

from typing import List, Optional, Union

import torch

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 KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"




def 					_lowercase     (				a_				:					Any					,a_				:					Tuple					,a_				:					Optional[int]=8				)				->	List[str]:




					'''simple docstring'''







					__magic_name__							=    height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__magic_name__							=    width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


class 				__UpperCamelCase				(					_UpperCAmelCase			):







	def __init__(   self: Optional[int]  , __UpperCamelCase: UNetaDConditionModel  , __UpperCamelCase: DDPMScheduler  , __UpperCamelCase: VQModel  , ):

						'''simple docstring'''





						super().__init__()

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: int  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: int      ):

						'''simple docstring'''





						if latents is None:
											__magic_name__							=    randn_tensor(lowerCamelCase_  , generator=lowerCamelCase_  , device=lowerCamelCase_  , dtype=lowerCamelCase_      )
						else:
											if latents.shape != shape:
																raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'      )
											__magic_name__							=    latents.to(lowerCamelCase_      )

						__magic_name__							=    latents * scheduler.init_noise_sigma
						return latents







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Optional[Any]=0      ):

						'''simple docstring'''





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

						__magic_name__							=    torch.device(F'cuda:{gpu_id}'      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: Union[str, Any]=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.'      )

						__magic_name__							=    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)

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

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







	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						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: Union[str, Any]  , __UpperCamelCase: Union[torch.FloatTensor, List[torch.FloatTensor]]  , __UpperCamelCase: Union[torch.FloatTensor, List[torch.FloatTensor]]  , __UpperCamelCase: int = 5_12  , __UpperCamelCase: int = 5_12  , __UpperCamelCase: int = 1_00  , __UpperCamelCase: float = 4.0  , __UpperCamelCase: int = 1  , __UpperCamelCase: Optional[Union[torch.Generator, List[torch.Generator]]] = None  , __UpperCamelCase: Optional[torch.FloatTensor] = None  , __UpperCamelCase: Optional[str] = "pil"  , __UpperCamelCase: bool = True  , ):

						'''simple docstring'''





						__magic_name__							=    self._execution_device

						__magic_name__							=    guidance_scale > 1.0

						if isinstance(lowerCamelCase_  , lowerCamelCase_      ):
											__magic_name__							=    torch.cat(lowerCamelCase_  , dim=0      )
						__magic_name__							=    image_embeds.shape[0] * num_images_per_prompt
						if isinstance(lowerCamelCase_  , lowerCamelCase_      ):
											__magic_name__							=    torch.cat(lowerCamelCase_  , dim=0      )

						if do_classifier_free_guidance:
											__magic_name__							=    image_embeds.repeat_interleave(lowerCamelCase_  , dim=0      )
											__magic_name__							=    negative_image_embeds.repeat_interleave(lowerCamelCase_  , dim=0      )

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

						self.scheduler.set_timesteps(lowerCamelCase_  , device=lowerCamelCase_      )
						__magic_name__							=    self.scheduler.timesteps

						__magic_name__							=    self.unet.config.in_channels

						__magic_name__							=    downscale_height_and_width(lowerCamelCase_  , lowerCamelCase_  , self.movq_scale_factor      )

						# create initial latent
						__magic_name__							=    self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)  , image_embeds.dtype  , lowerCamelCase_  , lowerCamelCase_  , lowerCamelCase_  , self.scheduler  , )

						for i, t in enumerate(self.progress_bar(lowerCamelCase_      )      ):
											# expand the latents if we are doing classifier free guidance
											__magic_name__							=    torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

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

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

											# compute the previous noisy sample x_t -> x_t-1
											__magic_name__							=    self.scheduler.step(
											    lowerCamelCase_  , lowerCamelCase_  , lowerCamelCase_  , generator=lowerCamelCase_  , )[0]
						# post-processing
						__magic_name__							=    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"]:
											__magic_name__							=    image * 0.5 + 0.5
											__magic_name__							=    image.clamp(0  , 1      )
											__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      ).float().numpy()

						if output_type == "pil":
											__magic_name__							=    self.numpy_to_pil(lowerCamelCase_      )

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=lowerCamelCase_      )







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  LayoutLMTokenizer
	_lowercase  :   List[str]       				=  LayoutLMTokenizerFast
	_lowercase  :   List[Any]       				=  True
	_lowercase  :   Optional[int]       				=  True







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						super().setUp()

						__magic_name__							=    [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__magic_name__							=    os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['vocab_file']      )
						with open(self.vocab_file  , 'w'  , encoding='utf-8'      ) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return LayoutLMTokenizer.from_pretrained(self.tmpdirname  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    'UNwant\u00E9d,running'
						__magic_name__							=    'unwanted, running'
						return input_text, output_text







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer_class(self.vocab_file      )

						__magic_name__							=    tokenizer.tokenize('UNwant\u00E9d,running'      )
						self.assertListEqual(__UpperCamelCase  , ['un', '##want', '##ed', ',', 'runn', '##ing']      )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase      )  , [7, 4, 5, 10, 8, 9]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						pass




