

import argparse

import torch

from diffusers import UNetaDConditionModel





def 			_lowercase    (      __UpperCamelCase     :		Optional[Any]   ,       __UpperCamelCase     :		List[str]   ,       __UpperCamelCase     :		Tuple   ,       __UpperCamelCase     :		str=None   ,       __UpperCamelCase     :		Any=None   ,       __UpperCamelCase     :		Optional[int]=None ):
      assert isinstance(__UpperCamelCase   ,       __UpperCamelCase ), "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__					=   old_checkpoint[path]
                  snake_case__					=   old_tensor.shape[0] // 3

                  snake_case__					=   (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

                  snake_case__					=   old_tensor.shape[0] // config["""num_head_channels"""] // 3

                  snake_case__					=   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
                  snake_case__	,			snake_case__	,			snake_case__					=   old_tensor.split(channels // num_heads   ,       dim=1 )

                  snake_case__					=   query.reshape(__UpperCamelCase )
                  snake_case__					=   key.reshape(__UpperCamelCase )
                  snake_case__					=   value.reshape(__UpperCamelCase )

      for path in paths:
            snake_case__					=   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__					=   new_path.replace(replacement["""old"""]   ,       replacement["""new"""] )

        # proj_attn.weight has to be converted from conv 1D to linear
            snake_case__					=   old_checkpoint[path["""old"""]]
            snake_case__					=   ["""proj_attn.weight"""]
            snake_case__					=   ["""proj_out.weight""", """proj_in.weight"""]
            if any(k in new_path for k in names ):
                  snake_case__					=   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__					=   weight[:, :, 0]
            else:
                  snake_case__					=   weight





def 			_lowercase    (      __UpperCamelCase     :		Any   ,       __UpperCamelCase     :		Optional[Any]=0 ):
      snake_case__					=   []
      for old_item in old_list:
            snake_case__					=   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 			_lowercase    (      __UpperCamelCase     :		List[Any]   ,       __UpperCamelCase     :		Union[str, Any]=1 ):
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
      else:
            return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )





def 			_lowercase    (      __UpperCamelCase     :		Union[str, Any]   ,       __UpperCamelCase     :		Any=0 ):
      snake_case__					=   []
      for old_item in old_list:
            mapping.append({"""old""": old_item, """new""": old_item} )

      return mapping





def 			_lowercase    (      __UpperCamelCase     :		Tuple   ,       __UpperCamelCase     :		str=0 ):
      snake_case__					=   []
      for old_item in old_list:
            snake_case__					=   old_item.replace("""in_layers.0"""   ,       """norm1""" )
            snake_case__					=   new_item.replace("""in_layers.2"""   ,       """conv1""" )

            snake_case__					=   new_item.replace("""out_layers.0"""   ,       """norm2""" )
            snake_case__					=   new_item.replace("""out_layers.3"""   ,       """conv2""" )

            snake_case__					=   new_item.replace("""emb_layers.1"""   ,       """time_emb_proj""" )
            snake_case__					=   new_item.replace("""skip_connection"""   ,       """conv_shortcut""" )

            snake_case__					=   shave_segments(__UpperCamelCase   ,       n_shave_prefix_segments=__UpperCamelCase )

            if "temopral_conv" not in old_item:
                  mapping.append({"""old""": old_item, """new""": new_item} )

      return mapping





def 			_lowercase    (      __UpperCamelCase     :		Any   ,       __UpperCamelCase     :		Any   ,       __UpperCamelCase     :		List[str]=None   ,       __UpperCamelCase     :		str=False ):
      snake_case__					=   {}
      snake_case__					=   list(checkpoint.keys() )

      snake_case__					=   """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__					=   """model_ema.""" + """""".join(key.split(""".""" )[1:] )
                        snake_case__					=   checkpoint.pop(__UpperCamelCase )
      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__					=   checkpoint.pop(__UpperCamelCase )

      snake_case__					=   {}

      snake_case__					=   unet_state_dict["""time_embed.0.weight"""]
      snake_case__					=   unet_state_dict["""time_embed.0.bias"""]
      snake_case__					=   unet_state_dict["""time_embed.2.weight"""]
      snake_case__					=   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__					=   unet_state_dict["""label_emb.0.0.weight"""]
            snake_case__					=   unet_state_dict["""label_emb.0.0.bias"""]
            snake_case__					=   unet_state_dict["""label_emb.0.2.weight"""]
            snake_case__					=   unet_state_dict["""label_emb.0.2.bias"""]
      else:
            raise NotImplementedError(F'''Not implemented `class_embed_type`: {config['class_embed_type']}''' )

      snake_case__					=   unet_state_dict["""input_blocks.0.0.weight"""]
      snake_case__					=   unet_state_dict["""input_blocks.0.0.bias"""]

      snake_case__					=   [v for v in unet_state_dict if v.startswith("""input_blocks.0.1""" )]
      snake_case__					=   renew_attention_paths(__UpperCamelCase )
      snake_case__					=   {"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
      assign_to_checkpoint(__UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

      snake_case__					=   unet_state_dict["""out.0.weight"""]
      snake_case__					=   unet_state_dict["""out.0.bias"""]
      snake_case__					=   unet_state_dict["""out.2.weight"""]
      snake_case__					=   unet_state_dict["""out.2.bias"""]

      # Retrieves the keys for the input blocks only
      snake_case__					=   len({""".""".join(layer.split(""".""" )[:2] ) for layer in unet_state_dict if """input_blocks""" in layer} )
      snake_case__					=   {
          layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
          for layer_id in range(__UpperCamelCase )
      }

      # Retrieves the keys for the middle blocks only
      snake_case__					=   len({""".""".join(layer.split(""".""" )[:2] ) for layer in unet_state_dict if """middle_block""" in layer} )
      snake_case__					=   {
          layer_id: [key for key in unet_state_dict if F'''middle_block.{layer_id}''' in key]
          for layer_id in range(__UpperCamelCase )
      }

      # Retrieves the keys for the output blocks only
      snake_case__					=   len({""".""".join(layer.split(""".""" )[:2] ) for layer in unet_state_dict if """output_blocks""" in layer} )
      snake_case__					=   {
          layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
          for layer_id in range(__UpperCamelCase )
      }

      for i in range(1   ,       __UpperCamelCase ):
            snake_case__					=   (i - 1) // (config["""layers_per_block"""] + 1)
            snake_case__					=   (i - 1) % (config["""layers_per_block"""] + 1)

            snake_case__					=   [
                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__					=   [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
            snake_case__					=   [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__					=   unet_state_dict.pop(
                      F'''input_blocks.{i}.op.weight''' )
                  snake_case__					=   unet_state_dict.pop(
                      F'''input_blocks.{i}.op.bias''' )

            snake_case__					=   renew_resnet_paths(__UpperCamelCase )
            snake_case__					=   {"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
            assign_to_checkpoint(
                __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

            snake_case__					=   [key for key in resnets if """temopral_conv""" in key]
            snake_case__					=   renew_temp_conv_paths(__UpperCamelCase )
            snake_case__					=   {
                """old""": F'''input_blocks.{i}.0.temopral_conv''',
                """new""": F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
            }
            assign_to_checkpoint(
                __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

            if len(__UpperCamelCase ):
                  snake_case__					=   renew_attention_paths(__UpperCamelCase )
                  snake_case__					=   {"""old""": F'''input_blocks.{i}.1''', """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
                  assign_to_checkpoint(
                      __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

            if len(__UpperCamelCase ):
                  snake_case__					=   renew_attention_paths(__UpperCamelCase )
                  snake_case__					=   {
                      """old""": F'''input_blocks.{i}.2''',
                      """new""": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                  }
                  assign_to_checkpoint(
                      __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

      snake_case__					=   middle_blocks[0]
      snake_case__					=   [key for key in resnet_a if """temopral_conv""" in key]
      snake_case__					=   middle_blocks[1]
      snake_case__					=   middle_blocks[2]
      snake_case__					=   middle_blocks[3]
      snake_case__					=   [key for key in resnet_a if """temopral_conv""" in key]

      snake_case__					=   renew_resnet_paths(__UpperCamelCase )
      snake_case__					=   {"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
      assign_to_checkpoint(
          __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       config=__UpperCamelCase   ,       additional_replacements=[meta_path] )

      snake_case__					=   renew_temp_conv_paths(__UpperCamelCase )
      snake_case__					=   {"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
      assign_to_checkpoint(
          __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       config=__UpperCamelCase   ,       additional_replacements=[meta_path] )

      snake_case__					=   renew_resnet_paths(__UpperCamelCase )
      snake_case__					=   {"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
      assign_to_checkpoint(
          __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       config=__UpperCamelCase   ,       additional_replacements=[meta_path] )

      snake_case__					=   renew_temp_conv_paths(__UpperCamelCase )
      snake_case__					=   {"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
      assign_to_checkpoint(
          __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       config=__UpperCamelCase   ,       additional_replacements=[meta_path] )

      snake_case__					=   renew_attention_paths(__UpperCamelCase )
      snake_case__					=   {"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
      assign_to_checkpoint(
          __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

      snake_case__					=   renew_attention_paths(__UpperCamelCase )
      snake_case__					=   {"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
      assign_to_checkpoint(
          __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

      for i in range(__UpperCamelCase ):
            snake_case__					=   i // (config["""layers_per_block"""] + 1)
            snake_case__					=   i % (config["""layers_per_block"""] + 1)
            snake_case__					=   [shave_segments(__UpperCamelCase   ,       2 ) for name in output_blocks[i]]
            snake_case__					=   {}

            for layer in output_block_layers:
                  snake_case__	,			snake_case__					=   layer.split(""".""" )[0], shave_segments(__UpperCamelCase   ,       1 )
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__UpperCamelCase )
                  else:
                        snake_case__					=   [layer_name]

            if len(__UpperCamelCase ) > 1:
                  snake_case__					=   [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
                  snake_case__					=   [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
                  snake_case__					=   [key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

                  snake_case__					=   renew_resnet_paths(__UpperCamelCase )
                  snake_case__					=   renew_resnet_paths(__UpperCamelCase )

                  snake_case__					=   {"""old""": F'''output_blocks.{i}.0''', """new""": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                  assign_to_checkpoint(
                      __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

                  snake_case__					=   [key for key in resnets if """temopral_conv""" in key]
                  snake_case__					=   renew_temp_conv_paths(__UpperCamelCase )
                  snake_case__					=   {
                      """old""": F'''output_blocks.{i}.0.temopral_conv''',
                      """new""": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
                  }
                  assign_to_checkpoint(
                      __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

                  snake_case__					=   {k: sorted(__UpperCamelCase ) for k, v in output_block_list.items()}
                  if ["conv.bias", "conv.weight"] in output_block_list.values():
                        snake_case__					=   list(output_block_list.values() ).index(["""conv.bias""", """conv.weight"""] )
                        snake_case__					=   unet_state_dict[
                            F'''output_blocks.{i}.{index}.conv.weight'''
                        ]
                        snake_case__					=   unet_state_dict[
                            F'''output_blocks.{i}.{index}.conv.bias'''
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(__UpperCamelCase ) == 2:
                              snake_case__					=   []

                  if len(__UpperCamelCase ):
                        snake_case__					=   renew_attention_paths(__UpperCamelCase )
                        snake_case__					=   {
                            """old""": F'''output_blocks.{i}.1''',
                            """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                        }
                        assign_to_checkpoint(
                            __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )

                  if len(__UpperCamelCase ):
                        snake_case__					=   renew_attention_paths(__UpperCamelCase )
                        snake_case__					=   {
                            """old""": F'''output_blocks.{i}.2''',
                            """new""": F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                        }
                        assign_to_checkpoint(
                            __UpperCamelCase   ,       __UpperCamelCase   ,       __UpperCamelCase   ,       additional_replacements=[meta_path]   ,       config=__UpperCamelCase )
            else:
                  snake_case__					=   renew_resnet_paths(__UpperCamelCase   ,       n_shave_prefix_segments=1 )
                  for path in resnet_0_paths:
                        snake_case__					=   """.""".join(["""output_blocks""", str(__UpperCamelCase ), path["""old"""]] )
                        snake_case__					=   """.""".join(["""up_blocks""", str(__UpperCamelCase ), """resnets""", str(__UpperCamelCase ), path["""new"""]] )
                        snake_case__					=   unet_state_dict[old_path]

                  snake_case__					=   [l for l in output_block_layers if """temopral_conv""" in l]
                  for path in temopral_conv_paths:
                        snake_case__					=   path.split("""temopral_conv.""" )[-1]
                        snake_case__					=   """.""".join(["""output_blocks""", str(__UpperCamelCase ), str(__UpperCamelCase ), """temopral_conv""", pruned_path] )
                        snake_case__					=   """.""".join(["""up_blocks""", str(__UpperCamelCase ), """temp_convs""", str(__UpperCamelCase ), pruned_path] )
                        snake_case__					=   unet_state_dict[old_path]

      return new_checkpoint


if __name__ == "__main__":
       lowerCAmelCase						:   int										=    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						:   Tuple										=    parser.parse_args()

       lowerCAmelCase						:   Dict										=    torch.load(args.checkpoint_path, map_location='''cpu''')
       lowerCAmelCase						:   List[Any]										=    UNetaDConditionModel()

       lowerCAmelCase						:   List[Any]										=    convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       lowerCAmelCase						:   Union[str, Any]										=    set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       lowerCAmelCase						:   Union[str, Any]										=    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 --









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowerCAmelCase						:   List[Any]										=    '''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

Can you answer this question about the text: \'{question}\''''
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




       UpperCamelCase__ :     int				    =       '''google/flan-t5-base'''
       UpperCamelCase__ :     int				    =       (
           '''This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '''
           '''text where to find the answer, and `question`, which is the question, and returns the answer to the question.'''
       )
       UpperCamelCase__ :     Tuple				    =       '''text_qa'''
       UpperCamelCase__ :     Tuple				    =       AutoTokenizer
       UpperCamelCase__ :     List[Any]				    =       AutoModelForSeqaSeqLM

       UpperCamelCase__ :     Optional[Any]				    =       ['''text''', '''text''']
       UpperCamelCase__ :     Any				    =       ['''text''']

       def UpperCAmelCase_						(						self       :						Tuple    ,		lowerCAmelCase__       :						str    ,		lowerCAmelCase__       :						str							)			->    List[Any]:
             snake_case__					=   QA_PROMPT.format(text=lowerCAmelCase__    ,		question=lowerCAmelCase__							)
             return self.pre_processor(lowerCAmelCase__    ,		return_tensors="""pt"""							)

       def UpperCAmelCase_						(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						Dict							)			->    List[Any]:
             snake_case__					=   self.model.generate(**lowerCAmelCase__							)

             snake_case__	,			snake_case__					=   inputs["""input_ids"""].shape
             snake_case__					=   output_ids.shape[0]

             return output_ids.reshape(lowerCAmelCase__    ,		out_b // in_b    ,		*output_ids.shape[1:]							)[0][0]

       def UpperCAmelCase_						(						self       :						Union[str, Any]    ,		lowerCAmelCase__       :						Dict							)			->    List[Any]:
             return self.pre_processor.decode(lowerCAmelCase__    ,		skip_special_tokens=lowerCAmelCase__    ,		clean_up_tokenization_spaces=lowerCAmelCase__							)






