






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


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

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




@require_sentencepiece
@require_tokenizers
class 			snake_case__   (							lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Union[str, Any] 				= CamembertTokenizer
       lowerCamelCase							:				int 				= CamembertTokenizerFast
       lowerCamelCase							:				int 				= True
       lowerCamelCase							:				Any 				= True



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            super().setUp()

            # We have a SentencePiece fixture for testing
            __snake_case			:Optional[int]          =						CamembertTokenizer(a__    )
            tokenizer.save_pretrained(self.tmpdirname    )



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






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						"""<pad>"""
            __snake_case			:List[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 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Dict          =						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__    )			,						10_04    )



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






            '''simple docstring'''





            self.assertEqual(self.get_tokenizer().vocab_size			,						10_05    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Any          =						CamembertTokenizer(a__    )
            tokenizer.save_pretrained(self.tmpdirname    )
            __snake_case			:str          =						CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

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

            __snake_case			:Dict          =						tokenizer.encode(a__    )
            __snake_case			:Optional[Any]          =						rust_tokenizer.encode(a__    )
            self.assertListEqual(a__			,						a__    )

            __snake_case			:Union[str, Any]          =						tokenizer.encode(a__			,						add_special_tokens=a__    )
            __snake_case			:List[str]          =						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)
            __snake_case			:List[Any]          =						tokenizer.convert_ids_to_tokens(a__    )
            __snake_case			:int          =						rust_tokenizer.tokenize(a__    )
            self.assertListEqual(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            if not self.test_rust_tokenizer:
                 return

            __snake_case			:Tuple          =						self.get_tokenizer()
            __snake_case			:str          =						self.get_rust_tokenizer()

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

            __snake_case			:Tuple          =						tokenizer.tokenize(a__    )
            __snake_case			:Dict          =						rust_tokenizer.tokenize(a__    )
            self.assertListEqual(a__			,						a__    )

            __snake_case			:int          =						tokenizer.encode(a__			,						add_special_tokens=a__    )
            __snake_case			:Optional[int]          =						rust_tokenizer.encode(a__			,						add_special_tokens=a__    )
            self.assertListEqual(a__			,						a__    )

            __snake_case			:List[Any]          =						self.get_rust_tokenizer()
            __snake_case			:Optional[int]          =						tokenizer.encode(a__    )
            __snake_case			:Union[str, Any]          =						rust_tokenizer.encode(a__    )
            self.assertListEqual(a__			,						a__    )



       @slow
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:str          =						{"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 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			:Optional[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=a__			,						model_name="""camembert-base"""			,						revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""			,						sequences=a__			,						)










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, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=7			,						a__=3			,						a__=18			,						a__=30			,						a__=4_00			,						a__=True			,						a__=32			,						a__=True			,						) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						parent
            __snake_case			:Dict          =						batch_size
            __snake_case			:Optional[Any]          =						num_channels
            __snake_case			:Dict          =						image_size
            __snake_case			:Dict          =						min_resolution
            __snake_case			:Dict          =						max_resolution
            __snake_case			:List[Any]          =						do_resize
            __snake_case			:Dict          =						size_divisor
            __snake_case			:Union[str, Any]          =						do_rescale



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return {
                "do_resize": self.do_resize,
                "size_divisor": self.size_divisor,
                "do_rescale": self.do_rescale,
            }




@require_torch
@require_vision
class 			snake_case__   (							lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= GLPNImageProcessor if is_vision_available() else None



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						GLPNImageProcessingTester(self    )



       @property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            return self.image_processor_tester.prepare_image_processor_dict()



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(a__			,						"""do_resize"""    )    )
            self.assertTrue(hasattr(a__			,						"""size_divisor"""    )    )
            self.assertTrue(hasattr(a__			,						"""resample"""    )    )
            self.assertTrue(hasattr(a__			,						"""do_rescale"""    )    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            __snake_case			:Optional[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						Image.Image    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:Union[str, Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            __snake_case			:List[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						numpify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						np.ndarray    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:int          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            __snake_case			:Optional[int]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						torchify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						torch.Tensor    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:List[Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )


