


"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase  		=			{
    """configuration_swin2sr""": ["""SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swin2SRConfig"""],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Swin2SRForImageSuperResolution""",
					    """Swin2SRModel""",
					    """Swin2SRPreTrainedModel""",
					]


try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			["""Swin2SRImageProcessor"""]


if TYPE_CHECKING:
					from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swinasr import (
										    SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinaSRForImageSuperResolution,
										    SwinaSRModel,
										    SwinaSRPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_swinasr import SwinaSRImageProcessor


else:
					import sys

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







'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      torch.save(model.state_dict()   ,	UpperCAmelCase   ,	_use_new_zipfile_serialization=UpperCAmelCase						)


if __name__ == "__main__":
 a__		: Tuple  							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






