

import argparse

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


logging.set_verbosity_info()




def        _lowercase  (					a_					:       str       ,a_					:       int       ,a_					:       Union[str, Any]       ,a_					:       Tuple       ,a_					:       Union[str, Any]					)					->						Tuple:




					'''simple docstring'''







					__magic_name__				=     BertConfig.from_pretrained(
					    'bert-large-cased'       ,vocab_size=a_       ,max_position_embeddings=5_1_2       ,is_decoder=a_       ,add_cross_attention=a_       ,)
					__magic_name__				=     bert_config.to_dict()
					del bert_config_dict["type_vocab_size"]
					__magic_name__				=     BertGenerationConfig(**a_					)
					if is_encoder:
										__magic_name__				=     BertGenerationEncoder(a_					)
					else:
										__magic_name__				=     BertGenerationDecoder(a_					)
					print(F'Building PyTorch model from configuration: {config}'					)

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

					# Save pytorch-model
					print(F'Save PyTorch model and config to {pytorch_dump_path}'					)
					model.save_pretrained(a_					)


if __name__ == "__main__":
						A__							=				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=50358, type=int, help="Vocab size of model")
						A__							=				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_torch_available, is_vision_available


_A      :     Tuple       	={
    '''configuration_imagegpt''': ['''IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ImageGPTConfig''', '''ImageGPTOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Dict       	=['''ImageGPTFeatureExtractor''']
			_A      :     Union[str, Any]       	=['''ImageGPTImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=[
			    '''IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ImageGPTForCausalImageModeling''',
			    '''ImageGPTForImageClassification''',
			    '''ImageGPTModel''',
			    '''ImageGPTPreTrainedModel''',
			    '''load_tf_weights_in_imagegpt''',
			]


if TYPE_CHECKING:
			from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
						from .image_processing_imagegpt import ImageGPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_imagegpt import (
						    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ImageGPTForCausalImageModeling,
						    ImageGPTForImageClassification,
						    ImageGPTModel,
						    ImageGPTPreTrainedModel,
						    load_tf_weights_in_imagegpt,
						)

else:
			import sys

			_A      :     str       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

