

"""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 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       int      ):
     A__				      =	json.load(open(_snake_case       , """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(_snake_case      )

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    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.'
 )
 SCREAMING_SNAKE_CASE_       :						Tuple					=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE					:       Tuple							   =							{
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							["""RealmTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							[
			    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """RealmEmbedder""",
			    """RealmForOpenQA""",
			    """RealmKnowledgeAugEncoder""",
			    """RealmPreTrainedModel""",
			    """RealmReader""",
			    """RealmScorer""",
			    """load_tf_weights_in_realm""",
			]
			SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							["""RealmRetriever"""]


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			SCREAMING_SNAKE_CASE					:       str							   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





