



"""simple docstring"""





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 	__UpperCAmelCase					(       snake_case_  :  List[Any]   ,	snake_case_  :  Any   )							->	Dict:






   """simple docstring"""


   _lowerCAmelCase					     =		UNetaDModel(**snake_case_   )
   _lowerCAmelCase					     =		checkpoint["""all_modules.0.W"""].data
   _lowerCAmelCase					     =		checkpoint["""all_modules.0.W"""].data
   _lowerCAmelCase					     =		checkpoint["""all_modules.1.weight"""].data
   _lowerCAmelCase					     =		checkpoint["""all_modules.1.bias"""].data

   _lowerCAmelCase					     =		checkpoint["""all_modules.2.weight"""].data
   _lowerCAmelCase					     =		checkpoint["""all_modules.2.bias"""].data

   _lowerCAmelCase					     =		checkpoint["""all_modules.3.weight"""].data
   _lowerCAmelCase					     =		checkpoint["""all_modules.3.bias"""].data

   _lowerCAmelCase					     =		checkpoint[list(checkpoint.keys()   )[-4]].data
   _lowerCAmelCase					     =		checkpoint[list(checkpoint.keys()   )[-3]].data
   _lowerCAmelCase					     =		checkpoint[list(checkpoint.keys()   )[-2]].data
   _lowerCAmelCase					     =		checkpoint[list(checkpoint.keys()   )[-1]].data

   _lowerCAmelCase					     =		4

   def set_attention_weights(snake_case_  :  List[Any]   ,	snake_case_  :  Tuple   ,	snake_case_  :  int   ):
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

   def set_resnet_weights(snake_case_  :  List[Any]   ,	snake_case_  :  Any   ,	snake_case_  :  int   ):
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
      _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.Dense_0.bias"""].data

      if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
         _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
         _lowerCAmelCase					     =		old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

   for i, block in enumerate(new_model_architecture.downsample_blocks   ):
      _lowerCAmelCase					     =		hasattr(snake_case_   ,	"""attentions"""   )
      for j in range(len(block.resnets   )   ):
         set_resnet_weights(block.resnets[j]   ,	snake_case_   ,	snake_case_   )
         module_index += 1
         if has_attentions:
            set_attention_weights(block.attentions[j]   ,	snake_case_   ,	snake_case_   )
            module_index += 1

      if hasattr(snake_case_   ,	"""downsamplers"""   ) and block.downsamplers is not None:
         set_resnet_weights(block.resnet_down   ,	snake_case_   ,	snake_case_   )
         module_index += 1
         _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
         _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.Conv_0.bias"""].data
         module_index += 1

   set_resnet_weights(new_model_architecture.mid_block.resnets[0]   ,	snake_case_   ,	snake_case_   )
   module_index += 1
   set_attention_weights(new_model_architecture.mid_block.attentions[0]   ,	snake_case_   ,	snake_case_   )
   module_index += 1
   set_resnet_weights(new_model_architecture.mid_block.resnets[1]   ,	snake_case_   ,	snake_case_   )
   module_index += 1

   for i, block in enumerate(new_model_architecture.up_blocks   ):
      _lowerCAmelCase					     =		hasattr(snake_case_   ,	"""attentions"""   )
      for j in range(len(block.resnets   )   ):
         set_resnet_weights(block.resnets[j]   ,	snake_case_   ,	snake_case_   )
         module_index += 1
      if has_attentions:
         set_attention_weights(
             block.attentions[0]   ,	snake_case_   ,	snake_case_   )  # why can there only be a single attention layer for up?
         module_index += 1

      if hasattr(snake_case_   ,	"""resnet_up"""   ) and block.resnet_up is not None:
         _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.weight"""].data
         _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.bias"""].data
         module_index += 1
         _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.weight"""].data
         _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.bias"""].data
         module_index += 1
         set_resnet_weights(block.resnet_up   ,	snake_case_   ,	snake_case_   )
         module_index += 1

   _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.weight"""].data
   _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.bias"""].data
   module_index += 1
   _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.weight"""].data
   _lowerCAmelCase					     =		checkpoint[F"""all_modules.{module_index}.bias"""].data

   return new_model_architecture.state_dict()


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     Optional[Any]          =	argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_path''',
      default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
      type=str,
      required=False,
      help='''Path to the checkpoint to convert.''',
  )

  parser.add_argument(
      '''--config_file''',
      default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
      type=str,
      required=False,
      help='''The config json file corresponding to the architecture.''',
  )

  parser.add_argument(
      '''--dump_path''',
      default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
      type=str,
      required=False,
      help='''Path to the output model.''',
  )

  SCREAMING_SNAKE_CASE				:     int          =	parser.parse_args()

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

  with open(args.config_file) as f:
    SCREAMING_SNAKE_CASE				:     int          =	json.loads(f.read())

  SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

  if "sde" in config:
    del config["sde"]

  SCREAMING_SNAKE_CASE				:     str          =	UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    SCREAMING_SNAKE_CASE				:     List[Any]          =	ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

    SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	ScoreSdeVePipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)




"""simple docstring"""





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
  raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     Tuple          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[Any]          =	'''Hello world! cécé herlolip'''






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  str   ,	snake_case_  :  bool   )							->	Optional[int]:






   """simple docstring"""


   _lowerCAmelCase					     =		FairseqRobertaModel.from_pretrained(snake_case_   )
   roberta.eval()  # disable dropout
   _lowerCAmelCase					     =		roberta.model.encoder.sentence_encoder
   _lowerCAmelCase					     =		RobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,	hidden_size=roberta.args.encoder_embed_dim   ,	num_hidden_layers=roberta.args.encoder_layers   ,	num_attention_heads=roberta.args.encoder_attention_heads   ,	intermediate_size=roberta.args.encoder_ffn_embed_dim   ,	max_position_embeddings=514   ,	type_vocab_size=1   ,	layer_norm_eps=1e-5   ,	)
   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our BERT config:"""   ,	snake_case_   )

   _lowerCAmelCase					     =		RobertaForSequenceClassification(snake_case_   ) if classification_head else RobertaForMaskedLM(snake_case_   )
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   _lowerCAmelCase					     =		roberta_sent_encoder.embed_tokens.weight
   _lowerCAmelCase					     =		roberta_sent_encoder.embed_positions.weight
   _lowerCAmelCase					     =		torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c RoBERTa doesn't use them.
   _lowerCAmelCase					     =		roberta_sent_encoder.emb_layer_norm.weight
   _lowerCAmelCase					     =		roberta_sent_encoder.emb_layer_norm.bias

   for i in range(config.num_hidden_layers   ):
      # Encoder: start of layer
      _lowerCAmelCase					     =		model.roberta.encoder.layer[i]
      _lowerCAmelCase					     =		roberta_sent_encoder.layers[i]

      # self attention
      _lowerCAmelCase					     =		layer.attention.self
      assert (
          roberta_layer.self_attn.k_proj.weight.data.shape
          == roberta_layer.self_attn.q_proj.weight.data.shape
          == roberta_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)   )
      )

      _lowerCAmelCase					     =		roberta_layer.self_attn.q_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.q_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn.k_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.k_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn.v_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.v_proj.bias

      # self-attention output
      _lowerCAmelCase					     =		layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      _lowerCAmelCase					     =		roberta_layer.self_attn.out_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.out_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn_layer_norm.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn_layer_norm.bias

      # intermediate
      _lowerCAmelCase					     =		layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      _lowerCAmelCase					     =		roberta_layer.fca.weight
      _lowerCAmelCase					     =		roberta_layer.fca.bias

      # output
      _lowerCAmelCase					     =		layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      _lowerCAmelCase					     =		roberta_layer.fca.weight
      _lowerCAmelCase					     =		roberta_layer.fca.bias
      _lowerCAmelCase					     =		roberta_layer.final_layer_norm.weight
      _lowerCAmelCase					     =		roberta_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].dense.weight
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].dense.bias
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.weight
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.bias
   else:
      # LM Head
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.dense.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.dense.bias
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.layer_norm.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.layer_norm.bias
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   _lowerCAmelCase					     =		roberta.encode(snake_case_   ).unsqueeze(0   )  # batch of size 1

   _lowerCAmelCase					     =		model(snake_case_   )[0]
   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""](roberta.extract_features(snake_case_   )   )
   else:
      _lowerCAmelCase					     =		roberta.model(snake_case_   )[0]
   print(our_output.shape   ,	their_output.shape   )
   _lowerCAmelCase					     =		torch.max(torch.abs(our_output - their_output   )   ).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
   _lowerCAmelCase					     =		torch.allclose(snake_case_   ,	snake_case_   ,	atol=1e-3   )
   print("""Do both models output the same tensors?"""   ,	"""🔥""" if success else """💩"""   )
   if not success:
      raise Exception("""Something went wRoNg"""   )

   pathlib.Path(snake_case_   ).mkdir(parents=snake_case_   ,	exist_ok=snake_case_   )
   print(F"""Saving model to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     Any          =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
  )
  SCREAMING_SNAKE_CASE				:     Any          =	parser.parse_args()
  convert_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )