



'''simple docstring'''






import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
def    lowerCamelCase__ (		_A       ):
  a		:      List[Any]       =  [tensor.shape for tensor in tensor_list]
  return all(shape == shapes[0] for shape in shapes[1:]       )

class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					StableDiffusionLatentUpscalePipeline
    lowercase__				=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """height""",
        """width""",
        """cross_attention_kwargs""",
        """negative_prompt_embeds""",
        """prompt_embeds""",
    }
    lowercase__				=					PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
    lowercase__				=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowercase__				=					frozenset(
        []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowercase__				=					frozenset([]			)

    lowercase__				=					True







    @property
    def     lowercase_ (   self				:						Tuple			):
      a		:      List[Any]       =  1
      a		:      str       =  4
      a		:      Optional[int]       =  (16, 16)

      a		:      Optional[Any]       =  floats_tensor((batch_size, num_channels) + sizes		,    rng=random.Random(0			)			).to(__snake_case			)
      return image







    def     lowercase_ (   self				:						Tuple			):
      torch.manual_seed(0			)
      a		:      Union[str, Any]       =  UNetaDConditionModel(
          act_fn='gelu'		,    attention_head_dim=8		,    norm_num_groups=__snake_case		,    block_out_channels=[32, 32, 64, 64]		,    time_cond_proj_dim=1_60		,    conv_in_kernel=1		,    conv_out_kernel=1		,    cross_attention_dim=32		,    down_block_types=(
              'KDownBlock2D',
              'KCrossAttnDownBlock2D',
              'KCrossAttnDownBlock2D',
              'KCrossAttnDownBlock2D',
          )		,    in_channels=8		,    mid_block_type=__snake_case		,    only_cross_attention=__snake_case		,    out_channels=5		,    resnet_time_scale_shift='scale_shift'		,    time_embedding_type='fourier'		,    timestep_post_act='gelu'		,    up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')		,    )
      a		:      str       =  AutoencoderKL(
          block_out_channels=[32, 32, 64, 64]		,    in_channels=3		,    out_channels=3		,    down_block_types=[
              'DownEncoderBlock2D',
              'DownEncoderBlock2D',
              'DownEncoderBlock2D',
              'DownEncoderBlock2D',
          ]		,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']		,    latent_channels=4		,    )
      a		:      Dict       =  EulerDiscreteScheduler(prediction_type='sample'			)
      a		:      str       =  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=10_00		,    hidden_act='quick_gelu'		,    projection_dim=5_12		,    )
      a		:      Any       =  CLIPTextModel(__snake_case			)
      a		:      Optional[Any]       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

      a		:      Optional[Any]       =  {
          'unet': model.eval(),
          'vae': vae.eval(),
          'scheduler': scheduler,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
      }

      return components







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Any=0			):
      if str(__snake_case			).startswith('mps'			):
        a		:      Optional[Any]       =  torch.manual_seed(__snake_case			)
      else:
        a		:      Union[str, Any]       =  torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
      a		:      Optional[int]       =  {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': self.dummy_image.cpu(),
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }
      return inputs







    def     lowercase_ (   self				:						Dict			):
      a		:      List[str]       =  'cpu'

      a		:      Tuple       =  self.get_dummy_components()
      a		:      str       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Dict       =  self.get_dummy_inputs(__snake_case			)
      a		:      List[str]       =  pipe(**__snake_case			).images
      a		:      List[str]       =  image[0, -3:, -3:, -1]

      self.assertEqual(image.shape		,    (1, 2_56, 2_56, 3)			)
      a		:      int       =  np.array(
          [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]			)
      a		:      List[Any]       =  np.abs(image_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(__snake_case		,    1e-3			)







    def     lowercase_ (   self				:						Optional[int]			):
      super().test_attention_slicing_forward_pass(expected_max_diff=7e-3			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      super().test_cpu_offload_forward_pass(expected_max_diff=3e-3			)







    def     lowercase_ (   self				:						List[Any]			):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      super().test_inference_batch_single_identical(expected_max_diff=7e-3			)







    def     lowercase_ (   self				:						Optional[Any]			):
      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3			)







    def     lowercase_ (   self				:						Tuple			):
      super().test_save_load_local(expected_max_difference=3e-3			)







    def     lowercase_ (   self				:						Optional[Any]			):
      super().test_save_load_optional_components(expected_max_difference=3e-3			)







    def     lowercase_ (   self				:						Tuple			):
      a		:      Dict       =  [
          'DDIMScheduler',
          'DDPMScheduler',
          'PNDMScheduler',
          'HeunDiscreteScheduler',
          'EulerAncestralDiscreteScheduler',
          'KDPM2DiscreteScheduler',
          'KDPM2AncestralDiscreteScheduler',
          'DPMSolverSDEScheduler',
      ]
      a		:      int       =  self.get_dummy_components()
      a		:      Dict       =  self.pipeline_class(**__snake_case			)

      # make sure that PNDM does not need warm-up
      pipe.scheduler.register_to_config(skip_prk_steps=__snake_case			)

      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)
      a		:      Any       =  self.get_dummy_inputs(__snake_case			)
      a		:      List[str]       =  2

      a		:      Any       =  []
      for scheduler_enum in KarrasDiffusionSchedulers:
        if scheduler_enum.name in skip_schedulers:
          # no sigma schedulers are not supported
          # no schedulers
          continue

        a		:      Optional[Any]       =  getattr(__snake_case		,    scheduler_enum.name			)
        a		:      Tuple       =  scheduler_cls.from_config(pipe.scheduler.config			)
        a		:      int       =  pipe(**__snake_case			)[0]
        outputs.append(__snake_case			)

      assert check_same_shape(__snake_case			)

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







    def     lowercase_ (   self				:						Optional[Any]			):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def     lowercase_ (   self				:						List[str]			):
      a		:      Dict       =  torch.manual_seed(33			)

      a		:      Optional[Any]       =  StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'		,    torch_dtype=torch.floataa			)
      pipe.to('cuda'			)

      a		:      Tuple       =  StableDiffusionLatentUpscalePipeline.from_pretrained(
          'stabilityai/sd-x2-latent-upscaler'		,    torch_dtype=torch.floataa			)
      upscaler.to('cuda'			)

      a		:      int       =  'a photo of an astronaut high resolution, unreal engine, ultra realistic'

      a		:      Dict       =  pipe(__snake_case		,    generator=__snake_case		,    output_type='latent'			).images

      a		:      Union[str, Any]       =  upscaler(
          prompt=__snake_case		,    image=__snake_case		,    num_inference_steps=20		,    guidance_scale=0		,    generator=__snake_case		,    output_type='np'		,    ).images[0]

      a		:      Dict       =  load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'			)
      assert np.abs((expected_image - image).mean()			) < 5e-2







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Optional[int]       =  torch.manual_seed(33			)

      a		:      Union[str, Any]       =  StableDiffusionLatentUpscalePipeline.from_pretrained(
          'stabilityai/sd-x2-latent-upscaler'		,    torch_dtype=torch.floataa			)
      upscaler.to('cuda'			)

      a		:      List[str]       =  'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

      a		:      List[str]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'			)

      a		:      str       =  upscaler(
          prompt=__snake_case		,    image=__snake_case		,    num_inference_steps=20		,    guidance_scale=0		,    generator=__snake_case		,    output_type='np'		,    ).images[0]

      a		:      str       =  load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'			)
      assert np.abs((expected_image - image).max()			) < 5e-2




'''simple docstring'''






from pathlib import Path

import fire
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  Path(_A       )
  a		:      Tuple       =  Path(_A       )
  dest_dir.mkdir(exist_ok=_A       )
  for path in src_dir.iterdir():
    a		:      Tuple       =  [x.rstrip() for x in list(path.open().readlines()       )][:n]
    a		:      List[str]       =  dest_dir.joinpath(path.name       )
    print(_A       )
    dest_path.open('w'       ).write('\n'.join(_A       )       )


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