

import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_lowerCamelCase       :     List[str]           =							logging.get_logger(__name__)





@dataclass
class       __UpperCAmelCase  :
			UpperCamelCase       =  4_2
			UpperCamelCase       =  4_2
			UpperCamelCase       =  None
			UpperCamelCase       =  None



			def  __magic_name__					(							self							:  Optional[Any]		):
					return json.dumps(dataclasses.asdict(self		),	indent=2		) + "\n"






@dataclass(frozen=UpperCAmelCase_		)
class       __UpperCAmelCase  :
			UpperCamelCase       =  4_2
			UpperCamelCase       =  None
			UpperCamelCase       =  None
			UpperCamelCase       =  None



			def  __magic_name__					(							self							:  List[str]		):
					return json.dumps(dataclasses.asdict(self		)		) + "\n"






class       __UpperCAmelCase  :



			def  __magic_name__					(							self							:  str,	__A							:  Optional[int]		):
					raise NotImplementedError()



			def  __magic_name__					(							self							:  Union[str, Any],	__A							:  Tuple		):
					raise NotImplementedError()



			def  __magic_name__					(							self							:  Dict,	__A							:  Optional[int]		):
					raise NotImplementedError()



			def  __magic_name__					(							self							:  List[Any],	__A							:  Any		):
					raise NotImplementedError()



			def  __magic_name__					(							self							:  Optional[Any]		):
					raise NotImplementedError()



			def  __magic_name__					(							self							:  int,	__A							:  List[Any]		):
					if len(self.get_labels()		) > 1:
							UpperCAmelCase :							Tuple								=				self.get_labels()[int(example.label		)]
					return example





			@classmethod
			def  __magic_name__					(							cls							:  Optional[int],	__A							:  List[Any],	__A							:  str=None		):
					with open(__A,	'''r''',	encoding='''utf-8-sig'''		) as f:
							return list(csv.reader(__A,	delimiter='''\t''',	quotechar=__A		)		)






class       __UpperCAmelCase  ( UpperCAmelCase_		):



			def __init__(							self							:  List[Any],	__A							:  Optional[Any]=None,	__A							:  Optional[Any]=None,	__A							:  Tuple="classification",	__A							:  Any=False		):
					UpperCAmelCase :							Tuple								=				[] if labels is None else labels
					UpperCAmelCase :							int								=				[] if examples is None else examples
					UpperCAmelCase :							Optional[int]								=				mode
					UpperCAmelCase :							Any								=				verbose



			def __len__(							self							:  List[str]		):
					return len(self.examples		)



			def __getitem__(							self							:  Optional[int],	__A							:  List[Any]		):
					if isinstance(__A,	__A		):
							return SingleSentenceClassificationProcessor(labels=self.labels,	examples=self.examples[idx]		)
					return self.examples[idx]



			@classmethod
			def  __magic_name__					(							cls							:  Union[str, Any],	__A							:  Dict,	__A							:  List[str]="",	__A							:  Optional[Any]=0,	__A							:  List[Any]=1,	__A							:  List[Any]=None,	__A							:  Union[str, Any]=False,	**__A							:  str		):
					UpperCAmelCase :							Optional[int]								=				cls(**__A		)
					processor.add_examples_from_csv(
					    __A,	split_name=__A,	column_label=__A,	column_text=__A,	column_id=__A,	skip_first_row=__A,	overwrite_labels=__A,	overwrite_examples=__A,	)
					return processor



			@classmethod
			def  __magic_name__					(							cls							:  str,	__A							:  Union[str, Any],	__A							:  Union[str, Any]=None,	**__A							:  List[Any]		):
					UpperCAmelCase :							List[Any]								=				cls(**__A		)
					processor.add_examples(__A,	labels=__A		)
					return processor



			def  __magic_name__					(							self							:  Dict,	__A							:  List[Any],	__A							:  Dict="",	__A							:  List[str]=0,	__A							:  Optional[Any]=1,	__A							:  List[str]=None,	__A							:  Any=False,	__A							:  Optional[Any]=False,	__A							:  Optional[Any]=False,	):
					UpperCAmelCase :							Union[str, Any]								=				self._read_tsv(__A		)
					if skip_first_row:
							UpperCAmelCase :							List[str]								=				lines[1:]
					UpperCAmelCase :							Union[str, Any]								=				[]
					UpperCAmelCase :							str								=				[]
					UpperCAmelCase :							Tuple								=				[]
					for i, line in enumerate(__A		):
							texts.append(line[column_text]		)
							labels.append(line[column_label]		)
							if column_id is not None:
									ids.append(line[column_id]		)
							else:
									UpperCAmelCase :							Optional[int]								=				F'''{split_name}-{i}''' if split_name else str(__A		)
									ids.append(__A		)

					return self.add_examples(
					    __A,	__A,	__A,	overwrite_labels=__A,	overwrite_examples=__A		)



			def  __magic_name__					(							self							:  int,	__A							:  List[str],	__A							:  Dict=None,	__A							:  Dict=None,	__A							:  Dict=False,	__A							:  Dict=False		):
					if labels is not None and len(__A		) != len(__A		):
							raise ValueError(
							    F'''Text and labels have mismatched lengths {len(__A		)} and {len(__A		)}'''		)
					if ids is not None and len(__A		) != len(__A		):
							raise ValueError(F'''Text and ids have mismatched lengths {len(__A		)} and {len(__A		)}'''		)
					if ids is None:
							UpperCAmelCase :							Dict								=				[None] * len(__A		)
					if labels is None:
							UpperCAmelCase :							Any								=				[None] * len(__A		)
					UpperCAmelCase :							str								=				[]
					UpperCAmelCase :							Optional[int]								=				set()
					for text_or_text_and_label, label, guid in zip(__A,	__A,	__A		):
							if isinstance(__A,	(tuple, list)		) and label is None:
									UpperCAmelCase				,		UpperCAmelCase :							str								=				text_or_text_and_label
							else:
									UpperCAmelCase :							Optional[Any]								=				text_or_text_and_label
							added_labels.add(__A		)
							examples.append(InputExample(guid=__A,	text_a=__A,	text_b=__A,	label=__A		)		)

					# Update examples
					if overwrite_examples:
							UpperCAmelCase :							Tuple								=				examples
					else:
							self.examples.extend(__A		)

					# Update labels
					if overwrite_labels:
							UpperCAmelCase :							List[Any]								=				list(__A		)
					else:
							UpperCAmelCase :							List[str]								=				list(set(self.labels		).union(__A		)		)

					return self.examples





			def  __magic_name__					(							self							:  Dict,	__A							:  int,	__A							:  List[Any]=None,	__A							:  Tuple=False,	__A							:  Union[str, Any]=0,	__A							:  Any=True,	__A							:  Dict=None,	):
					if max_length is None:
							UpperCAmelCase :							Tuple								=				tokenizer.max_len

					UpperCAmelCase :							Dict								=				{label: i for i, label in enumerate(self.labels		)}

					UpperCAmelCase :							Optional[Any]								=				[]
					for ex_index, example in enumerate(self.examples		):
							if ex_index % 1_0_0_0_0 == 0:
									logger.info(F'''Tokenizing example {ex_index}'''		)

							UpperCAmelCase :							Any								=				tokenizer.encode(
							    example.text_a,	add_special_tokens=__A,	max_length=min(__A,	tokenizer.max_len		),	)
							all_input_ids.append(__A		)

					UpperCAmelCase :							Union[str, Any]								=				max(len(__A		) for input_ids in all_input_ids		)

					UpperCAmelCase :							List[Any]								=				[]
					for ex_index, (input_ids, example) in enumerate(zip(__A,	self.examples		)		):
							if ex_index % 1_0_0_0_0 == 0:
									logger.info(F'''Writing example {ex_index}/{len(self.examples		)}'''		)
							# The mask has 1 for real tokens and 0 for padding tokens. Only real
							# tokens are attended to.
							UpperCAmelCase :							List[str]								=				[1 if mask_padding_with_zero else 0] * len(__A		)

							# Zero-pad up to the sequence length.
							UpperCAmelCase :							List[Any]								=				batch_length - len(__A		)
							if pad_on_left:
									UpperCAmelCase :							Dict								=				([pad_token] * padding_length) + input_ids
									UpperCAmelCase :							List[Any]								=				([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
							else:
									UpperCAmelCase :							Optional[Any]								=				input_ids + ([pad_token] * padding_length)
									UpperCAmelCase :							int								=				attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

							if len(__A		) != batch_length:
									raise ValueError(F'''Error with input length {len(__A		)} vs {batch_length}'''		)
							if len(__A		) != batch_length:
									raise ValueError(F'''Error with input length {len(__A		)} vs {batch_length}'''		)

							if self.mode == "classification":
									UpperCAmelCase :							Optional[int]								=				label_map[example.label]
							elif self.mode == "regression":
									UpperCAmelCase :							Optional[Any]								=				float(example.label		)
							else:
									raise ValueError(self.mode		)

							if ex_index < 5 and self.verbose:
									logger.info('''*** Example ***'''		)
									logger.info(F'''guid: {example.guid}'''		)
									logger.info(F'''input_ids: {" ".join([str(__A		) for x in input_ids]		)}'''		)
									logger.info(F'''attention_mask: {" ".join([str(__A		) for x in attention_mask]		)}'''		)
									logger.info(F'''label: {example.label} (id = {label})'''		)

							features.append(InputFeatures(input_ids=__A,	attention_mask=__A,	label=__A		)		)

					if return_tensors is None:
							return features
					elif return_tensors == "tf":
							if not is_tf_available():
									raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''		)
							import tensorflow as tf

							def gen():
									for ex in features:
											yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

							UpperCAmelCase :							Tuple								=				tf.data.Dataset.from_generator(
							    __A,	({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa),	({'''input_ids''': tf.TensorShape([None]		), '''attention_mask''': tf.TensorShape([None]		)}, tf.TensorShape([]		)),	)
							return dataset
					elif return_tensors == "pt":
							if not is_torch_available():
									raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''		)
							import torch
							from torch.utils.data import TensorDataset

							UpperCAmelCase :							Optional[Any]								=				torch.tensor([f.input_ids for f in features],	dtype=torch.long		)
							UpperCAmelCase :							Dict								=				torch.tensor([f.attention_mask for f in features],	dtype=torch.long		)
							if self.mode == "classification":
									UpperCAmelCase :							Optional[int]								=				torch.tensor([f.label for f in features],	dtype=torch.long		)
							elif self.mode == "regression":
									UpperCAmelCase :							Dict								=				torch.tensor([f.label for f in features],	dtype=torch.float		)

							UpperCAmelCase :							Any								=				TensorDataset(__A,	__A,	__A		)
							return dataset
					else:
							raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''		)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCamelCase       :     int           =							{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase       :     Tuple           =							["MBart50Tokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase       :     List[Any]           =							["MBart50TokenizerFast"]


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbartaa import MBartaaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
     import sys

     _lowerCamelCase       :     Optional[int]           =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





