code: 
  
  
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=None      ,		lowercase_=None  )			->						Optional[Any]:
         assert isinstance(lowercase_      ,		lowercase_  ), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
         # Splits the attention layers into three variables.
         if attention_paths_to_split is not None:
                for path, path_map in attention_paths_to_split.items():
                       _snake_case      :							Optional[int]     				=						old_checkpoint[path]
                       _snake_case      :							Dict     				=						old_tensor.shape[0] // 3
  
                       _snake_case      :							List[Any]     				=						(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)
  
                       _snake_case      :							Optional[int]     				=						old_tensor.shape[0] // config['''num_head_channels'''] // 3
  
                       _snake_case      :							Tuple     				=						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
                       _snake_case      :							Union[str, Any]     				=						old_tensor.split(channels // num_heads      ,		dim=1  )
  
                       _snake_case      :							Optional[Any]     				=						query.reshape(lowercase_  )
                       _snake_case      :							Optional[Any]     				=						key.reshape(lowercase_  )
                       _snake_case      :							Tuple     				=						value.reshape(lowercase_  )
  
         for path in paths:
                _snake_case      :							Optional[int]     				=						path['''new''']
  
                # These have already been assigned
                if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                       continue
  
                if additional_replacements is not None:
                       for replacement in additional_replacements:
                              _snake_case      :							Tuple     				=						new_path.replace(replacement['''old''']      ,		replacement['''new''']  )
  
          # proj_attn.weight has to be converted from conv 1D to linear
                _snake_case      :							str     				=						old_checkpoint[path['''old''']]
                _snake_case      :							Dict     				=						['''proj_attn.weight''']
                _snake_case      :							Union[str, Any]     				=						['''proj_out.weight''', '''proj_in.weight''']
                if any(k in new_path for k in names  ):
                       _snake_case      :							Tuple     				=						weight[:, :, 0]
                elif any(k in new_path for k in names_a  ) and len(weight.shape  ) > 2 and ".attentions." not in new_path:
                       _snake_case      :							int     				=						weight[:, :, 0]
                else:
                       _snake_case      :							List[str]     				=						weight
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_=0  )			->						Optional[int]:
         _snake_case      :							Union[str, Any]     				=						[]
         for old_item in old_list:
                _snake_case      :							List[Any]     				=						old_item
  
                #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
                #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
  
                #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
                #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
  
                #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
  
                mapping.append({'''old''': old_item, '''new''': new_item}  )
  
         return mapping
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_=1  )			->						int:
         if n_shave_prefix_segments >= 0:
                return ".".join(path.split('''.'''  )[n_shave_prefix_segments:]  )
         else:
                return ".".join(path.split('''.'''  )[:n_shave_prefix_segments]  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_=0  )			->						List[Any]:
         _snake_case      :							Optional[Any]     				=						[]
         for old_item in old_list:
                mapping.append({'''old''': old_item, '''new''': old_item}  )
  
         return mapping
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_=0  )			->						str:
         _snake_case      :							Union[str, Any]     				=						[]
         for old_item in old_list:
                _snake_case      :							int     				=						old_item.replace('''in_layers.0'''      ,		'''norm1'''  )
                _snake_case      :							Tuple     				=						new_item.replace('''in_layers.2'''      ,		'''conv1'''  )
  
                _snake_case      :							Optional[int]     				=						new_item.replace('''out_layers.0'''      ,		'''norm2'''  )
                _snake_case      :							Tuple     				=						new_item.replace('''out_layers.3'''      ,		'''conv2'''  )
  
                _snake_case      :							Union[str, Any]     				=						new_item.replace('''emb_layers.1'''      ,		'''time_emb_proj'''  )
                _snake_case      :							Any     				=						new_item.replace('''skip_connection'''      ,		'''conv_shortcut'''  )
  
                _snake_case      :							Optional[Any]     				=						shave_segments(lowercase_      ,		n_shave_prefix_segments=lowercase_  )
  
                if "temopral_conv" not in old_item:
                       mapping.append({'''old''': old_item, '''new''': new_item}  )
  
         return mapping
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=False  )			->						Optional[int]:
         _snake_case      :							str     				=						{}
         _snake_case      :							List[str]     				=						list(checkpoint.keys()  )
  
         _snake_case      :							Union[str, Any]     				=						'''model.diffusion_model.'''
  
         # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
         if sum(k.startswith('''model_ema'''  ) for k in keys  ) > 100 and extract_ema:
                print(f'''Checkpoint {path} has both EMA and non-EMA weights.'''  )
                print(
                    '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
                    ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'''  )
                for key in keys:
                       if key.startswith('''model.diffusion_model'''  ):
                              _snake_case      :							Optional[Any]     				=						'''model_ema.''' + ''''''.join(key.split('''.'''  )[1:]  )
                              _snake_case      :							List[str]     				=						checkpoint.pop(lowercase_  )
         else:
                if sum(k.startswith('''model_ema'''  ) for k in keys  ) > 100:
                       print(
                           '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
                           ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'''  )
  
                for key in keys:
                       _snake_case      :							Tuple     				=						checkpoint.pop(lowercase_  )
  
         _snake_case      :							Tuple     				=						{}
  
         _snake_case      :							Tuple     				=						unet_state_dict['''time_embed.0.weight''']
         _snake_case      :							Optional[int]     				=						unet_state_dict['''time_embed.0.bias''']
         _snake_case      :							Any     				=						unet_state_dict['''time_embed.2.weight''']
         _snake_case      :							List[Any]     				=						unet_state_dict['''time_embed.2.bias''']
  
         if config["class_embed_type"] is None:
                # No parameters to port
                ...
         elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
                _snake_case      :							int     				=						unet_state_dict['''label_emb.0.0.weight''']
                _snake_case      :							List[Any]     				=						unet_state_dict['''label_emb.0.0.bias''']
                _snake_case      :							Tuple     				=						unet_state_dict['''label_emb.0.2.weight''']
                _snake_case      :							Union[str, Any]     				=						unet_state_dict['''label_emb.0.2.bias''']
         else:
                raise NotImplementedError(f'''Not implemented `class_embed_type`: {config['class_embed_type']}'''  )
  
         _snake_case      :							List[Any]     				=						unet_state_dict['''input_blocks.0.0.weight''']
         _snake_case      :							Tuple     				=						unet_state_dict['''input_blocks.0.0.bias''']
  
         _snake_case      :							Dict     				=						[v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''  )]
         _snake_case      :							Dict     				=						renew_attention_paths(lowercase_  )
         _snake_case      :							str     				=						{'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
         assign_to_checkpoint(lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
         _snake_case      :							str     				=						unet_state_dict['''out.0.weight''']
         _snake_case      :							Any     				=						unet_state_dict['''out.0.bias''']
         _snake_case      :							int     				=						unet_state_dict['''out.2.weight''']
         _snake_case      :							Optional[int]     				=						unet_state_dict['''out.2.bias''']
  
         # Retrieves the keys for the input blocks only
         _snake_case      :							Optional[int]     				=						len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in unet_state_dict if '''input_blocks''' in layer}  )
         _snake_case      :							List[str]     				=						{
             layer_id: [key for key in unet_state_dict if f'''input_blocks.{layer_id}''' in key]
             for layer_id in range(lowercase_  )
         }
  
         # Retrieves the keys for the middle blocks only
         _snake_case      :							List[Any]     				=						len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in unet_state_dict if '''middle_block''' in layer}  )
         _snake_case      :							Union[str, Any]     				=						{
             layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
             for layer_id in range(lowercase_  )
         }
  
         # Retrieves the keys for the output blocks only
         _snake_case      :							Union[str, Any]     				=						len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in unet_state_dict if '''output_blocks''' in layer}  )
         _snake_case      :							Tuple     				=						{
             layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
             for layer_id in range(lowercase_  )
         }
  
         for i in range(1      ,		lowercase_  ):
                _snake_case      :							Tuple     				=						(i - 1) // (config['''layers_per_block'''] + 1)
                _snake_case      :							str     				=						(i - 1) % (config['''layers_per_block'''] + 1)
  
                _snake_case      :							int     				=						[
                    key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key and f'''input_blocks.{i}.0.op''' not in key
                ]
                _snake_case      :							Optional[Any]     				=						[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
                _snake_case      :							Optional[int]     				=						[key for key in input_blocks[i] if f'''input_blocks.{i}.2''' in key]
  
                if f'''input_blocks.{i}.op.weight''' in unet_state_dict:
                       _snake_case      :							List[str]     				=						unet_state_dict.pop(
                           f'''input_blocks.{i}.op.weight'''  )
                       _snake_case      :							Any     				=						unet_state_dict.pop(
                           f'''input_blocks.{i}.op.bias'''  )
  
                _snake_case      :							str     				=						renew_resnet_paths(lowercase_  )
                _snake_case      :							List[Any]     				=						{'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                assign_to_checkpoint(
                    lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
                _snake_case      :							Union[str, Any]     				=						[key for key in resnets if '''temopral_conv''' in key]
                _snake_case      :							int     				=						renew_temp_conv_paths(lowercase_  )
                _snake_case      :							int     				=						{
                    '''old''': f'''input_blocks.{i}.0.temopral_conv''',
                    '''new''': f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
                }
                assign_to_checkpoint(
                    lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
                if len(lowercase_  ):
                       _snake_case      :							Dict     				=						renew_attention_paths(lowercase_  )
                       _snake_case      :							Union[str, Any]     				=						{'''old''': f'''input_blocks.{i}.1''', '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
                       assign_to_checkpoint(
                           lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
                if len(lowercase_  ):
                       _snake_case      :							Any     				=						renew_attention_paths(lowercase_  )
                       _snake_case      :							Tuple     				=						{
                           '''old''': f'''input_blocks.{i}.2''',
                           '''new''': f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                       }
                       assign_to_checkpoint(
                           lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
         _snake_case      :							Dict     				=						middle_blocks[0]
         _snake_case      :							List[str]     				=						[key for key in resnet_a if '''temopral_conv''' in key]
         _snake_case      :							int     				=						middle_blocks[1]
         _snake_case      :							int     				=						middle_blocks[2]
         _snake_case      :							List[str]     				=						middle_blocks[3]
         _snake_case      :							List[Any]     				=						[key for key in resnet_a if '''temopral_conv''' in key]
  
         _snake_case      :							List[str]     				=						renew_resnet_paths(lowercase_  )
         _snake_case      :							Any     				=						{'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
         assign_to_checkpoint(
             lowercase_      ,		lowercase_      ,		lowercase_      ,		config=lowercase_      ,		additional_replacements=[meta_path]  )
  
         _snake_case      :							Optional[Any]     				=						renew_temp_conv_paths(lowercase_  )
         _snake_case      :							Any     				=						{'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
         assign_to_checkpoint(
             lowercase_      ,		lowercase_      ,		lowercase_      ,		config=lowercase_      ,		additional_replacements=[meta_path]  )
  
         _snake_case      :							Tuple     				=						renew_resnet_paths(lowercase_  )
         _snake_case      :							Tuple     				=						{'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
         assign_to_checkpoint(
             lowercase_      ,		lowercase_      ,		lowercase_      ,		config=lowercase_      ,		additional_replacements=[meta_path]  )
  
         _snake_case      :							List[str]     				=						renew_temp_conv_paths(lowercase_  )
         _snake_case      :							Tuple     				=						{'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
         assign_to_checkpoint(
             lowercase_      ,		lowercase_      ,		lowercase_      ,		config=lowercase_      ,		additional_replacements=[meta_path]  )
  
         _snake_case      :							Any     				=						renew_attention_paths(lowercase_  )
         _snake_case      :							str     				=						{'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
         assign_to_checkpoint(
             lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
         _snake_case      :							Optional[Any]     				=						renew_attention_paths(lowercase_  )
         _snake_case      :							Any     				=						{'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
         assign_to_checkpoint(
             lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
         for i in range(lowercase_  ):
                _snake_case      :							Any     				=						i // (config['''layers_per_block'''] + 1)
                _snake_case      :							Dict     				=						i % (config['''layers_per_block'''] + 1)
                _snake_case      :							Union[str, Any]     				=						[shave_segments(lowercase_      ,		2  ) for name in output_blocks[i]]
                _snake_case      :							Optional[int]     				=						{}
  
                for layer in output_block_layers:
                       _snake_case      :							str     				=						layer.split('''.'''  )[0], shave_segments(lowercase_      ,		1  )
                       if layer_id in output_block_list:
                              output_block_list[layer_id].append(lowercase_  )
                       else:
                              _snake_case      :							Any     				=						[layer_name]
  
                if len(lowercase_  ) > 1:
                       _snake_case      :							Optional[int]     				=						[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
                       _snake_case      :							int     				=						[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
                       _snake_case      :							Dict     				=						[key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]
  
                       _snake_case      :							Tuple     				=						renew_resnet_paths(lowercase_  )
                       _snake_case      :							Optional[Any]     				=						renew_resnet_paths(lowercase_  )
  
                       _snake_case      :							int     				=						{'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                       assign_to_checkpoint(
                           lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
                       _snake_case      :							Union[str, Any]     				=						[key for key in resnets if '''temopral_conv''' in key]
                       _snake_case      :							List[Any]     				=						renew_temp_conv_paths(lowercase_  )
                       _snake_case      :							Optional[int]     				=						{
                           '''old''': f'''output_blocks.{i}.0.temopral_conv''',
                           '''new''': f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
                       }
                       assign_to_checkpoint(
                           lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
                       _snake_case      :							Optional[int]     				=						{k: sorted(lowercase_  ) for k, v in output_block_list.items()}
                       if ["conv.bias", "conv.weight"] in output_block_list.values():
                              _snake_case      :							List[Any]     				=						list(output_block_list.values()  ).index(['''conv.bias''', '''conv.weight''']  )
                              _snake_case      :							List[str]     				=						unet_state_dict[
                                  f'''output_blocks.{i}.{index}.conv.weight'''
                              ]
                              _snake_case      :							Any     				=						unet_state_dict[
                                  f'''output_blocks.{i}.{index}.conv.bias'''
                              ]
  
                              # Clear attentions as they have been attributed above.
                              if len(lowercase_  ) == 2:
                                     _snake_case      :							List[str]     				=						[]
  
                       if len(lowercase_  ):
                              _snake_case      :							Union[str, Any]     				=						renew_attention_paths(lowercase_  )
                              _snake_case      :							Tuple     				=						{
                                  '''old''': f'''output_blocks.{i}.1''',
                                  '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                              }
                              assign_to_checkpoint(
                                  lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
  
                       if len(lowercase_  ):
                              _snake_case      :							Tuple     				=						renew_attention_paths(lowercase_  )
                              _snake_case      :							Optional[int]     				=						{
                                  '''old''': f'''output_blocks.{i}.2''',
                                  '''new''': f'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                              }
                              assign_to_checkpoint(
                                  lowercase_      ,		lowercase_      ,		lowercase_      ,		additional_replacements=[meta_path]      ,		config=lowercase_  )
                else:
                       _snake_case      :							Union[str, Any]     				=						renew_resnet_paths(lowercase_      ,		n_shave_prefix_segments=1  )
                       for path in resnet_0_paths:
                              _snake_case      :							Optional[int]     				=						'''.'''.join(['''output_blocks''', str(lowercase_  ), path['''old''']]  )
                              _snake_case      :							Union[str, Any]     				=						'''.'''.join(['''up_blocks''', str(lowercase_  ), '''resnets''', str(lowercase_  ), path['''new''']]  )
                              _snake_case      :							Tuple     				=						unet_state_dict[old_path]
  
                       _snake_case      :							Tuple     				=						[l for l in output_block_layers if '''temopral_conv''' in l]
                       for path in temopral_conv_paths:
                              _snake_case      :							Union[str, Any]     				=						path.split('''temopral_conv.'''  )[-1]
                              _snake_case      :							Union[str, Any]     				=						'''.'''.join(['''output_blocks''', str(lowercase_  ), str(lowercase_  ), '''temopral_conv''', pruned_path]  )
                              _snake_case      :							int     				=						'''.'''.join(['''up_blocks''', str(lowercase_  ), '''temp_convs''', str(lowercase_  ), pruned_path]  )
                              _snake_case      :							Optional[Any]     				=						unet_state_dict[old_path]
  
         return new_checkpoint
  
  
  if __name__ == "__main__":
     lowerCAmelCase_   =   argparse.ArgumentParser()
  
     parser.add_argument(
         "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
     )
     parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
     lowerCAmelCase_   =   parser.parse_args()
  
     lowerCAmelCase_   =   torch.load(args.checkpoint_path, map_location="cpu")
     lowerCAmelCase_   =   UNetaDConditionModel()
  
     lowerCAmelCase_   =   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
     lowerCAmelCase_   =   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
     lowerCAmelCase_   =   set(converted_ckpt.keys()) - set(unet.state_dict().keys())
  
     assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"
  
     # load state_dict
     unet.load_state_dict(converted_ckpt)
  
     unet.save_pretrained(args.dump_path)
  
     # -- finish converting the unet --
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  
  import pytorch_lightning as pl
  from pytorch_lightning.utilities import rank_zero_info
  
  from transformers import (
      AdamW,
      AutoConfig,
      AutoModel,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForTokenClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      PretrainedConfig,
      PreTrainedTokenizer,
  )
  from transformers.optimization import (
      Adafactor,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )
  from transformers.utils.versions import require_version
  
  
  lowerCAmelCase_   =   logging.getLogger(__name__)
  
  require_version("pytorch_lightning>=1.0.4")
  
  lowerCAmelCase_   =   {
      "base": AutoModel,
      "sequence-classification": AutoModelForSequenceClassification,
      "question-answering": AutoModelForQuestionAnswering,
      "pretraining": AutoModelForPreTraining,
      "token-classification": AutoModelForTokenClassification,
      "language-modeling": AutoModelWithLMHead,
      "summarization": AutoModelForSeqaSeqLM,
      "translation": AutoModelForSeqaSeqLM,
  }
  
  
  # update this and the import above to support new schedulers from transformers.optimization
  lowerCAmelCase_   =   {
      "linear": get_linear_schedule_with_warmup,
      "cosine": get_cosine_schedule_with_warmup,
      "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
      "polynomial": get_polynomial_decay_schedule_with_warmup,
      # '': get_constant_schedule,             # not supported for now
      # '': get_constant_schedule_with_warmup, # not supported for now
  }
  lowerCAmelCase_   =   sorted(arg_to_scheduler.keys())
  lowerCAmelCase_   =   "{" + ", ".join(arg_to_scheduler_choices) + "}"
  
  class 				A    (pl.LightningModule				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_="base"     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  **lowercase_     ,  )      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               # TODO: move to self.save_hyperparameters()
               # self.save_hyperparameters()
               # can also expand arguments into trainer signature for easier reading
  
               self.save_hyperparameters(lowercase_					)
               _snake_case      :							Union[str, Any]     				=						0
               _snake_case      :							Any     				=						Path(self.hparams.output_dir					)
               _snake_case      :							Optional[Any]     				=						self.hparams.cache_dir if self.hparams.cache_dir else None
               if config is None:
                      _snake_case      :							List[Any]     				=						AutoConfig.from_pretrained(
                          self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,  **({'''num_labels''': num_labels} if num_labels is not None else {})     ,  cache_dir=lowercase_     ,  **lowercase_     ,  )
               else:
                      _snake_case      :							PretrainedConfig     				=						config
  
               _snake_case      :							Optional[Any]     				=						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
               for p in extra_model_params:
                      if getattr(self.hparams     ,  lowercase_     ,  lowercase_					):
                             assert hasattr(self.config     ,  lowercase_					), F'''model config doesn\'t have a `{p}` attribute'''
                             setattr(self.config     ,  lowercase_     ,  getattr(self.hparams     ,  lowercase_					)					)
  
               if tokenizer is None:
                      _snake_case      :							List[Any]     				=						AutoTokenizer.from_pretrained(
                          self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,  cache_dir=lowercase_     ,  )
               else:
                      _snake_case      :							PreTrainedTokenizer     				=						tokenizer
               _snake_case      :							str     				=						MODEL_MODES[mode]
               if model is None:
                      _snake_case      :							Optional[Any]     				=						self.model_type.from_pretrained(
                          self.hparams.model_name_or_path     ,  from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path					)     ,  config=self.config     ,  cache_dir=lowercase_     ,  )
               else:
                      _snake_case      :							Optional[int]     				=						model
  
  
  
        def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.model_type.from_pretrained(*lowercase_     ,  **lowercase_					)
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						arg_to_scheduler[self.hparams.lr_scheduler]
               _snake_case      :							Optional[int]     				=						get_schedule_func(
                   self.opt     ,  num_warmup_steps=self.hparams.warmup_steps     ,  num_training_steps=self.total_steps()					)
               _snake_case      :							int     				=						{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
               return scheduler
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.model
               _snake_case      :							List[Any]     				=						['''bias''', '''LayerNorm.weight''']
               _snake_case      :							Optional[Any]     				=						[
                   {
                       '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
                       '''weight_decay''': self.hparams.weight_decay,
                   },
                   {
                       '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
                       '''weight_decay''': 0.0,
                   },
               ]
               if self.hparams.adafactor:
                      _snake_case      :							Optional[int]     				=						Adafactor(
                          lowercase_     ,  lr=self.hparams.learning_rate     ,  scale_parameter=lowercase_     ,  relative_step=lowercase_					)
  
               else:
                      _snake_case      :							Optional[Any]     				=						AdamW(
                          lowercase_     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon					)
               _snake_case      :							Optional[int]     				=						optimizer
  
               _snake_case      :							Tuple     				=						self.get_lr_scheduler()
  
               return [optimizer], [scheduler]
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               return self.validation_step(lowercase_     ,  lowercase_					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               return self.validation_end(lowercase_					)
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						max(1     ,  self.hparams.gpus					)  # TODO: consider num_tpu_cores
               _snake_case      :							Dict     				=						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
               return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   int:
  
  
  
               '''simple docstring'''
  
               if stage == "test":
                      _snake_case      :							Optional[Any]     				=						len(self.test_dataloader().dataset					)
               else:
                      _snake_case      :							Any     				=						self.get_dataloader('''train'''     ,  self.hparams.train_batch_size     ,  shuffle=lowercase_					)
                      _snake_case      :							Union[str, Any]     				=						len(self.train_dataloader().dataset					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = False					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               raise NotImplementedError('''You must implement this for your task'''					)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               return self.train_loader
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               return self.get_dataloader('''dev'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               return self.get_dataloader('''test'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowercase_					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               return os.path.join(
                   self.hparams.data_dir     ,  '''cached_{}_{}_{}'''.format(
                       lowercase_     ,  list(filter(lowercase_     ,  self.hparams.model_name_or_path.split('''/'''					)					)					).pop()     ,  str(self.hparams.max_seq_length					)     ,  )     ,  )
  
  
  
        @pl.utilities.rank_zero_only
        def    __a (					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.output_dir.joinpath('''best_tfmr'''					)
               _snake_case      :							str     				=						self.step_count
               self.model.save_pretrained(lowercase_					)
               self.tokenizer.save_pretrained(lowercase_					)
  
  
  
        @staticmethod
        def    __a (					lowercase_     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               parser.add_argument(
                   '''--model_name_or_path'''     ,  default=lowercase_     ,  type=lowercase_     ,  required=lowercase_     ,  help='''Path to pretrained model or model identifier from huggingface.co/models'''     ,  )
               parser.add_argument(
                   '''--config_name'''     ,  default=''''''     ,  type=lowercase_     ,  help='''Pretrained config name or path if not the same as model_name'''					)
               parser.add_argument(
                   '''--tokenizer_name'''     ,  default=lowercase_     ,  type=lowercase_     ,  help='''Pretrained tokenizer name or path if not the same as model_name'''     ,  )
               parser.add_argument(
                   '''--cache_dir'''     ,  default=''''''     ,  type=lowercase_     ,  help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''     ,  )
               parser.add_argument(
                   '''--encoder_layerdrop'''     ,  type=lowercase_     ,  help='''Encoder layer dropout probability (Optional). Goes into model.config'''     ,  )
               parser.add_argument(
                   '''--decoder_layerdrop'''     ,  type=lowercase_     ,  help='''Decoder layer dropout probability (Optional). Goes into model.config'''     ,  )
               parser.add_argument(
                   '''--dropout'''     ,  type=lowercase_     ,  help='''Dropout probability (Optional). Goes into model.config'''     ,  )
               parser.add_argument(
                   '''--attention_dropout'''     ,  type=lowercase_     ,  help='''Attention dropout probability (Optional). Goes into model.config'''     ,  )
               parser.add_argument('''--learning_rate'''     ,  default=5E-5     ,  type=lowercase_     ,  help='''The initial learning rate for Adam.'''					)
               parser.add_argument(
                   '''--lr_scheduler'''     ,  default='''linear'''     ,  choices=lowercase_     ,  metavar=lowercase_     ,  type=lowercase_     ,  help='''Learning rate scheduler'''     ,  )
               parser.add_argument('''--weight_decay'''     ,  default=0.0     ,  type=lowercase_     ,  help='''Weight decay if we apply some.'''					)
               parser.add_argument('''--adam_epsilon'''     ,  default=1E-8     ,  type=lowercase_     ,  help='''Epsilon for Adam optimizer.'''					)
               parser.add_argument('''--warmup_steps'''     ,  default=0     ,  type=lowercase_     ,  help='''Linear warmup over warmup_steps.'''					)
               parser.add_argument('''--num_workers'''     ,  default=4     ,  type=lowercase_     ,  help='''kwarg passed to DataLoader'''					)
               parser.add_argument('''--num_train_epochs'''     ,  dest='''max_epochs'''     ,  default=3     ,  type=lowercase_					)
               parser.add_argument('''--train_batch_size'''     ,  default=32     ,  type=lowercase_					)
               parser.add_argument('''--eval_batch_size'''     ,  default=32     ,  type=lowercase_					)
               parser.add_argument('''--adafactor'''     ,  action='''store_true'''					)
  
  
  class 				A    (pl.Callback				):
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               if (
                   trainer.is_global_zero and trainer.global_rank == 0
               ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
                      pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.
  
  
  class 				A    (pl.Callback				):
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						trainer.lr_schedulers[0]['''scheduler''']
               _snake_case      :							Any     				=						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
               pl_module.logger.log_metrics(lowercase_					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               rank_zero_info('''***** Validation results *****'''					)
               _snake_case      :							Tuple     				=						trainer.callback_metrics
               # Log results
               for key in sorted(lowercase_					):
                      if key not in ["log", "progress_bar"]:
                             rank_zero_info('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               rank_zero_info('''***** Test results *****'''					)
               _snake_case      :							int     				=						trainer.callback_metrics
               # Log and save results to file
               _snake_case      :							Dict     				=						os.path.join(pl_module.hparams.output_dir     ,  '''test_results.txt'''					)
               with open(lowercase_     ,  '''w'''					) as writer:
                      for key in sorted(lowercase_					):
                             if key not in ["log", "progress_bar"]:
                                    rank_zero_info('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)
                                    writer.write('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						None:
         #  To allow all pl args uncomment the following line
         #  parser = pl.Trainer.add_argparse_args(parser)
         parser.add_argument(
             '''--output_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		help='''The output directory where the model predictions and checkpoints will be written.'''      ,		)
         parser.add_argument(
             '''--fp16'''      ,		action='''store_true'''      ,		help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''      ,		)
  
         parser.add_argument(
             '''--fp16_opt_level'''      ,		type=lowercase_      ,		default='''O2'''      ,		help=(
                 '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
                 '''See details at https://nvidia.github.io/apex/amp.html'''
             )      ,		)
         parser.add_argument('''--n_tpu_cores'''      ,		dest='''tpu_cores'''      ,		type=lowercase_  )
         parser.add_argument('''--max_grad_norm'''      ,		dest='''gradient_clip_val'''      ,		default=1.0      ,		type=lowercase_      ,		help='''Max gradient norm'''  )
         parser.add_argument('''--do_train'''      ,		action='''store_true'''      ,		help='''Whether to run training.'''  )
         parser.add_argument('''--do_predict'''      ,		action='''store_true'''      ,		help='''Whether to run predictions on the test set.'''  )
         parser.add_argument(
             '''--gradient_accumulation_steps'''      ,		dest='''accumulate_grad_batches'''      ,		type=lowercase_      ,		default=1      ,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''      ,		)
         parser.add_argument('''--seed'''      ,		type=lowercase_      ,		default=42      ,		help='''random seed for initialization'''  )
         parser.add_argument(
             '''--data_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''      ,		)
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=True      ,		lowercase_=None      ,		lowercase_=[]      ,		lowercase_=None      ,		lowercase_=None      ,		**lowercase_      ,		)			->						Optional[Any]:
         pl.seed_everything(args.seed  )
  
         # init model
         _snake_case      :							int     				=						Path(model.hparams.output_dir  )
         odir.mkdir(exist_ok=lowercase_  )
  
         # add custom checkpoints
         if checkpoint_callback is None:
                _snake_case      :							Union[str, Any]     				=						pl.callbacks.ModelCheckpoint(
                    filepath=args.output_dir      ,		prefix='''checkpoint'''      ,		monitor='''val_loss'''      ,		mode='''min'''      ,		save_top_k=1  )
         if early_stopping_callback:
                extra_callbacks.append(lowercase_  )
         if logging_callback is None:
                _snake_case      :							Optional[int]     				=						LoggingCallback()
  
         _snake_case      :							Union[str, Any]     				=						{}
  
         # TODO: remove with PyTorch 1.6 since pl uses native amp
         if args.fpaa:
                _snake_case      :							Any     				=						16
                # train_params["amp_level"] = args.fp16_opt_level
  
         if args.gpus > 1:
                _snake_case      :							List[str]     				=						'''auto'''  # "ddp"
                _snake_case      :							Dict     				=						'''ddp'''
  
         _snake_case      :							Optional[int]     				=						args.accumulate_grad_batches
         _snake_case      :							Optional[Any]     				=						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
         _snake_case      :							Tuple     				=						'''auto'''
  
         _snake_case      :							int     				=						pl.Trainer.from_argparse_args(
             lowercase_      ,		weights_summary=lowercase_      ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,		logger=lowercase_      ,		**lowercase_      ,		)
  
         if args.do_train:
                trainer.fit(lowercase_  )
  
         return trainer
  
  
  
  
  
  
  