





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


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

snake_case_     :       List[str]						=  "pt" if is_torch_available() else "tf"


@require_sentencepiece
@require_tokenizers
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   str									=       CamembertTokenizer
							__a					:   Optional[int]									=       CamembertTokenizerFast
							__a					:   List[Any]									=       True
							__a					:   Union[str, Any]									=       True

							def     UpperCAmelCase__	(							self							:     int   )					->       Tuple:







													"""simple docstring"""
													super().setUp()

													# We have a SentencePiece fixture for testing
													UpperCAmelCase_			:      Optional[Any]     						=  CamembertTokenizer(__magic_name__   )
													tokenizer.save_pretrained(self.tmpdirname   )

							def     UpperCAmelCase__	(							self							:     Any   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  '''<pad>'''
													UpperCAmelCase_			:      Union[str, Any]     						=  1

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

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







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  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(__magic_name__   )       ,     10_04   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Tuple:







													"""simple docstring"""
													self.assertEqual(self.get_tokenizer().vocab_size       ,     10_05   )

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







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  CamembertTokenizer(__magic_name__   )
													tokenizer.save_pretrained(self.tmpdirname   )
													UpperCAmelCase_			:      Optional[Any]     						=  CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

													UpperCAmelCase_			:      Optional[int]     						=  '''I was born in 92000, and this is falsé.'''

													UpperCAmelCase_			:      Any     						=  tokenizer.encode(__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  rust_tokenizer.encode(__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  tokenizer.encode(__magic_name__       ,     add_special_tokens=__magic_name__   )
													UpperCAmelCase_			:      List[str]     						=  rust_tokenizer.encode(__magic_name__       ,     add_special_tokens=__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

													# <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_			:      List[Any]     						=  tokenizer.convert_ids_to_tokens(__magic_name__   )
													UpperCAmelCase_			:      Any     						=  rust_tokenizer.tokenize(__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

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







													"""simple docstring"""
													if not self.test_rust_tokenizer:
																			return

													UpperCAmelCase_			:      List[str]     						=  self.get_tokenizer()
													UpperCAmelCase_			:      Union[str, Any]     						=  self.get_rust_tokenizer()

													UpperCAmelCase_			:      List[Any]     						=  '''I was born in 92000, and this is falsé.'''

													UpperCAmelCase_			:      int     						=  tokenizer.tokenize(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  rust_tokenizer.tokenize(__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      int     						=  tokenizer.encode(__magic_name__       ,     add_special_tokens=__magic_name__   )
													UpperCAmelCase_			:      Any     						=  rust_tokenizer.encode(__magic_name__       ,     add_special_tokens=__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  self.get_rust_tokenizer()
													UpperCAmelCase_			:      int     						=  tokenizer.encode(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  rust_tokenizer.encode(__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )



							@slow
							def     UpperCAmelCase__	(							self							:     Any   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      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.
													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=__magic_name__       ,     model_name='''camembert-base'''       ,     revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''       ,     sequences=__magic_name__       ,     )












'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


snake_case_     :       List[str]						=  False


class 					__a      (unittest.TestCase     ):
							pass





@nightly
@require_torch_gpu
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       str:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      List[Any]     						=  load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''   )

													UpperCAmelCase_			:      Optional[int]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Union[str, Any]     						=  pipe.dual_guided(
													    prompt='''first prompt'''       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=2       ,     output_type='''numpy'''       ,     ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(__magic_name__   )
																			UpperCAmelCase_			:      Optional[int]     						=  VersatileDiffusionPipeline.from_pretrained(__magic_name__       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Any     						=  generator.manual_seed(0   )
													UpperCAmelCase_			:      Dict     						=  pipe.dual_guided(
													    prompt='''first prompt'''       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=2       ,     output_type='''numpy'''       ,     ).images

													assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"



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







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Union[str, Any]     						=  '''cyberpunk 2077'''
													UpperCAmelCase_			:      Union[str, Any]     						=  load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''   )
													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe.dual_guided(
													    prompt=__magic_name__       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=50       ,     output_type='''numpy'''       ,     ).images

													UpperCAmelCase_			:      List[str]     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      Union[str, Any]     						=  np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]   )

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

													UpperCAmelCase_			:      Tuple     						=  '''A painting of a squirrel eating a burger '''
													UpperCAmelCase_			:      Optional[int]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      List[Any]     						=  pipe.text_to_image(
													    prompt=__magic_name__       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=50       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      Any     						=  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]   )

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

													UpperCAmelCase_			:      Tuple     						=  pipe.image_variation(__magic_name__       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Optional[Any]     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      List[str]     						=  np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]   )

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





