





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

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


_UpperCAmelCase       : Optional[Any]			     =       False

@skip_mps
class      lowerCAmelCase							(      __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	StableDiffusionAttendAndExcitePipeline
 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase__									=	TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}    )
 UpperCAmelCase__									=	TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase__									=	TEXT_TO_IMAGE_IMAGE_PARAMS
 @classmethod
 def   A_			(   cls						:   Optional[Any]			)    ->			List[Any]:
     super().setUpClass()
     torch.use_deterministic_algorithms(UpperCAmelCase			)
 @classmethod
 def   A_			(   cls						:   Optional[Any]			)    ->			str:
     super().tearDownClass()
     torch.use_deterministic_algorithms(UpperCAmelCase			)
 def   A_			(   self						:   Any			)    ->			Union[str, Any]:
     torch.manual_seed(0			)
     lowerCamelCase__					:   Optional[int]      				=	UNetaDConditionModel(
         block_out_channels=(32, 64)    ,  layers_per_block=1    ,  sample_size=32    ,  in_channels=4    ,  out_channels=4    ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,  cross_attention_dim=32    ,  attention_head_dim=(2, 4)    ,  use_linear_projection=UpperCAmelCase    ,  )
     lowerCamelCase__					:   Optional[Any]      				=	DDIMScheduler(
         beta_start=0.0_0_0_8_5    ,  beta_end=0.0_1_2    ,  beta_schedule='scaled_linear'    ,  clip_sample=UpperCAmelCase    ,  set_alpha_to_one=UpperCAmelCase    ,  )
     torch.manual_seed(0			)
     lowerCamelCase__					:   Optional[int]      				=	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			)
     lowerCamelCase__					:   Any      				=	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=512    ,  )
     lowerCamelCase__					:   Optional[int]      				=	CLIPTextModel(UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

     lowerCamelCase__					:   str      				=	{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }

     return components
 def   A_			(   self						:   int    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   List[Any]=0			)    ->			Tuple:
     if str(UpperCAmelCase			).startswith('mps'			):
         lowerCamelCase__					:   Optional[Any]      				=	torch.manual_seed(UpperCAmelCase			)
     else:
         lowerCamelCase__					:   int      				=	torch.Generator(device=UpperCAmelCase			).manual_seed(UpperCAmelCase			)
     lowerCamelCase__					:   Dict      				=	{
         'prompt': 'a cat and a frog',
         'token_indices': [2, 5],
         'generator': generator,
         'num_inference_steps': 1,
         'guidance_scale': 6.0,
         'output_type': 'numpy',
         'max_iter_to_alter': 2,
         'thresholds': {0: 0.7},
     }
     return inputs
 def   A_			(   self						:   int			)    ->			Optional[Any]:
     lowerCamelCase__					:   str      				=	'cpu'

     lowerCamelCase__					:   Dict      				=	self.get_dummy_components()
     lowerCamelCase__					:   Any      				=	self.pipeline_class(**UpperCAmelCase			)
     pipe.to(UpperCAmelCase			)
     pipe.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	self.get_dummy_inputs(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	pipe(**UpperCAmelCase			).images
     lowerCamelCase__					:   int      				=	image[0, -3:, -3:, -1]

     self.assertEqual(image.shape    ,  (1, 64, 64, 3)			)
     lowerCamelCase__					:   Union[str, Any]      				=	np.array(
         [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]			)
     lowerCamelCase__					:   Dict      				=	np.abs(image_slice.flatten() - expected_slice			).max()
     self.assertLessEqual(UpperCAmelCase    ,  1e-3			)
 def   A_			(   self						:   List[Any]			)    ->			Tuple:
     super().test_cpu_offload_forward_pass(expected_max_diff=5e-4			)
 def   A_			(   self						:   str			)    ->			Optional[Any]:
     # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
     self._test_inference_batch_consistent(batch_sizes=[1, 2]			)
 def   A_			(   self						:   Optional[int]			)    ->			Union[str, Any]:
     self._test_inference_batch_single_identical(batch_size=2    ,  expected_max_diff=7e-4			)
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3			)
 def   A_			(   self						:   Dict			)    ->			str:
     super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4			)
 def   A_			(   self						:   Dict			)    ->			Union[str, Any]:
     super().test_save_load_local(expected_max_difference=5e-4			)







 def   A_			(   self						:   Union[str, Any]			)    ->			Any:
     super().test_save_load_optional_components(expected_max_difference=4e-4			)


@require_torch_gpu
@slow
class      lowerCAmelCase							(      unittest.TestCase    ):
 @classmethod
 def   A_			(   cls						:   Optional[Any]			)    ->			int:
     super().setUpClass()
     torch.use_deterministic_algorithms(UpperCAmelCase			)
 @classmethod
 def   A_			(   cls						:   int			)    ->			Optional[int]:
     super().tearDownClass()
     torch.use_deterministic_algorithms(UpperCAmelCase			)
 def   A_			(   self						:   Optional[Any]			)    ->			Dict:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







 def   A_			(   self						:   Any			)    ->			List[Any]:
     lowerCamelCase__					:   Tuple      				=	torch.manual_seed(51			)

     lowerCamelCase__					:   str      				=	StableDiffusionAttendAndExcitePipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'    ,  safety_checker=UpperCAmelCase    ,  torch_dtype=torch.floataa			)
     pipe.to('cuda'			)

     lowerCamelCase__					:   List[Any]      				=	'a painting of an elephant with glasses'
     lowerCamelCase__					:   Tuple      				=	[5, 7]

     lowerCamelCase__					:   str      				=	pipe(
         prompt=UpperCAmelCase    ,  token_indices=UpperCAmelCase    ,  guidance_scale=7.5    ,  generator=UpperCAmelCase    ,  num_inference_steps=5    ,  max_iter_to_alter=5    ,  output_type='numpy'    ,  ).images[0]

     lowerCamelCase__					:   Any      				=	load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'			)
     assert np.abs((expected_image - image).max()			) < 5e-1












from pathlib import Path

import fire


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   Union[str, Any]      				=	Path(_UpperCAmelCase				)
    lowerCamelCase__					:   int      				=	Path(_UpperCAmelCase				)
    dest_dir.mkdir(exist_ok=_UpperCAmelCase				)
    for path in src_dir.iterdir():
        lowerCamelCase__					:   Tuple      				=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
        lowerCamelCase__					:   Optional[int]      				=	dest_dir.joinpath(path.name				)
        print(_UpperCAmelCase				)
        dest_path.open('w'				).write('\n'.join(_UpperCAmelCase				)				)


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





