



"""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 			_snake_case     (   _SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		unittest.TestCase):
				UpperCamelCase__			:   List[Any]       						=StableDiffusionXLImgaImgPipeline
				UpperCamelCase__			:   int       						=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				UpperCamelCase__			:   Tuple       						=PipelineTesterMixin.required_optional_params - {"""latents"""}
				UpperCamelCase__			:   List[str]       						=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				UpperCamelCase__			:   Union[str, Any]       						=IMAGE_TO_IMAGE_IMAGE_PARAMS
				UpperCamelCase__			:   Any       						=IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 							A__ (  self   :  Union[str, Any]  ):
							torch.manual_seed(0  )
							lowercase__   					=			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__   					=			EulerDiscreteScheduler(
							    beta_start=0.00085,   beta_end=0.012,   steps_offset=1,   beta_schedule="scaled_linear",   timestep_spacing="leading",   )
							torch.manual_seed(0  )
							lowercase__   					=			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__   					=			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,   hidden_act="gelu",   projection_dim=32,   )
							lowercase__   					=			CLIPTextModel(__lowercase  )
							lowercase__   					=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip",   local_files_only=__lowercase  )

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

							lowercase__   					=			{
							    "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 							A__ (  self   :  Union[str, Any],   __lowercase   :  List[str],   __lowercase   :  str=0  ):
							lowercase__   					=			floats_tensor((1, 3, 32, 32),   rng=random.Random(__lowercase  )  ).to(__lowercase  )
							lowercase__   					=			image / 2 + 0.5
							if str(__lowercase  ).startswith("mps"  ):
										lowercase__   					=			torch.manual_seed(__lowercase  )
							else:
										lowercase__   					=			torch.Generator(device=__lowercase  ).manual_seed(__lowercase  )
							lowercase__   					=			{
							    "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 							A__ (  self   :  Optional[Any]  ):
							lowercase__   					=			"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowercase__   					=			self.get_dummy_components()
							lowercase__   					=			StableDiffusionXLImgaImgPipeline(**__lowercase  )
							lowercase__   					=			sd_pipe.to(__lowercase  )
							sd_pipe.set_progress_bar_config(disable=__lowercase  )

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

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

							lowercase__   					=			np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]  )

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




				def 							A__ (  self   :  str  ):
							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )




				def 							A__ (  self   :  List[Any]  ):
							super().test_inference_batch_single_identical(expected_max_diff=3e-3  )




				def 							A__ (  self   :  List[str]  ):
							pass




				def 							A__ (  self   :  List[Any]  ):
							lowercase__   					=			self.get_dummy_components()
							lowercase__   					=			StableDiffusionXLImgaImgPipeline(**__lowercase  )
							lowercase__   					=			sd_pipe.to(__lowercase  )
							lowercase__   					=			sd_pipe.to(__lowercase  )
							sd_pipe.set_progress_bar_config(disable=__lowercase  )

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

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

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

							(
							    (
							    lowercase__				
							)					,    (
							    lowercase__				
							)					,    (
							    lowercase__				
							)					,    (
							    lowercase__				
							)					,    				
							)   					=			sd_pipe.encode_prompt(__lowercase,   negative_prompt=__lowercase  )

							lowercase__   					=			sd_pipe(
							    **__lowercase,   prompt_embeds=__lowercase,   negative_prompt_embeds=__lowercase,   pooled_prompt_embeds=__lowercase,   negative_pooled_prompt_embeds=__lowercase,   )
							lowercase__   					=			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 			_snake_case     (   unittest.TestCase):




				def 							A__ (  self   :  str  ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def 							A__ (  self   :  str,   __lowercase   :  Optional[Any],   __lowercase   :  str="cpu",   __lowercase   :  List[str]=torch.floataa,   __lowercase   :  List[Any]=0  ):
							lowercase__   					=			torch.Generator(device=__lowercase  ).manual_seed(__lowercase  )
							lowercase__   					=			np.random.RandomState(__lowercase  ).standard_normal((1, 4, 64, 64)  )
							lowercase__   					=			torch.from_numpy(__lowercase  ).to(device=__lowercase,   dtype=__lowercase  )
							lowercase__   					=			{
							    "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 							A__ (  self   :  Any  ):
							lowercase__   					=			DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"  )
							pipe.to(__lowercase  )
							pipe.set_progress_bar_config(disable=__lowercase  )

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

							assert image.shape == (1, 512, 512, 3)
							lowercase__   					=			np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]  )
							assert np.abs(image_slice - expected_slice  ).max() < 7e-3









from pathlib import Path

import fire



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
			for path in src_dir.iterdir():
						lowercase__   					=			[x.rstrip() for x in list(path.open().readlines()					)][:n]
						lowercase__   					=			dest_dir.joinpath(path.name					)
						print(SCREAMING_SNAKE_CASE_					)
						dest_path.open("w"					).write("\n".join(SCREAMING_SNAKE_CASE_					)					)


if __name__ == "__main__":
							fire.Fire(minify)




