





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase    =	{
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	['''CamembertTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	['''CamembertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''CamembertForCausalLM''',
         '''CamembertForMaskedLM''',
         '''CamembertForMultipleChoice''',
         '''CamembertForQuestionAnswering''',
         '''CamembertForSequenceClassification''',
         '''CamembertForTokenClassification''',
         '''CamembertModel''',
         '''CamembertPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFCamembertForCausalLM''',
         '''TFCamembertForMaskedLM''',
         '''TFCamembertForMultipleChoice''',
         '''TFCamembertForQuestionAnswering''',
         '''TFCamembertForSequenceClassification''',
         '''TFCamembertForTokenClassification''',
         '''TFCamembertModel''',
         '''TFCamembertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_camembert import CamembertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_camembert_fast import CamembertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_camembert import (
              CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              CamembertForCausalLM,
              CamembertForMaskedLM,
              CamembertForMultipleChoice,
              CamembertForQuestionAnswering,
              CamembertForSequenceClassification,
              CamembertForTokenClassification,
              CamembertModel,
              CamembertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_camembert import (
              TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCamembertForCausalLM,
              TFCamembertForMaskedLM,
              TFCamembertForMultipleChoice,
              TFCamembertForQuestionAnswering,
              TFCamembertForSequenceClassification,
              TFCamembertForTokenClassification,
              TFCamembertModel,
              TFCamembertPreTrainedModel,
          )

else:
     import sys

     _UpperCamelCase    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








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


_UpperCamelCase    =	logging.get_logger(__name__)




@dataclass
class 			_lowerCamelCase	:



      """simple docstring"""






      UpperCAmelCase_  : str
      UpperCAmelCase_  : str
      UpperCAmelCase_  : Optional[str]				      =None
      UpperCAmelCase_  : Optional[str]				      =None







      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






        return json.dumps(dataclasses.asdict(self				)			,  indent=2				) + "\n"




@dataclass(frozen=a      )
class 			_lowerCamelCase	:



      """simple docstring"""






      UpperCAmelCase_  : List[int]
      UpperCAmelCase_  : Optional[List[int]]				      =None
      UpperCAmelCase_  : Optional[List[int]]				      =None
      UpperCAmelCase_  : Optional[Union[int, float]]				      =None







      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        return json.dumps(dataclasses.asdict(self				)				) + "\n"




class 			_lowerCamelCase	:



      """simple docstring"""






      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				Dict:






        '''simple docstring'''






        raise NotImplementedError()







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				int:






        '''simple docstring'''






        raise NotImplementedError()







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				List[str]:






        '''simple docstring'''






        raise NotImplementedError()







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				str:






        '''simple docstring'''






        raise NotImplementedError()







      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        raise NotImplementedError()







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        if len(self.get_labels()				) > 1:
          __snake_case				:			Any            =    self.get_labels()[int(example.label				)]
        return example







      @classmethod
      def   UpperCAmelCase						(	cls			,  UpperCAmelCase			,  UpperCAmelCase=None				)    ->				str:






        '''simple docstring'''






        with open(UpperCAmelCase			,  "r"			,  encoding="utf-8-sig"				) as f:
          return list(csv.reader(UpperCAmelCase			,  delimiter="\t"			,  quotechar=UpperCAmelCase				)				)




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  UpperCAmelCase="classification"			,  UpperCAmelCase=False				)    ->				str:






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    [] if labels is None else labels
        __snake_case				:			str            =    [] if examples is None else examples
        __snake_case				:			str            =    mode
        __snake_case				:			Tuple            =    verbose







      def __len__(	self				)    ->				Dict:






        '''simple docstring'''






        return len(self.examples				)







      def __getitem__(	self			,  UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        if isinstance(UpperCAmelCase			,  UpperCAmelCase				):
          return SingleSentenceClassificationProcessor(labels=self.labels			,  examples=self.examples[idx]				)
        return self.examples[idx]







      @classmethod
      def   UpperCAmelCase						(	cls			,  UpperCAmelCase			,  UpperCAmelCase=""			,  UpperCAmelCase=0			,  UpperCAmelCase=1			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  **UpperCAmelCase				)    ->				int:






        '''simple docstring'''






        __snake_case				:			Dict            =    cls(**UpperCAmelCase				)
        processor.add_examples_from_csv(
            UpperCAmelCase			,  split_name=UpperCAmelCase			,  column_label=UpperCAmelCase			,  column_text=UpperCAmelCase			,  column_id=UpperCAmelCase			,  skip_first_row=UpperCAmelCase			,  overwrite_labels=UpperCAmelCase			,  overwrite_examples=UpperCAmelCase			,  )
        return processor







      @classmethod
      def   UpperCAmelCase						(	cls			,  UpperCAmelCase			,  UpperCAmelCase=None			,  **UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			Dict            =    cls(**UpperCAmelCase				)
        processor.add_examples(UpperCAmelCase			,  labels=UpperCAmelCase				)
        return processor







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase=""			,  UpperCAmelCase=0			,  UpperCAmelCase=1			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  UpperCAmelCase=False			,  UpperCAmelCase=False			,  )    ->				Dict:






        '''simple docstring'''






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

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







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  UpperCAmelCase=False				)    ->				Optional[Any]:






        '''simple docstring'''






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

        # Update examples
        if overwrite_examples:
          __snake_case				:			Union[str, Any]            =    examples
        else:
          self.examples.extend(UpperCAmelCase				)

        # Update labels
        if overwrite_labels:
          __snake_case				:			Optional[Any]            =    list(UpperCAmelCase				)
        else:
          __snake_case				:			Dict            =    list(set(self.labels				).union(UpperCAmelCase				)				)

        return self.examples







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  UpperCAmelCase=0			,  UpperCAmelCase=True			,  UpperCAmelCase=None			,  )    ->				str:






        '''simple docstring'''






        if max_length is None:
          __snake_case				:			Tuple            =    tokenizer.max_len

        __snake_case				:			Union[str, Any]            =    {label: i for i, label in enumerate(self.labels				)}

        __snake_case				:			Union[str, Any]            =    []
        for ex_index, example in enumerate(self.examples				):
          if ex_index % 10000 == 0:
            logger.info(F"""Tokenizing example {ex_index}"""				)

          __snake_case				:			Union[str, Any]            =    tokenizer.encode(
              example.text_a			,  add_special_tokens=UpperCAmelCase			,  max_length=min(UpperCAmelCase			,  tokenizer.max_len				)			,  )
          all_input_ids.append(UpperCAmelCase				)

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

        __snake_case				:			str            =    []
        for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase			,  self.examples				)				):
          if ex_index % 10000 == 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.
          __snake_case				:			Optional[Any]            =    [1 if mask_padding_with_zero else 0] * len(UpperCAmelCase				)

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

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

          if self.mode == "classification":
            __snake_case				:			str            =    label_map[example.label]
          elif self.mode == "regression":
            __snake_case				:			List[str]            =    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(UpperCAmelCase				) for x in input_ids]				)}"""				)
            logger.info(F"""attention_mask: {" ".join([str(UpperCAmelCase				) for x in attention_mask]				)}"""				)
            logger.info(F"""label: {example.label} (id = {label})"""				)

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

        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)

          __snake_case				:			int            =    tf.data.Dataset.from_generator(
              UpperCAmelCase			,  ({"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

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

          __snake_case				:			List[Any]            =    TensorDataset(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)
          return dataset
        else:
          raise ValueError("return_tensors should be one of 'tf' or 'pt'"				)

