


'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def      UpperCAmelCase      (		lowerCamelCase_  :str ):







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

		return orig_key





def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		for key in orig_state_dict.copy().keys():
				snake_case_		:							Union[str, Any]			       =						orig_state_dict.pop(lowerCamelCase_ )

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

		snake_case_		:							Tuple			       =						orig_state_dict["""cls.predictions.decoder.bias"""]
		snake_case_		:							Optional[int]			       =						torch.arange(lowerCamelCase_ ).expand((1, -1) ) + 2

		return orig_state_dict





def      UpperCAmelCase      (		lowerCamelCase_  :Dict				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		snake_case_		:							List[Any]			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )["""model_state_dict"""]
		snake_case_		:							Optional[int]			       =						YosoConfig.from_json_file(lowerCamelCase_ )
		snake_case_		:							List[str]			       =						YosoForMaskedLM(lowerCamelCase_ )

		snake_case_		:							Optional[Any]			       =						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	:	int     =     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'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch
@require_torch
@require_optimum
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoTokenizer.from_pretrained(_UpperCamelCase			)
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						tokenizer("""This is me"""  ,return_tensors="""pt"""			)

									snake_case_		:							List[str]			       =						model.to_bettertransformer()

									self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									snake_case_		:							List[str]			       =						model.generate(**_UpperCamelCase			)

									snake_case_		:							Dict			       =						model.reverse_bettertransformer()

									self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

											self.assertFalse(
											    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

											snake_case_		:							Optional[Any]			       =						model_reloaded.generate(**_UpperCamelCase			)
											self.assertTrue(torch.allclose(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						model.to_bettertransformer()

									with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(_UpperCamelCase			):
													model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							Any			       =						model.reverse_bettertransformer()
											model.save_pretrained(_UpperCamelCase			)