
'''simple docstring'''
import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 		A_(     A						:		Tuple		,     A						:		Optional[Any]		,     A						:		Dict):
							# Initialise PyTorch model
							UpperCamelCase   			=     BertConfig.from_json_file(A)
							print(f'''Building PyTorch model from configuration: {config}''')
							UpperCamelCase   			=     BertForPreTraining(A)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(A		,     A		,     A)

							# Save pytorch-model
							print(f'''Save PyTorch model to {pytorch_dump_path}''')
							torch.save(model.state_dict()		,     A)


if __name__ == "__main__":
						lowerCAmelCase					:    Optional[int]           = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
						)
						parser.add_argument(
						    '--bert_config_file',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'The config json file corresponding to the pre-trained BERT model. \n'
						        'This specifies the model architecture.'
						    ),
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						lowerCAmelCase					:    int           = parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase					:    Tuple           = TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase					:    Optional[int]           = TaTokenizerFast

lowerCAmelCase					:    Any           = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[int]           = [
						    'MT5EncoderModel',
						    'MT5ForConditionalGeneration',
						    'MT5ForQuestionAnswering',
						    'MT5Model',
						    'MT5PreTrainedModel',
						    'MT5Stack',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Dict           = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[Any]           = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(
						    __name__,
						    globals()['__file__'],
						    _import_structure,
						    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
						    module_spec=__spec__,
						)




