




'''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 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			BarthezTokenizer
			__SCREAMING_SNAKE_CASE    =			BarthezTokenizerFast
			__SCREAMING_SNAKE_CASE    =			True
			__SCREAMING_SNAKE_CASE    =			True





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						super().setUp()

						__snake_case			=		BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"							)
						tokenizer.save_pretrained(self.tmpdirname							)
						tokenizer.save_pretrained(self.tmpdirname      ,		legacy_format=a_							)
						__snake_case			=		tokenizer





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		"<pad>"
						__snake_case			=		1

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





			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		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(a_							)      ,		101_122							)





			def 							A (      self						:    Any							):



						"""simple docstring"""




						self.assertEqual(self.get_tokenizer().vocab_size      ,		101_122							)





			@require_torch
			def 							A (      self						:    Dict							):



						"""simple docstring"""




						__snake_case			=		["A long paragraph for summarization.", "Another paragraph for summarization."]
						__snake_case			=		[0, 57, 3_018, 70_307, 91, 2]

						__snake_case			=		self.tokenizer(
						    a_      ,		max_length=len(a_							)      ,		padding=a_      ,		truncation=a_      ,		return_tensors="pt"							)
						self.assertIsInstance(a_      ,		a_							)

						self.assertEqual((2, 6)      ,		batch.input_ids.shape							)
						self.assertEqual((2, 6)      ,		batch.attention_mask.shape							)
						__snake_case			=		batch.input_ids.tolist()[0]
						self.assertListEqual(a_      ,		a_							)





			def 							A (      self						:    List[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(a_							)
						__snake_case			=		rust_tokenizer.tokenize(a_							)
						self.assertListEqual(a_      ,		a_							)

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

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






			@slow
			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		{"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 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.
						__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=a_      ,		model_name="moussaKam/mbarthez"      ,		revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"      ,		sequences=a_      ,		)












"""simple docstring"""




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


if is_vision_available():
		import PIL


a		:					int	      =     logging.get_logger(__name__)


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Optional[int]    =["""pixel_values"""]






							def __init__(  self ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 1 / 255 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					None:
								super().__init__(**lowerCAmelCase__ )
								a :						Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
								a :						Tuple      =							get_size_dict(lowerCAmelCase__ )
								a :						Any      =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								a :						Dict      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						Optional[int]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return resize(
								    lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   resample=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return center_crop(lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								return rescale(lowerCAmelCase__ ,   scale=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






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






							def        __a  (  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 ,   **lowerCAmelCase__ ,   )      ->					PIL.Image.Image:
								a :						List[Any]      =							do_resize if do_resize is not None else self.do_resize
								a :						str      =							resample if resample is not None else self.resample
								a :						Dict      =							do_center_crop if do_center_crop is not None else self.do_center_crop
								a :						Union[str, Any]      =							do_rescale if do_rescale is not None else self.do_rescale
								a :						str      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								a :						Dict      =							do_normalize if do_normalize is not None else self.do_normalize
								a :						List[str]      =							image_mean if image_mean is not None else self.image_mean
								a :						Union[str, Any]      =							image_std if image_std is not None else self.image_std

								a :						str      =							size if size is not None else self.size
								a :						Any      =							get_size_dict(lowerCAmelCase__ )
								a :						Dict      =							crop_size if crop_size is not None else self.crop_size
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						str      =							make_list_of_images(lowerCAmelCase__ )

								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." )

								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.
								a :						Optional[Any]      =							[to_numpy_array(lowerCAmelCase__ ) for image in images]

								if do_resize:
									a :						Optional[int]      =							[self.resize(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ ) for image in images]

								if do_center_crop:
									a :						Dict      =							[self.center_crop(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ) for image in images]

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

								if do_normalize:
									a :						Optional[Any]      =							[self.normalize(image=lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ) for image in images]

								a :						Optional[Any]      =							[to_channel_dimension_format(lowerCAmelCase__ ,   lowerCAmelCase__ ) for image in images]

								a :						Tuple      =							{"pixel_values": images}
								return BatchFeature(data=lowerCAmelCase__ ,   tensor_type=lowerCAmelCase__ )


