



"""simple docstring"""
import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Optional[int]										= BarthezTokenizer
					lowercase__   :       List[str]										= BarthezTokenizerFast
					lowercase__   :       int										= True
					lowercase__   :       List[str]										= True

					def 			snake_case__      (    self   ):
									super().setUp()

									_lowerCamelCase        =				BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''   )
									tokenizer.save_pretrained(self.tmpdirname   )
									tokenizer.save_pretrained(self.tmpdirname				,       legacy_format=lowerCamelCase__   )
									_lowerCamelCase        =				tokenizer

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''<pad>'''
									_lowerCamelCase        =				1

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				list(self.get_tokenizer().get_vocab().keys()   )

									self.assertEqual(vocab_keys[0]				,       '''<s>'''   )
									self.assertEqual(vocab_keys[1]				,       '''<pad>'''   )
									self.assertEqual(vocab_keys[-1]				,       '''<mask>'''   )
									self.assertEqual(len(lowerCamelCase__   )				,       1_0_1_1_2_2   )

					def 			snake_case__      (    self   ):
									self.assertEqual(self.get_tokenizer().vocab_size				,       1_0_1_1_2_2   )

					@require_torch
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									_lowerCamelCase        =				[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

									_lowerCamelCase        =				self.tokenizer(
									    lowerCamelCase__				,       max_length=len(lowerCamelCase__   )				,       padding=lowerCamelCase__				,       truncation=lowerCamelCase__				,       return_tensors='''pt'''   )
									self.assertIsInstance(lowerCamelCase__				,       lowerCamelCase__   )

									self.assertEqual((2, 6)				,       batch.input_ids.shape   )
									self.assertEqual((2, 6)				,       batch.attention_mask.shape   )
									_lowerCamelCase        =				batch.input_ids.tolist()[0]
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									if not self.test_rust_tokenizer:
													return

									_lowerCamelCase        =				self.get_tokenizer()
									_lowerCamelCase        =				self.get_rust_tokenizer()

									_lowerCamelCase        =				'''I was born in 92000, and this is falsé.'''

									_lowerCamelCase        =				tokenizer.tokenize(lowerCamelCase__   )
									_lowerCamelCase        =				rust_tokenizer.tokenize(lowerCamelCase__   )
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )

									_lowerCamelCase        =				tokenizer.encode(lowerCamelCase__				,       add_special_tokens=lowerCamelCase__   )
									_lowerCamelCase        =				rust_tokenizer.encode(lowerCamelCase__				,       add_special_tokens=lowerCamelCase__   )
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )

									_lowerCamelCase        =				self.get_rust_tokenizer()
									_lowerCamelCase        =				tokenizer.encode(lowerCamelCase__   )
									_lowerCamelCase        =				rust_tokenizer.encode(lowerCamelCase__   )
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )





					@slow
					def 			snake_case__      (    self   ):
									# fmt: off
									_lowerCamelCase        =				{'''input_ids''': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], '''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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									_lowerCamelCase        =				[
									    '''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='''moussaKam/mbarthez'''				,       revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''				,       sequences=lowerCamelCase__				,       )





"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Tuple										= StableDiffusionXLImgaImgPipeline
					lowercase__   :       List[str]										= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
					lowercase__   :       int										= PipelineTesterMixin.required_optional_params - {'latents'}
					lowercase__   :       Union[str, Any]										= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					lowercase__   :       Dict										= IMAGE_TO_IMAGE_IMAGE_PARAMS
					lowercase__   :       List[str]										= IMAGE_TO_IMAGE_IMAGE_PARAMS

					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=4				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       attention_head_dim=(2, 4)				,       use_linear_projection=lowerCamelCase__				,       addition_embed_type='''text_time'''				,       addition_time_embed_dim=8				,       transformer_layers_per_block=(1, 2)				,       projection_class_embeddings_input_dim=8_0				,       cross_attention_dim=6_4				,       )
									_lowerCamelCase        =				EulerDiscreteScheduler(
									    beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       steps_offset=1				,       beta_schedule='''scaled_linear'''				,       timestep_spacing='''leading'''				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				AutoencoderKL(
									    block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       sample_size=1_2_8				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       hidden_act='''gelu'''				,       projection_dim=3_2				,       )
									_lowerCamelCase        =				CLIPTextModel(lowerCamelCase__   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,       local_files_only=lowerCamelCase__   )

									_lowerCamelCase        =				CLIPTextModelWithProjection(lowerCamelCase__   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,       local_files_only=lowerCamelCase__   )

									_lowerCamelCase        =				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''text_encoder_2''': text_encoder_a,
									    '''tokenizer_2''': tokenizer_a,
									    # "safety_checker": None,
									    # "feature_extractor": None,
									}
									return components

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
									_lowerCamelCase        =				image / 2 + 0.5
									if str(lowerCamelCase__   ).startswith('''mps'''   ):
													_lowerCamelCase        =				torch.manual_seed(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 5.0,
									    '''output_type''': '''numpy''',
									    '''strength''': 0.7_5,
									}
									return inputs

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionXLImgaImgPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe(**lowerCamelCase__   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)

									_lowerCamelCase        =				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]   )

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

					def 			snake_case__      (    self   ):
									super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )

					def 			snake_case__      (    self   ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3   )

					def 			snake_case__      (    self   ):
									pass





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionXLImgaImgPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									# forward without prompt embeds
									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				3 * ['''this is a negative prompt''']
									_lowerCamelCase        =				negative_prompt
									_lowerCamelCase        =				3 * [inputs['''prompt''']]

									_lowerCamelCase        =				sd_pipe(**lowerCamelCase__   )
									_lowerCamelCase        =				output.images[0, -3:, -3:, -1]

									# forward with prompt embeds
									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				3 * ['''this is a negative prompt''']
									_lowerCamelCase        =				3 * [inputs.pop('''prompt'''   )]

									(
									    (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     
									)        =				sd_pipe.encode_prompt(lowerCamelCase__				,       negative_prompt=lowerCamelCase__   )

									_lowerCamelCase        =				sd_pipe(
									    **lowerCamelCase__				,       prompt_embeds=lowerCamelCase__				,       negative_prompt_embeds=lowerCamelCase__				,       pooled_prompt_embeds=lowerCamelCase__				,       negative_pooled_prompt_embeds=lowerCamelCase__				,       )
									_lowerCamelCase        =				output.images[0, -3:, -3:, -1]

									# make sure that it's equal
									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4


@slow
@require_torch_gpu
class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__="cpu"				,       lowerCamelCase__=torch.floataa				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				np.random.RandomState(lowerCamelCase__   ).standard_normal((1, 4, 6_4, 6_4)   )
									_lowerCamelCase        =				torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__				,       dtype=lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''   )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				pipe(**lowerCamelCase__   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									_lowerCamelCase        =				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]   )
									assert np.abs(image_slice - expected_slice   ).max() < 7e-3
