



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()



@skip_mps
class       _lowercase				(  lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				StableDiffusionModelEditingPipeline
	__A		      =				TEXT_TO_IMAGE_PARAMS
	__A		      =				TEXT_TO_IMAGE_BATCH_PARAMS
	__A		      =				TEXT_TO_IMAGE_IMAGE_PARAMS
	__A		      =				TEXT_TO_IMAGE_IMAGE_PARAMS




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						torch.manual_seed(0						)
						a   	=  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")						,			cross_attention_dim=32						,			)
						a   	=  DDIMScheduler()
						torch.manual_seed(0						)
						a   	=  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						,			)
						torch.manual_seed(0						)
						a   	=  CLIPTextConfig(
						    bos_token_id=0						,			eos_token_id=2						,			hidden_size=32						,			intermediate_size=37						,			layer_norm_eps=1E-0_5						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=1000						,			)
						a   	=  CLIPTextModel(lowerCamelCase_						)
						a   	=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

						a   	=  {
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_=0						):
						"""simple docstring"""

						a   	=  torch.manual_seed(lowerCamelCase_						)
						a   	=  {
						    "prompt": "A field of roses",
						    "generator": generator,
						    # Setting height and width to None to prevent OOMs on CPU.
						    "height": None,
						    "width": None,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  "cpu"  # ensure determinism for the device-dependent torch.Generator
						a   	=  self.get_dummy_components()
						a   	=  StableDiffusionModelEditingPipeline(**lowerCamelCase_						)
						a   	=  sd_pipe.to(lowerCamelCase_						)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_						)

						a   	=  self.get_dummy_inputs(lowerCamelCase_						)
						a   	=  sd_pipe(**lowerCamelCase_						).images
						a   	=  image[0, -3:, -3:, -1]
						assert image.shape == (1, 64, 64, 3)

						a   	=  np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]						)

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  "cpu"  # ensure determinism for the device-dependent torch.Generator
						a   	=  self.get_dummy_components()
						a   	=  StableDiffusionModelEditingPipeline(**lowerCamelCase_						)
						a   	=  sd_pipe.to(lowerCamelCase_						)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_						)

						a   	=  self.get_dummy_inputs(lowerCamelCase_						)
						a   	=  "french fries"
						a   	=  sd_pipe(**lowerCamelCase_						,			negative_prompt=lowerCamelCase_						)
						a   	=  output.images
						a   	=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						a   	=  np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]						)

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  "cpu"  # ensure determinism for the device-dependent torch.Generator
						a   	=  self.get_dummy_components()
						a   	=  EulerAncestralDiscreteScheduler(
						    beta_start=0.0_0085						,			beta_end=0.012						,			beta_schedule="scaled_linear"						)
						a   	=  StableDiffusionModelEditingPipeline(**lowerCamelCase_						)
						a   	=  sd_pipe.to(lowerCamelCase_						)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_						)

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

						assert image.shape == (1, 64, 64, 3)

						a   	=  np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]						)

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  "cpu"  # ensure determinism for the device-dependent torch.Generator
						a   	=  self.get_dummy_components()
						a   	=  PNDMScheduler()
						a   	=  StableDiffusionModelEditingPipeline(**lowerCamelCase_						)
						a   	=  sd_pipe.to(lowerCamelCase_						)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_						)

						a   	=  self.get_dummy_inputs(lowerCamelCase_						)
						# the pipeline does not expect pndm so test if it raises error.
						with self.assertRaises(lowerCamelCase_						):
											a   	=  sd_pipe(**lowerCamelCase_						).images




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						super().test_inference_batch_single_identical(expected_max_diff=5E-3						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)




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





	"""simple docstring"""




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_=0						):
						"""simple docstring"""

						a   	=  torch.manual_seed(lowerCamelCase_						)
						a   	=  {
						    "prompt": "A field of roses",
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 7.5,
						    "output_type": "numpy",
						}
						return inputs




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  "CompVis/stable-diffusion-v1-4"
						a   	=  StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase_						,			safety_checker=lowerCamelCase_						)
						pipe.to(lowerCamelCase_						)
						pipe.set_progress_bar_config(disable=lowerCamelCase_						)
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 512, 512, 3)

						a   	=  np.array(
						    [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]						)

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

						# make sure image changes after editing
						pipe.edit_model("A pack of roses"						,			"A pack of blue roses"						)

						a   	=  pipe(**lowerCamelCase_						).images
						a   	=  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)

						assert np.abs(expected_slice - image_slice						).max() > 1E-1




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						a   	=  "CompVis/stable-diffusion-v1-4"
						a   	=  DDIMScheduler.from_pretrained(lowerCamelCase_						,			subfolder="scheduler"						)
						a   	=  StableDiffusionModelEditingPipeline.from_pretrained(
						    lowerCamelCase_						,			scheduler=lowerCamelCase_						,			safety_checker=lowerCamelCase_						)
						a   	=  pipe.to(lowerCamelCase_						)
						pipe.set_progress_bar_config(disable=lowerCamelCase_						)
						pipe.enable_attention_slicing(1						)
						pipe.enable_sequential_cpu_offload()

						a   	=  self.get_inputs()
						a   	=  pipe(**lowerCamelCase_						)

						a   	=  torch.cuda.max_memory_allocated()
						# make sure that less than 4.4 GB is allocated
						assert mem_bytes < 4.4 * 10**9





import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def        a( A			:       Dict			)						->			Optional[int]:
					"""simple docstring"""





					return EnvironmentCommand()







class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						a   	=  parser.add_parser("env"						,			help="Print relevant system environment info."						)
						download_parser.set_defaults(func=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "`datasets` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "Huggingface_hub version": huggingface_hub.__version__,
						    "PyArrow version": pyarrow.__version__,
						    "Pandas version": pandas.__version__,
						}

						print("\nCopy-and-paste the text below in your GitHub issue.\n"						)
						print(self.format_dict(lowerCamelCase_						)						)

						return info




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"
