



"""simple docstring"""





import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
SCREAMING_SNAKE_CASE				:     Optional[Any]          =	'''src/diffusers'''
SCREAMING_SNAKE_CASE				:     Any          =	'''docs/source/en'''
SCREAMING_SNAKE_CASE				:     int          =	'''.'''






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






   """simple docstring"""


   with open(snake_case_   ,	"""r"""   ,	encoding="""utf-8"""   ,	newline="""\n"""   ) as f:
      _lowerCAmelCase					     =		f.readlines()
   # Find the start prompt.
   _lowerCAmelCase					     =		0
   while not lines[start_index].startswith(snake_case_   ):
      start_index += 1
   start_index += 1

   _lowerCAmelCase					     =		start_index
   while not lines[end_index].startswith(snake_case_   ):
      end_index += 1
   end_index -= 1

   while len(lines[start_index]   ) <= 1:
      start_index += 1
   while len(lines[end_index]   ) <= 1:
      end_index -= 1
   end_index += 1
   return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE				:     str          =	'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE				:     List[str]          =	re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
SCREAMING_SNAKE_CASE				:     Optional[int]          =	re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE				:     int          =	re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the diffusers module imported is the one in the repo.
SCREAMING_SNAKE_CASE				:     int          =	importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
SCREAMING_SNAKE_CASE				:     List[Any]          =	spec.loader.load_module()






def 	__UpperCAmelCase					(       snake_case_  :  Dict   )							->	Optional[int]:






   """simple docstring"""


   _lowerCAmelCase					     =		re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""   ,	snake_case_   )
   return [m.group(0   ) for m in matches]






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






   """simple docstring"""


   _lowerCAmelCase					     =		2 if text == """✅""" or text == """❌""" else len(snake_case_   )
   _lowerCAmelCase					     =		(width - text_length) // 2
   _lowerCAmelCase					     =		width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent






def 	__UpperCAmelCase					(       )							->	Tuple:






   """simple docstring"""


   _lowerCAmelCase					     =		diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   _lowerCAmelCase					     =		{
       name: config_mapping_names[code]
       for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
       if code in config_mapping_names
   }
   _lowerCAmelCase					     =		{name: config.replace("""ConfigMixin"""   ,	""""""   ) for name, config in model_name_to_config.items()}

   # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )

   # Let's lookup through all diffusers object (once).
   for attr_name in dir(snake_case_   ):
      _lowerCAmelCase					     =		None
      if attr_name.endswith("""Tokenizer"""   ):
         _lowerCAmelCase					     =		slow_tokenizers
         _lowerCAmelCase					     =		attr_name[:-9]
      elif attr_name.endswith("""TokenizerFast"""   ):
         _lowerCAmelCase					     =		fast_tokenizers
         _lowerCAmelCase					     =		attr_name[:-13]
      elif _re_tf_models.match(snake_case_   ) is not None:
         _lowerCAmelCase					     =		tf_models
         _lowerCAmelCase					     =		_re_tf_models.match(snake_case_   ).groups()[0]
      elif _re_flax_models.match(snake_case_   ) is not None:
         _lowerCAmelCase					     =		flax_models
         _lowerCAmelCase					     =		_re_flax_models.match(snake_case_   ).groups()[0]
      elif _re_pt_models.match(snake_case_   ) is not None:
         _lowerCAmelCase					     =		pt_models
         _lowerCAmelCase					     =		_re_pt_models.match(snake_case_   ).groups()[0]

      if lookup_dict is not None:
         while len(snake_case_   ) > 0:
            if attr_name in model_name_to_prefix.values():
               _lowerCAmelCase					     =		True
               break
            # Try again after removing the last word in the name
            _lowerCAmelCase					     =		"""""".join(camel_case_split(snake_case_   )[:-1]   )

    # Let's build that table!
   _lowerCAmelCase					     =		list(model_name_to_config.keys()   )
   model_names.sort(key=str.lower   )
   _lowerCAmelCase					     =		["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
   # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
   _lowerCAmelCase					     =		[len(snake_case_   ) + 2 for c in columns]
   _lowerCAmelCase					     =		max([len(snake_case_   ) for name in model_names]   ) + 2

   # Build the table per se
   _lowerCAmelCase					     =		"""|""" + """|""".join([_center_text(snake_case_   ,	snake_case_   ) for c, w in zip(snake_case_   ,	snake_case_   )]   ) + """|\n"""
   # Use ":-----:" format to center-aligned table cell texts
   table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]   ) + "|\n"

   _lowerCAmelCase					     =		{True: """✅""", False: """❌"""}
   for name in model_names:
      _lowerCAmelCase					     =		model_name_to_prefix[name]
      _lowerCAmelCase					     =		[
          name,
          check[slow_tokenizers[prefix]],
          check[fast_tokenizers[prefix]],
          check[pt_models[prefix]],
          check[tf_models[prefix]],
          check[flax_models[prefix]],
      ]
      table += "|" + "|".join([_center_text(snake_case_   ,	snake_case_   ) for l, w in zip(snake_case_   ,	snake_case_   )]   ) + "|\n"
   return table






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






   """simple docstring"""


   _lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase					     =		_find_text_in_file(
       filename=os.path.join(snake_case_   ,	"""index.mdx"""   )   ,	start_prompt="""<!--This table is updated automatically from the auto modules"""   ,	end_prompt="""<!-- End table-->"""   ,	)
   _lowerCAmelCase					     =		get_model_table_from_auto_modules()

   if current_table != new_table:
      if overwrite:
         with open(os.path.join(snake_case_   ,	"""index.mdx"""   )   ,	"""w"""   ,	encoding="""utf-8"""   ,	newline="""\n"""   ) as f:
            f.writelines(lines[:start_index] + [new_table] + lines[end_index:]   )
      else:
         raise ValueError(
             """The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."""   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     str          =	argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	parser.parse_args()

  check_model_table(args.fix_and_overwrite)




"""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
  )