'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ChineseCLIPImageProcessor




class 		SCREAMING_SNAKE_CASE  (       unittest.TestCase						):







				'''simple docstring'''





				def __init__(    self       :    List[Any]							,		lowercase__       :    Any							,		lowercase__       :    str=7							,		lowercase__       :    Optional[Any]=3							,		lowercase__       :    List[Any]=18							,		lowercase__       :    List[Any]=30							,		lowercase__       :    Tuple=400							,		lowercase__       :    Optional[Any]=True							,		lowercase__       :    Union[str, Any]=None							,		lowercase__       :    List[Any]=True							,		lowercase__       :    List[str]=None							,		lowercase__       :    Optional[int]=True							,		lowercase__       :    Dict=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,		lowercase__       :    str=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,		lowercase__       :    int=True							,		)				->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase      :      Optional[int]       =    size if size is not None else {"height": 224, "width": 224}
										_UpperCamelCase      :      int       =    crop_size if crop_size is not None else {"height": 18, "width": 18}
										_UpperCamelCase      :      int       =    parent
										_UpperCamelCase      :      List[str]       =    batch_size
										_UpperCamelCase      :      int       =    num_channels
										_UpperCamelCase      :      List[Any]       =    image_size
										_UpperCamelCase      :      Dict       =    min_resolution
										_UpperCamelCase      :      Dict       =    max_resolution
										_UpperCamelCase      :      Any       =    do_resize
										_UpperCamelCase      :      int       =    size
										_UpperCamelCase      :      int       =    do_center_crop
										_UpperCamelCase      :      Dict       =    crop_size
										_UpperCamelCase      :      List[Any]       =    do_normalize
										_UpperCamelCase      :      List[str]       =    image_mean
										_UpperCamelCase      :      Tuple       =    image_std
										_UpperCamelCase      :      Any       =    do_convert_rgb





				def 			snake_case__					(    self       :    Dict       )				->List[Any]:


										'''simple docstring'''




										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}







				def 			snake_case__					(    self       :    Optional[Any]							,		lowercase__       :    Optional[Any]=False							,		lowercase__       :    Any=False							,		lowercase__       :    List[Any]=False       )				->Optional[Any]:


										'''simple docstring'''




										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																_UpperCamelCase      :      Optional[int]       =    []
																for i in range(self.batch_size       ):
																						image_inputs.append(
																						    np.random.randint(
																						        255							,		size=(self.num_channels, self.max_resolution, self.max_resolution)							,		dtype=np.uinta       )       )
										else:
																_UpperCamelCase      :      List[Any]       =    []
																for i in range(self.batch_size       ):
																						_UpperCamelCase      :      Any       =    np.random.choice(np.arange(self.min_resolution							,		self.max_resolution       )							,		2       )
																						image_inputs.append(np.random.randint(255							,		size=(self.num_channels, width, height)							,		dtype=np.uinta       )       )

										if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																_UpperCamelCase      :      Any       =    [Image.fromarray(np.moveaxis(lowercase__							,		0							,		-1       )       ) for x in image_inputs]

										if torchify:
																_UpperCamelCase      :      str       =    [torch.from_numpy(lowercase__       ) for x in image_inputs]

										return image_inputs





@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE  (       SCREAMING_SNAKE_CASE ,			unittest.TestCase						):







				'''simple docstring'''
				UpperCAmelCase__					      =				ChineseCLIPImageProcessor if is_vision_available() else None





				def 			snake_case__					(    self       :    Any       )				->str:


										'''simple docstring'''




										_UpperCamelCase      :      Optional[Any]       =    ChineseCLIPImageProcessingTester(self							,		do_center_crop=lowercase__       )





				@property
				def 			snake_case__					(    self       :    int       )				->Union[str, Any]:


										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()





				def 			snake_case__					(    self       :    List[Any]       )				->Dict:


										'''simple docstring'''




										_UpperCamelCase      :      Dict       =    self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(lowercase__							,		"do_resize"       )       )
										self.assertTrue(hasattr(lowercase__							,		"size"       )       )
										self.assertTrue(hasattr(lowercase__							,		"do_center_crop"       )       )
										self.assertTrue(hasattr(lowercase__							,		"center_crop"       )       )
										self.assertTrue(hasattr(lowercase__							,		"do_normalize"       )       )
										self.assertTrue(hasattr(lowercase__							,		"image_mean"       )       )
										self.assertTrue(hasattr(lowercase__							,		"image_std"       )       )
										self.assertTrue(hasattr(lowercase__							,		"do_convert_rgb"       )       )





				def 			snake_case__					(    self       :    Tuple       )				->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase      :      str       =    self.image_processing_class.from_dict(self.image_processor_dict       )
										self.assertEqual(image_processor.size							,		{"height": 224, "width": 224}       )
										self.assertEqual(image_processor.crop_size							,		{"height": 18, "width": 18}       )

										_UpperCamelCase      :      List[str]       =    self.image_processing_class.from_dict(self.image_processor_dict							,		size=42							,		crop_size=84       )
										self.assertEqual(image_processor.size							,		{"shortest_edge": 42}       )
										self.assertEqual(image_processor.crop_size							,		{"height": 84, "width": 84}       )





				def 			snake_case__					(    self       :    str       )				->Optional[int]:


										'''simple docstring'''




										pass





				def 			snake_case__					(    self       :    Optional[int]       )				->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase      :      List[str]       =    self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										_UpperCamelCase      :      Dict       =    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__       )
										for image in image_inputs:
																self.assertIsInstance(lowercase__							,		Image.Image       )

										# Test not batched input
										_UpperCamelCase      :      List[str]       =    image_processing(image_inputs[0]							,		return_tensors="pt"       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,		(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,		)

										# Test batched
										_UpperCamelCase      :      Optional[Any]       =    image_processing(lowercase__							,		return_tensors="pt"       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,		(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,		)





				def 			snake_case__					(    self       :    int       )				->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase      :      str       =    self.image_processing_class(**self.image_processor_dict       )
										# create random numpy tensors
										_UpperCamelCase      :      Optional[Any]       =    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__							,		numpify=lowercase__       )
										for image in image_inputs:
																self.assertIsInstance(lowercase__							,		np.ndarray       )

										# Test not batched input
										_UpperCamelCase      :      Tuple       =    image_processing(image_inputs[0]							,		return_tensors="pt"       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,		(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,		)

										# Test batched
										_UpperCamelCase      :      str       =    image_processing(lowercase__							,		return_tensors="pt"       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,		(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,		)







				def 			snake_case__					(    self       :    str       )				->str:


										'''simple docstring'''




										_UpperCamelCase      :      Tuple       =    self.image_processing_class(**self.image_processor_dict       )
										# create random PyTorch tensors
										_UpperCamelCase      :      Dict       =    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__							,		torchify=lowercase__       )
										for image in image_inputs:
																self.assertIsInstance(lowercase__							,		torch.Tensor       )

										# Test not batched input
										_UpperCamelCase      :      int       =    image_processing(image_inputs[0]							,		return_tensors="pt"       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,		(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,		)

										# Test batched
										_UpperCamelCase      :      List[Any]       =    image_processing(lowercase__							,		return_tensors="pt"       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,		(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,		)





@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE  (       SCREAMING_SNAKE_CASE ,			unittest.TestCase						):







				'''simple docstring'''
				UpperCAmelCase__					      =				ChineseCLIPImageProcessor if is_vision_available() else None





				def 			snake_case__					(    self       :    List[str]       )				->int:


										'''simple docstring'''




										_UpperCamelCase      :      Union[str, Any]       =    ChineseCLIPImageProcessingTester(self							,		num_channels=4							,		do_center_crop=lowercase__       )
										_UpperCamelCase      :      Optional[Any]       =    3





				@property
				def 			snake_case__					(    self       :    Tuple       )				->List[str]:


										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()





				def 			snake_case__					(    self       :    Union[str, Any]       )				->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase      :      List[Any]       =    self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(lowercase__							,		"do_resize"       )       )
										self.assertTrue(hasattr(lowercase__							,		"size"       )       )
										self.assertTrue(hasattr(lowercase__							,		"do_center_crop"       )       )
										self.assertTrue(hasattr(lowercase__							,		"center_crop"       )       )
										self.assertTrue(hasattr(lowercase__							,		"do_normalize"       )       )
										self.assertTrue(hasattr(lowercase__							,		"image_mean"       )       )
										self.assertTrue(hasattr(lowercase__							,		"image_std"       )       )
										self.assertTrue(hasattr(lowercase__							,		"do_convert_rgb"       )       )





				def 			snake_case__					(    self       :    Optional[Any]       )				->List[Any]:


										'''simple docstring'''




										pass







				def 			snake_case__					(    self       :    Union[str, Any]       )				->Dict:


										'''simple docstring'''




										_UpperCamelCase      :      Optional[Any]       =    self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										_UpperCamelCase      :      List[str]       =    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__       )
										for image in image_inputs:
																self.assertIsInstance(lowercase__							,		Image.Image       )

										# Test not batched input
										_UpperCamelCase      :      Optional[int]       =    image_processing(image_inputs[0]							,		return_tensors="pt"       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,		(
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,		)

										# Test batched
										_UpperCamelCase      :      Union[str, Any]       =    image_processing(lowercase__							,		return_tensors="pt"       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,		(
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,		)






'''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


_UpperCamelCase 							=  get_tests_dir("""fixtures/test_sentencepiece.model""")
_UpperCamelCase 							=  get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_UpperCamelCase 							=  """pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				CamembertTokenizer
     lowerCamelCase__       						=				CamembertTokenizerFast
     lowerCamelCase__       						=				True
     lowerCamelCase__       						=				True


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          super().setUp()

          # We have a SentencePiece fixture for testing
          __A	:       List[Any]      =							CamembertTokenizer(lowerCamelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							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(lowerCamelCase		)			,     1004		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.assertEqual(self.get_tokenizer().vocab_size			,     1005		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							CamembertTokenizer(lowerCamelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
          __A	:       Optional[int]      =							CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

          __A	:       str      =							"I was born in 92000, and this is falsé."

          __A	:       List[Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Tuple      =							rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Dict      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          __A	:       str      =							rust_tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          # <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)
          __A	:       int      =							tokenizer.convert_ids_to_tokens(lowerCamelCase		)
          __A	:       Optional[int]      =							rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          if not self.test_rust_tokenizer:
               return

          __A	:       Tuple      =							self.get_tokenizer()
          __A	:       List[Any]      =							self.get_rust_tokenizer()

          __A	:       Any      =							"I was born in 92000, and this is falsé."

          __A	:       int      =							tokenizer.tokenize(lowerCamelCase		)
          __A	:       Any      =							rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Any      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          __A	:       Optional[int]      =							rust_tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Union[str, Any]      =							self.get_rust_tokenizer()
          __A	:       Optional[Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Dict      =							rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							{"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.
          __A	:       int      =							[
              "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="camembert-base"			,     revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"			,     sequences=lowerCamelCase			,     )

