



from ...processing_utils import ProcessorMixin


class       _lowercase   (a_      ):
				'''simple docstring'''







				lowercase__   						=						"""WhisperFeatureExtractor"""
				lowercase__   						=						"""WhisperTokenizer"""

				def __init__( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									super().__init__(snake_case__     , snake_case__    )
									UpperCamelCase_		  =   self.feature_extractor
									UpperCamelCase_		  =   False

				def       _lowerCamelCase					( self     , snake_case__=None     , snake_case__=None     , snake_case__=True    ):

									'''simple docstring'''


									return self.tokenizer.get_decoder_prompt_ids(task=snake_case__     , language=snake_case__     , no_timestamps=snake_case__    )

				def __call__( self     , *snake_case__     , **snake_case__    ):

									'''simple docstring'''


									if self._in_target_context_manager:
														return self.current_processor(*snake_case__     , **snake_case__    )

									UpperCamelCase_		  =   kwargs.pop("audio"     , snake_case__    )
									UpperCamelCase_		  =   kwargs.pop("sampling_rate"     , snake_case__    )
									UpperCamelCase_		  =   kwargs.pop("text"     , snake_case__    )
									if len(snake_case__    ) > 0:
														UpperCamelCase_		  =   args[0]
														UpperCamelCase_		  =   args[1:]

									if audio is None and text is None:
														raise ValueError("You need to specify either an `audio` or `text` input to process."    )

									if audio is not None:
														UpperCamelCase_		  =   self.feature_extractor(snake_case__     , *snake_case__     , sampling_rate=snake_case__     , **snake_case__    )
									if text is not None:
														UpperCamelCase_		  =   self.tokenizer(snake_case__     , **snake_case__    )

									if text is None:
														return inputs

									elif audio is None:
														return encodings
									else:
														UpperCamelCase_		  =   encodings["input_ids"]
														return inputs

				def       _lowerCamelCase					( self     , *snake_case__     , **snake_case__    ):

									'''simple docstring'''


									return self.tokenizer.batch_decode(*snake_case__     , **snake_case__    )

				def       _lowerCamelCase					( self     , *snake_case__     , **snake_case__    ):

									'''simple docstring'''


									return self.tokenizer.decode(*snake_case__     , **snake_case__    )

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__="np"    ):

									'''simple docstring'''


									return self.tokenizer.get_prompt_ids(snake_case__     , return_tensors=snake_case__    )





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       _lowercase   (a_      ,   a_      ,   unittest.TestCase      ):
				'''simple docstring'''







				lowercase__   						=						StableDiffusionXLImgaImgPipeline
				lowercase__   						=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				lowercase__   						=						PipelineTesterMixin.required_optional_params - {"""latents"""}
				lowercase__   						=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowercase__   						=						IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowercase__   						=						IMAGE_TO_IMAGE_IMAGE_PARAMS

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									torch.manual_seed(0    )
									UpperCamelCase_		  =   UNetaDConditionModel(
									    block_out_channels=(32, 64)     , layers_per_block=2     , sample_size=32     , in_channels=4     , out_channels=4     , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     , attention_head_dim=(2, 4)     , use_linear_projection=snake_case__     , addition_embed_type="text_time"     , addition_time_embed_dim=8     , transformer_layers_per_block=(1, 2)     , projection_class_embeddings_input_dim=80     , cross_attention_dim=64     , )
									UpperCamelCase_		  =   EulerDiscreteScheduler(
									    beta_start=0.00_085     , beta_end=0.012     , steps_offset=1     , beta_schedule="scaled_linear"     , timestep_spacing="leading"     , )
									torch.manual_seed(0    )
									UpperCamelCase_		  =   AutoencoderKL(
									    block_out_channels=[32, 64]     , in_channels=3     , out_channels=3     , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     , latent_channels=4     , sample_size=128     , )
									torch.manual_seed(0    )
									UpperCamelCase_		  =   CLIPTextConfig(
									    bos_token_id=0     , eos_token_id=2     , hidden_size=32     , intermediate_size=37     , layer_norm_eps=1e-05     , num_attention_heads=4     , num_hidden_layers=5     , pad_token_id=1     , vocab_size=1000     , hidden_act="gelu"     , projection_dim=32     , )
									UpperCamelCase_		  =   CLIPTextModel(snake_case__    )
									UpperCamelCase_		  =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     , local_files_only=snake_case__    )

									UpperCamelCase_		  =   CLIPTextModelWithProjection(snake_case__    )
									UpperCamelCase_		  =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     , local_files_only=snake_case__    )

									UpperCamelCase_		  =   {
									    "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       _lowerCamelCase					( self     , snake_case__     , snake_case__=0    ):

									'''simple docstring'''


									UpperCamelCase_		  =   floats_tensor((1, 3, 32, 32)     , rng=random.Random(snake_case__    )    ).to(snake_case__    )
									UpperCamelCase_		  =   image / 2 + 0.5
									if str(snake_case__    ).startswith("mps"    ):
														UpperCamelCase_		  =   torch.manual_seed(snake_case__    )
									else:
														UpperCamelCase_		  =   torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
									UpperCamelCase_		  =   {
									    "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": 0.75,
									}
									return inputs

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   "cpu"  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase_		  =   self.get_dummy_components()
									UpperCamelCase_		  =   StableDiffusionXLImgaImgPipeline(**snake_case__    )
									UpperCamelCase_		  =   sd_pipe.to(snake_case__    )
									sd_pipe.set_progress_bar_config(disable=snake_case__    )

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

									assert image.shape == (1, 32, 32, 3)

									UpperCamelCase_		  =   np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]    )

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

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									super().test_attention_slicing_forward_pass(expected_max_diff=3e-3    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									super().test_inference_batch_single_identical(expected_max_diff=3e-3    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									pass

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.get_dummy_components()
									UpperCamelCase_		  =   StableDiffusionXLImgaImgPipeline(**snake_case__    )
									UpperCamelCase_		  =   sd_pipe.to(snake_case__    )
									UpperCamelCase_		  =   sd_pipe.to(snake_case__    )
									sd_pipe.set_progress_bar_config(disable=snake_case__    )

									# forward without prompt embeds
									UpperCamelCase_		  =   self.get_dummy_inputs(snake_case__    )
									UpperCamelCase_		  =   3 * ["this is a negative prompt"]
									UpperCamelCase_		  =   negative_prompt
									UpperCamelCase_		  =   3 * [inputs["prompt"]]

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

									# forward with prompt embeds
									UpperCamelCase_		  =   self.get_dummy_inputs(snake_case__    )
									UpperCamelCase_		  =   3 * ["this is a negative prompt"]
									UpperCamelCase_		  =   3 * [inputs.pop("prompt"    )]

									(
									    (
									    UpperCamelCase_
									)  ,    (
									    UpperCamelCase_
									)  ,    (
									    UpperCamelCase_
									)  ,    (
									    UpperCamelCase_
									)  ,    
									)		  =   sd_pipe.encode_prompt(snake_case__     , negative_prompt=snake_case__    )

									UpperCamelCase_		  =   sd_pipe(
									    **snake_case__     , prompt_embeds=snake_case__     , negative_prompt_embeds=snake_case__     , pooled_prompt_embeds=snake_case__     , negative_pooled_prompt_embeds=snake_case__     , )
									UpperCamelCase_		  =   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       _lowercase   (unittest.TestCase      ):
				'''simple docstring'''







				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__="cpu"     , snake_case__=torch.floataa     , snake_case__=0    ):

									'''simple docstring'''


									UpperCamelCase_		  =   torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
									UpperCamelCase_		  =   np.random.RandomState(snake_case__    ).standard_normal((1, 4, 64, 64)    )
									UpperCamelCase_		  =   torch.from_numpy(snake_case__    ).to(device=snake_case__     , dtype=snake_case__    )
									UpperCamelCase_		  =   {
									    "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       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"    )
									pipe.to(snake_case__    )
									pipe.set_progress_bar_config(disable=snake_case__    )

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

									assert image.shape == (1, 512, 512, 3)
									UpperCamelCase_		  =   np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]    )
									assert np.abs(image_slice - expected_slice    ).max() < 7e-3
