





'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, 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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class      snake_case     (    __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=StableDiffusionInstructPixaPixPipeline
 SCREAMING_SNAKE_CASE_	:					Tuple					=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
 SCREAMING_SNAKE_CASE_	:					List[Any]					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 SCREAMING_SNAKE_CASE_	:					Tuple					=IMAGE_TO_IMAGE_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE_	:					List[str]					=IMAGE_TO_IMAGE_IMAGE_PARAMS






 def        _lowerCamelCase					(       self       :							List[Any]	):
        torch.manual_seed(0	)
        __UpperCamelCase       =  UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,	layers_per_block=2							,	sample_size=3_2							,	in_channels=8							,	out_channels=4							,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,	cross_attention_dim=3_2							,	)
        __UpperCamelCase       =  PNDMScheduler(skip_prk_steps=__A	)
        torch.manual_seed(0	)
        __UpperCamelCase       =  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							,	)
        torch.manual_seed(0	)
        __UpperCamelCase       =  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							,	)
        __UpperCamelCase       =  CLIPTextModel(__A	)
        __UpperCamelCase       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

        __UpperCamelCase       =  {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components






 def        _lowerCamelCase					(       self       :							str							,	__A       :							Dict							,	__A       :							Any=0	):
        __UpperCamelCase       =  floats_tensor((1, 3, 3_2, 3_2)							,	rng=random.Random(__A	)	).to(__A	)
        __UpperCamelCase       =  image.cpu().permute(0							,	2							,	3							,	1	)[0]
        __UpperCamelCase       =  Image.fromarray(np.uinta(__A	)	).convert('RGB'	)
        if str(__A	).startswith('mps'	):
               __UpperCamelCase       =  torch.manual_seed(__A	)
        else:
               __UpperCamelCase       =  torch.Generator(device=__A	).manual_seed(__A	)
        __UpperCamelCase       =  {
            'prompt': 'A painting of a squirrel eating a burger',
            'image': image,
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'image_guidance_scale': 1,
            'output_type': 'numpy',
        }
        return inputs






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase       =  self.get_dummy_components()
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline(**__A	)
        __UpperCamelCase       =  sd_pipe.to(__A	)
        sd_pipe.set_progress_bar_config(disable=__A	)

        __UpperCamelCase       =  self.get_dummy_inputs(__A	)
        __UpperCamelCase       =  sd_pipe(**__A	).images
        __UpperCamelCase       =  image[0, -3:, -3:, -1]
        assert image.shape == (1, 3_2, 3_2, 3)
        __UpperCamelCase       =  np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase       =  self.get_dummy_components()
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline(**__A	)
        __UpperCamelCase       =  sd_pipe.to(__A	)
        sd_pipe.set_progress_bar_config(disable=__A	)

        __UpperCamelCase       =  self.get_dummy_inputs(__A	)
        __UpperCamelCase       =  'french fries'
        __UpperCamelCase       =  sd_pipe(**__A							,	negative_prompt=__A	)
        __UpperCamelCase       =  output.images
        __UpperCamelCase       =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 3_2, 3_2, 3)
        __UpperCamelCase       =  np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase       =  self.get_dummy_components()
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline(**__A	)
        __UpperCamelCase       =  sd_pipe.to(__A	)
        sd_pipe.set_progress_bar_config(disable=__A	)

        __UpperCamelCase       =  self.get_dummy_inputs(__A	)
        __UpperCamelCase       =  [inputs['prompt']] * 2

        __UpperCamelCase       =  np.array(inputs['image']	).astype(np.floataa	) / 255.0
        __UpperCamelCase       =  torch.from_numpy(__A	).unsqueeze(0	).to(__A	)
        __UpperCamelCase       =  image / 2 + 0.5
        __UpperCamelCase       =  image.permute(0							,	3							,	1							,	2	)
        __UpperCamelCase       =  image.repeat(2							,	1							,	1							,	1	)

        __UpperCamelCase       =  sd_pipe(**__A	).images
        __UpperCamelCase       =  image[-1, -3:, -3:, -1]

        assert image.shape == (2, 3_2, 3_2, 3)
        __UpperCamelCase       =  np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase       =  self.get_dummy_components()
        __UpperCamelCase       =  EulerAncestralDiscreteScheduler(
            beta_start=0.0_0085							,	beta_end=0.012							,	beta_schedule='scaled_linear'	)
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline(**__A	)
        __UpperCamelCase       =  sd_pipe.to(__A	)
        sd_pipe.set_progress_bar_config(disable=__A	)

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

        __UpperCamelCase       =  [round(__A							,	4	) for x in image_slice.flatten().tolist()]
        print(','.join([str(__A	) for x in slice]	)	)

        assert image.shape == (1, 3_2, 3_2, 3)
        __UpperCamelCase       =  np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






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






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  self.get_dummy_components()
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline(**__A	)
        __UpperCamelCase       =  VaeImageProcessor(do_resize=__A							,	do_normalize=__A	)
        __UpperCamelCase       =  pipe.to(__A	)
        pipe.set_progress_bar_config(disable=__A	)

        __UpperCamelCase       =  pipe(**self.get_dummy_inputs_by_type(__A							,	input_image_type='pt'	)	)[0]

        __UpperCamelCase       =  components['vae']
        __UpperCamelCase       =  self.get_dummy_inputs_by_type(__A							,	input_image_type='pt'	)

        for image_param in self.image_latents_params:
               if image_param in inputs.keys():
                      __UpperCamelCase       =  vae.encode(inputs[image_param]	).latent_dist.mode()

        __UpperCamelCase       =  pipe(**__A	)[0]

        __UpperCamelCase       =  np.abs(out - out_latents_inputs	).max()
        self.assertLess(__A							,	1e-4							,	'passing latents as image input generate different result from passing image'	)






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




 """simple docstring"""



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






 def        _lowerCamelCase					(       self       :							int							,	__A       :							int=0	):
        __UpperCamelCase       =  torch.manual_seed(__A	)
        __UpperCamelCase       =  load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'	)
        __UpperCamelCase       =  {
            'prompt': 'turn him into a cyborg',
            'image': image,
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'image_guidance_scale': 1.0,
            'output_type': 'numpy',
        }
        return inputs






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
            'timbrooks/instruct-pix2pix'							,	safety_checker=__A	)
        pipe.to(__A	)
        pipe.set_progress_bar_config(disable=__A	)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        __UpperCamelCase       =  np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]	)

        assert np.abs(expected_slice - image_slice	).max() < 1e-3






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
            'timbrooks/instruct-pix2pix'							,	safety_checker=__A	)
        __UpperCamelCase       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
        pipe.to(__A	)
        pipe.set_progress_bar_config(disable=__A	)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        __UpperCamelCase       =  np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]	)

        assert np.abs(expected_slice - image_slice	).max() < 1e-3






 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
            'timbrooks/instruct-pix2pix'							,	safety_checker=__A	)
        __UpperCamelCase       =  DDIMScheduler.from_config(pipe.scheduler.config	)
        pipe.to(__A	)
        pipe.set_progress_bar_config(disable=__A	)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        __UpperCamelCase       =  np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]	)

        assert np.abs(expected_slice - image_slice	).max() < 1e-3






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  0

        def callback_fn(__A       :							int							,	__A       :							int							,	__A       :							torch.FloatTensor	) -> None:
               __UpperCamelCase       =  True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                      __UpperCamelCase       =  latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      __UpperCamelCase       =  latents[0, -3:, -3:, -1]
                      __UpperCamelCase       =  np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]	)

                      assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
               elif step == 2:
                      __UpperCamelCase       =  latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      __UpperCamelCase       =  latents[0, -3:, -3:, -1]
                      __UpperCamelCase       =  np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]	)

                      assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

        __UpperCamelCase       =  False

        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
            'timbrooks/instruct-pix2pix'							,	safety_checker=__A							,	torch_dtype=torch.floataa	)
        __UpperCamelCase       =  pipe.to(__A	)
        pipe.set_progress_bar_config(disable=__A	)
        pipe.enable_attention_slicing()

        __UpperCamelCase       =  self.get_inputs()
        pipe(**__A							,	callback=__A							,	callback_steps=1	)
        assert callback_fn.has_been_called
        assert number_of_steps == 3






 def        _lowerCamelCase					(       self       :							List[str]	):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
            'timbrooks/instruct-pix2pix'							,	safety_checker=__A							,	torch_dtype=torch.floataa	)
        __UpperCamelCase       =  pipe.to(__A	)
        pipe.set_progress_bar_config(disable=__A	)
        pipe.enable_attention_slicing(1	)
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase       =  self.get_inputs()
        __UpperCamelCase       =  pipe(**__A	)

        __UpperCamelCase       =  torch.cuda.max_memory_allocated()
        # make sure that less than 2.2 GB is allocated
        assert mem_bytes < 2.2 * 1_0**9






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.get_inputs()
        # resize to resolution that is divisible by 8 but not 16 or 32
        __UpperCamelCase       =  inputs['image'].resize((5_0_4, 5_0_4)	)

        __UpperCamelCase       =  'timbrooks/instruct-pix2pix'
        __UpperCamelCase       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
            __A							,	safety_checker=__A							,	)
        pipe.to(__A	)
        pipe.set_progress_bar_config(disable=__A	)
        pipe.enable_attention_slicing()

        __UpperCamelCase       =  pipe(**__A	)
        __UpperCamelCase       =  output.images[0]

        __UpperCamelCase       =  image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

        assert image.shape == (5_0_4, 5_0_4, 3)
        __UpperCamelCase       =  np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-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


a__							:							Tuple ='''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)
