import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						str						=      CycleDiffusionPipeline
    a__	:						List[str]						=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """negative_prompt""",
        """height""",
        """width""",
        """negative_prompt_embeds""",
    }
    a__	:						Union[str, Any]						=      PipelineTesterMixin.required_optional_params - {"""latents"""}
    a__	:						List[Any]						=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""} )
    a__	:						List[str]						=      IMAGE_TO_IMAGE_IMAGE_PARAMS
    a__	:						str						=      IMAGE_TO_IMAGE_IMAGE_PARAMS







    def 			UpperCamelCase__   (	self)       ->	Any:
        torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		UNetaDConditionModel(
            block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
        __UpperCamelCase     :Any			  =		DDIMScheduler(
            beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , num_train_timesteps=1_000 , clip_sample=__lowercase , set_alpha_to_one=__lowercase , )
        torch.manual_seed(0)
        __UpperCamelCase     :Any			  =		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 , )
        torch.manual_seed(0)
        __UpperCamelCase     :Tuple			  =		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=1_000 , )
        __UpperCamelCase     :List[str]			  =		CLIPTextModel(__lowercase)
        __UpperCamelCase     :Tuple			  =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        __UpperCamelCase     :str			  =		{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=0)       ->	str:
        __UpperCamelCase     :List[str]			  =		floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowercase)).to(__lowercase)
        __UpperCamelCase     :List[Any]			  =		image / 2 + 0.5
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase     :Optional[int]			  =		torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase     :Optional[int]			  =		torch.Generator(device=__lowercase).manual_seed(__lowercase)
        __UpperCamelCase     :Any			  =		{
            '''prompt''': '''An astronaut riding an elephant''',
            '''source_prompt''': '''An astronaut riding a horse''',
            '''image''': image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''eta''': 0.1,
            '''strength''': 0.8,
            '''guidance_scale''': 3,
            '''source_guidance_scale''': 1,
            '''output_type''': '''numpy''',
        }
        return inputs







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[Any]			  =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

        __UpperCamelCase     :Optional[Any]			  =		self.get_dummy_components()
        __UpperCamelCase     :List[str]			  =		CycleDiffusionPipeline(**__lowercase)
        __UpperCamelCase     :Tuple			  =		pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :List[Any]			  =		self.get_dummy_inputs(__lowercase)
        __UpperCamelCase     :Tuple			  =		pipe(**__lowercase)
        __UpperCamelCase     :Tuple			  =		output.images

        __UpperCamelCase     :Union[str, Any]			  =		images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __UpperCamelCase     :int			  =		np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







    @unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''')
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Any			  =		self.get_dummy_components()
        for name, module in components.items():
            if hasattr(__lowercase , '''half'''):
                __UpperCamelCase     :Any			  =		module.half()
        __UpperCamelCase     :int			  =		CycleDiffusionPipeline(**__lowercase)
        __UpperCamelCase     :Optional[int]			  =		pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :Optional[Any]			  =		self.get_dummy_inputs(__lowercase)
        __UpperCamelCase     :str			  =		pipe(**__lowercase)
        __UpperCamelCase     :Any			  =		output.images

        __UpperCamelCase     :str			  =		images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __UpperCamelCase     :Optional[Any]			  =		np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







    @skip_mps
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        return super().test_save_load_local()







    @unittest.skip('''non-deterministic pipeline''')
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        return super().test_inference_batch_single_identical()







    @skip_mps
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def 			UpperCamelCase__   (	self)       ->	Any:
        return super().test_save_load_optional_components()







    @skip_mps
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        return super().test_attention_slicing_forward_pass()


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






    '''simple docstring'''






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







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :int			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/cycle-diffusion/black_colored_car.png''')
        __UpperCamelCase     :str			  =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''')
        __UpperCamelCase     :int			  =		init_image.resize((512, 512))

        __UpperCamelCase     :List[Any]			  =		'''CompVis/stable-diffusion-v1-4'''
        __UpperCamelCase     :Optional[Any]			  =		DDIMScheduler.from_pretrained(__lowercase , subfolder='''scheduler''')
        __UpperCamelCase     :Optional[Any]			  =		CycleDiffusionPipeline.from_pretrained(
            __lowercase , scheduler=__lowercase , safety_checker=__lowercase , torch_dtype=torch.floataa , revision='''fp16''')

        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()

        __UpperCamelCase     :Dict			  =		'''A black colored car'''
        __UpperCamelCase     :List[str]			  =		'''A blue colored car'''

        __UpperCamelCase     :Optional[int]			  =		torch.manual_seed(0)
        __UpperCamelCase     :Optional[int]			  =		pipe(
            prompt=__lowercase , source_prompt=__lowercase , image=__lowercase , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=__lowercase , output_type='''np''' , )
        __UpperCamelCase     :List[Any]			  =		output.images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(image - expected_image).max() < 5E-1







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :str			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/cycle-diffusion/black_colored_car.png''')
        __UpperCamelCase     :str			  =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''')
        __UpperCamelCase     :List[Any]			  =		init_image.resize((512, 512))

        __UpperCamelCase     :Optional[int]			  =		'''CompVis/stable-diffusion-v1-4'''
        __UpperCamelCase     :str			  =		DDIMScheduler.from_pretrained(__lowercase , subfolder='''scheduler''')
        __UpperCamelCase     :List[str]			  =		CycleDiffusionPipeline.from_pretrained(__lowercase , scheduler=__lowercase , safety_checker=__lowercase)

        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()

        __UpperCamelCase     :Tuple			  =		'''A black colored car'''
        __UpperCamelCase     :str			  =		'''A blue colored car'''

        __UpperCamelCase     :Tuple			  =		torch.manual_seed(0)
        __UpperCamelCase     :int			  =		pipe(
            prompt=__lowercase , source_prompt=__lowercase , image=__lowercase , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=__lowercase , output_type='''np''' , )
        __UpperCamelCase     :Optional[Any]			  =		output.images

        assert np.abs(image - expected_image).max() < 2E-2













"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


