
from typing import List

import numpy as np






def      lowerCamelCase__       (       __lowerCAmelCase						:						dict       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			{key: len(__lowerCAmelCase       ) for key, value in gen_kwargs.items() if isinstance(__lowerCAmelCase					,							__lowerCAmelCase       )}
			if len(set(lists_lengths.values()       )       ) > 1:
						raise RuntimeError(
						    (
						        "Sharding is ambiguous for this dataset: "
						        + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
						        + "\n".join(F"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()       )
						        + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
						        + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
						    )       )
			lowerCAmelCase_      			=			max(lists_lengths.values()					,							default=0       )
			return max(1					,							__lowerCAmelCase       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			for group_idx in range(__lowerCAmelCase       ):
						lowerCAmelCase_      			=			num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
						if num_shards_to_add == 0:
									break
						lowerCAmelCase_      			=			shards_indices_per_group[-1].stop if shards_indices_per_group else 0
						lowerCAmelCase_      			=			range(__lowerCAmelCase					,							start + num_shards_to_add       )
						shards_indices_per_group.append(__lowerCAmelCase       )
			return shards_indices_per_group






def      lowerCamelCase__       (       __lowerCAmelCase						:						dict					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			_number_of_shards_in_gen_kwargs(__lowerCAmelCase       )
			if num_shards == 1:
						return [dict(__lowerCAmelCase       )]
			else:
						lowerCAmelCase_      			=			_distribute_shards(num_shards=__lowerCAmelCase					,							max_num_jobs=__lowerCAmelCase       )
						return [
						    {
						        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
						        if isinstance(__lowerCAmelCase					,							__lowerCAmelCase       )
						        else value
						        for key, value in gen_kwargs.items()
						    }
						    for group_idx in range(len(__lowerCAmelCase       )       )
						]






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[dict]       ):




			"""simple docstring"""


			return {
			    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
			    if isinstance(gen_kwargs_list[0][key]					,							__lowerCAmelCase       )
			    else gen_kwargs_list[0][key]
			    for key in gen_kwargs_list[0]
			}






def      lowerCamelCase__       (       __lowerCAmelCase						:						np.random.Generator					,							__lowerCAmelCase						:						dict       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			{len(__lowerCAmelCase       ) for value in gen_kwargs.values() if isinstance(__lowerCAmelCase					,							__lowerCAmelCase       )}
			lowerCAmelCase_      			=			{}
			for size in list_sizes:
						lowerCAmelCase_      			=			list(range(__lowerCAmelCase       )       )
						rng.shuffle(indices_per_size[size]       )
			# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
			lowerCAmelCase_      			=			dict(__lowerCAmelCase       )
			for key, value in shuffled_kwargs.items():
						if isinstance(__lowerCAmelCase					,							__lowerCAmelCase       ):
									lowerCAmelCase_      			=			[value[i] for i in indices_per_size[len(__lowerCAmelCase       )]]
			return shuffled_kwargs





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 			_lowerCAmelCase      (			__a      ,   __a      ,   __a      ,   unittest.TestCase ):
				_lowercase    	=StableUnCLIPPipeline
				_lowercase    	=TEXT_TO_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_BATCH_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				_lowercase    	=False




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			32
							lowerCAmelCase_      			=			embedder_hidden_size

							# prior components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=_UpperCamelCase       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			PriorTransformer(
							    num_attention_heads=2       ,       attention_head_dim=12       ,       embedding_dim=_UpperCamelCase       ,       num_layers=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDPMScheduler(
							    variance_type="fixed_small_log"       ,       prediction_type="sample"       ,       num_train_timesteps=1_000       ,       clip_sample=_UpperCamelCase       ,       clip_sample_range=5.0       ,       beta_schedule="squaredcos_cap_v2"       ,       )

							# regular denoising components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase  )
							lowerCAmelCase_      			=			DDPMScheduler(beta_schedule="squaredcos_cap_v2"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=32       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			UNetaDConditionModel(
							    sample_size=32       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")       ,       up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")       ,       block_out_channels=(32, 64)       ,       attention_head_dim=(2, 4)       ,       class_embed_type="projection"       ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,       cross_attention_dim=_UpperCamelCase       ,       layers_per_block=1       ,       upcast_attention=_UpperCamelCase       ,       use_linear_projection=_UpperCamelCase       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDIMScheduler(
							    beta_schedule="scaled_linear"       ,       beta_start=0.00085       ,       beta_end=0.012       ,       prediction_type="v_prediction"       ,       set_alpha_to_one=_UpperCamelCase       ,       steps_offset=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			AutoencoderKL()

							lowerCAmelCase_      			=			{
							    # prior components
							    "prior_tokenizer": prior_tokenizer,
							    "prior_text_encoder": prior_text_encoder,
							    "prior": prior,
							    "prior_scheduler": prior_scheduler,
							    # image noising components
							    "image_normalizer": image_normalizer,
							    "image_noising_scheduler": image_noising_scheduler,
							    # regular denoising components
							    "tokenizer": tokenizer,
							    "text_encoder": text_encoder,
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							}

							return components




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=0  )       ->   Tuple:
							if str(_UpperCamelCase  ).startswith("mps"  ):
										lowerCAmelCase_      			=			torch.manual_seed(_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )
							lowerCAmelCase_      			=			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "prior_num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			torch_device == "cpu"

							self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			torch_device in ["cpu", "mps"]

							self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase  )





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




				def   __a		(      self  )       ->   List[str]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"  )

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			torch.Generator(device="cpu"  ).manual_seed(0  )
							lowerCAmelCase_      			=			pipe("anime turle"       ,       generator=_UpperCamelCase       ,       output_type="np"  )

							lowerCAmelCase_      			=			output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Optional[Any]:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							lowerCAmelCase_      			=			pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			pipe(
							    "anime turtle"       ,       prior_num_inference_steps=2       ,       num_inference_steps=2       ,       output_type="np"       ,       )

							lowerCAmelCase_      			=			torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9



