





"""simple docstring"""
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							A						=    json.load(open(lowercase__   ,    "r"			)			)
							A						=    GPTNeoConfig(
							    hidden_size=config_json["n_embd"]   ,    num_layers=config_json["n_layer"]   ,    num_heads=config_json["n_head"]   ,    attention_types=config_json["attention_types"]   ,    max_position_embeddings=config_json["n_positions"]   ,    resid_dropout=config_json["res_dropout"]   ,    embed_dropout=config_json["embed_dropout"]   ,    attention_dropout=config_json["attn_dropout"]   ,    )
							print(F"Building PyTorch model from configuration: {config}"			)
							A						=    GPTNeoForCausalLM(lowercase__			)

							# Load weights from tf checkpoint
							load_tf_weights_in_gpt_neo(lowercase__   ,    lowercase__   ,    lowercase__			)

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


if __name__ == "__main__":
		_snake_case	:						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(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained mesh-tf 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.'
		)
		_snake_case	:						Union[str, Any]					=					parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__SCREAMING_SNAKE_CASE	:							str           =  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Any           =  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  [
		    '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieMForMultipleChoice''',
		    '''ErnieMForQuestionAnswering''',
		    '''ErnieMForSequenceClassification''',
		    '''ErnieMForTokenClassification''',
		    '''ErnieMModel''',
		    '''ErnieMPreTrainedModel''',
		    '''ErnieMForInformationExtraction''',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

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

