






"""simple docstring"""
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				# Initialise PyTorch model
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						BertConfig.from_pretrained(
				    'bert-large-cased',	vocab_size=lowerCamelCase,	max_position_embeddings=512,	is_decoder=lowerCamelCase,	add_cross_attention=lowerCamelCase,	)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						bert_config.to_dict()
				del bert_config_dict["type_vocab_size"]
				_SCREAMING_SNAKE_CASE      :		int							=						BertGenerationConfig(**lowerCamelCase					)
				if is_encoder:
								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						BertGenerationEncoder(lowerCamelCase					)
				else:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						BertGenerationDecoder(lowerCamelCase					)
				print(f"""Building PyTorch model from configuration: {config}"""					)

				# Load weights from tf checkpoint
				load_tf_weights_in_bert_generation(
				    lowerCamelCase,	lowerCamelCase,	model_class='bert',	is_encoder_named_decoder=lowerCamelCase,	is_encoder=lowerCamelCase,	)

				# Save pytorch-model
				print(f"""Save PyTorch model and config to {pytorch_dump_path}"""					)
				model.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
			lowerCAmelCase__							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--is_encoder_named_decoder',
			    action='store_true',
			    help='If decoder has to be renamed to encoder in PyTorch model.',
			)
			parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
			parser.add_argument('--vocab_size', default=50_358, type=int, help='Vocab size of model')
			lowerCAmelCase__							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_hub_path,
			    args.pytorch_dump_path,
			    args.is_encoder_named_decoder,
			    args.vocab_size,
			    is_encoder=args.is_encoder,
			)









"""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,
)


lowerCAmelCase__							= {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__							= ['BigBirdTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__							= ['BigBirdTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__							= [
			    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'BigBirdForCausalLM',
			    'BigBirdForMaskedLM',
			    'BigBirdForMultipleChoice',
			    'BigBirdForPreTraining',
			    'BigBirdForQuestionAnswering',
			    'BigBirdForSequenceClassification',
			    'BigBirdForTokenClassification',
			    'BigBirdLayer',
			    'BigBirdModel',
			    'BigBirdPreTrainedModel',
			    'load_tf_weights_in_big_bird',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__							= [
			    'FlaxBigBirdForCausalLM',
			    'FlaxBigBirdForMaskedLM',
			    'FlaxBigBirdForMultipleChoice',
			    'FlaxBigBirdForPreTraining',
			    'FlaxBigBirdForQuestionAnswering',
			    'FlaxBigBirdForSequenceClassification',
			    'FlaxBigBirdForTokenClassification',
			    'FlaxBigBirdModel',
			    'FlaxBigBirdPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_big_bird import BigBirdTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_big_bird_fast import BigBirdTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_big_bird import (
						    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BigBirdForCausalLM,
						    BigBirdForMaskedLM,
						    BigBirdForMultipleChoice,
						    BigBirdForPreTraining,
						    BigBirdForQuestionAnswering,
						    BigBirdForSequenceClassification,
						    BigBirdForTokenClassification,
						    BigBirdLayer,
						    BigBirdModel,
						    BigBirdPreTrainedModel,
						    load_tf_weights_in_big_bird,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_big_bird import (
						    FlaxBigBirdForCausalLM,
						    FlaxBigBirdForMaskedLM,
						    FlaxBigBirdForMultipleChoice,
						    FlaxBigBirdForPreTraining,
						    FlaxBigBirdForQuestionAnswering,
						    FlaxBigBirdForSequenceClassification,
						    FlaxBigBirdForTokenClassification,
						    FlaxBigBirdModel,
						    FlaxBigBirdPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase__							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

