
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 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						Optional[int]:
				# Initialise PyTorch model
				__lowercase								= json.load(open(SCREAMING_SNAKE_CASE   ,							'r'      )      )
				__lowercase								= 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}"""      )
				__lowercase								= GPTNeoForCausalLM(SCREAMING_SNAKE_CASE      )

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__							=    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__							=    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


											super().__init__(pad_token_id=_UpperCAmelCase					,			**_UpperCAmelCase    )

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





							@property
							def       a__			(			self  :       Any    )						->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""


											if self.task == "multiple-choice":
															__lowercase								= {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															__lowercase								= {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]    )





