


'''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       a				(      __a			, __a			, __a			, __a			, __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			TapasConfig.from_json_file(__a       )
					# set absolute/relative position embeddings parameter
					UpperCamelCase__       :Optional[Any]							 =			reset_position_index_per_cell

					# set remaining parameters of TapasConfig as well as the model based on the task
					if task == "SQA":
										UpperCamelCase__       :List[str]							 =			TapasForQuestionAnswering(config=__a       )
					elif task == "WTQ":
										# run_task_main.py hparams
										UpperCamelCase__       :Dict							 =			4
										UpperCamelCase__       :List[Any]							 =			True
										# hparam_utils.py hparams
										UpperCamelCase__       :Any							 =			0.6_6_4_6_9_4
										UpperCamelCase__       :List[Any]							 =			0.2_0_7_9_5_1
										UpperCamelCase__       :Tuple							 =			0.1_2_1_1_9_4
										UpperCamelCase__       :int							 =			True
										UpperCamelCase__       :List[str]							 =			True
										UpperCamelCase__       :Dict							 =			False
										UpperCamelCase__       :Dict							 =			0.0_3_5_2_5_1_3

										UpperCamelCase__       :List[str]							 =			TapasForQuestionAnswering(config=__a       )
					elif task == "WIKISQL_SUPERVISED":
										# run_task_main.py hparams
										UpperCamelCase__       :Optional[int]							 =			4
										UpperCamelCase__       :Optional[int]							 =			False
										# hparam_utils.py hparams
										UpperCamelCase__       :Optional[Any]							 =			3_6.4_5_1_9
										UpperCamelCase__       :Tuple							 =			0.9_0_3_4_2_1
										UpperCamelCase__       :Union[str, Any]							 =			2_2_2.0_8_8
										UpperCamelCase__       :Any							 =			True
										UpperCamelCase__       :Tuple							 =			True
										UpperCamelCase__       :str							 =			True
										UpperCamelCase__       :Any							 =			0.7_6_3_1_4_1

										UpperCamelCase__       :List[str]							 =			TapasForQuestionAnswering(config=__a       )
					elif task == "TABFACT":
										UpperCamelCase__       :List[Any]							 =			TapasForSequenceClassification(config=__a       )
					elif task == "MLM":
										UpperCamelCase__       :Optional[int]							 =			TapasForMaskedLM(config=__a       )
					elif task == "INTERMEDIATE_PRETRAINING":
										UpperCamelCase__       :List[str]							 =			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}'''       )
					UpperCamelCase__       :Optional[int]							 =			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt'''			, model_max_length=512       )
					tokenizer.save_pretrained(__a       )

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


if __name__ == "__main__":
		__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.'''
		)
		__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,
		)



'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case											=					logging.get_logger(__name__)

__snake_case											=					{'''vocab_file''': '''vocab.txt'''}

__snake_case											=					{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

__snake_case											=					{
    '''openbmb/cpm-ant-10b''': 1024,
}


def       a				(      __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			collections.OrderedDict()
					with open(__a			, '''r'''			, encoding='''utf-8'''       ) as reader:
										UpperCamelCase__       :Dict							 =			reader.readlines()
					for index, token in enumerate(__a       ):
										UpperCamelCase__       :str							 =			token.rstrip('''\n'''       )
										UpperCamelCase__       :Optional[int]							 =			index
					return vocab
class        lowercase     (							A__ ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_="<unk>"   ,       UpperCamelCase_=200							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			vocab
							UpperCamelCase__       :List[str]							 =			unk_token
							UpperCamelCase__       :Tuple							 =			max_input_chars_per_word




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			list(UpperCamelCase_							)
							if len(UpperCamelCase_							) > self.max_input_chars_per_word:
												return [self.unk_token]

							UpperCamelCase__       :List[Any]							 =			0
							UpperCamelCase__       :str							 =			[]
							while start < len(UpperCamelCase_							):
												UpperCamelCase__       :int							 =			len(UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			None
												while start < end:
																	UpperCamelCase__       :int							 =			''''''.join(chars[start:end]							)
																	if substr in self.vocab:
																						UpperCamelCase__       :List[Any]							 =			substr
																						break
																	end -= 1
												if cur_substr is None:
																	sub_tokens.append(self.unk_token							)
																	start += 1
												else:
																	sub_tokens.append(UpperCamelCase_							)
																	UpperCamelCase__       :Any							 =			end

							return sub_tokens



class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    VOCAB_FILES_NAMES
		_a   			=    PRETRAINED_VOCAB_FILES_MAP
		_a   			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a   			=    ['input_ids', 'attention_mask']
		_a   			=    False




		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_="<d>"   ,       UpperCamelCase_="</d>"   ,       UpperCamelCase_="<s>"   ,       UpperCamelCase_="</s>"   ,       UpperCamelCase_="<pad>"   ,       UpperCamelCase_="<unk>"   ,       UpperCamelCase_="</n>"   ,       UpperCamelCase_="</_>"   ,       UpperCamelCase_="left"   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							requires_backends(self   ,       ['''jieba''']							)
							super().__init__(
							    bod_token=UpperCamelCase_   ,       eod_token=UpperCamelCase_   ,       bos_token=UpperCamelCase_   ,       eos_token=UpperCamelCase_   ,       pad_token=UpperCamelCase_   ,       unk_token=UpperCamelCase_   ,       line_token=UpperCamelCase_   ,       space_token=UpperCamelCase_   ,       padding_side=UpperCamelCase_   ,       **UpperCamelCase_   ,       )
							UpperCamelCase__       :Tuple							 =			bod_token
							UpperCamelCase__       :Dict							 =			eod_token
							UpperCamelCase__       :Optional[int]							 =			load_vocab(UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			self.encoder[space_token]
							UpperCamelCase__       :List[Any]							 =			self.encoder[line_token]

							del self.encoder[space_token]
							del self.encoder[line_token]

							UpperCamelCase__       :Union[str, Any]							 =			collections.OrderedDict(sorted(self.encoder.items()   ,       key=lambda UpperCamelCase_							: x[1]							)							)
							UpperCamelCase__       :Union[str, Any]							 =			{v: k for k, v in self.encoder.items()}

							UpperCamelCase__       :List[Any]							 =			WordpieceTokenizer(vocab=self.encoder   ,       unk_token=self.unk_token							)




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.encoder[self.bod_token]




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.encoder[self.eod_token]




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.encoder["\n"]




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return len(self.encoder							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return dict(self.encoder   ,       **self.added_tokens_encoder							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			[]
							for x in jieba.cut(UpperCamelCase_   ,       cut_all=UpperCamelCase_							):
												output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCamelCase_							)							)
							return output_tokens




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			[i for i in token_ids if i >= 0]
							UpperCamelCase__       :Optional[int]							 =			[
							    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
							]
							return super()._decode(UpperCamelCase_   ,       **UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return token in self.encoder




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return "".join(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return self.encoder.get(UpperCamelCase_   ,       self.encoder.get(self.unk_token							)							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return self.decoder.get(UpperCamelCase_   ,       self.unk_token							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None							):

							'''simple docstring'''


							if os.path.isdir(UpperCamelCase_							):
												UpperCamelCase__       :int							 =			os.path.join(
												    UpperCamelCase_   ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
							else:
												UpperCamelCase__       :str							 =			(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
							UpperCamelCase__       :Any							 =			0
							if " " in self.encoder:
												UpperCamelCase__       :Dict							 =			self.encoder[''' ''']
												del self.encoder[" "]
							if "\n" in self.encoder:
												UpperCamelCase__       :List[str]							 =			self.encoder['''\n''']
												del self.encoder["\n"]
							UpperCamelCase__       :List[str]							 =			collections.OrderedDict(sorted(self.encoder.items()   ,       key=lambda UpperCamelCase_							: x[1]							)							)
							with open(UpperCamelCase_   ,       '''w'''   ,       encoding='''utf-8'''							) as writer:
												for token, token_index in self.encoder.items():
																	if index != token_index:
																						logger.warning(
																						    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																						    ''' Please check that the vocabulary is not corrupted!'''							)
																						UpperCamelCase__       :Any							 =			token_index
																	writer.write(token + '''\n'''							)
																	index += 1
							return (vocab_file,)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None							):

							'''simple docstring'''


							if token_ids_a is None:
												return [self.bos_token_id] + token_ids_a
							return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = False							):

							'''simple docstring'''


							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=UpperCamelCase_   ,       token_ids_a=UpperCamelCase_   ,       already_has_special_tokens=UpperCamelCase_							)

							if token_ids_a is not None:
												return [1] + ([0] * len(UpperCamelCase_							)) + [1] + ([0] * len(UpperCamelCase_							))
							return [1] + ([0] * len(UpperCamelCase_							))