

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase     					=  {
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase     					=  [
      'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TvltModel',
      'TvltForPreTraining',
      'TvltForAudioVisualClassification',
      'TvltPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase     					=  ['TvltImageProcessor']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

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









import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







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

     return orig_key







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Any:

     """simple docstring"""







     for key in orig_state_dict.copy().keys():
          _lowerCAmelCase :       List[str]	   =     orig_state_dict.pop(SCREAMING_SNAKE_CASE					)

          if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
               continue
          else:
               _lowerCAmelCase :       int	   =     val

     _lowerCAmelCase :       Tuple	   =     orig_state_dict["cls.predictions.decoder.bias"]
     _lowerCAmelCase :       Any	   =     (
         torch.arange(config.max_position_embeddings					).expand((1, -1)					) + 2
     )

     return orig_state_dict







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Optional[Any]	   =     torch.load(SCREAMING_SNAKE_CASE		,				map_location="cpu"					)["model_state_dict"]
     _lowerCAmelCase :       Optional[Any]	   =     NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       List[str]	   =     NystromformerForMaskedLM(SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Union[str, Any]	   =     convert_checkpoint_helper(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)

     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     					=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The json file for Nystromformer model config.',
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  __UpperCAmelCase     					=  parser.parse_args()
  convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






