





"""simple docstring"""





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a							=							logging.get_logger(__name__)

_a							=							{'vocab_file': 'sentencepiece.bpe.model'}

_a							=							{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

_a							=							{
    'camembert-base': 5_12,
}

_a							=							'▁'




class  _lowerCAmelCase  (     A_       ):







 """simple docstring"""







 __UpperCAmelCase     : int       		=     VOCAB_FILES_NAMES
 __UpperCAmelCase     : List[str]       		=     PRETRAINED_VOCAB_FILES_MAP
 __UpperCAmelCase     : Dict       		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __UpperCAmelCase     : List[Any]       		=     ["input_ids", "attention_mask"]


 def __init__(      self							:      List[Any], UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      Tuple="<s>", UpperCAmelCase__							:      Tuple="</s>", UpperCAmelCase__							:      List[str]="</s>", UpperCAmelCase__							:      Tuple="<s>", UpperCAmelCase__							:      Optional[Any]="<unk>", UpperCAmelCase__							:      Optional[Any]="<pad>", UpperCAmelCase__							:      Any="<mask>", UpperCAmelCase__							:      Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"], UpperCAmelCase__							:      Optional[Dict[str, Any]] = None, **UpperCAmelCase__							:      Tuple, ):
    # Mask token behave like a normal word, i.e. include the space before it
    __lowercase								=						AddedToken(_lowerCamelCase, lstrip=_lowerCamelCase, rstrip=_lowerCamelCase     ) if isinstance(_lowerCamelCase, _lowerCamelCase     ) else mask_token

    __lowercase								=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=_lowerCamelCase, eos_token=_lowerCamelCase, unk_token=_lowerCamelCase, sep_token=_lowerCamelCase, cls_token=_lowerCamelCase, pad_token=_lowerCamelCase, mask_token=_lowerCamelCase, additional_special_tokens=_lowerCamelCase, sp_model_kwargs=self.sp_model_kwargs, **_lowerCamelCase, )
    __lowercase								=						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
    self.sp_model.Load(str(_lowerCamelCase     )     )
    __lowercase								=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    __lowercase								=						{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
    __lowercase								=						len(self.fairseq_tokens_to_ids     )
    __lowercase								=						len(self.sp_model     ) + len(self.fairseq_tokens_to_ids     )
    __lowercase								=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      List[int], UpperCAmelCase__							:      Optional[List[int]] = None     ):

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


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      List[int], UpperCAmelCase__							:      Optional[List[int]] = None, UpperCAmelCase__							:      bool = False     ):
    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase, token_ids_a=_lowerCamelCase, already_has_special_tokens=_lowerCamelCase     )

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


 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      List[int], UpperCAmelCase__							:      Optional[List[int]] = None     ):
    __lowercase								=						[self.sep_token_id]
    __lowercase								=						[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]


 @property
 def  _lowercase			(      self							:      List[str]     ):
    return len(self.fairseq_tokens_to_ids     ) + len(self.sp_model     )


 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						{self.convert_ids_to_tokens(_lowerCamelCase     ): i for i in range(self.vocab_size     )}
    vocab.update(self.added_tokens_encoder     )
    return vocab


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      str     ):
    return self.sp_model.encode(_lowerCamelCase, out_type=_lowerCamelCase     )


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      Optional[int]     ):
    if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(_lowerCamelCase     ) == 0:
       # Convert sentence piece unk token to fairseq unk token index
       return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(_lowerCamelCase     )


 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      Optional[Any]     ):
    if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      int     ):
    __lowercase								=						[]
    __lowercase								=						""
    __lowercase								=						False
    for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
          if not prev_is_special:
             out_string += " "
          out_string += self.sp_model.decode(_lowerCamelCase     ) + token
          __lowercase								=						True
          __lowercase								=						[]
       else:
          current_sub_tokens.append(_lowerCamelCase     )
          __lowercase								=						False
    out_string += self.sp_model.decode(_lowerCamelCase     )
    return out_string.strip()


 def __getstate__(      self							:      Union[str, Any]     ):
    __lowercase								=						self.__dict__.copy()
    __lowercase								=						None
    return state


 def __setstate__(      self							:      Optional[Any], UpperCAmelCase__							:      Dict     ):
    __lowercase								=						d

    # for backward compatibility
    if not hasattr(self, "sp_model_kwargs"     ):
       __lowercase								=						{}

    __lowercase								=						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
    self.sp_model.Load(self.vocab_file     )



 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      str, UpperCAmelCase__							:      Optional[str] = None     ):
    if not os.path.isdir(_lowerCamelCase     ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
       return
    __lowercase								=						os.path.join(
        _lowerCamelCase, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

    if os.path.abspath(self.vocab_file     ) != os.path.abspath(_lowerCamelCase     ) and os.path.isfile(self.vocab_file     ):
       copyfile(self.vocab_file, _lowerCamelCase     )
    elif not os.path.isfile(self.vocab_file     ):
       with open(_lowerCamelCase, "wb"     ) as fi:
          __lowercase								=						self.sp_model.serialized_model_proto()
          fi.write(_lowerCamelCase     )

    return (out_vocab_file,)







"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_a							=							{'LayoutLMv2Config', 'LayoutLMv3Config'}




@is_pipeline_test
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : str       		=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 __UpperCAmelCase     : str       		=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

 if model_mapping is not None:
  __UpperCAmelCase     : Optional[Any]       		=     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
 if tf_model_mapping is not None:
  __UpperCAmelCase     : Tuple       		=     {
      config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
  }


 @require_torch
 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						pipeline(
        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    __lowercase								=						text_classifier("This is great !", top_k=2     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]     )

    __lowercase								=						text_classifier(["This is great !", "This is bad"], top_k=2     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
        ], )

    __lowercase								=						text_classifier("This is great !", top_k=1     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    # Legacy behavior
    __lowercase								=						text_classifier("This is great !", return_all_scores=UpperCAmelCase__     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    __lowercase								=						text_classifier("This is great !", return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]     )

    __lowercase								=						text_classifier(["This is great !", "Something else"], return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
        ], )

    __lowercase								=						text_classifier(["This is great !", "Something else"], return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            {"label": "LABEL_0", "score": 0.504},
            {"label": "LABEL_0", "score": 0.504},
        ], )


 @require_torch
 def  _lowercase			(      self							:      Dict     ):
    import torch

    __lowercase								=						pipeline(
        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt", device=torch.device("cpu"     ), )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )


 @require_tf
 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						pipeline(
        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )


 @slow
 @require_torch
 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						pipeline("text-classification"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("This is bad !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "NEGATIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("Birds are a type of animal"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 0.988}]     )


 @slow
 @require_tf
 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						pipeline("text-classification", framework="tf"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("This is bad !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "NEGATIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("Birds are a type of animal"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 0.988}]     )


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      str, UpperCAmelCase__							:      int, UpperCAmelCase__							:      Tuple     ):
    __lowercase								=						TextClassificationPipeline(model=UpperCAmelCase__, tokenizer=UpperCAmelCase__     )
    return text_classifier, ["HuggingFace is in", "This is another test"]



 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      str     ):
    __lowercase								=						text_classifier.model
    # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
    __lowercase								=						"HuggingFace is in"
    __lowercase								=						text_classifier(UpperCAmelCase__     )

    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]     )
    self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )

    __lowercase								=						["HuggingFace is in ", "Paris is in France"]
    __lowercase								=						text_classifier(UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}], )
    self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )
    self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()     )

    # Forcing to get all results with `top_k=None`
    # This is NOT the legacy format
    __lowercase								=						text_classifier(UpperCAmelCase__, top_k=UpperCAmelCase__     )
    __lowercase								=						len(model.config.idalabel.values()     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [[{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N, [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N], )

    __lowercase								=						{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
    __lowercase								=						text_classifier(UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, )
    self.assertTrue(outputs["label"] in model.config.idalabel.values()     )

    # This might be used a text pair, but tokenizer + pipe interaction
    # makes it hard to understand that it's not using the pair properly
    # https://github.com/huggingface/transformers/issues/17305
    # We disabled this usage instead as it was outputting wrong outputs.
    __lowercase								=						[["HuggingFace is in ", "Paris is in France"]]
    with self.assertRaises(UpperCAmelCase__     ):
       text_classifier(UpperCAmelCase__     )

    # This used to be valid for doing text pairs
    # We're keeping it working because of backward compatibility
    __lowercase								=						text_classifier([[["HuggingFace is in ", "Paris is in France"]]]     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}], )
    self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )




