



'''simple docstring'''




import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
a     :    List[str]      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	List[Any]							  =	os.path.abspath(_lowercase	)
							logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''	)
							# Load weights from TF model
							UpperCAmelCase  :	Optional[Any]							  =	tf.train.list_variables(_lowercase	)
							UpperCAmelCase  :	Any							  =	[]
							UpperCAmelCase  :	List[str]							  =	[]
							UpperCAmelCase  :	Any							  =	[]
							for full_name, shape in init_vars:
														# logger.info(f"Loading TF weight {name} with shape {shape}")
														UpperCAmelCase  :	Optional[Any]							  =	full_name.split("""/"""	)
														if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
																					logger.info(F'''Skipping non-model layer {full_name}'''	)
																					continue
														if "optimizer" in full_name:
																					logger.info(F'''Skipping optimization layer {full_name}'''	)
																					continue
														if name[0] == "model":
																					# ignore initial 'model'
																					UpperCAmelCase  :	List[str]							  =	name[1:]
														# figure out how many levels deep the name is
														UpperCAmelCase  :	Any							  =	0
														for _name in name:
																					if _name.startswith("""layer_with_weights"""	):
																												depth += 1
																					else:
																												break
														layer_depth.append(_lowercase	)
														# read data
														UpperCAmelCase  :	Dict							  =	tf.train.load_variable(_lowercase			,       _lowercase	)
														names.append("""/""".join(_lowercase	)	)
														arrays.append(_lowercase	)
							logger.info(F'''Read a total of {len(_lowercase	):,} layers'''	)

							# Sanity check
							if len(set(_lowercase	)	) != 1:
														raise ValueError(F'''Found layer names with different depths (layer depth {list(set(_lowercase	)	)})'''	)
							UpperCAmelCase  :	Tuple							  =	list(set(_lowercase	)	)[0]
							if layer_depth != 1:
														raise ValueError(
														    """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
														    """ heads."""	)

							# convert layers
							logger.info("""Converting weights..."""	)
							for full_name, array in zip(_lowercase			,       _lowercase	):
														UpperCAmelCase  :	Optional[Any]							  =	full_name.split("""/"""	)
														UpperCAmelCase  :	Optional[Any]							  =	model
														UpperCAmelCase  :	str							  =	[]
														for i, m_name in enumerate(_lowercase	):
																					if m_name == ".ATTRIBUTES":
																												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																												break
																					if m_name.startswith("""layer_with_weights"""	):
																												UpperCAmelCase  :	Union[str, Any]							  =	int(m_name.split("""-"""	)[-1]	)
																												if layer_num <= 2:
																																			# embedding layers
																																			# layer_num 0: word_embeddings
																																			# layer_num 1: position_embeddings
																																			# layer_num 2: token_type_embeddings
																																			continue
																												elif layer_num == 3:
																																			# embedding LayerNorm
																																			trace.extend(["""embeddings""", """LayerNorm"""]	)
																																			UpperCAmelCase  :	int							  =	getattr(_lowercase			,       """embeddings"""	)
																																			UpperCAmelCase  :	Optional[int]							  =	getattr(_lowercase			,       """LayerNorm"""	)
																												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																																			# encoder layers
																																			trace.extend(["""encoder""", """layer""", str(layer_num - 4	)]	)
																																			UpperCAmelCase  :	Any							  =	getattr(_lowercase			,       """encoder"""	)
																																			UpperCAmelCase  :	Union[str, Any]							  =	getattr(_lowercase			,       """layer"""	)
																																			UpperCAmelCase  :	str							  =	pointer[layer_num - 4]
																												elif layer_num == config.num_hidden_layers + 4:
																																			# pooler layer
																																			trace.extend(["""pooler""", """dense"""]	)
																																			UpperCAmelCase  :	int							  =	getattr(_lowercase			,       """pooler"""	)
																																			UpperCAmelCase  :	Optional[Any]							  =	getattr(_lowercase			,       """dense"""	)
																					elif m_name == "embeddings":
																												trace.append("""embeddings"""	)
																												UpperCAmelCase  :	Any							  =	getattr(_lowercase			,       """embeddings"""	)
																												if layer_num == 0:
																																			trace.append("""word_embeddings"""	)
																																			UpperCAmelCase  :	Tuple							  =	getattr(_lowercase			,       """word_embeddings"""	)
																												elif layer_num == 1:
																																			trace.append("""position_embeddings"""	)
																																			UpperCAmelCase  :	Dict							  =	getattr(_lowercase			,       """position_embeddings"""	)
																												elif layer_num == 2:
																																			trace.append("""token_type_embeddings"""	)
																																			UpperCAmelCase  :	Optional[int]							  =	getattr(_lowercase			,       """token_type_embeddings"""	)
																												else:
																																			raise ValueError(F'''Unknown embedding layer with name {full_name}'''	)
																												trace.append("""weight"""	)
																												UpperCAmelCase  :	str							  =	getattr(_lowercase			,       """weight"""	)
																					elif m_name == "_attention_layer":
																												# self-attention layer
																												trace.extend(["""attention""", """self"""]	)
																												UpperCAmelCase  :	Optional[Any]							  =	getattr(_lowercase			,       """attention"""	)
																												UpperCAmelCase  :	Optional[int]							  =	getattr(_lowercase			,       """self"""	)
																					elif m_name == "_attention_layer_norm":
																												# output attention norm
																												trace.extend(["""attention""", """output""", """LayerNorm"""]	)
																												UpperCAmelCase  :	List[str]							  =	getattr(_lowercase			,       """attention"""	)
																												UpperCAmelCase  :	Any							  =	getattr(_lowercase			,       """output"""	)
																												UpperCAmelCase  :	List[Any]							  =	getattr(_lowercase			,       """LayerNorm"""	)
																					elif m_name == "_attention_output_dense":
																												# output attention dense
																												trace.extend(["""attention""", """output""", """dense"""]	)
																												UpperCAmelCase  :	Optional[Any]							  =	getattr(_lowercase			,       """attention"""	)
																												UpperCAmelCase  :	List[str]							  =	getattr(_lowercase			,       """output"""	)
																												UpperCAmelCase  :	Any							  =	getattr(_lowercase			,       """dense"""	)
																					elif m_name == "_output_dense":
																												# output dense
																												trace.extend(["""output""", """dense"""]	)
																												UpperCAmelCase  :	Dict							  =	getattr(_lowercase			,       """output"""	)
																												UpperCAmelCase  :	Any							  =	getattr(_lowercase			,       """dense"""	)
																					elif m_name == "_output_layer_norm":
																												# output dense
																												trace.extend(["""output""", """LayerNorm"""]	)
																												UpperCAmelCase  :	List[str]							  =	getattr(_lowercase			,       """output"""	)
																												UpperCAmelCase  :	Union[str, Any]							  =	getattr(_lowercase			,       """LayerNorm"""	)
																					elif m_name == "_key_dense":
																												# attention key
																												trace.append("""key"""	)
																												UpperCAmelCase  :	List[str]							  =	getattr(_lowercase			,       """key"""	)
																					elif m_name == "_query_dense":
																												# attention query
																												trace.append("""query"""	)
																												UpperCAmelCase  :	Optional[Any]							  =	getattr(_lowercase			,       """query"""	)
																					elif m_name == "_value_dense":
																												# attention value
																												trace.append("""value"""	)
																												UpperCAmelCase  :	Any							  =	getattr(_lowercase			,       """value"""	)
																					elif m_name == "_intermediate_dense":
																												# attention intermediate dense
																												trace.extend(["""intermediate""", """dense"""]	)
																												UpperCAmelCase  :	Optional[int]							  =	getattr(_lowercase			,       """intermediate"""	)
																												UpperCAmelCase  :	Any							  =	getattr(_lowercase			,       """dense"""	)
																					elif m_name == "_output_layer_norm":
																												# output layer norm
																												trace.append("""output"""	)
																												UpperCAmelCase  :	Union[str, Any]							  =	getattr(_lowercase			,       """output"""	)
																					# weights & biases
																					elif m_name in ["bias", "beta"]:
																												trace.append("""bias"""	)
																												UpperCAmelCase  :	Union[str, Any]							  =	getattr(_lowercase			,       """bias"""	)
																					elif m_name in ["kernel", "gamma"]:
																												trace.append("""weight"""	)
																												UpperCAmelCase  :	Optional[int]							  =	getattr(_lowercase			,       """weight"""	)
																					else:
																												logger.warning(F'''Ignored {m_name}'''	)
        # for certain layers reshape is necessary
														UpperCAmelCase  :	Optional[int]							  =	""".""".join(_lowercase	)
														if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""			,       _lowercase	) or re.match(
														    R"""(\S+)\.attention\.output\.dense\.weight"""			,       _lowercase	):
																					UpperCAmelCase  :	Tuple							  =	array.reshape(pointer.data.shape	)
														if "kernel" in full_name:
																					UpperCAmelCase  :	Union[str, Any]							  =	array.transpose()
														if pointer.shape == array.shape:
																					UpperCAmelCase  :	Tuple							  =	torch.from_numpy(_lowercase	)
														else:
																					raise ValueError(
																					    F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
																					    F''' {array.shape}'''	)
														logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}'''	)
							return model



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[Any]:
							# Instantiate model
							logger.info(F'''Loading model based on config from {config_path}...'''	)
							UpperCAmelCase  :	str							  =	BertConfig.from_json_file(_lowercase	)
							UpperCAmelCase  :	List[Any]							  =	BertModel(_lowercase	)

							# Load weights from checkpoint
							logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...'''	)
							load_tfa_weights_in_bert(_lowercase			,       _lowercase			,       _lowercase	)

							# Save pytorch-model
							logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...'''	)
							torch.save(model.state_dict()			,       _lowercase	)


if __name__ == "__main__":
							a     :    str      						=					argparse.ArgumentParser()
							parser.add_argument(
							    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
							)
							parser.add_argument(
							    """--bert_config_file""",
							    type=str,
							    required=True,
							    help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""",
							    type=str,
							    required=True,
							    help="""Path to the output PyTorch model (must include filename).""",
							)
							a     :    Optional[Any]      						=					parser.parse_args()
							convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_barthez import BarthezTokenizer
else:
							a     :    int      						=					None

a     :    List[Any]      						=					logging.get_logger(__name__)

a     :    Dict      						=					{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a     :    Union[str, Any]      						=					{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

a     :    List[Any]      						=					{
    """moussaKam/mbarthez""": 1_0_2_4,
    """moussaKam/barthez""": 1_0_2_4,
    """moussaKam/barthez-orangesum-title""": 1_0_2_4,
}

a     :    int      						=					"""▁"""

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase    =				['input_ids', 'attention_mask']
	lowercase    =				BarthezTokenizer





	def __init__(						self	,	A=None	,	A=None	,	A="<s>"	,	A="</s>"	,	A="</s>"	,	A="<s>"	,	A="<unk>"	,	A="<pad>"	,	A="<mask>"	,	**A	,	)    ->    List[Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase  :	Tuple							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else mask_token

								super().__init__(
								    A	,	tokenizer_file=A	,	bos_token=A	,	eos_token=A	,	unk_token=A	,	sep_token=A	,	cls_token=A	,	pad_token=A	,	mask_token=A	,	**A	,	)

								UpperCAmelCase  :	Union[str, Any]							  =	vocab_file
								UpperCAmelCase  :	int							  =	False if not self.vocab_file else True





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase  :	Optional[int]							  =	[self.cls_token_id]
								UpperCAmelCase  :	List[str]							  =	[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Optional[int]							  =	[self.sep_token_id]
								UpperCAmelCase  :	List[Any]							  =	[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]






	def 				_lowercase(						self	,	A	,	A = None					)    ->    Tuple[str]:
								if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""					)

								if not os.path.isdir(A					):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								UpperCAmelCase  :	str							  =	os.path.join(
								    A	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(A					):
															copyfile(self.vocab_file	,	A					)

								return (out_vocab_file,)

