
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_						: Any         =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Union[str, Any]         =  ["""BloomTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[int]         =  [
							    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BloomForCausalLM""",
							    """BloomModel""",
							    """BloomPreTrainedModel""",
							    """BloomForSequenceClassification""",
							    """BloomForTokenClassification""",
							    """BloomForQuestionAnswering""",
							]

if TYPE_CHECKING:
							from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bloom_fast import BloomTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bloom import (
														    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BloomForCausalLM,
														    BloomForQuestionAnswering,
														    BloomForSequenceClassification,
														    BloomForTokenClassification,
														    BloomModel,
														    BloomPreTrainedModel,
														)

else:
							import sys

							lowerCamelCase_						: Tuple         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

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


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






