





"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
       import PIL

a_			=      logging.get_logger(__name__)







def 		__lowercase					(       snake_case_  :  Optional[Any]						)       ->List[List[ImageInput]]:



    '''simple docstring'''







    if isinstance(_lowerCAmelCase    ,(list, tuple)						) and isinstance(videos[0]    ,(list, tuple)						) and is_valid_image(videos[0][0]						):
        return videos

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

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

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






class 		__snake_case (		lowerCamelCase__					):





 """simple docstring"""


 _lowerCamelCase				=					["""pixel_values"""]

 def __init__(   self ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = PILImageResampling.BILINEAR ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = True ,						__lowerCamelCase = 1 / 255 ,						__lowerCamelCase = True ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )
     __A   :						str									=					size if size is not None else {"shortest_edge": 256}
     __A   :						str									=					get_size_dict(__lowerCamelCase ,						default_to_square=__lowerCamelCase  )
     __A   :						Any									=					crop_size if crop_size is not None else {"height": 224, "width": 224}
     __A   :						Union[str, Any]									=					get_size_dict(__lowerCamelCase ,						param_name='''crop_size'''  )

     __A   :						int									=					do_resize
     __A   :						Union[str, Any]									=					size
     __A   :						List[Any]									=					do_center_crop
     __A   :						Dict									=					crop_size
     __A   :						List[Any]									=					resample
     __A   :						Optional[int]									=					do_rescale
     __A   :						Union[str, Any]									=					rescale_factor
     __A   :						Tuple									=					offset
     __A   :						Optional[int]									=					do_normalize
     __A   :						Dict									=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __A   :						int									=					image_std if image_std is not None else IMAGENET_STANDARD_STD

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = PILImageResampling.BILINEAR ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						Optional[Any]									=					get_size_dict(__lowerCamelCase ,						default_to_square=__lowerCamelCase  )
     if "shortest_edge" in size:
         __A   :						Any									=					get_resize_output_image_size(__lowerCamelCase ,						size['''shortest_edge'''] ,						default_to_square=__lowerCamelCase  )
     elif "height" in size and "width" in size:
         __A   :						Optional[int]									=					(size["height"], size["width"])
     else:
         raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""  )
     return resize(__lowerCamelCase ,						size=__lowerCamelCase ,						resample=__lowerCamelCase ,						data_format=__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						str									=					get_size_dict(__lowerCamelCase  )
     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(__lowerCamelCase ,						size=(size['''height'''], size['''width''']) ,						data_format=__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						List[Any]									=					image.astype(np.floataa  )
     if offset:
         __A   :						Optional[Any]									=					image - (scale / 2)
     return rescale(__lowerCamelCase ,						scale=__lowerCamelCase ,						data_format=__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     return normalize(__lowerCamelCase ,						mean=__lowerCamelCase ,						std=__lowerCamelCase ,						data_format=__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = ChannelDimension.FIRST ,						):



     '''simple docstring'''




     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.'''  )

     if offset and not do_rescale:
         raise ValueError('''For offset, do_rescale must also be set to True.'''  )

     # All transformations expect numpy arrays.
     __A   :						Dict									=					to_numpy_array(__lowerCamelCase  )

     if do_resize:
         __A   :						Any									=					self.resize(image=__lowerCamelCase ,						size=__lowerCamelCase ,						resample=__lowerCamelCase  )

     if do_center_crop:
         __A   :						Tuple									=					self.center_crop(__lowerCamelCase ,						size=__lowerCamelCase  )

     if do_rescale:
         __A   :						Optional[Any]									=					self.rescale(image=__lowerCamelCase ,						scale=__lowerCamelCase ,						offset=__lowerCamelCase  )

     if do_normalize:
         __A   :						List[str]									=					self.normalize(image=__lowerCamelCase ,						mean=__lowerCamelCase ,						std=__lowerCamelCase  )

     __A   :						str									=					to_channel_dimension_format(__lowerCamelCase ,						__lowerCamelCase  )
     return image







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = ChannelDimension.FIRST ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						str									=					do_resize if do_resize is not None else self.do_resize
     __A   :						int									=					resample if resample is not None else self.resample
     __A   :						Union[str, Any]									=					do_center_crop if do_center_crop is not None else self.do_center_crop
     __A   :						Dict									=					do_rescale if do_rescale is not None else self.do_rescale
     __A   :						int									=					rescale_factor if rescale_factor is not None else self.rescale_factor
     __A   :						Any									=					offset if offset is not None else self.offset
     __A   :						Any									=					do_normalize if do_normalize is not None else self.do_normalize
     __A   :						str									=					image_mean if image_mean is not None else self.image_mean
     __A   :						str									=					image_std if image_std is not None else self.image_std

     __A   :						Optional[Any]									=					size if size is not None else self.size
     __A   :						List[Any]									=					get_size_dict(__lowerCamelCase ,						default_to_square=__lowerCamelCase  )
     __A   :						Any									=					crop_size if crop_size is not None else self.crop_size
     __A   :						Dict									=					get_size_dict(__lowerCamelCase ,						param_name='''crop_size'''  )

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

     __A   :						str									=					make_batched(__lowerCamelCase  )

     __A   :						str									=					[
         [
             self._preprocess_image(
                 image=__lowerCamelCase ,						do_resize=__lowerCamelCase ,						size=__lowerCamelCase ,						resample=__lowerCamelCase ,						do_center_crop=__lowerCamelCase ,						crop_size=__lowerCamelCase ,						do_rescale=__lowerCamelCase ,						rescale_factor=__lowerCamelCase ,						offset=__lowerCamelCase ,						do_normalize=__lowerCamelCase ,						image_mean=__lowerCamelCase ,						image_std=__lowerCamelCase ,						data_format=__lowerCamelCase ,						)
             for img in video
         ]
         for video in videos
     ]

     __A   :						Tuple									=					{"pixel_values": videos}
     return BatchFeature(data=__lowerCamelCase ,						tensor_type=__lowerCamelCase  )








"""simple docstring"""



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					BarthezTokenizer
 _lowerCamelCase				=					BarthezTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     __A   :						List[Any]									=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''  )
     tokenizer.save_pretrained(self.tmpdirname  )
     tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=__lowerCamelCase  )
     __A   :						Any									=					tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''<pad>'''
     __A   :						Union[str, Any]									=					1

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					list(self.get_tokenizer().get_vocab().keys()  )

     self.assertEqual(vocab_keys[0] ,						'''<s>'''  )
     self.assertEqual(vocab_keys[1] ,						'''<pad>'''  )
     self.assertEqual(vocab_keys[-1] ,						'''<mask>'''  )
     self.assertEqual(len(__lowerCamelCase  ) ,						10_1122  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size ,						10_1122  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						Any									=					[0, 57, 3018, 7_0307, 91, 2]

     __A   :						List[Any]									=					self.tokenizer(
         __lowerCamelCase ,						max_length=len(__lowerCamelCase  ) ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )

     self.assertEqual((2, 6) ,						batch.input_ids.shape  )
     self.assertEqual((2, 6) ,						batch.attention_mask.shape  )
     __A   :						Any									=					batch.input_ids.tolist()[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A   :						Tuple									=					self.get_tokenizer()
     __A   :						Dict									=					self.get_rust_tokenizer()

     __A   :						Union[str, Any]									=					'''I was born in 92000, and this is falsé.'''

     __A   :						List[Any]									=					tokenizer.tokenize(__lowerCamelCase  )
     __A   :						Optional[int]									=					rust_tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					self.get_rust_tokenizer()
     __A   :						Optional[int]									=					tokenizer.encode(__lowerCamelCase  )
     __A   :						Any									=					rust_tokenizer.encode(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
     # fmt: on

     # moussaKam/mbarthez is a french model. So we also use french texts.
     __A   :						Union[str, Any]									=					[
         '''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=__lowerCamelCase ,						model_name='''moussaKam/mbarthez''' ,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,						sequences=__lowerCamelCase ,						)




