

'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__						:       Optional[Any]										=						logging.get_logger(__name__)




class     a			(				__lowerCamelCase			,			__lowerCamelCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    'maskformer-swin'

						__UpperCAmelCase			     =    {
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(							self					:      Any			,			snake_case_					:      Any=2_2_4			,			snake_case_					:      List[str]=4			,			snake_case_					:      Optional[int]=3			,			snake_case_					:      Optional[int]=9_6			,			snake_case_					:      List[str]=[2, 2, 6, 2]			,			snake_case_					:      Optional[Any]=[3, 6, 1_2, 2_4]			,			snake_case_					:      str=7			,			snake_case_					:      int=4.0			,			snake_case_					:      Optional[int]=True			,			snake_case_					:      Union[str, Any]=0.0			,			snake_case_					:      Optional[int]=0.0			,			snake_case_					:      Any=0.1			,			snake_case_					:      Union[str, Any]="gelu"			,			snake_case_					:      int=False			,			snake_case_					:      Optional[int]=0.0_2			,			snake_case_					:      Optional[Any]=1e-5			,			snake_case_					:      Optional[int]=None			,			snake_case_					:      List[Any]=None			,			**snake_case_					:      Union[str, Any]			,			):



								'''simple docstring'''



								super().__init__(**UpperCamelCase_  )

								snake_case__						:       Optional[int]       						=  image_size
								snake_case__						:       Optional[Any]       						=  patch_size
								snake_case__						:       Tuple       						=  num_channels
								snake_case__						:       int       						=  embed_dim
								snake_case__						:       List[Any]       						=  depths
								snake_case__						:       str       						=  len(UpperCamelCase_  )
								snake_case__						:       int       						=  num_heads
								snake_case__						:       Optional[int]       						=  window_size
								snake_case__						:       List[str]       						=  mlp_ratio
								snake_case__						:       Union[str, Any]       						=  qkv_bias
								snake_case__						:       Dict       						=  hidden_dropout_prob
								snake_case__						:       List[Any]       						=  attention_probs_dropout_prob
								snake_case__						:       Union[str, Any]       						=  drop_path_rate
								snake_case__						:       Any       						=  hidden_act
								snake_case__						:       Any       						=  use_absolute_embeddings
								snake_case__						:       Dict       						=  layer_norm_eps
								snake_case__						:       Optional[int]       						=  initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								snake_case__						:       List[Any]       						=  int(embed_dim * 2 ** (len(UpperCamelCase_  ) - 1)  )
								snake_case__						:       List[str]       						=  ['''stem'''] + [F"""stage{idx}""" for idx in range(1			,			len(UpperCamelCase_  ) + 1  )]
								snake_case__						,       snake_case__						:       List[Any]       						=  get_aligned_output_features_output_indices(
								    out_features=UpperCamelCase_			,			out_indices=UpperCamelCase_			,			stage_names=self.stage_names  )






'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  BigBirdForPreTraining(__lowerCAmelCase     )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__lowerCAmelCase   ,							__lowerCAmelCase   ,							is_trivia_qa=__lowerCAmelCase     )

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
		model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
					lowerCAmelCase__						:       Union[str, Any]										=						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(
					    """--big_bird_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."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					lowerCAmelCase__						:       Optional[int]										=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)



