
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  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:List[Any]         =	UnCLIPImageVariationPipeline
	a			:Tuple         =	IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
	a			:Union[str, Any]         =	IMAGE_VARIATION_BATCH_PARAMS

	a			:str         =	[
	    'generator',
	    'return_dict',
	    'decoder_num_inference_steps',
	    'super_res_num_inference_steps',
	]
	a			:Optional[int]         =	False
	@property
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     int:
					return 3_2
	@property
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Union[str, Any]:
					return 3_2
	@property
	def 						_lowercase  (       self  :			Any       )       ->     Union[str, Any]:
					return self.time_input_dim
	@property
	def 						_lowercase  (       self  :			Dict       )       ->     Optional[int]:
					return self.time_input_dim * 4
	@property
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Dict:
					return 1_0_0
	@property
	def 						_lowercase  (       self  :			Dict       )       ->     str:
					lowercase_       =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
					return tokenizer
	@property
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     List[Any]:
					torch.manual_seed(0       )
					lowercase_       =					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=3_7       ,							layer_norm_eps=1e-05       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=1_0_0_0       ,							)
					return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_       )
	@property
	def 						_lowercase  (       self  :			int       )       ->     int:
					torch.manual_seed(0       )
					lowercase_       =					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=3_2       ,							intermediate_size=3_7       ,							patch_size=1       ,							)
					return CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE_       )
	@property
	def 						_lowercase  (       self  :			Any       )       ->     Dict:
					torch.manual_seed(0       )

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

					lowercase_       =					UnCLIPTextProjModel(**SCREAMING_SNAKE_CASE_       )
					return model
	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[str]:
					torch.manual_seed(0       )

					lowercase_       =					{
					    '''sample_size''': 3_2,
					    # 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''',
					}

					lowercase_       =					UNetaDConditionModel(**SCREAMING_SNAKE_CASE_       )
					return model
	@property
	def 						_lowercase  (       self  :			Tuple       )       ->     List[Any]:
					return {
					    "sample_size": 6_4,
					    "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 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[Any]:
					torch.manual_seed(0       )

					lowercase_       =					UNetaDModel(**self.dummy_super_res_kwargs       )
					return model
	@property
	def 						_lowercase  (       self  :			Dict       )       ->     Optional[Any]:
					# seeded differently to get different unet than `self.dummy_super_res_first`
					torch.manual_seed(1       )

					lowercase_       =					UNetaDModel(**self.dummy_super_res_kwargs       )
					return model
	def 						_lowercase  (       self  :			int       )       ->     Optional[Any]:
					lowercase_       =					self.dummy_decoder
					lowercase_       =					self.dummy_text_proj
					lowercase_       =					self.dummy_text_encoder
					lowercase_       =					self.dummy_tokenizer
					lowercase_       =					self.dummy_super_res_first
					lowercase_       =					self.dummy_super_res_last

					lowercase_       =					UnCLIPScheduler(
					    variance_type='''learned_range'''       ,							prediction_type='''epsilon'''       ,							num_train_timesteps=1_0_0_0       ,							)

					lowercase_       =					UnCLIPScheduler(
					    variance_type='''fixed_small_log'''       ,							prediction_type='''epsilon'''       ,							num_train_timesteps=1_0_0_0       ,							)

					lowercase_       =					CLIPImageProcessor(crop_size=3_2       ,							size=3_2       )

					lowercase_       =					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 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0       ,							SCREAMING_SNAKE_CASE_  :			Any=True       )       ->     Union[str, Any]:
					lowercase_       =					floats_tensor((1, 3, 3_2, 3_2)       ,							rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )
					if str(SCREAMING_SNAKE_CASE_       ).startswith('''mps'''       ):
									lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )

					if pil_image:
									lowercase_       =					input_image * 0.5 + 0.5
									lowercase_       =					input_image.clamp(0       ,							1       )
									lowercase_       =					input_image.cpu().permute(0       ,							2       ,							3       ,							1       ).float().numpy()
									lowercase_       =					DiffusionPipeline.numpy_to_pil(SCREAMING_SNAKE_CASE_       )[0]

					return {
					    "image": input_image,
					    "generator": generator,
					    "decoder_num_inference_steps": 2,
					    "super_res_num_inference_steps": 2,
					    "output_type": "np",
					}
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Union[str, Any]:
					lowercase_       =					'''cpu'''

					lowercase_       =					self.get_dummy_components()

					lowercase_       =					self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,							pil_image=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					pipe(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					output.images

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,							pil_image=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					pipe(
					    **SCREAMING_SNAKE_CASE_       ,							return_dict=SCREAMING_SNAKE_CASE_       ,							)[0]

					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 6_4, 6_4, 3)

					lowercase_       =					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 						_lowercase  (       self  :			str       )       ->     Optional[Any]:
					lowercase_       =					'''cpu'''

					lowercase_       =					self.get_dummy_components()

					lowercase_       =					self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,							pil_image=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					pipe(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					output.images

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,							pil_image=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					pipe(
					    **SCREAMING_SNAKE_CASE_       ,							return_dict=SCREAMING_SNAKE_CASE_       ,							)[0]

					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 6_4, 6_4, 3)

					lowercase_       =					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 						_lowercase  (       self  :			str       )       ->     int:
					lowercase_       =					'''cpu'''

					lowercase_       =					self.get_dummy_components()

					lowercase_       =					self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,							pil_image=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    pipeline_inputs['''image'''],
					    pipeline_inputs['''image'''],
					]

					lowercase_       =					pipe(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					output.images

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,							pil_image=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    tuple_pipeline_inputs['''image'''],
					    tuple_pipeline_inputs['''image'''],
					]

					lowercase_       =					pipe(
					    **SCREAMING_SNAKE_CASE_       ,							return_dict=SCREAMING_SNAKE_CASE_       ,							)[0]

					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (2, 6_4, 6_4, 3)

					lowercase_       =					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 						_lowercase  (       self  :			Any       )       ->     List[Any]:
					lowercase_       =					torch.device('''cpu'''       )
					class  lowercase__:



						"""simple docstring"""




						a			:Tuple         =	1

					lowercase_       =					self.get_dummy_components()

					lowercase_       =					self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )
					lowercase_       =					pipe.decoder.dtype
					lowercase_       =					1

					lowercase_       =					(
					    batch_size,
					    pipe.decoder.config.in_channels,
					    pipe.decoder.config.sample_size,
					    pipe.decoder.config.sample_size,
					)
					lowercase_       =					pipe.prepare_latents(
					    SCREAMING_SNAKE_CASE_       ,							dtype=SCREAMING_SNAKE_CASE_       ,							device=SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							latents=SCREAMING_SNAKE_CASE_       ,							scheduler=DummyScheduler()       )

					lowercase_       =					(
					    batch_size,
					    pipe.super_res_first.config.in_channels // 2,
					    pipe.super_res_first.config.sample_size,
					    pipe.super_res_first.config.sample_size,
					)
					lowercase_       =					pipe.prepare_latents(
					    SCREAMING_SNAKE_CASE_       ,							dtype=SCREAMING_SNAKE_CASE_       ,							device=SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							latents=SCREAMING_SNAKE_CASE_       ,							scheduler=DummyScheduler()       )

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,							pil_image=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					pipe(
					    **SCREAMING_SNAKE_CASE_       ,							decoder_latents=SCREAMING_SNAKE_CASE_       ,							super_res_latents=SCREAMING_SNAKE_CASE_       ).images

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,							pil_image=SCREAMING_SNAKE_CASE_       )
					# Don't pass image, instead pass embedding
					lowercase_       =					pipeline_inputs.pop('''image'''       )
					lowercase_       =					pipe.image_encoder(SCREAMING_SNAKE_CASE_       ).image_embeds

					lowercase_       =					pipe(
					    **SCREAMING_SNAKE_CASE_       ,							decoder_latents=SCREAMING_SNAKE_CASE_       ,							super_res_latents=SCREAMING_SNAKE_CASE_       ,							image_embeddings=SCREAMING_SNAKE_CASE_       ,							).images

					# make sure passing text embeddings manually is identical
					assert np.abs(img_out_a - img_out_a       ).max() < 1e-4
	@skip_mps
	def 						_lowercase  (       self  :			Tuple       )       ->     List[str]:
					lowercase_       =					torch_device == '''cpu'''

					# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
					lowercase_       =					1e-2

					self._test_attention_slicing_forward_pass(
					    test_max_difference=SCREAMING_SNAKE_CASE_       ,							expected_max_diff=SCREAMING_SNAKE_CASE_       )
	@skip_mps
	def 						_lowercase  (       self  :			List[str]       )       ->     str:
					lowercase_       =					torch_device == '''cpu'''
					lowercase_       =					True
					lowercase_       =					[
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]

					self._test_inference_batch_single_identical(
					    test_max_difference=SCREAMING_SNAKE_CASE_       ,							relax_max_difference=SCREAMING_SNAKE_CASE_       ,							additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_       ,							)
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Tuple:
					lowercase_       =					[
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]

					if torch_device == "mps":
									# TODO: MPS errors with larger batch sizes
									lowercase_       =					[2, 3]
									self._test_inference_batch_consistent(
									    batch_sizes=SCREAMING_SNAKE_CASE_       ,							additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_       ,							)
					else:
									self._test_inference_batch_consistent(
									    additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_       )
	@skip_mps
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Tuple:
					return super().test_dict_tuple_outputs_equivalent()
	@skip_mps
	def 						_lowercase  (       self  :			str       )       ->     Union[str, Any]:
					return super().test_save_load_local()




	@skip_mps
	def 						_lowercase  (       self  :			str       )       ->     List[str]:
					return super().test_save_load_optional_components()

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



	"""simple docstring"""




	def 						_lowercase  (       self  :			Optional[int]       )       ->     Union[str, Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




	def 						_lowercase  (       self  :			Optional[int]       )       ->     Optional[Any]:
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''       )
					lowercase_       =					load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''       )

					lowercase_       =					UnCLIPImageVariationPipeline.from_pretrained(
					    '''kakaobrain/karlo-v1-alpha-image-variations'''       ,							torch_dtype=torch.floataa       )
					lowercase_       =					pipeline.to(SCREAMING_SNAKE_CASE_       )
					pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					torch.Generator(device='''cpu'''       ).manual_seed(0       )
					lowercase_       =					pipeline(
					    SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''np'''       ,							)

					lowercase_       =					output.images[0]

					assert image.shape == (2_5_6, 2_5_6, 3)

					assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							1_5       )






import argparse
from collections import defaultdict

import yaml


__a  			=					'docs/source/en/_toctree.yml'




def 				a						(							snake_case__:					Dict					):


				'''simple docstring'''
				lowercase_       =					defaultdict(snake_case__					)
				for doc in model_doc:
								counts[doc["local"]] += 1
				lowercase_       =					[key for key, value in counts.items() if value > 1]

				lowercase_       =					[]
				for duplicate_key in duplicates:
								lowercase_       =					list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
								if len(snake_case__					) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
												    '''others.'''					)
								# Only add this once
								new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

				# Sort
				return sorted(snake_case__						,   key=lambda snake_case__					: s["title"].lower()					)




def 				a						(							snake_case__:					List[Any]=False					):


				'''simple docstring'''
				with open(snake_case__						,   encoding='''utf-8'''					) as f:
								lowercase_       =					yaml.safe_load(f.read()					)

				# Get to the API doc
				lowercase_       =					0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				lowercase_       =					content[api_idx]['''sections''']

				# Then to the model doc
				lowercase_       =					0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				lowercase_       =					api_doc[model_idx]['''sections''']

				lowercase_       =					[(idx, section) for idx, section in enumerate(snake_case__					) if '''sections''' in section]
				lowercase_       =					False
				for idx, modality_doc in modalities_docs:
								lowercase_       =					modality_doc['''sections''']
								lowercase_       =					clean_model_doc_toc(snake_case__					)

								if old_modality_doc != new_modality_doc:
												lowercase_       =					True
												if overwrite:
																lowercase_       =					new_modality_doc

				if diff:
								if overwrite:
												lowercase_       =					model_doc
												lowercase_       =					api_doc
												with open(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
																f.write(yaml.dump(snake_case__						,   allow_unicode=snake_case__					)					)
								else:
												raise ValueError(
												    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__a  			=					parser.parse_args()

						check_model_doc(args.fix_and_overwrite)




