



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_  :	List[str] 				=					get_tests_dir("""fixtures/test_sentencepiece.model""")
lowerCamelCase_  :	List[str] 				=					get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

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

@require_sentencepiece
@require_tokenizers
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  List[str]    =	CamembertTokenizer
			__UpperCamelCase :  List[str]    =	CamembertTokenizerFast
			__UpperCamelCase :  Dict    =	True
			__UpperCamelCase :  Dict    =	True

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							UpperCamelCase_:							List[str]		=  CamembertTokenizer(snake_case_ )
							tokenizer.save_pretrained(self.tmpdirname )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Any		=  """<pad>"""
							UpperCamelCase_:							Optional[int]		=  1

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

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							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(snake_case_ )  ,		1004 )

			def  lowerCAmelCase__		(    self	:    Dict ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,		1005 )

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Union[str, Any]		=  CamembertTokenizer(snake_case_ )
							tokenizer.save_pretrained(self.tmpdirname )
							UpperCamelCase_:							str		=  CamembertTokenizerFast.from_pretrained(self.tmpdirname )

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

							UpperCamelCase_:							List[str]		=  tokenizer.encode(snake_case_ )
							UpperCamelCase_:							str		=  rust_tokenizer.encode(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

							UpperCamelCase_:							List[str]		=  tokenizer.encode(snake_case_  ,		add_special_tokens=snake_case_ )
							UpperCamelCase_:							Optional[int]		=  rust_tokenizer.encode(snake_case_  ,		add_special_tokens=snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

							# <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)
							UpperCamelCase_:							int		=  tokenizer.convert_ids_to_tokens(snake_case_ )
							UpperCamelCase_:							Dict		=  rust_tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							str		=  self.get_tokenizer()
							UpperCamelCase_:							str		=  self.get_rust_tokenizer()

							UpperCamelCase_:							List[str]		=  """I was born in 92000, and this is falsé."""

							UpperCamelCase_:							Optional[Any]		=  tokenizer.tokenize(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  rust_tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

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

							UpperCamelCase_:							List[str]		=  self.get_rust_tokenizer()
							UpperCamelCase_:							List[Any]		=  tokenizer.encode(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  rust_tokenizer.encode(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )




			@slow
			def  lowerCAmelCase__		(    self	:    str ):
							# fmt: off
							UpperCamelCase_:							str		=  {"""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.
							UpperCamelCase_:							List[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=snake_case_  ,		model_name="""camembert-base"""  ,		revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""  ,		sequences=snake_case_  ,		)








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor

class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Tuple=7  ,		snake_case_	:    Optional[Any]=3  ,		snake_case_	:    Dict=18  ,		snake_case_	:    Dict=30  ,		snake_case_	:    Union[str, Any]=400  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    Any=None  ,		snake_case_	:    List[str]=True  ,		):
							UpperCamelCase_:							Dict		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Union[str, Any]		=  parent
							UpperCamelCase_:							Tuple		=  batch_size
							UpperCamelCase_:							List[str]		=  num_channels
							UpperCamelCase_:							Optional[int]		=  image_size
							UpperCamelCase_:							Dict		=  min_resolution
							UpperCamelCase_:							Optional[int]		=  max_resolution
							UpperCamelCase_:							str		=  do_resize
							UpperCamelCase_:							Tuple		=  size
							UpperCamelCase_:							Dict		=  do_normalize




			def  lowerCAmelCase__		(    self	:    str ):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
							            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
							        ] ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}




@require_torch
@require_vision
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	ImageGPTImageProcessor if is_vision_available() else None

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  ImageGPTImageProcessingTester(self )

			@property
			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(snake_case_  ,		"""clusters""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""size""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize""" ) )

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18} )

							UpperCamelCase_:							Tuple		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42 )
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42} )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict )
							UpperCamelCase_:							Optional[int]		=  json.loads(image_processor.to_json_string() )
							for key, value in self.image_processor_dict.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		obj[key] ) )
											else:
															self.assertEqual(obj[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							int		=  os.path.join(snake_case_  ,		"""image_processor.json""" )
											image_processor_first.to_json_file(snake_case_ )
											UpperCamelCase_:							Any		=  self.image_processing_class.from_json_file(snake_case_ ).to_dict()

							UpperCamelCase_:							str		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(snake_case_ )
											UpperCamelCase_:							Optional[int]		=  self.image_processing_class.from_pretrained(snake_case_ ).to_dict()

							UpperCamelCase_:							Union[str, Any]		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )




			@unittest.skip("""ImageGPT requires clusters at initialization""" )
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							pass







def     A__   (  )					->		Optional[int]:
				UpperCamelCase_:							Optional[int]		=  load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,      split="""test"""  )

				UpperCamelCase_:							Tuple		=  Image.open(dataset[4]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[5]["""file"""]  )

				UpperCamelCase_:							List[str]		=  [imagea, imagea]

				return images





@require_vision
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )

							UpperCamelCase_:							List[str]		=  prepare_images()

							# test non-batched
							UpperCamelCase_:							List[str]		=  image_processing(images[0]  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(1, 1024) )

							UpperCamelCase_:							Union[str, Any]		=  [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,		snake_case_ )

							# test batched
							UpperCamelCase_:							Optional[int]		=  image_processing(snake_case_  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(2, 1024) )

							UpperCamelCase_:							str		=  [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,		snake_case_ )






