

'''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 		__SCREAMING_SNAKE_CASE       ( __snake_case	,       __snake_case	,       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    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										torch.manual_seed(0      )
										lowercase   :       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=_lowercase		,				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		,				)
										lowercase   :       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      )
										lowercase   :       Tuple								=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      )
										lowercase   :       str								=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		,				)
										lowercase   :       Optional[Any]								=CLIPTextModel(_lowercase      )
										lowercase   :       Tuple								=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		,				local_files_only=_lowercase      )

										lowercase   :       List[Any]								=CLIPTextModelWithProjection(_lowercase      )
										lowercase   :       Optional[int]								=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		,				local_files_only=_lowercase      )

										lowercase   :       Optional[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    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		List[Any]=0      ):
										'''simple docstring'''
										lowercase   :       str								=floats_tensor((1, 3, 32, 32)		,				rng=random.Random(_lowercase      )      ).to(_lowercase      )
										lowercase   :       List[Any]								=image / 2 + 0.5
										if str(_lowercase      ).startswith('''mps'''      ):
																lowercase   :       Tuple								=torch.manual_seed(_lowercase      )
										else:
																lowercase   :       Optional[Any]								=torch.Generator(device=_lowercase      ).manual_seed(_lowercase      )
										lowercase   :       List[Any]								={
										    """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    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								="""cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowercase   :       int								=self.get_dummy_components()
										lowercase   :       Optional[int]								=StableDiffusionXLImgaImgPipeline(**_lowercase      )
										lowercase   :       Any								=sd_pipe.to(_lowercase      )
										sd_pipe.set_progress_bar_config(disable=_lowercase      )

										lowercase   :       Optional[Any]								=self.get_dummy_inputs(_lowercase      )
										lowercase   :       Optional[int]								=sd_pipe(**_lowercase      ).images
										lowercase   :       Union[str, Any]								=image[0, -3:, -3:, -1]

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

										lowercase   :       str								=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    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										pass







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Tuple								=self.get_dummy_components()
										lowercase   :       List[Any]								=StableDiffusionXLImgaImgPipeline(**_lowercase      )
										lowercase   :       Union[str, Any]								=sd_pipe.to(_lowercase      )
										lowercase   :       List[Any]								=sd_pipe.to(_lowercase      )
										sd_pipe.set_progress_bar_config(disable=_lowercase      )

										# forward without prompt embeds
										lowercase   :       Dict								=self.get_dummy_inputs(_lowercase      )
										lowercase   :       str								=3 * ["""this is a negative prompt"""]
										lowercase   :       Union[str, Any]								=negative_prompt
										lowercase   :       str								=3 * [inputs["""prompt"""]]

										lowercase   :       Union[str, Any]								=sd_pipe(**_lowercase      )
										lowercase   :       Any								=output.images[0, -3:, -3:, -1]

										# forward with prompt embeds
										lowercase   :       Optional[int]								=self.get_dummy_inputs(_lowercase      )
										lowercase   :       List[str]								=3 * ["""this is a negative prompt"""]
										lowercase   :       Dict								=3 * [inputs.pop('''prompt'''      )]

										(
										    lowercase     
										)   :       str								=sd_pipe.encode_prompt(_lowercase		,				negative_prompt=_lowercase      )

										lowercase   :       List[str]								=sd_pipe(
										    **_lowercase		,				prompt_embeds=_lowercase		,				negative_prompt_embeds=_lowercase		,				pooled_prompt_embeds=_lowercase		,				negative_pooled_prompt_embeds=_lowercase		,				)
										lowercase   :       Any								=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    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		str="cpu"		,				UpperCAmelCase__    :		Union[str, Any]=torch.floataa		,				UpperCAmelCase__    :		Optional[int]=0      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=torch.Generator(device=_lowercase      ).manual_seed(_lowercase      )
										lowercase   :       Any								=np.random.RandomState(_lowercase      ).standard_normal((1, 4, 64, 64)      )
										lowercase   :       List[Any]								=torch.from_numpy(_lowercase      ).to(device=_lowercase		,				dtype=_lowercase      )
										lowercase   :       str								={
										    """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    :		Dict      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''      )
										pipe.to(_lowercase      )
										pipe.set_progress_bar_config(disable=_lowercase      )

										lowercase   :       List[Any]								=self.get_inputs(_lowercase      )
										lowercase   :       Optional[Any]								=pipe(**_lowercase      ).images
										lowercase   :       List[str]								=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase   :       Tuple								=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'''





# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


UpperCamelCase_							=					"""3"""

print("""Python version:""", sys.version)

print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())

try:
							import torch

							print("""Torch version:""", torch.__version__)
							print("""Cuda available:""", torch.cuda.is_available())
							print("""Cuda version:""", torch.version.cuda)
							print("""CuDNN version:""", torch.backends.cudnn.version())
							print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
							print("""Torch version:""", None)

try:
							import transformers

							print("""transformers version:""", transformers.__version__)
except ImportError:
							print("""transformers version:""", None)





