

'''simple docstring'''





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											=     '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'





def a(lowercase__			, lowercase__			, lowercase__=8	):





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





def a(lowercase__			, lowercase__=512			, lowercase__=512	):





	'''simple docstring'''
	snake_case_      		= pil_image.resize((w, h)			, resample=Image.BICUBIC			, reducing_gap=1	)
	snake_case_      		= np.array(pil_image.convert('RGB'	)	)
	snake_case_      		= arr.astype(np.floataa	) / 127.5 - 1
	snake_case_      		= np.transpose(lowercase__			, [2, 0, 1]	)
	snake_case_      		= torch.from_numpy(lowercase__	).unsqueeze(0	)
	return image






class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__()

				self.register_modules(
				    unet=__UpperCamelCase ,				scheduler=__UpperCamelCase ,				movq=__UpperCamelCase ,				)
				snake_case_      		= 2 ** (len(self.movq.config.block_out_channels							) - 1)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= min(int(num_inference_steps * strength							) ,				__UpperCamelCase							)

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

				return timesteps, num_inference_steps - t_start


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=None							):

				"""simple docstring"""


				if not isinstance(__UpperCamelCase ,				(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(__UpperCamelCase							)}"""							)

				snake_case_      		= image.to(device=__UpperCamelCase ,				dtype=__UpperCamelCase							)

				snake_case_      		= batch_size * num_images_per_prompt

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

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

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

					snake_case_      		= self.movq.config.scaling_factor * init_latents

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

				snake_case_      		= init_latents.shape
				snake_case_      		= randn_tensor(__UpperCamelCase ,				generator=__UpperCamelCase ,				device=__UpperCamelCase ,				dtype=__UpperCamelCase							)

				# get latents
				snake_case_      		= self.scheduler.add_noise(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

				snake_case_      		= init_latents

				return latents


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=0							):

				"""simple docstring"""


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

				snake_case_      		= torch.device(f"""cuda:{gpu_id}"""							)

				snake_case_      		= [
				    self.unet,
				    self.movq,
				]
				for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
						cpu_offload(__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=0							):

				"""simple docstring"""


				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.'							)

				snake_case_      		= torch.device(f"""cuda:{gpu_id}"""							)

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

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

				# We'll offload the last model manually.
				snake_case_      		= hook


			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				if not hasattr(self.unet ,				'_hf_hook'							):
					return self.device
				for module in self.unet.modules():
					if (
					    hasattr(__UpperCamelCase ,				'_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(__UpperCamelCase							)
			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = 5_12 ,				__UpperCamelCase = 5_12 ,				__UpperCamelCase = 1_00 ,				__UpperCamelCase = 4.0 ,				__UpperCamelCase = 0.3 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = None ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				):

				"""simple docstring"""


				snake_case_      		= self._execution_device

				snake_case_      		= guidance_scale > 1.0

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

				if do_classifier_free_guidance:
					snake_case_      		= image_embeds.repeat_interleave(__UpperCamelCase ,				dim=0							)
					snake_case_      		= negative_image_embeds.repeat_interleave(__UpperCamelCase ,				dim=0							)

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

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

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

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

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

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

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

				# post-processing
				snake_case_      		= self.movq.decode(__UpperCamelCase ,				force_not_quantize=__UpperCamelCase							)['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"]:
					snake_case_      		= image * 0.5 + 0.5
					snake_case_      		= image.clamp(0 ,				1							)
					snake_case_      		= image.cpu().permute(0 ,				2 ,				3 ,				1							).float().numpy()

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(__UpperCamelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__UpperCamelCase							)



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


A											=     '3'

print('Python version:', sys.version)

print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())

try:
		import torch

		print('Torch version:', torch.__version__)
		print('Cuda available:', torch.cuda.is_available())
		print('Cuda version:', torch.version.cuda)
		print('CuDNN version:', torch.backends.cudnn.version())
		print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
		print('Torch version:', None)

try:
		import transformers

		print('transformers version:', transformers.__version__)
except ImportError:
		print('transformers version:', None)
