

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_barthez import BarthezTokenizer
else:
		A											=     None

A											=     logging.get_logger(__name__)

A											=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A											=     {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

A											=     {
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

A											=     '▁'


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  PRETRAINED_VOCAB_FILES_MAP
			__A						  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A						  =  ["""input_ids""", """attention_mask"""]
			__A						  =  BarthezTokenizer


			def __init__(    self ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase="<s>" ,				__UpperCamelCase="</s>" ,				__UpperCamelCase="</s>" ,				__UpperCamelCase="<s>" ,				__UpperCamelCase="<unk>" ,				__UpperCamelCase="<pad>" ,				__UpperCamelCase="<mask>" ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= AddedToken(__UpperCamelCase ,				lstrip=__UpperCamelCase ,				rstrip=__UpperCamelCase							) if isinstance(__UpperCamelCase ,				__UpperCamelCase							) else mask_token

				super().__init__(
				    __UpperCamelCase ,				tokenizer_file=__UpperCamelCase ,				bos_token=__UpperCamelCase ,				eos_token=__UpperCamelCase ,				unk_token=__UpperCamelCase ,				sep_token=__UpperCamelCase ,				cls_token=__UpperCamelCase ,				pad_token=__UpperCamelCase ,				mask_token=__UpperCamelCase ,				**__UpperCamelCase ,				)

				snake_case_      		= vocab_file
				snake_case_      		= False if not self.vocab_file else True


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				snake_case_      		= [self.cls_token_id]
				snake_case_      		= [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= [self.sep_token_id]
				snake_case_      		= [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 + sep + token_ids_a + sep							) * [0]







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
					    'tokenizer.'							)

				if not os.path.isdir(__UpperCamelCase							):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
					return
				snake_case_      		= os.path.join(
				    __UpperCamelCase ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

				if os.path.abspath(self.vocab_file							) != os.path.abspath(__UpperCamelCase							):
					copyfile(self.vocab_file ,				__UpperCamelCase							)

				return (out_vocab_file,)



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(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	# 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_      		= TapasConfig.from_json_file(lowercase__	)
	# set absolute/relative position embeddings parameter
	snake_case_      		= reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= True
		# hparam_utils.py hparams
		snake_case_      		= 0.66_4694
		snake_case_      		= 0.20_7951
		snake_case_      		= 0.12_1194
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= False
		snake_case_      		= 0.035_2513

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= False
		# hparam_utils.py hparams
		snake_case_      		= 36.4519
		snake_case_      		= 0.90_3421
		snake_case_      		= 222.088
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= 0.76_3141

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

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

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

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


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