




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class lowerCAmelCase__			(  UpperCamelCase_,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    UnCLIPImageVariationPipeline
			lowerCamelCase__   =    IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
			lowerCamelCase__   =    IMAGE_VARIATION_BATCH_PARAMS

			lowerCamelCase__   =    [
			    """generator""",
			    """return_dict""",
			    """decoder_num_inference_steps""",
			    """super_res_num_inference_steps""",
			]
			lowerCamelCase__   =    False
			@property
			def       A_					(						self						):
							return 32
			@property
			def       A_					(						self						):
							return 32
			@property
			def       A_					(						self						):
							return self.time_input_dim
			@property
			def       A_					(						self						):
							return self.time_input_dim * 4
			@property
			def       A_					(						self						):
							return 100
			@property
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)
							return tokenizer
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)
							_lowerCamelCase  :    List[Any]					=					CLIPTextConfig(
							    bos_token_id=0						,   eos_token_id=2						,   hidden_size=self.text_embedder_hidden_size						,   projection_dim=self.text_embedder_hidden_size						,   intermediate_size=37						,   layer_norm_eps=1E-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1000						,   )
							return CLIPTextModelWithProjection(lowercase						)
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)
							_lowerCamelCase  :    str					=					CLIPVisionConfig(
							    hidden_size=self.text_embedder_hidden_size						,   projection_dim=self.text_embedder_hidden_size						,   num_hidden_layers=5						,   num_attention_heads=4						,   image_size=32						,   intermediate_size=37						,   patch_size=1						,   )
							return CLIPVisionModelWithProjection(lowercase						)
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)

							_lowerCamelCase  :    Any					=					{
							    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
							    '''time_embed_dim''': self.time_embed_dim,
							    '''cross_attention_dim''': self.cross_attention_dim,
							}

							_lowerCamelCase  :    Union[str, Any]					=					UnCLIPTextProjModel(**lowercase						)
							return model
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)

							_lowerCamelCase  :    str					=					{
							    '''sample_size''': 32,
							    # RGB in channels
							    '''in_channels''': 3,
							    # Out channels is double in channels because predicts mean and variance
							    '''out_channels''': 6,
							    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
							    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
							    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
							    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
							    '''layers_per_block''': 1,
							    '''cross_attention_dim''': self.cross_attention_dim,
							    '''attention_head_dim''': 4,
							    '''resnet_time_scale_shift''': '''scale_shift''',
							    '''class_embed_type''': '''identity''',
							}

							_lowerCamelCase  :    Union[str, Any]					=					UNetaDConditionModel(**lowercase						)
							return model
			@property
			def       A_					(						self						):
							return {
							    "sample_size": 64,
							    "layers_per_block": 1,
							    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
							    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
							    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    "in_channels": 6,
							    "out_channels": 3,
							}
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)

							_lowerCamelCase  :    Any					=					UNetaDModel(**self.dummy_super_res_kwargs						)
							return model
			@property
			def       A_					(						self						):
							# seeded differently to get different unet than `self.dummy_super_res_first`
							torch.manual_seed(1						)

							_lowerCamelCase  :    Union[str, Any]					=					UNetaDModel(**self.dummy_super_res_kwargs						)
							return model
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.dummy_decoder
							_lowerCamelCase  :    Dict					=					self.dummy_text_proj
							_lowerCamelCase  :    Any					=					self.dummy_text_encoder
							_lowerCamelCase  :    Any					=					self.dummy_tokenizer
							_lowerCamelCase  :    Union[str, Any]					=					self.dummy_super_res_first
							_lowerCamelCase  :    int					=					self.dummy_super_res_last

							_lowerCamelCase  :    Any					=					UnCLIPScheduler(
							    variance_type='learned_range'						,   prediction_type='epsilon'						,   num_train_timesteps=1000						,   )

							_lowerCamelCase  :    int					=					UnCLIPScheduler(
							    variance_type='fixed_small_log'						,   prediction_type='epsilon'						,   num_train_timesteps=1000						,   )

							_lowerCamelCase  :    Dict					=					CLIPImageProcessor(crop_size=32						,   size=32						)

							_lowerCamelCase  :    List[str]					=					self.dummy_image_encoder

							return {
							    "decoder": decoder,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "text_proj": text_proj,
							    "feature_extractor": feature_extractor,
							    "image_encoder": image_encoder,
							    "super_res_first": super_res_first,
							    "super_res_last": super_res_last,
							    "decoder_scheduler": decoder_scheduler,
							    "super_res_scheduler": super_res_scheduler,
							}
			def       A_					(						self						,   lowercase						,   lowercase=0						,   lowercase=True						):
							_lowerCamelCase  :    Tuple					=					floats_tensor((1, 3, 32, 32)						,   rng=random.Random(lowercase						)						).to(lowercase						)
							if str(lowercase						).startswith('mps'						):
											_lowerCamelCase  :    Optional[Any]					=					torch.manual_seed(lowercase						)
							else:
											_lowerCamelCase  :    Tuple					=					torch.Generator(device=lowercase						).manual_seed(lowercase						)

							if pil_image:
											_lowerCamelCase  :    Optional[int]					=					input_image * 0.5 + 0.5
											_lowerCamelCase  :    Optional[int]					=					input_image.clamp(0						,   1						)
											_lowerCamelCase  :    int					=					input_image.cpu().permute(0						,   2						,   3						,   1						).float().numpy()
											_lowerCamelCase  :    Union[str, Any]					=					DiffusionPipeline.numpy_to_pil(lowercase						)[0]

							return {
							    "image": input_image,
							    "generator": generator,
							    "decoder_num_inference_steps": 2,
							    "super_res_num_inference_steps": 2,
							    "output_type": "np",
							}
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					'''cpu'''

							_lowerCamelCase  :    Any					=					self.get_dummy_components()

							_lowerCamelCase  :    Union[str, Any]					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    str					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    int					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    Optional[int]					=					pipe(**lowercase						)
							_lowerCamelCase  :    Tuple					=					output.images

							_lowerCamelCase  :    Optional[int]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					pipe(
							    **lowercase						,   return_dict=lowercase						,   )[0]

							_lowerCamelCase  :    Any					=					image[0, -3:, -3:, -1]
							_lowerCamelCase  :    List[str]					=					image_from_tuple[0, -3:, -3:, -1]

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

							_lowerCamelCase  :    Union[str, Any]					=					np.array(
							    [
							        0.99_97,
							        0.00_02,
							        0.99_97,
							        0.99_97,
							        0.99_69,
							        0.00_23,
							        0.99_97,
							        0.99_69,
							        0.99_70,
							    ]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					'''cpu'''

							_lowerCamelCase  :    Union[str, Any]					=					self.get_dummy_components()

							_lowerCamelCase  :    Union[str, Any]					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    Dict					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    List[Any]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    int					=					pipe(**lowercase						)
							_lowerCamelCase  :    Dict					=					output.images

							_lowerCamelCase  :    List[str]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    Any					=					pipe(
							    **lowercase						,   return_dict=lowercase						,   )[0]

							_lowerCamelCase  :    Optional[int]					=					image[0, -3:, -3:, -1]
							_lowerCamelCase  :    Union[str, Any]					=					image_from_tuple[0, -3:, -3:, -1]

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

							_lowerCamelCase  :    str					=					np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					'''cpu'''

							_lowerCamelCase  :    Optional[int]					=					self.get_dummy_components()

							_lowerCamelCase  :    Union[str, Any]					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    int					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)
							_lowerCamelCase  :    Optional[int]					=					[
							    pipeline_inputs['''image'''],
							    pipeline_inputs['''image'''],
							]

							_lowerCamelCase  :    Any					=					pipe(**lowercase						)
							_lowerCamelCase  :    Tuple					=					output.images

							_lowerCamelCase  :    Tuple					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					[
							    tuple_pipeline_inputs['''image'''],
							    tuple_pipeline_inputs['''image'''],
							]

							_lowerCamelCase  :    Optional[Any]					=					pipe(
							    **lowercase						,   return_dict=lowercase						,   )[0]

							_lowerCamelCase  :    Any					=					image[0, -3:, -3:, -1]
							_lowerCamelCase  :    Dict					=					image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (2, 64, 64, 3)

							_lowerCamelCase  :    str					=					np.array(
							    [
							        0.99_97,
							        0.99_89,
							        0.00_08,
							        0.00_21,
							        0.99_60,
							        0.00_18,
							        0.00_14,
							        0.00_02,
							        0.99_33,
							    ]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					torch.device('cpu'						)






							class lowerCAmelCase__			:






										'''simple docstring'''



										lowerCamelCase__   =    1

							_lowerCamelCase  :    Optional[int]					=					self.get_dummy_components()

							_lowerCamelCase  :    int					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    List[Any]					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Any					=					torch.Generator(device=lowercase						).manual_seed(0						)
							_lowerCamelCase  :    Any					=					pipe.decoder.dtype
							_lowerCamelCase  :    Dict					=					1

							_lowerCamelCase  :    Optional[int]					=					(
							    batch_size,
							    pipe.decoder.config.in_channels,
							    pipe.decoder.config.sample_size,
							    pipe.decoder.config.sample_size,
							)
							_lowerCamelCase  :    Any					=					pipe.prepare_latents(
							    lowercase						,   dtype=lowercase						,   device=lowercase						,   generator=lowercase						,   latents=lowercase						,   scheduler=DummyScheduler()						)

							_lowerCamelCase  :    Any					=					(
							    batch_size,
							    pipe.super_res_first.config.in_channels // 2,
							    pipe.super_res_first.config.sample_size,
							    pipe.super_res_first.config.sample_size,
							)
							_lowerCamelCase  :    Dict					=					pipe.prepare_latents(
							    lowercase						,   dtype=lowercase						,   device=lowercase						,   generator=lowercase						,   latents=lowercase						,   scheduler=DummyScheduler()						)

							_lowerCamelCase  :    List[str]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    int					=					pipe(
							    **lowercase						,   decoder_latents=lowercase						,   super_res_latents=lowercase						).images

							_lowerCamelCase  :    List[Any]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)
							# Don't pass image, instead pass embedding
							_lowerCamelCase  :    Optional[int]					=					pipeline_inputs.pop('image'						)
							_lowerCamelCase  :    Dict					=					pipe.image_encoder(lowercase						).image_embeds

							_lowerCamelCase  :    Optional[int]					=					pipe(
							    **lowercase						,   decoder_latents=lowercase						,   super_res_latents=lowercase						,   image_embeddings=lowercase						,   ).images

							# make sure passing text embeddings manually is identical
							assert np.abs(img_out_a - img_out_a						).max() < 1E-4
			@skip_mps
			def       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					torch_device == '''cpu'''

							# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
							_lowerCamelCase  :    Optional[int]					=					1E-2

							self._test_attention_slicing_forward_pass(
							    test_max_difference=lowercase						,   expected_max_diff=lowercase						)
			@skip_mps
			def       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					torch_device == '''cpu'''
							_lowerCamelCase  :    Optional[int]					=					True
							_lowerCamelCase  :    Any					=					[
							    '''decoder_num_inference_steps''',
							    '''super_res_num_inference_steps''',
							]

							self._test_inference_batch_single_identical(
							    test_max_difference=lowercase						,   relax_max_difference=lowercase						,   additional_params_copy_to_batched_inputs=lowercase						,   )
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					[
							    '''decoder_num_inference_steps''',
							    '''super_res_num_inference_steps''',
							]

							if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											_lowerCamelCase  :    List[Any]					=					[2, 3]
											self._test_inference_batch_consistent(
											    batch_sizes=lowercase						,   additional_params_copy_to_batched_inputs=lowercase						,   )
							else:
											self._test_inference_batch_consistent(
											    additional_params_copy_to_batched_inputs=lowercase						)
			@skip_mps
			def       A_					(						self						):
							return super().test_dict_tuple_outputs_equivalent()
			@skip_mps
			def       A_					(						self						):
							return super().test_save_load_local()






			@skip_mps
			def       A_					(						self						):
							return super().test_save_load_optional_components()






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






			'''simple docstring'''



			def       A_					(						self						):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'						)
							_lowerCamelCase  :    int					=					load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'						)

							_lowerCamelCase  :    Tuple					=					UnCLIPImageVariationPipeline.from_pretrained(
							    'kakaobrain/karlo-v1-alpha-image-variations'						,   torch_dtype=torch.floataa						)
							_lowerCamelCase  :    Any					=					pipeline.to(lowercase						)
							pipeline.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Any					=					torch.Generator(device='cpu'						).manual_seed(0						)
							_lowerCamelCase  :    Any					=					pipeline(
							    lowercase						,   generator=lowercase						,   output_type='np'						,   )

							_lowerCamelCase  :    Tuple					=					output.images[0]

							assert image.shape == (256, 256, 3)

							assert_mean_pixel_difference(lowercase						,   lowercase						,   15						)











"""simple docstring"""






from pathlib import Path

import fire







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    int					=					Path(lowercase__	)
				_lowerCamelCase  :    List[Any]					=					Path(lowercase__	)
				dest_dir.mkdir(exist_ok=lowercase__	)
				for path in src_dir.iterdir():
								_lowerCamelCase  :    str					=					[x.rstrip() for x in list(path.open().readlines()	)][:n]
								_lowerCamelCase  :    Any					=					dest_dir.joinpath(path.name	)
								print(lowercase__	)
								dest_path.open('w'	).write('\n'.join(lowercase__	)	)


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