



'''simple docstring'''


import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase							       =			logging.get_logger(__name__)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


					'''simple docstring'''







					a_       		=RobertaPreLayerNormConfig.from_pretrained(
					    SCREAMING_SNAKE_CASE__       ,	architectures=["RobertaPreLayerNormForMaskedLM"]       )

					# convert state_dict
					a_       		=torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE__       ,	filename="pytorch_model.bin"       )       )
					a_       		={}
					for tensor_key, tensor_value in original_state_dict.items():
										# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
										if tensor_key.startswith("roberta."       ):
															a_       		="""roberta_prelayernorm.""" + tensor_key[len("roberta."       ) :]

										# The original implementation contains weights which are not used, remove them from the state_dict
										if tensor_key.endswith(".self.LayerNorm.weight"       ) or tensor_key.endswith(".self.LayerNorm.bias"       ):
															continue

										a_       		=tensor_value

					a_       		=RobertaPreLayerNormForMaskedLM.from_pretrained(
					    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE__       ,	config=SCREAMING_SNAKE_CASE__       ,	state_dict=SCREAMING_SNAKE_CASE__       )
					model.save_pretrained(SCREAMING_SNAKE_CASE__       )

					# convert tokenizer
					a_       		=AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       )
					tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
		lowercase							       =			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowercase							       =			parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase							       =			{
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''VisionEncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''TFVisionEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


