





"""simple docstring"""




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    str    ,       _lowercase						:    str					) ->List[str]:






	'''simple docstring'''






	def get_masked_lm_array(_lowercase						:    str					):
		a      :		List[Any]	=				F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		a      :		Union[str, Any]	=				tf.train.load_variable(_lowercase    ,       _lowercase					)

		if "kernel" in name:
			a      :		Optional[int]	=				array.transpose()

		return torch.from_numpy(_lowercase					)

	def get_encoder_array(_lowercase						:    str					):
		a      :		List[str]	=				F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		a      :		Dict	=				tf.train.load_variable(_lowercase    ,       _lowercase					)

		if "kernel" in name:
			a      :		Optional[Any]	=				array.transpose()

		return torch.from_numpy(_lowercase					)

	def get_encoder_layer_array(_lowercase						:    int    ,       _lowercase						:    str					):
		a      :		str	=				F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		a      :		Dict	=				tf.train.load_variable(_lowercase    ,       _lowercase					)

		if "kernel" in name:
			a      :		int	=				array.transpose()

		return torch.from_numpy(_lowercase					)

	def get_encoder_attention_layer_array(_lowercase						:    int    ,       _lowercase						:    str    ,       _lowercase						:    Optional[Any]					):
		a      :		Optional[int]	=				F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		a      :		Optional[int]	=				tf.train.load_variable(_lowercase    ,       _lowercase					)
		a      :		Optional[Any]	=				array.reshape(_lowercase					)

		if "kernel" in name:
			a      :		int	=				array.transpose()

		return torch.from_numpy(_lowercase					)

	print(F"""Loading model based on config from {config_path}..."""					)
	a      :		Optional[int]	=				BertConfig.from_json_file(_lowercase					)
	a      :		str	=				BertForMaskedLM(_lowercase					)

	# Layers
	for layer_index in range(0    ,       config.num_hidden_layers					):
		a      :		BertLayer	=				model.bert.encoder.layer[layer_index]

		# Self-attention
		a      :		BertSelfAttention	=				layer.attention.self

		a      :		List[Any]	=				get_encoder_attention_layer_array(
		    _lowercase    ,       "_query_dense/kernel"    ,       self_attn.query.weight.data.shape					)
		a      :		Optional[int]	=				get_encoder_attention_layer_array(
		    _lowercase    ,       "_query_dense/bias"    ,       self_attn.query.bias.data.shape					)
		a      :		List[Any]	=				get_encoder_attention_layer_array(
		    _lowercase    ,       "_key_dense/kernel"    ,       self_attn.key.weight.data.shape					)
		a      :		str	=				get_encoder_attention_layer_array(
		    _lowercase    ,       "_key_dense/bias"    ,       self_attn.key.bias.data.shape					)
		a      :		Optional[Any]	=				get_encoder_attention_layer_array(
		    _lowercase    ,       "_value_dense/kernel"    ,       self_attn.value.weight.data.shape					)
		a      :		Union[str, Any]	=				get_encoder_attention_layer_array(
		    _lowercase    ,       "_value_dense/bias"    ,       self_attn.value.bias.data.shape					)

		# Self-attention Output
		a      :		BertSelfOutput	=				layer.attention.output

		a      :		str	=				get_encoder_attention_layer_array(
		    _lowercase    ,       "_output_dense/kernel"    ,       self_output.dense.weight.data.shape					)
		a      :		Union[str, Any]	=				get_encoder_attention_layer_array(
		    _lowercase    ,       "_output_dense/bias"    ,       self_output.dense.bias.data.shape					)

		a      :		int	=				get_encoder_layer_array(_lowercase    ,       "_attention_layer_norm/gamma"					)
		a      :		Dict	=				get_encoder_layer_array(_lowercase    ,       "_attention_layer_norm/beta"					)

		# Intermediate
		a      :		BertIntermediate	=				layer.intermediate

		a      :		Tuple	=				get_encoder_layer_array(_lowercase    ,       "_intermediate_dense/kernel"					)
		a      :		Any	=				get_encoder_layer_array(_lowercase    ,       "_intermediate_dense/bias"					)

		# Output
		a      :		BertOutput	=				layer.output

		a      :		List[str]	=				get_encoder_layer_array(_lowercase    ,       "_output_dense/kernel"					)
		a      :		Optional[int]	=				get_encoder_layer_array(_lowercase    ,       "_output_dense/bias"					)

		a      :		Tuple	=				get_encoder_layer_array(_lowercase    ,       "_output_layer_norm/gamma"					)
		a      :		str	=				get_encoder_layer_array(_lowercase    ,       "_output_layer_norm/beta"					)

	# Embeddings
	a      :		int	=				get_encoder_array("_position_embedding_layer/embeddings"					)
	a      :		Any	=				get_encoder_array("_type_embedding_layer/embeddings"					)
	a      :		Tuple	=				get_encoder_array("_embedding_norm_layer/gamma"					)
	a      :		Optional[Any]	=				get_encoder_array("_embedding_norm_layer/beta"					)

	# LM Head
	a      :		Union[str, Any]	=				model.cls.predictions.transform

	a      :		Optional[Any]	=				get_masked_lm_array("dense/kernel"					)
	a      :		int	=				get_masked_lm_array("dense/bias"					)

	a      :		List[Any]	=				get_masked_lm_array("layer_norm/gamma"					)
	a      :		Tuple	=				get_masked_lm_array("layer_norm/beta"					)

	a      :		Any	=				get_masked_lm_array("embedding_table"					)

	# Pooling
	a      :		Dict	=				BertPooler(config=_lowercase					)
	a      :		BertPooler	=				get_encoder_array("_pooler_layer/kernel"					)
	a      :		BertPooler	=				get_encoder_array("_pooler_layer/bias"					)

	# Export final model
	model.save_pretrained(_lowercase					)

	# Integration test - should load without any errors ;)
	a      :		Any	=				BertForMaskedLM.from_pretrained(_lowercase					)
	print(new_model.eval()					)

	print("Model conversion was done sucessfully!"					)


if __name__ == "__main__":
		a						: Tuple     	=			argparse.ArgumentParser()
		parser.add_argument(
		    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping 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.''',
		)
		a						: Dict     	=			parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









"""simple docstring"""




import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


a						: int     	=			'''<<<<<<< This should probably be modified because it mentions: '''

a						: Tuple     	=			'''=======
>>>>>>>
'''

a						: List[Any]     	=			[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

a						: Union[str, Any]     	=			[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R'''tfds\.core''', R'''datasets'''),
    (R'''tf\.io\.gfile\.GFile''', R'''open'''),
    (R'''tf\.([\w\d]+)''', R'''datasets.Value(\'\1\')'''),
    (R'''tfds\.features\.Text\(\)''', R'''datasets.Value(\'string\')'''),
    (R'''tfds\.features\.Text\(''', R'''datasets.Value(\'string\'),'''),
    (R'''features\s*=\s*tfds.features.FeaturesDict\(''', R'''features=datasets.Features('''),
    (R'''tfds\.features\.FeaturesDict\(''', R'''dict('''),
    (R'''The TensorFlow Datasets Authors''', R'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (R'''tfds\.''', R'''datasets.'''),
    (R'''dl_manager\.manual_dir''', R'''self.config.data_dir'''),
    (R'''self\.builder_config''', R'''self.config'''),
]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Namespace					) ->Any:






	'''simple docstring'''






	return ConvertCommand(args.tfds_path    ,       args.datasets_directory					)







class        __UpperCamelCase				(						a__						):






							@staticmethod
							def     __a   (   lowerCAmelCase__      )	-> int:
								a      :		Any	=				parser.add_parser(
								    "convert"      ,					help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."      ,					)
								train_parser.add_argument(
								    "--tfds_path"      ,					type=lowerCAmelCase__      ,					required=lowerCAmelCase__      ,					help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."      ,					)
								train_parser.add_argument(
								    "--datasets_directory"      ,					type=lowerCAmelCase__      ,					required=lowerCAmelCase__      ,					help="Path to the HuggingFace Datasets folder."      )
								train_parser.set_defaults(func=lowerCAmelCase__      )






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					*lowerCAmelCase__      )	-> Tuple:
								a      :		List[str]	=				get_logger("datasets-cli/converting"      )

								a      :		Optional[int]	=				tfds_path
								a      :		str	=				datasets_directory






							def     __a   (   self      )	-> str:
								if os.path.isdir(self._tfds_path      ):
									a      :		int	=				os.path.abspath(self._tfds_path      )
								elif os.path.isfile(self._tfds_path      ):
									a      :		Optional[Any]	=				os.path.dirname(self._tfds_path      )
								else:
									raise ValueError("--tfds_path is neither a directory nor a file. Please check path."      )

								a      :		str	=				os.path.abspath(self._datasets_directory      )

								self._logger.info(f"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""      )

								a      :		str	=				[]
								a      :		Union[str, Any]	=				[]
								a      :		str	=				{}

								if os.path.isdir(self._tfds_path      ):
									a      :		Dict	=				os.listdir(lowerCAmelCase__      )
								else:
									a      :		Dict	=				[os.path.basename(self._tfds_path      )]

								for f_name in file_names:
									self._logger.info(f"""Looking at file {f_name}"""      )
									a      :		Any	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		Dict	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )

									if not os.path.isfile(lowerCAmelCase__      ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
										self._logger.info("Skipping file"      )
										continue

									with open(lowerCAmelCase__      ,					encoding="utf-8"      ) as f:
										a      :		Any	=				f.readlines()

									a      :		Tuple	=				[]
									a      :		Any	=				False
									a      :		Dict	=				False
									a      :		Dict	=				[]
									for line in lines:
										a      :		Tuple	=				line

										# Convert imports
										if "import tensorflow.compat.v2 as tf" in out_line:
											continue
										elif "@tfds.core" in out_line:
											continue
										elif "builder=self" in out_line:
											continue
										elif "import tensorflow_datasets.public_api as tfds" in out_line:
											a      :		Optional[int]	=				"import datasets\n"
										elif "import tensorflow" in out_line:
											# order is important here
											a      :		Optional[Any]	=				""
											continue
										elif "from absl import logging" in out_line:
											a      :		Any	=				"from datasets import logging\n"
										elif "getLogger" in out_line:
											a      :		int	=				out_line.replace("getLogger"      ,					"get_logger"      )
										elif any(expression in out_line for expression in TO_HIGHLIGHT      ):
											a      :		Optional[Any]	=				True
											a      :		Optional[Any]	=				list(filter(lambda lowerCAmelCase__      : e in out_line      ,					lowerCAmelCase__      )      )
											out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowerCAmelCase__      ) + "\n"      )
											out_lines.append(lowerCAmelCase__      )
											out_lines.append(lowerCAmelCase__      )
											continue
										else:
											for pattern, replacement in TO_CONVERT:
												a      :		int	=				re.sub(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )

                # Take care of saving utilities (to later move them together with main script)
										if "tensorflow_datasets" in out_line:
											a      :		int	=				re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)"      ,					lowerCAmelCase__      )
											tfds_imports.extend(imp.strip() for imp in match.group(1      ).split(","      )      )
											a      :		List[str]	=				"from . import " + match.group(1      )

										# Check we have not forget anything
										if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
											raise ValueError(f"""Error converting {out_line.strip()}"""      )

										if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
											a      :		str	=				True
										out_lines.append(lowerCAmelCase__      )

									if is_builder or "wmt" in f_name:
										# We create a new directory for each dataset
										a      :		Tuple	=				f_name.replace(".py"      ,					""      )
										a      :		Optional[Any]	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )
										a      :		Dict	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )
										os.makedirs(lowerCAmelCase__      ,					exist_ok=lowerCAmelCase__      )
										self._logger.info(f"""Adding directory {output_dir}"""      )
										imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}      )
									else:
										# Utilities will be moved at the end
										utils_files.append(lowerCAmelCase__      )

									if needs_manual_update:
										with_manual_update.append(lowerCAmelCase__      )

									with open(lowerCAmelCase__      ,					"w"      ,					encoding="utf-8"      ) as f:
										f.writelines(lowerCAmelCase__      )
									self._logger.info(f"""Converted in {output_file}"""      )

								for utils_file in utils_files:
									try:
										a      :		int	=				os.path.basename(lowerCAmelCase__      )
										a      :		int	=				imports_to_builder_map[f_name.replace(".py"      ,					""      )]
										self._logger.info(f"""Moving {dest_folder} to {utils_file}"""      )
										shutil.copy(lowerCAmelCase__      ,					lowerCAmelCase__      )
									except KeyError:
										self._logger.error(f"""Cannot find destination folder for {utils_file}. Please copy manually."""      )

								if with_manual_update:
									for file_path in with_manual_update:
										self._logger.warning(
										    f"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""      )


