
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__a         =   logging.get_logger(__name__)  # pylint: disable=invalid-name

__a         =   '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''




def 							a		(				snake_case__:   Optional[Any]		,  snake_case__:   List[Any]		,  snake_case__:   List[str]=8   ):


				'''simple docstring'''
				lowercase_	    =   height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowercase_	    =   width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




def 							a		(				snake_case__:   List[str]		,  snake_case__:   int=512		,  snake_case__:   Optional[int]=512   ):


				'''simple docstring'''
				lowercase_	    =   pil_image.resize((w, h)		,  resample=Image.BICUBIC		,  reducing_gap=1   )
				lowercase_	    =   np.array(pil_image.convert('''RGB'''   )   )
				lowercase_	    =   arr.astype(np.floataa   ) / 1_2_7.5 - 1
				lowercase_	    =   np.transpose(snake_case__		,  [2, 0, 1]   )
				lowercase_	    =   torch.from_numpy(snake_case__   ).unsqueeze(0   )
				return image


class 					lowercase__(							UpperCAmelCase_				):



	"""simple docstring"""




	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     UNetaDConditionModel			,				SCREAMING_SNAKE_CASE_    :     DDPMScheduler			,				SCREAMING_SNAKE_CASE_    :     VQModel			,				)			->	Optional[Any]:
					super().__init__()

					self.register_modules(
					    unet=__lowerCamelCase			,				scheduler=__lowerCamelCase			,				movq=__lowerCamelCase			,				)
					lowercase_	    =   2 ** (len(self.movq.config.block_out_channels			) - 1)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	Optional[Any]:
					lowercase_	    =   min(int(num_inference_steps * strength			)			,				__lowerCamelCase			)

					lowercase_	    =   max(num_inference_steps - init_timestep			,				0			)
					lowercase_	    =   self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=None			)			->	Optional[int]:




					if not isinstance(__lowerCamelCase			,				(torch.Tensor, PIL.Image.Image, list)			):
									raise ValueError(
									    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowerCamelCase			)}'''			)

					lowercase_	    =   image.to(device=__lowerCamelCase			,				dtype=__lowerCamelCase			)

					lowercase_	    =   batch_size * num_images_per_prompt

					if image.shape[1] == 4:
									lowercase_	    =   image

					else:
									if isinstance(__lowerCamelCase			,				__lowerCamelCase			) and len(__lowerCamelCase			) != batch_size:
													raise ValueError(
													    f'''You have passed a list of generators of length {len(__lowerCamelCase			)}, but requested an effective batch'''
													    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

									elif isinstance(__lowerCamelCase			,				__lowerCamelCase			):
													lowercase_	    =   [
													    self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(__lowerCamelCase			)
													]
													lowercase_	    =   torch.cat(__lowerCamelCase			,				dim=0			)
									else:
													lowercase_	    =   self.movq.encode(__lowerCamelCase			).latent_dist.sample(__lowerCamelCase			)

									lowercase_	    =   self.movq.config.scaling_factor * init_latents

					lowercase_	    =   torch.cat([init_latents]			,				dim=0			)

					lowercase_	    =   init_latents.shape
					lowercase_	    =   randn_tensor(__lowerCamelCase			,				generator=__lowerCamelCase			,				device=__lowerCamelCase			,				dtype=__lowerCamelCase			)

					# get latents
					lowercase_	    =   self.scheduler.add_noise(__lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			)

					lowercase_	    =   init_latents

					return latents
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0			)			->	Dict:




					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError('''Please install accelerate via `pip install accelerate`'''			)

					lowercase_	    =   torch.device(f'''cuda:{gpu_id}'''			)

					lowercase_	    =   [
					    self.unet,
					    self.movq,
					]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(__lowerCamelCase			,				__lowerCamelCase			)
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     Dict=0			)			->	Tuple:




					if is_accelerate_available() and is_accelerate_version('''>='''			,				'''0.17.0.dev0'''			):
									from accelerate import cpu_offload_with_hook
					else:
									raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''			)

					lowercase_	    =   torch.device(f'''cuda:{gpu_id}'''			)

					if self.device.type != "cpu":
									self.to('''cpu'''			,				silence_dtype_warnings=__lowerCamelCase			)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					lowercase_	    =   None
					for cpu_offloaded_model in [self.unet, self.movq]:
									lowercase_    ,      lowercase_	    =   cpu_offload_with_hook(__lowerCamelCase			,				__lowerCamelCase			,				prev_module_hook=__lowerCamelCase			)

					# We'll offload the last model manually.
					lowercase_	    =   hook
	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 		_lowercase    (			self    :     int			)			->	List[str]:




					if not hasattr(self.unet			,				'''_hf_hook'''			):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(__lowerCamelCase			,				'''_hf_hook'''			)
									    and hasattr(module._hf_hook			,				'''execution_device'''			)
									    and module._hf_hook.execution_device is not None
									):
													return torch.device(module._hf_hook.execution_device			)
					return self.device




	@torch.no_grad()
	@replace_example_docstring(__lowerCamelCase			)
	def __call__(			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[torch.FloatTensor, List[torch.FloatTensor]]			,				SCREAMING_SNAKE_CASE_    :     Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]			,				SCREAMING_SNAKE_CASE_    :     Union[torch.FloatTensor, List[torch.FloatTensor]]			,				SCREAMING_SNAKE_CASE_    :     int = 5_1_2			,				SCREAMING_SNAKE_CASE_    :     int = 5_1_2			,				SCREAMING_SNAKE_CASE_    :     int = 1_0_0			,				SCREAMING_SNAKE_CASE_    :     float = 4.0			,				SCREAMING_SNAKE_CASE_    :     float = 0.3			,				SCREAMING_SNAKE_CASE_    :     int = 1			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[torch.Generator, List[torch.Generator]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = "pil"			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				)			->	List[Any]:
					lowercase_	    =   self._execution_device

					lowercase_	    =   guidance_scale > 1.0

					if isinstance(__lowerCamelCase			,				__lowerCamelCase			):
									lowercase_	    =   torch.cat(__lowerCamelCase			,				dim=0			)
					lowercase_	    =   image_embeds.shape[0]
					if isinstance(__lowerCamelCase			,				__lowerCamelCase			):
									lowercase_	    =   torch.cat(__lowerCamelCase			,				dim=0			)

					if do_classifier_free_guidance:
									lowercase_	    =   image_embeds.repeat_interleave(__lowerCamelCase			,				dim=0			)
									lowercase_	    =   negative_image_embeds.repeat_interleave(__lowerCamelCase			,				dim=0			)

					lowercase_	    =   torch.cat([negative_image_embeds, image_embeds]			,				dim=0			).to(dtype=self.unet.dtype			,				device=__lowerCamelCase			)

					if not isinstance(__lowerCamelCase			,				__lowerCamelCase			):
									lowercase_	    =   [image]
					if not all(isinstance(__lowerCamelCase			,				(PIL.Image.Image, torch.Tensor)			) for i in image			):
									raise ValueError(
									    f'''Input is in incorrect format: {[type(__lowerCamelCase			) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''			)

					lowercase_	    =   torch.cat([prepare_image(__lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			) for i in image]			,				dim=0			)
					lowercase_	    =   image.to(dtype=image_embeds.dtype			,				device=__lowerCamelCase			)

					lowercase_	    =   self.movq.encode(__lowerCamelCase			)['''latents''']
					lowercase_	    =   latents.repeat_interleave(__lowerCamelCase			,				dim=0			)
					self.scheduler.set_timesteps(__lowerCamelCase			,				device=__lowerCamelCase			)
					lowercase_    ,      lowercase_	    =   self.get_timesteps(__lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			)
					lowercase_	    =   timesteps[:1].repeat(batch_size * num_images_per_prompt			)
					lowercase_    ,      lowercase_	    =   downscale_height_and_width(__lowerCamelCase			,				__lowerCamelCase			,				self.movq_scale_factor			)
					lowercase_	    =   self.prepare_latents(
					    __lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			,				image_embeds.dtype			,				__lowerCamelCase			,				__lowerCamelCase			)
					for i, t in enumerate(self.progress_bar(__lowerCamelCase			)			):
									# expand the latents if we are doing classifier free guidance
									lowercase_	    =   torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

									lowercase_	    =   {'''image_embeds''': image_embeds}
									lowercase_	    =   self.unet(
									    sample=__lowerCamelCase			,				timestep=__lowerCamelCase			,				encoder_hidden_states=__lowerCamelCase			,				added_cond_kwargs=__lowerCamelCase			,				return_dict=__lowerCamelCase			,				)[0]

									if do_classifier_free_guidance:
													lowercase_    ,      lowercase_	    =   noise_pred.split(latents.shape[1]			,				dim=1			)
													lowercase_    ,      lowercase_	    =   noise_pred.chunk(2			)
													lowercase_    ,      lowercase_	    =   variance_pred.chunk(2			)
													lowercase_	    =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													lowercase_	    =   torch.cat([noise_pred, variance_pred_text]			,				dim=1			)

									if not (
									    hasattr(self.scheduler.config			,				'''variance_type'''			)
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
													lowercase_    ,      lowercase_	    =   noise_pred.split(latents.shape[1]			,				dim=1			)

									# compute the previous noisy sample x_t -> x_t-1
									lowercase_	    =   self.scheduler.step(
									    __lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			,				generator=__lowerCamelCase			,				)[0]

					# post-processing
					lowercase_	    =   self.movq.decode(__lowerCamelCase			,				force_not_quantize=__lowerCamelCase			)['''sample''']

					if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''			)

					if output_type in ["np", "pil"]:
									lowercase_	    =   image * 0.5 + 0.5
									lowercase_	    =   image.clamp(0			,				1			)
									lowercase_	    =   image.cpu().permute(0			,				2			,				3			,				1			).float().numpy()

					if output_type == "pil":
									lowercase_	    =   self.numpy_to_pil(__lowerCamelCase			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=__lowerCamelCase			)











import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   '''http://www.mocksite.com/file1.txt'''
SCREAMING_SNAKE_CASE_   :      str			=   '''"text": ["foo", "foo"]'''
SCREAMING_SNAKE_CASE_   :      Optional[int]			=   '''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''

class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		2_00
	__UpperCamelCase   =		{'''Content-Length''': '''100'''}
	__UpperCamelCase   =		{}




	def    UpperCAmelCase			(						self							:     str					,      **__lowerCamelCase							:     Optional[int]   )       -> str:

			'''simple docstring'''




			return [bytes(__lowerCamelCase					,      'utf-8'   )]





def  SCREAMING_SNAKE_CASE				(      *snake_case						,   **snake_case							)		->    int:
		return MockResponse()



@pytest.mark.parametrize('urls_type'						,   [str, list, dict]							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Optional[Any]:
		import requests

		monkeypatch.setattr(snake_case						,   'request'						,   snake_case							)

		__lowercase											=     URL
		if issubclass(snake_case						,   snake_case							):
				__lowercase											=     url
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     [url]
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     {'train': url}
		__lowercase											=     'dummy'
		__lowercase											=     'downloads'
		__lowercase											=     tmp_path
		__lowercase											=     DownloadConfig(
		    cache_dir=os.path.join(snake_case						,   snake_case							)						,   use_etag=snake_case						,   )
		__lowercase											=     DownloadManager(dataset_name=snake_case						,   download_config=snake_case							)
		__lowercase											=     dl_manager.download(snake_case							)
		__lowercase											=     urls
		for downloaded_paths in [downloaded_paths]:
				if isinstance(snake_case						,   snake_case							):
						__lowercase											=     [downloaded_paths]
						__lowercase											=     [urls]
				elif isinstance(snake_case						,   snake_case							):
						assert "train" in downloaded_paths.keys()
						__lowercase											=     downloaded_paths.values()
						__lowercase											=     urls.values()
				assert downloaded_paths
				for downloaded_path, input_url in zip(snake_case						,   snake_case							):
						assert downloaded_path == dl_manager.downloaded_paths[input_url]
						__lowercase											=     Path(snake_case							)
						__lowercase											=     downloaded_path.parts
						assert parts[-1] == HASH
						assert parts[-2] == cache_subdir
						assert downloaded_path.exists()
						__lowercase											=     downloaded_path.read_text()
						assert content == CONTENT
						__lowercase											=     downloaded_path.with_suffix('.json'							)
						assert metadata_downloaded_path.exists()
						__lowercase											=     json.loads(metadata_downloaded_path.read_text()							)
						assert metadata_content == {"url": URL, "etag": None}



@pytest.mark.parametrize('paths_type'						,   [str, list, dict]							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Union[str, Any]:
		__lowercase											=     str(snake_case							)
		if issubclass(snake_case						,   snake_case							):
				__lowercase											=     filename
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     [filename]
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     {'train': filename}
		__lowercase											=     'dummy'
		__lowercase											=     xz_file.parent
		__lowercase											=     'extracted'
		__lowercase											=     DownloadConfig(
		    cache_dir=snake_case						,   use_etag=snake_case						,   )
		__lowercase											=     DownloadManager(dataset_name=snake_case						,   download_config=snake_case							)
		__lowercase											=     dl_manager.extract(snake_case							)
		__lowercase											=     paths
		for extracted_paths in [extracted_paths]:
				if isinstance(snake_case						,   snake_case							):
						__lowercase											=     [extracted_paths]
						__lowercase											=     [paths]
				elif isinstance(snake_case						,   snake_case							):
						assert "train" in extracted_paths.keys()
						__lowercase											=     extracted_paths.values()
						__lowercase											=     paths.values()
				assert extracted_paths
				for extracted_path, input_path in zip(snake_case						,   snake_case							):
						assert extracted_path == dl_manager.extracted_paths[input_path]
						__lowercase											=     Path(snake_case							)
						__lowercase											=     extracted_path.parts
						assert parts[-1] == hash_url_to_filename(snake_case						,   etag=snake_case							)
						assert parts[-2] == extracted_subdir
						assert extracted_path.exists()
						__lowercase											=     extracted_path.read_text()
						__lowercase											=     text_file.read_text()
						assert extracted_file_content == expected_file_content



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:
		assert path.endswith('.jsonl'							)
		for num_items, line in enumerate(snake_case						,   start=1							):
				__lowercase											=     json.loads(line.decode('utf-8'							)							)
				assert item.keys() == {"col_1", "col_2", "col_3"}
		assert num_items == 4



@pytest.mark.parametrize('archive_jsonl'						,   ['tar_jsonl_path', 'zip_jsonl_path']							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:
		__lowercase											=     request.getfixturevalue(snake_case							)
		__lowercase											=     DownloadManager()
		for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
				_test_jsonl(snake_case						,   snake_case							)
		assert num_jsonl == 2



@pytest.mark.parametrize('archive_nested_jsonl'						,   ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Any:
		__lowercase											=     request.getfixturevalue(snake_case							)
		__lowercase											=     DownloadManager()
		for num_tar, (path, file) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
				for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
						_test_jsonl(snake_case						,   snake_case							)
		assert num_tar == 1
		assert num_jsonl == 2



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     DownloadManager()
		for num_file, file in enumerate(dl_manager.iter_files(snake_case							)						,   start=1							):
				assert os.path.basename(snake_case							) == ("test.txt" if num_file == 1 else "train.txt")
		assert num_file == 2




