





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     int	 =    "detr"
						__magic_name__:     List[str]	 =    ["past_key_values"]
						__magic_name__:     Tuple	 =    {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}






						def __init__( self : Dict						,				_A : Optional[int]=True						,				_A : List[Any]=None						,				_A : Optional[Any]=3						,				_A : Optional[Any]=100						,				_A : int=6						,				_A : List[str]=2048						,				_A : Any=8						,				_A : Optional[Any]=6						,				_A : Tuple=2048						,				_A : Tuple=8						,				_A : Any=0.0						,				_A : Union[str, Any]=0.0						,				_A : List[str]=True						,				_A : List[Any]="relu"						,				_A : Optional[Any]=256						,				_A : int=0.1						,				_A : Any=0.0						,				_A : str=0.0						,				_A : Any=0.0_2						,				_A : Tuple=1.0						,				_A : Optional[Any]=False						,				_A : Optional[int]="sine"						,				_A : List[Any]="resnet50"						,				_A : Tuple=True						,				_A : int=False						,				_A : Optional[int]=1						,				_A : Optional[int]=5						,				_A : Tuple=2						,				_A : Dict=1						,				_A : Union[str, Any]=1						,				_A : Optional[Any]=5						,				_A : str=2						,				_A : Any=0.1						,				**_A : List[str]						,				)							->      int:







							"""simple docstring"""


							if backbone_config is not None and use_timm_backbone:
								raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'		)

							if not use_timm_backbone:
								if backbone_config is None:
									logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
									snake_case_						:     int     =		CONFIG_MAPPING['resnet'](out_features=['stage4']		)
								elif isinstance(_A						,				_A		):
									snake_case_						:     int     =		backbone_config.get('model_type'		)
									snake_case_						:     Tuple     =		CONFIG_MAPPING[backbone_model_type]
									snake_case_						:     Optional[int]     =		config_class.from_dict(_A		)
								# set timm attributes to None
								snake_case_  ,snake_case_  ,snake_case_						:     Tuple     =		None, None, None

							snake_case_						:     Union[str, Any]     =		use_timm_backbone
							snake_case_						:     Optional[Any]     =		backbone_config
							snake_case_						:     List[str]     =		num_channels
							snake_case_						:     Optional[int]     =		num_queries
							snake_case_						:     Optional[int]     =		d_model
							snake_case_						:     Optional[int]     =		encoder_ffn_dim
							snake_case_						:     List[str]     =		encoder_layers
							snake_case_						:     List[str]     =		encoder_attention_heads
							snake_case_						:     Optional[int]     =		decoder_ffn_dim
							snake_case_						:     str     =		decoder_layers
							snake_case_						:     Tuple     =		decoder_attention_heads
							snake_case_						:     List[str]     =		dropout
							snake_case_						:     Tuple     =		attention_dropout
							snake_case_						:     Tuple     =		activation_dropout
							snake_case_						:     int     =		activation_function
							snake_case_						:     Union[str, Any]     =		init_std
							snake_case_						:     List[Any]     =		init_xavier_std
							snake_case_						:     int     =		encoder_layerdrop
							snake_case_						:     List[Any]     =		decoder_layerdrop
							snake_case_						:     Dict     =		encoder_layers
							snake_case_						:     Optional[int]     =		auxiliary_loss
							snake_case_						:     Tuple     =		position_embedding_type
							snake_case_						:     Optional[int]     =		backbone
							snake_case_						:     Optional[int]     =		use_pretrained_backbone
							snake_case_						:     int     =		dilation
							# Hungarian matcher
							snake_case_						:     int     =		class_cost
							snake_case_						:     List[str]     =		bbox_cost
							snake_case_						:     Optional[Any]     =		giou_cost
							# Loss coefficients
							snake_case_						:     List[str]     =		mask_loss_coefficient
							snake_case_						:     Dict     =		dice_loss_coefficient
							snake_case_						:     Optional[Any]     =		bbox_loss_coefficient
							snake_case_						:     List[str]     =		giou_loss_coefficient
							snake_case_						:     Dict     =		eos_coefficient
							super().__init__(is_encoder_decoder=_A						,				**_A		)






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      int:







							"""simple docstring"""


							return self.encoder_attention_heads






						@property
						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							return self.d_model






						@classmethod
						def UpperCAmelCase_			( cls : Tuple						,				_A : PretrainedConfig						,				**_A : Union[str, Any]		)							->      str:







							"""simple docstring"""


							return cls(backbone_config=_A						,				**_A		)






						def UpperCAmelCase_			( self : Any		)							->      Dict[str, any]:







							"""simple docstring"""


							snake_case_						:     str     =		copy.deepcopy(self.__dict__		)
							if output["backbone_config"] is not None:
								snake_case_						:     Optional[int]     =		self.backbone_config.to_dict()
							snake_case_						:     str     =		self.__class__.model_type
							return output
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Any	 =    version.parse("1.11"   )






						@property
						def UpperCAmelCase_			( self : Dict		)							->      Mapping[str, Mapping[int, str]]:







							"""simple docstring"""


							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							        ('pixel_mask', {0: 'batch'}),
							    ]		)






						@property
						def UpperCAmelCase_			( self : Tuple		)							->      float:







							"""simple docstring"""


							return 1E-5






						@property
						def UpperCAmelCase_			( self : int		)							->      int:







							"""simple docstring"""


							return 12








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_						:     Optional[int]     =		TapasConfig.from_json_file(__a				)
	# set absolute/relative position embeddings parameter
	snake_case_						:     List[Any]     =		reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_						:     Optional[int]     =		4
		snake_case_						:     List[str]     =		True
		# hparam_utils.py hparams
		snake_case_						:     Optional[Any]     =		0.664694
		snake_case_						:     Dict     =		0.207951
		snake_case_						:     Tuple     =		0.121194
		snake_case_						:     Dict     =		True
		snake_case_						:     int     =		True
		snake_case_						:     int     =		False
		snake_case_						:     str     =		0.0352513

		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_						:     int     =		4
		snake_case_						:     Optional[int]     =		False
		# hparam_utils.py hparams
		snake_case_						:     str     =		36.4519
		snake_case_						:     Optional[Any]     =		0.903421
		snake_case_						:     List[Any]     =		222.088
		snake_case_						:     Optional[int]     =		True
		snake_case_						:     Optional[Any]     =		True
		snake_case_						:     str     =		True
		snake_case_						:     int     =		0.763141

		snake_case_						:     str     =		TapasForQuestionAnswering(config=__a				)
	elif task == "TABFACT":
		snake_case_						:     List[Any]     =		TapasForSequenceClassification(config=__a				)
	elif task == "MLM":
		snake_case_						:     Optional[int]     =		TapasForMaskedLM(config=__a				)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_						:     Tuple     =		TapasModel(config=__a				)
	else:
		raise ValueError(f"""Task {task} not supported."""				)

	print(f"""Building PyTorch model from configuration: {config}"""				)
	# Load weights from tf checkpoint
	load_tf_weights_in_tapas(__a				,	__a				,	__a				)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
	model.save_pretrained(__a				)

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
	snake_case_						:     str     =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,	model_max_length=5_12				)
	tokenizer.save_pretrained(__a				)

	print('Used relative position embeddings:'				,	model.config.reset_position_index_per_cell				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
						)
						parser.add_argument(
						    """--reset_position_index_per_cell""",
						    default=False,
						    action="""store_true""",
						    help="""Whether to use relative position embeddings or not. Defaults to True.""",
						)
						parser.add_argument(
						    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
						)
						parser.add_argument(
						    """--tapas_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained TAPAS 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."""
						)
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.task,
						    args.reset_position_index_per_cell,
						    args.tf_checkpoint_path,
						    args.tapas_config_file,
						    args.pytorch_dump_path,
						)

