




'''simple docstring'''

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 						__magic_name__       (      __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					if "model" in orig_key:
										snake_case_		     =							orig_key.replace('''model.''', ''''''      )
					if "norm1" in orig_key:
										snake_case_		     =							orig_key.replace('''norm1''', '''attention.output.LayerNorm'''      )
					if "norm2" in orig_key:
										snake_case_		     =							orig_key.replace('''norm2''', '''output.LayerNorm'''      )
					if "norm" in orig_key:
										snake_case_		     =							orig_key.replace('''norm''', '''LayerNorm'''      )
					if "transformer" in orig_key:
										snake_case_		     =							orig_key.split('''.'''      )[0].split('''_'''      )[-1]
										snake_case_		     =							orig_key.replace(F"transformer_{layer_num}", F"encoder.layer.{layer_num}"      )
					if "mha.attn" in orig_key:
										snake_case_		     =							orig_key.replace('''mha.attn''', '''attention.self'''      )
					if "mha" in orig_key:
										snake_case_		     =							orig_key.replace('''mha''', '''attention'''      )
					if "W_q" in orig_key:
										snake_case_		     =							orig_key.replace('''W_q''', '''self.query'''      )
					if "W_k" in orig_key:
										snake_case_		     =							orig_key.replace('''W_k''', '''self.key'''      )
					if "W_v" in orig_key:
										snake_case_		     =							orig_key.replace('''W_v''', '''self.value'''      )
					if "ff1" in orig_key:
										snake_case_		     =							orig_key.replace('''ff1''', '''intermediate.dense'''      )
					if "ff2" in orig_key:
										snake_case_		     =							orig_key.replace('''ff2''', '''output.dense'''      )
					if "ff" in orig_key:
										snake_case_		     =							orig_key.replace('''ff''', '''output.dense'''      )
					if "mlm_class" in orig_key:
										snake_case_		     =							orig_key.replace('''mlm.mlm_class''', '''cls.predictions.decoder'''      )
					if "mlm" in orig_key:
										snake_case_		     =							orig_key.replace('''mlm''', '''cls.predictions.transform'''      )
					if "cls" not in orig_key:
										snake_case_		     =							'''yoso.''' + orig_key

					return orig_key


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										snake_case_		     =							orig_state_dict.pop(lowerCAmelCase__      )

										if ("pooler" in key) or ("sen_class" in key):
															continue
										else:
															snake_case_		     =							val

					snake_case_		     =							orig_state_dict['''cls.predictions.decoder.bias''']
					snake_case_		     =							torch.arange(lowerCAmelCase__      ).expand((1, -1)      ) + 2

					return orig_state_dict


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							torch.load(lowerCAmelCase__, map_location='''cpu'''      )['''model_state_dict''']
					snake_case_		     =							YosoConfig.from_json_file(lowerCAmelCase__      )
					snake_case_		     =							YosoForMaskedLM(lowerCAmelCase__      )

					snake_case_		     =							convert_checkpoint_helper(config.max_position_embeddings, lowerCAmelCase__      )

					print(model.load_state_dict(lowerCAmelCase__      )      )
					model.eval()
					model.save_pretrained(lowerCAmelCase__      )

					print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"      )


if __name__ == "__main__":
							a    :							Optional[Any]										=    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for YOSO model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							a    :							int										=    parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



"""simple docstring"""


from typing import TYPE_CHECKING

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


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



