




'''simple docstring'''

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


a       :						Optional[int]								=		get_tests_dir('''fixtures/test_sentencepiece.model''')
a       :						Dict								=		get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

a       :						Tuple								=		"""pt""" if is_torch_available() else """tf"""



@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE__  (			UpperCAmelCase__				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			CamembertTokenizer
			__SCREAMING_SNAKE_CASE    =			CamembertTokenizerFast
			__SCREAMING_SNAKE_CASE    =			True
			__SCREAMING_SNAKE_CASE    =			True





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						super().setUp()

						# We have a SentencePiece fixture for testing
						__snake_case			=		CamembertTokenizer(_UpperCAmelCase							)
						tokenizer.save_pretrained(self.tmpdirname							)





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		"<pad>"
						__snake_case			=		1

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





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		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(_UpperCAmelCase							)      ,		1_004							)





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						self.assertEqual(self.get_tokenizer().vocab_size      ,		1_005							)





			def 							A (      self						:    str							):



						"""simple docstring"""




						__snake_case			=		CamembertTokenizer(_UpperCAmelCase							)
						tokenizer.save_pretrained(self.tmpdirname							)
						__snake_case			=		CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

						__snake_case			=		"I was born in 92000, and this is falsé."

						__snake_case			=		tokenizer.encode(_UpperCAmelCase							)
						__snake_case			=		rust_tokenizer.encode(_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase      ,		_UpperCAmelCase							)

						__snake_case			=		tokenizer.encode(_UpperCAmelCase      ,		add_special_tokens=_UpperCAmelCase							)
						__snake_case			=		rust_tokenizer.encode(_UpperCAmelCase      ,		add_special_tokens=_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase      ,		_UpperCAmelCase							)

						# <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)
						__snake_case			=		tokenizer.convert_ids_to_tokens(_UpperCAmelCase							)
						__snake_case			=		rust_tokenizer.tokenize(_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase      ,		_UpperCAmelCase							)





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						if not self.test_rust_tokenizer:
									return

						__snake_case			=		self.get_tokenizer()
						__snake_case			=		self.get_rust_tokenizer()

						__snake_case			=		"I was born in 92000, and this is falsé."

						__snake_case			=		tokenizer.tokenize(_UpperCAmelCase							)
						__snake_case			=		rust_tokenizer.tokenize(_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase      ,		_UpperCAmelCase							)

						__snake_case			=		tokenizer.encode(_UpperCAmelCase      ,		add_special_tokens=_UpperCAmelCase							)
						__snake_case			=		rust_tokenizer.encode(_UpperCAmelCase      ,		add_special_tokens=_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase      ,		_UpperCAmelCase							)

						__snake_case			=		self.get_rust_tokenizer()
						__snake_case			=		tokenizer.encode(_UpperCAmelCase							)
						__snake_case			=		rust_tokenizer.encode(_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase      ,		_UpperCAmelCase							)






			@slow
			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		{"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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.
						__snake_case			=		[
						    "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=_UpperCAmelCase      ,		model_name="camembert-base"      ,		revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"      ,		sequences=_UpperCAmelCase      ,		)













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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


snake_case__   :						str													=   logging.get_logger(__name__)






def      _snake_case     (__lowercase):
   if isinstance(__lowercase     ,   (list, tuple)) and isinstance(videos[0]     ,   (list, tuple)) and is_valid_image(videos[0][0]):
      return videos

   elif isinstance(__lowercase     ,   (list, tuple)) and is_valid_image(videos[0]):
      return [videos]

   elif is_valid_image(__lowercase):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}""")


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = ["""pixel_values"""]




  def __init__(  self		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = PILImageResampling.BILINEAR		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		_UpperCAmelCase = 1 / 255		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			None:
     super().__init__(**_UpperCAmelCase			)
     UpperCamelCase_						=  size if size is not None else {'shortest_edge': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		param_name='crop_size'			)

     UpperCamelCase_						=  do_resize
     UpperCamelCase_						=  size
     UpperCamelCase_						=  do_center_crop
     UpperCamelCase_						=  crop_size
     UpperCamelCase_						=  resample
     UpperCamelCase_						=  do_rescale
     UpperCamelCase_						=  rescale_factor
     UpperCamelCase_						=  do_normalize
     UpperCamelCase_						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     UpperCamelCase_						=  image_std if image_std is not None else IMAGENET_STANDARD_STD




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = PILImageResampling.BILINEAR		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     if "shortest_edge" in size:
        UpperCamelCase_						=  get_resize_output_image_size(_UpperCAmelCase		,		size['shortest_edge']		,		default_to_square=_UpperCAmelCase			)
     elif "height" in size and "width" in size:
        UpperCamelCase_						=  (size['height'], size['width'])
     else:
        raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""			)
     return resize(_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase			)
     if "height" not in size or "width" not in size:
        raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""			)
     return center_crop(_UpperCAmelCase		,		size=(size['height'], size['width'])		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			int:
     return rescale(_UpperCAmelCase		,		scale=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     return normalize(_UpperCAmelCase		,		mean=_UpperCAmelCase		,		std=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = ChannelDimension.FIRST		,		)      ->			np.ndarray:
     if do_resize and size is None or resample is None:
        raise ValueError('Size and resample 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.'			)

     # All transformations expect numpy arrays.
     UpperCamelCase_						=  to_numpy_array(_UpperCAmelCase			)

     if do_resize:
        UpperCamelCase_						=  self.resize(image=_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase			)

     if do_center_crop:
        UpperCamelCase_						=  self.center_crop(_UpperCAmelCase		,		size=_UpperCAmelCase			)

     if do_rescale:
        UpperCamelCase_						=  self.rescale(image=_UpperCAmelCase		,		scale=_UpperCAmelCase			)

     if do_normalize:
        UpperCamelCase_						=  self.normalize(image=_UpperCAmelCase		,		mean=_UpperCAmelCase		,		std=_UpperCAmelCase			)

     UpperCamelCase_						=  to_channel_dimension_format(_UpperCAmelCase		,		_UpperCAmelCase			)
     return image




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = ChannelDimension.FIRST		,		**_UpperCAmelCase		,		)      ->			PIL.Image.Image:
     UpperCamelCase_						=  do_resize if do_resize is not None else self.do_resize
     UpperCamelCase_						=  resample if resample is not None else self.resample
     UpperCamelCase_						=  do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCamelCase_						=  do_rescale if do_rescale is not None else self.do_rescale
     UpperCamelCase_						=  rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCamelCase_						=  do_normalize if do_normalize is not None else self.do_normalize
     UpperCamelCase_						=  image_mean if image_mean is not None else self.image_mean
     UpperCamelCase_						=  image_std if image_std is not None else self.image_std

     UpperCamelCase_						=  size if size is not None else self.size
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  crop_size if crop_size is not None else self.crop_size
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		param_name='crop_size'			)

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

     UpperCamelCase_						=  make_batched(_UpperCAmelCase			)

     UpperCamelCase_						=  [
         [
             self._preprocess_image(
                 image=_UpperCAmelCase		,		do_resize=_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase		,		do_center_crop=_UpperCAmelCase		,		crop_size=_UpperCAmelCase		,		do_rescale=_UpperCAmelCase		,		rescale_factor=_UpperCAmelCase		,		do_normalize=_UpperCAmelCase		,		image_mean=_UpperCAmelCase		,		image_std=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		)
             for img in video
         ]
         for video in videos
     ]

     UpperCamelCase_						=  {'pixel_values': videos}
     return BatchFeature(data=_UpperCAmelCase		,		tensor_type=_UpperCAmelCase			)





