





'''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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   str:







	'''simple docstring'''







	lowerCamelCase__              =	BertConfig.from_json_file(__snake_case							)
	print(F'Building PyTorch model from configuration: {config}'							)
	lowerCamelCase__              =	BertForPreTraining(__snake_case							)

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

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


if __name__ == "__main__":
			_a						=   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."
			)
			_a						=   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










from typing import TYPE_CHECKING

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


_a						=   {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   ["FunnelTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FunnelBaseModel",
			    "FunnelForMaskedLM",
			    "FunnelForMultipleChoice",
			    "FunnelForPreTraining",
			    "FunnelForQuestionAnswering",
			    "FunnelForSequenceClassification",
			    "FunnelForTokenClassification",
			    "FunnelModel",
			    "FunnelPreTrainedModel",
			    "load_tf_weights_in_funnel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFFunnelBaseModel",
			    "TFFunnelForMaskedLM",
			    "TFFunnelForMultipleChoice",
			    "TFFunnelForPreTraining",
			    "TFFunnelForQuestionAnswering",
			    "TFFunnelForSequenceClassification",
			    "TFFunnelForTokenClassification",
			    "TFFunnelModel",
			    "TFFunnelPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



