




'''simple docstring'''

import gc
import unittest

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

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

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class __magic_name__(						__lowerCAmelCase    ,		__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					List[str]    =  StableDiffusionXLPipeline
						UpperCAmelCase_		:					List[str]    =  TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase_		:					Dict    =  TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase_		:					Dict    =  TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase_		:					Optional[Any]    =  TEXT_TO_IMAGE_IMAGE_PARAMS






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						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=__UpperCamelCase					,   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					,   )
									snake_case__          =						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       )
									snake_case__          =						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       )
									snake_case__          =						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					,   )
									snake_case__          =						CLIPTextModel(__UpperCamelCase       )
									snake_case__          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					,   local_files_only=__UpperCamelCase       )

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

									snake_case__          =						{
									    """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						:						str					,   __UpperCamelCase						:						Dict					,   __UpperCamelCase						:						Tuple=0       ):



									'''simple docstring'''






									if str(__UpperCamelCase       ).startswith("""mps"""       ):
												snake_case__          =						torch.manual_seed(__UpperCamelCase       )
									else:
												snake_case__          =						torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
									snake_case__          =						{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 5.0,
									    """output_type""": """numpy""",
									}
									return inputs






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






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

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

									assert image.shape == (1, 6_4, 6_4, 3)
									snake_case__          =						np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]       )

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






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__          =						self.get_dummy_components()
									snake_case__          =						StableDiffusionXLPipeline(**__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

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

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

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

									snake_case__          =						sd_pipe(
									    **__UpperCamelCase					,   prompt_embeds=__UpperCamelCase					,   negative_prompt_embeds=__UpperCamelCase					,   pooled_prompt_embeds=__UpperCamelCase					,   negative_pooled_prompt_embeds=__UpperCamelCase					,   )
									snake_case__          =						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






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






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







						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






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







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






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						int					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						Optional[int]="cpu"					,   __UpperCamelCase						:						List[Any]=torch.floataa					,   __UpperCamelCase						:						Tuple=0       ):



									'''simple docstring'''






									snake_case__          =						torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
									snake_case__          =						np.random.RandomState(__UpperCamelCase       ).standard_normal((1, 4, 6_4, 6_4)       )
									snake_case__          =						torch.from_numpy(__UpperCamelCase       ).to(device=__UpperCamelCase					,   dtype=__UpperCamelCase       )
									snake_case__          =						{
									    """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						:						Tuple       ):



									'''simple docstring'''






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

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

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									snake_case__          =						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'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a__         =     {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CvtForImageClassification''',
		    '''CvtModel''',
		    '''CvtPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCvtForImageClassification''',
		    '''TFCvtModel''',
		    '''TFCvtPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cvt import (
				    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CvtForImageClassification,
				    CvtModel,
				    CvtPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_cvt import (
				    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCvtForImageClassification,
				    TFCvtModel,
				    TFCvtPreTrainedModel,
				)


else:
		import sys

		a__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)