

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  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				CamembertTokenizer
			lowerCamelCase_					:					Tuple	      =				CamembertTokenizerFast
			lowerCamelCase_					:					List[Any]	      =				True
			lowerCamelCase_					:					List[Any]	      =				True






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:   Tuple           =  CamembertTokenizer(__lowerCamelCase   )
							tokenizer.save_pretrained(self.tmpdirname   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  """<pad>"""
							snake_case_						:   int           =  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   )					->       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(__lowerCamelCase   ) ,							1004   )






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



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






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


							'''simple docstring'''



							snake_case_						:   Any           =  CamembertTokenizer(__lowerCamelCase   )
							tokenizer.save_pretrained(self.tmpdirname   )
							snake_case_						:   Dict           =  CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

							snake_case_						:   int           =  """I was born in 92000, and this is falsé."""

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

							snake_case_						:   Any           =  tokenizer.encode(__lowerCamelCase ,							add_special_tokens=__lowerCamelCase   )
							snake_case_						:   Any           =  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)
							snake_case_						:   Tuple           =  tokenizer.convert_ids_to_tokens(__lowerCamelCase   )
							snake_case_						:   Any           =  rust_tokenizer.tokenize(__lowerCamelCase   )
							self.assertListEqual(__lowerCamelCase ,							__lowerCamelCase   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							if not self.test_rust_tokenizer:
											return

							snake_case_						:   Dict           =  self.get_tokenizer()
							snake_case_						:   str           =  self.get_rust_tokenizer()

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

							snake_case_						:   List[Any]           =  tokenizer.tokenize(__lowerCamelCase   )
							snake_case_						:   Dict           =  rust_tokenizer.tokenize(__lowerCamelCase   )
							self.assertListEqual(__lowerCamelCase ,							__lowerCamelCase   )

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

							snake_case_						:   Optional[int]           =  self.get_rust_tokenizer()
							snake_case_						:   Dict           =  tokenizer.encode(__lowerCamelCase   )
							snake_case_						:   Optional[int]           =  rust_tokenizer.encode(__lowerCamelCase   )
							self.assertListEqual(__lowerCamelCase ,							__lowerCamelCase   )






			@slow
			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Optional[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.
							snake_case_						:   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 ,							)







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class  __lowerCAmelCase	(      unittest.TestCase					):






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


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  UNetaDModel(
							    block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=3 ,							out_channels=3 ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,							)
							return model






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2




@slow
@require_torch
class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
