
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      _SCREAMING_SNAKE_CASE				(		UpperCAmelCase__,     UpperCAmelCase__,     unittest.TestCase				):
				lowerCamelCase_	  =  StableDiffusionXLImgaImgPipeline
				lowerCamelCase_	  =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
				lowerCamelCase_	  =  PipelineTesterMixin.required_optional_params - {'''latents'''}
				lowerCamelCase_	  =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowerCamelCase_	  =  IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowerCamelCase_	  =  IMAGE_TO_IMAGE_IMAGE_PARAMS





				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								torch.manual_seed(0				)
								A  :	str				=					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=__lowerCAmelCase       ,   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       ,   )
								A  :	Dict				=					EulerDiscreteScheduler(
								    beta_start=0.0_00_85       ,   beta_end=0.0_12       ,   steps_offset=1       ,   beta_schedule='''scaled_linear'''       ,   timestep_spacing='''leading'''       ,   )
								torch.manual_seed(0				)
								A  :	Dict				=					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				)
								A  :	int				=					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       ,   )
								A  :	Optional[int]				=					CLIPTextModel(__lowerCAmelCase				)
								A  :	List[str]				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,   local_files_only=__lowerCAmelCase				)

								A  :	Any				=					CLIPTextModelWithProjection(__lowerCAmelCase				)
								A  :	Dict				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,   local_files_only=__lowerCAmelCase				)

								A  :	int				=					{
								    '''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 							_UpperCAmelCase    (		self			:		Optional[int]       ,   snake_case_			:		List[str]       ,   snake_case_			:		str=0				):


								"""simple docstring"""

								A  :	int				=					floats_tensor((1, 3, 32, 32)       ,   rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)
								A  :	Dict				=					image / 2 + 0.5
								if str(__lowerCAmelCase				).startswith('''mps'''				):
												A  :	Union[str, Any]				=					torch.manual_seed(__lowerCAmelCase				)
								else:
												A  :	int				=					torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)
								A  :	List[str]				=					{
								    '''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 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								A  :	List[str]				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								A  :	Optional[int]				=					self.get_dummy_components()
								A  :	int				=					StableDiffusionXLImgaImgPipeline(**__lowerCAmelCase				)
								A  :	List[str]				=					sd_pipe.to(__lowerCAmelCase				)
								sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

								A  :	List[Any]				=					self.get_dummy_inputs(__lowerCAmelCase				)
								A  :	List[str]				=					sd_pipe(**__lowerCAmelCase				).images
								A  :	Optional[int]				=					image[0, -3:, -3:, -1]

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

								A  :	Tuple				=					np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]				)

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





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

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





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

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





				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								pass






				def 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								A  :	List[str]				=					self.get_dummy_components()
								A  :	Union[str, Any]				=					StableDiffusionXLImgaImgPipeline(**__lowerCAmelCase				)
								A  :	Union[str, Any]				=					sd_pipe.to(__lowerCAmelCase				)
								A  :	Dict				=					sd_pipe.to(__lowerCAmelCase				)
								sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

								# forward without prompt embeds
								A  :	Union[str, Any]				=					self.get_dummy_inputs(__lowerCAmelCase				)
								A  :	Union[str, Any]				=					3 * ['''this is a negative prompt''']
								A  :	Optional[int]				=					negative_prompt
								A  :	List[str]				=					3 * [inputs['''prompt''']]

								A  :	List[str]				=					sd_pipe(**__lowerCAmelCase				)
								A  :	Any				=					output.images[0, -3:, -3:, -1]

								# forward with prompt embeds
								A  :	List[str]				=					self.get_dummy_inputs(__lowerCAmelCase				)
								A  :	Union[str, Any]				=					3 * ['''this is a negative prompt''']
								A  :	str				=					3 * [inputs.pop('''prompt'''				)]

								(
								    (
								    A       
								)			,							(
								    A       
								)			,							(
								    A       
								)			,							(
								    A       
								)			,							       
								)  :	Union[str, Any]				=					sd_pipe.encode_prompt(__lowerCAmelCase       ,   negative_prompt=__lowerCAmelCase				)

								A  :	Tuple				=					sd_pipe(
								    **__lowerCAmelCase       ,   prompt_embeds=__lowerCAmelCase       ,   negative_prompt_embeds=__lowerCAmelCase       ,   pooled_prompt_embeds=__lowerCAmelCase       ,   negative_pooled_prompt_embeds=__lowerCAmelCase       ,   )
								A  :	List[str]				=					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      _SCREAMING_SNAKE_CASE				(		unittest.TestCase				):





				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

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





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]       ,   snake_case_			:		Tuple       ,   snake_case_			:		Optional[int]="cpu"       ,   snake_case_			:		Union[str, Any]=torch.floataa       ,   snake_case_			:		Any=0				):


								"""simple docstring"""

								A  :	List[Any]				=					torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)
								A  :	str				=					np.random.RandomState(__lowerCAmelCase				).standard_normal((1, 4, 64, 64)				)
								A  :	List[str]				=					torch.from_numpy(__lowerCAmelCase				).to(device=__lowerCAmelCase       ,   dtype=__lowerCAmelCase				)
								A  :	Any				=					{
								    '''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 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								A  :	str				=					DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''				)
								pipe.to(__lowerCAmelCase				)
								pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

								A  :	Tuple				=					self.get_inputs(__lowerCAmelCase				)
								A  :	str				=					pipe(**__lowerCAmelCase				).images
								A  :	Any				=					image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								A  :	Optional[int]				=					np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]				)
								assert np.abs(image_slice - expected_slice				).max() < 7E-3





'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_lowercase  =			logging.get_logger(__name__)
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Tuple       ,		*__lowerCAmelCase     :  List[str]       ,		**__lowerCAmelCase     :  List[str]      ):
							warnings.warn(
							    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use VideoMAEImageProcessor instead.'       ,		__lowerCAmelCase       ,		)
							super().__init__(*__lowerCAmelCase       ,		**__lowerCAmelCase      )

