"""simple docstring"""




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__lowerCAmelCase    :							int													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Union[str, Any]													=						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase    :							Optional[int]													=						{
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

__lowerCAmelCase    :							Optional[Any]													=						{"allegro/herbert-base-cased": 5_14}
__lowerCAmelCase    :							List[str]													=						{}

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	Optional[int]			=    VOCAB_FILES_NAMES
					UpperCamelCase_ :	Union[str, Any]			=    PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase_ :	List[Any]			=    PRETRAINED_INIT_CONFIGURATION
					UpperCamelCase_ :	Optional[int]			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase_ :	List[str]			=    HerbertTokenizer




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     List[str]=None  ,						snake_case__						:     List[Any]=None  ,						snake_case__						:     Any=None  ,						snake_case__						:     Dict="<s>"  ,						snake_case__						:     Optional[Any]="<unk>"  ,						snake_case__						:     Optional[int]="<pad>"  ,						snake_case__						:     List[str]="<mask>"  ,						snake_case__						:     Dict="</s>"  ,						**snake_case__						:     int  ,						):
							super().__init__(
							    snake_case__  ,						snake_case__  ,						tokenizer_file=snake_case__  ,						cls_token=snake_case__  ,						unk_token=snake_case__  ,						pad_token=snake_case__  ,						mask_token=snake_case__  ,						sep_token=snake_case__  ,						**snake_case__  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None     ):
							lowerCAmelCase__        =   [self.cls_token_id]
							lowerCAmelCase__        =   [self.sep_token_id]
							if token_ids_a is None:
									return cls + token_ids_a + sep

							return cls + token_ids_a + sep + token_ids_a + sep




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None  ,						snake_case__						:     bool = False     ):
							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=snake_case__  ,						token_ids_a=snake_case__  ,						already_has_special_tokens=snake_case__     )

							if token_ids_a is None:
									return [1] + ([0] * len(snake_case__     )) + [1]
							return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None     ):
							lowerCAmelCase__        =   [self.sep_token_id]
							lowerCAmelCase__        =   [self.cls_token_id]

							if token_ids_a is None:
									return len(cls + token_ids_a + sep     ) * [0]
							return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     str  ,						snake_case__						:     Optional[str] = None     ):
							lowerCAmelCase__        =   self._tokenizer.model.save(snake_case__  ,						name=snake_case__     )
							return tuple(snake_case__     )





"""simple docstring"""




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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TapasConfig.from_json_file(lowerCamelCase__     )
		# set absolute/relative position embeddings parameter
		lowerCAmelCase__        =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WTQ":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   True
				# hparam_utils.py hparams
				lowerCAmelCase__        =   0.66_46_94
				lowerCAmelCase__        =   0.20_79_51
				lowerCAmelCase__        =   0.12_11_94
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   False
				lowerCAmelCase__        =   0.0_35_25_13

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   False
				# hparam_utils.py hparams
				lowerCAmelCase__        =   36.45_19
				lowerCAmelCase__        =   0.90_34_21
				lowerCAmelCase__        =   2_22.0_88
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   0.76_31_41

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "TABFACT":
				lowerCAmelCase__        =   TapasForSequenceClassification(config=lowerCamelCase__     )
		elif task == "MLM":
				lowerCAmelCase__        =   TapasForMaskedLM(config=lowerCamelCase__     )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__        =   TapasModel(config=lowerCamelCase__     )
		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(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

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

		# Save tokenizer files
		print(f"""Save tokenizer files to {pytorch_dump_path}"""     )
		lowerCAmelCase__        =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""			,  model_max_length=512     )
		tokenizer.save_pretrained(lowerCamelCase__     )

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


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						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,
			)




