


"""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
lowercase  		=			"""src/diffusers"""
lowercase  		=			"""docs/source/en"""
lowercase  		=			"""."""



def 			A__		(					_UpperCAmelCase    :      Optional[Any]				,    _UpperCAmelCase    :      Optional[int]				,    _UpperCAmelCase    :      str	)							->	Optional[Any]:

	'''simple docstring'''


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

	snake_case__  :						Union[str, Any]		=      start_index
	while not lines[end_index].startswith(_UpperCAmelCase	):
		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 |
lowercase  		=			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowercase  		=			re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowercase  		=			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.
lowercase  		=			re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]	)							->	Tuple:

	'''simple docstring'''


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



def 			A__		(					_UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      List[str]	)							->	Optional[int]:

	'''simple docstring'''


	snake_case__  :						List[Any]		=      2 if text == "✅" or text == "❌" else len(_UpperCAmelCase	)
	snake_case__  :						Optional[Any]		=      (width - text_length) // 2
	snake_case__  :						Union[str, Any]		=      width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent



def 			A__		(					)							->	Any:

	'''simple docstring'''


	snake_case__  :						Optional[Any]		=      diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	snake_case__  :						Optional[int]		=      {
	    name: config_mapping_names[code]
	    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_mapping_names
	}
	snake_case__  :						int		=      {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.
	snake_case__  :						Optional[Any]		=      collections.defaultdict(_UpperCAmelCase	)
	snake_case__  :						Optional[Any]		=      collections.defaultdict(_UpperCAmelCase	)
	snake_case__  :						Tuple		=      collections.defaultdict(_UpperCAmelCase	)
	snake_case__  :						List[Any]		=      collections.defaultdict(_UpperCAmelCase	)
	snake_case__  :						List[Any]		=      collections.defaultdict(_UpperCAmelCase	)

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

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

    # Let's build that table!
	snake_case__  :						Union[str, Any]		=      list(model_name_to_config.keys()	)
	model_names.sort(key=str.lower	)
	snake_case__  :						Optional[Any]		=      ["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).
	snake_case__  :						Union[str, Any]		=      [len(_UpperCAmelCase	) + 2 for c in columns]
	snake_case__  :						Union[str, Any]		=      max([len(_UpperCAmelCase	) for name in model_names]	) + 2

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

	snake_case__  :						Union[str, Any]		=      {True: "✅", False: "❌"}
	for name in model_names:
		snake_case__  :						str		=      model_name_to_prefix[name]
		snake_case__  :						Union[str, Any]		=      [
		    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(_UpperCAmelCase				,    _UpperCAmelCase	) for l, w in zip(_UpperCAmelCase				,    _UpperCAmelCase	)]	) + "|\n"
	return table



def 			A__		(					_UpperCAmelCase    :      str=False	)							->	List[Any]:

	'''simple docstring'''


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

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(_UpperCAmelCase				,    "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__":
					lowercase  		=			argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					lowercase  		=			parser.parse_args()

					check_model_table(args.fix_and_overwrite)










'''simple docstring'''



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="Hello world! cécé herlolip"


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						int:
				snake_case__  ,      snake_case__      :   int      =    os.path.split(UpperCamelCase__			)
				snake_case__      :   List[Any]      =    DataaVecTextModel.from_pretrained(
				    UpperCamelCase__   ,				checkpoint_file=UpperCamelCase__			)
				dataavec.eval()  # disable dropout
				snake_case__      :   Dict      =    dataavec.models[0]
				snake_case__      :   int      =    dataavec_model.encoder.sentence_encoder
				snake_case__      :   List[Any]      =    DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings   ,				hidden_size=dataavec_model.args.encoder_embed_dim   ,				num_hidden_layers=dataavec_model.args.encoder_layers   ,				num_attention_heads=dataavec_model.args.encoder_attention_heads   ,				intermediate_size=dataavec_model.args.encoder_ffn_embed_dim   ,				max_position_embeddings=514   ,				type_vocab_size=1   ,				layer_norm_eps=1e-5   ,				)
				if classification_head:
								snake_case__      :   int      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase__			)

				snake_case__      :   Optional[int]      =    DataaVecTextForSequenceClassification(UpperCamelCase__			) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Optional[Any]      =    dataavec_sent_encoder.embed_tokens.weight
				snake_case__      :   Union[str, Any]      =    dataavec_sent_encoder.embed_positions.weight
				snake_case__      :   Any      =    torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				snake_case__      :   Tuple      =    dataavec_sent_encoder.layernorm_embedding.weight
				snake_case__      :   Dict      =    dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								snake_case__      :   BertLayer      =    model.dataavec_text.encoder.layer[i]
								snake_case__      :   TransformerSentenceEncoderLayer      =    dataavec_sent_encoder.layers[i]

								# self attention
								snake_case__      :   BertSelfAttention      =    layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)

								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.q_proj.weight
								snake_case__      :   List[str]      =    dataavec_layer.self_attn.q_proj.bias
								snake_case__      :   str      =    dataavec_layer.self_attn.k_proj.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.k_proj.bias
								snake_case__      :   Optional[Any]      =    dataavec_layer.self_attn.v_proj.weight
								snake_case__      :   int      =    dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__      :   BertSelfOutput      =    layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								snake_case__      :   Optional[int]      =    dataavec_layer.self_attn.out_proj.weight
								snake_case__      :   str      =    dataavec_layer.self_attn.out_proj.bias
								snake_case__      :   int      =    dataavec_layer.self_attn_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   BertIntermediate      =    layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   str      =    dataavec_layer.fca.bias

								# output
								snake_case__      :   BertOutput      =    layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   List[Any]      =    dataavec_layer.fca.bias
								snake_case__      :   str      =    dataavec_layer.final_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Any      =    dataavec.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.weight
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.bias
								snake_case__      :   Union[str, Any]      =    dataavec_model.encoder.lm_head.layer_norm.weight
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__      :   torch.Tensor      =    dataavec.encode(UpperCamelCase__			).unsqueeze(0			)  # batch of size 1

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

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


if __name__ == "__main__":
				_lowercase       :						Any						=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--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."
				)
				_lowercase       :						Tuple						=parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)




