

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_        =			get_tests_dir('''fixtures/test_sentencepiece.model''')
snake_case_        =			get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

snake_case_        =			'''pt''' if is_torch_available() else '''tf'''


@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Union[str, Any]														=					CamembertTokenizer
    __lowerCamelCase						:	Dict														=					CamembertTokenizerFast
    __lowerCamelCase						:	Optional[int]														=					True
    __lowerCamelCase						:	List[Any]														=					True





    def 				snake_case_    (				self):
         super().setUp()

         # We have a SentencePiece fixture for testing
         lowercase__   :       Any				=	CamembertTokenizer(a)
         tokenizer.save_pretrained(self.tmpdirname)





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	'<pad>'
         lowercase__   :       str				=	1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(a)						,       a)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(a)						,       a)





    def 				snake_case_    (				self):
         lowercase__   :       str				=	list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]						,       '<s>NOTUSED')
         self.assertEqual(vocab_keys[1]						,       '<pad>')
         self.assertEqual(vocab_keys[-1]						,       '<mask>')
         self.assertEqual(len(a)						,       1004)





    def 				snake_case_    (				self):
         self.assertEqual(self.get_tokenizer().vocab_size						,       1005)





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	CamembertTokenizer(a)
         tokenizer.save_pretrained(self.tmpdirname)
         lowercase__   :       Tuple				=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

         lowercase__   :       Optional[Any]				=	'I was born in 92000, and this is falsé.'

         lowercase__   :       int				=	tokenizer.encode(a)
         lowercase__   :       Optional[Any]				=	rust_tokenizer.encode(a)
         self.assertListEqual(a						,       a)

         lowercase__   :       str				=	tokenizer.encode(a						,       add_special_tokens=a)
         lowercase__   :       int				=	rust_tokenizer.encode(a						,       add_special_tokens=a)
         self.assertListEqual(a						,       a)

         # <unk> tokens are not the same for `rust` than for `slow`.
         # Because spm gives back raw token instead of `unk` in EncodeAsPieces
         # tokens = tokenizer.tokenize(sequence)
         lowercase__   :       Optional[int]				=	tokenizer.convert_ids_to_tokens(a)
         lowercase__   :       Tuple				=	rust_tokenizer.tokenize(a)
         self.assertListEqual(a						,       a)





    def 				snake_case_    (				self):
         if not self.test_rust_tokenizer:
              return

         lowercase__   :       str				=	self.get_tokenizer()
         lowercase__   :       str				=	self.get_rust_tokenizer()

         lowercase__   :       Any				=	'I was born in 92000, and this is falsé.'

         lowercase__   :       Union[str, Any]				=	tokenizer.tokenize(a)
         lowercase__   :       Any				=	rust_tokenizer.tokenize(a)
         self.assertListEqual(a						,       a)

         lowercase__   :       str				=	tokenizer.encode(a						,       add_special_tokens=a)
         lowercase__   :       List[Any]				=	rust_tokenizer.encode(a						,       add_special_tokens=a)
         self.assertListEqual(a						,       a)

         lowercase__   :       Dict				=	self.get_rust_tokenizer()
         lowercase__   :       Any				=	tokenizer.encode(a)
         lowercase__   :       str				=	rust_tokenizer.encode(a)
         self.assertListEqual(a						,       a)





    @slow
    def 				snake_case_    (				self):
         # fmt: off
         lowercase__   :       Dict				=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
         # fmt: on

         # camembert is a french model. So we also use french texts.
         lowercase__   :       Dict				=	[
             'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
             'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
             'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
             'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
             'telles que la traduction et la synthèse de texte.',
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=a						,       model_name='camembert-base'						,       revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'						,       sequences=a						,       )




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case_        =			logging.get_logger(__name__)


if is_vision_available():
       import PIL


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Optional[Any]														=					["""pixel_values"""]





    def __init__(				self						,       a = True						,       a = None						,       a = PILImageResampling.BICUBIC						,       a = True						,       a = None						,       a = True						,       a = 1 / 255						,       a = True						,       a = None						,       a = None						,       a = True						,       **a						,       ):
         super().__init__(**a)
         lowercase__   :       List[str]				=	size if size is not None else {'shortest_edge': 224}
         lowercase__   :       str				=	get_size_dict(a						,       default_to_square=a)
         lowercase__   :       Optional[int]				=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       default_to_square=a						,       param_name='crop_size')

         lowercase__   :       List[str]				=	do_resize
         lowercase__   :       List[Any]				=	size
         lowercase__   :       Tuple				=	resample
         lowercase__   :       int				=	do_center_crop
         lowercase__   :       Union[str, Any]				=	crop_size
         lowercase__   :       int				=	do_rescale
         lowercase__   :       List[str]				=	rescale_factor
         lowercase__   :       Tuple				=	do_normalize
         lowercase__   :       str				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase__   :       Optional[Any]				=	image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase__   :       List[Any]				=	do_convert_rgb





    def 				snake_case_    (				self						,       a						,       a						,       a = PILImageResampling.BICUBIC						,       a = None						,       **a						,       ):
         lowercase__   :       str				=	get_size_dict(a						,       default_to_square=a)
         if "shortest_edge" not in size:
              raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
         lowercase__   :       str				=	get_resize_output_image_size(a						,       size=size['shortest_edge']						,       default_to_square=a)
         return resize(a						,       size=a						,       resample=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         lowercase__   :       List[str]				=	get_size_dict(a)
         if "height" not in size or "width" not in size:
              raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
         return center_crop(a						,       size=(size['height'], size['width'])						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         return rescale(a						,       scale=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a = None						,       **a						,       ):
         return normalize(a						,       mean=a						,       std=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = ChannelDimension.FIRST						,       **a						,       ):
         lowercase__   :       int				=	do_resize if do_resize is not None else self.do_resize
         lowercase__   :       Tuple				=	size if size is not None else self.size
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       param_name='size'						,       default_to_square=a)
         lowercase__   :       Optional[Any]				=	resample if resample is not None else self.resample
         lowercase__   :       int				=	do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase__   :       List[Any]				=	crop_size if crop_size is not None else self.crop_size
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       param_name='crop_size'						,       default_to_square=a)
         lowercase__   :       Dict				=	do_rescale if do_rescale is not None else self.do_rescale
         lowercase__   :       int				=	rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__   :       List[str]				=	do_normalize if do_normalize is not None else self.do_normalize
         lowercase__   :       Optional[Any]				=	image_mean if image_mean is not None else self.image_mean
         lowercase__   :       List[str]				=	image_std if image_std is not None else self.image_std
         lowercase__   :       Any				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowercase__   :       str				=	make_list_of_images(a)

         if not valid_images(a):
              raise ValueError(
                  'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                  'torch.Tensor, tf.Tensor or jax.ndarray.')

         if do_resize and size is None:
              raise ValueError('Size must be specified if do_resize is True.')

         if do_center_crop and crop_size is None:
              raise ValueError('Crop size must be specified if do_center_crop is True.')

         if do_rescale and rescale_factor is None:
              raise ValueError('Rescale factor must be specified if do_rescale is True.')

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.')

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              lowercase__   :       str				=	[convert_to_rgb(a) for image in images]

         # All transformations expect numpy arrays.
         lowercase__   :       Dict				=	[to_numpy_array(a) for image in images]

         if do_resize:
              lowercase__   :       Tuple				=	[self.resize(image=a						,       size=a						,       resample=a) for image in images]

         if do_center_crop:
              lowercase__   :       List[str]				=	[self.center_crop(image=a						,       size=a) for image in images]

         if do_rescale:
              lowercase__   :       str				=	[self.rescale(image=a						,       scale=a) for image in images]

         if do_normalize:
              lowercase__   :       Tuple				=	[self.normalize(image=a						,       mean=a						,       std=a) for image in images]

         lowercase__   :       Optional[int]				=	[to_channel_dimension_format(a						,       a) for image in images]

         lowercase__   :       Dict				=	{'pixel_values': images}
         return BatchFeature(data=a						,       tensor_type=a)

