


'''simple docstring'''


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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

				return orig_key







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				for key in orig_state_dict.copy().keys():
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      orig_state_dict.pop(SCREAMING_SNAKE_CASE__     )

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

				_SCREAMING_SNAKE_CASE :       Tuple     		=      orig_state_dict["""cls.predictions.decoder.bias"""]
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.arange(SCREAMING_SNAKE_CASE__     ).expand((1, -1)     ) + 2

				return orig_state_dict







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       str     		=      torch.load(SCREAMING_SNAKE_CASE__							,      map_location="""cpu"""     )["""model_state_dict"""]
				_SCREAMING_SNAKE_CASE :       List[str]     		=      YosoConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Any     		=      YosoForMaskedLM(SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       List[Any]     		=      convert_checkpoint_helper(config.max_position_embeddings							,      SCREAMING_SNAKE_CASE__     )

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

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


if __name__ == "__main__":
				UpperCAmelCase_		:   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.'
				)
				UpperCAmelCase_		:   Union[str, Any]							   =		parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:   Dict							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      Optional[int]      	=  """canine"""






							def __init__(    self    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=1_6384    ,					__snake_case=16    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=0    ,					__snake_case=0XE000    ,					__snake_case=0XE001    ,					__snake_case=4    ,					__snake_case=4    ,					__snake_case=8    ,					__snake_case=1_6384    ,					__snake_case=128    ,					**__snake_case    ,					):
											super().__init__(pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					**__snake_case  )

											_SCREAMING_SNAKE_CASE :       str     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       Any     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Dict     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Tuple     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       List[str]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       str     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Dict     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      layer_norm_eps

											# Character config:
											_SCREAMING_SNAKE_CASE :       Any     		=      downsampling_rate
											_SCREAMING_SNAKE_CASE :       List[str]     		=      upsampling_kernel_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hash_functions
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      num_hash_buckets
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      local_transformer_stride




