"""simple docstring"""




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,
)


__lowerCAmelCase    :							Optional[int]													=						logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCAmelCase    :							List[Any]													=						"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=8     ):





		"""simple docstring"""




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






class       a_      (     __UpperCamelCase			):




					def __init__(	self						:     int  ,						snake_case__						:     UNetaDConditionModel  ,						snake_case__						:     DDPMScheduler  ,						snake_case__						:     VQModel  ,						):
							super().__init__()

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Dict  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     str  ,						snake_case__						:     Any  ,						snake_case__						:     str  ,						snake_case__						:     Union[str, Any]     ):
							if latents is None:
									lowerCAmelCase__        =   randn_tensor(snake_case__  ,						generator=snake_case__  ,						device=snake_case__  ,						dtype=snake_case__     )
							else:
									if latents.shape != shape:
											raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""     )
									lowerCAmelCase__        =   latents.to(snake_case__     )

							lowerCAmelCase__        =   latents * scheduler.init_noise_sigma
							return latents




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     str=0     ):
							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

							lowerCAmelCase__        =   torch.device(F"""cuda:{gpu_id}"""     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     List[Any]=0     ):
							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."""     )

							lowerCAmelCase__        =   torch.device(F"""cuda:{gpu_id}"""     )

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

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

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




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							if not hasattr(self.unet  ,						"""_hf_hook"""     ):
									return self.device
							for module in self.unet.modules():
									if (
									    hasattr(snake_case__  ,						"""_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(snake_case__     )
					def __call__(	self						:     Optional[int]  ,						snake_case__						:     Union[torch.FloatTensor, List[torch.FloatTensor]]  ,						snake_case__						:     Union[torch.FloatTensor, List[torch.FloatTensor]]  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     int = 512  ,						snake_case__						:     int = 512  ,						snake_case__						:     int = 100  ,						snake_case__						:     float = 4.0  ,						snake_case__						:     int = 1  ,						snake_case__						:     Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,						snake_case__						:     Optional[torch.FloatTensor] = None  ,						snake_case__						:     Optional[str] = "pil"  ,						snake_case__						:     bool = True  ,						):
							lowerCAmelCase__        =   self._execution_device

							lowerCAmelCase__        =   guidance_scale > 1.0

							if isinstance(snake_case__  ,						snake_case__     ):
									lowerCAmelCase__        =   torch.cat(snake_case__  ,						dim=0     )
							if isinstance(snake_case__  ,						snake_case__     ):
									lowerCAmelCase__        =   torch.cat(snake_case__  ,						dim=0     )
							if isinstance(snake_case__  ,						snake_case__     ):
									lowerCAmelCase__        =   torch.cat(snake_case__  ,						dim=0     )

							lowerCAmelCase__        =   image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
									lowerCAmelCase__        =   image_embeds.repeat_interleave(snake_case__  ,						dim=0     )
									lowerCAmelCase__        =   negative_image_embeds.repeat_interleave(snake_case__  ,						dim=0     )
									lowerCAmelCase__        =   hint.repeat_interleave(snake_case__  ,						dim=0     )

							lowerCAmelCase__        =   torch.cat([negative_image_embeds, image_embeds]  ,						dim=0     ).to(dtype=self.unet.dtype  ,						device=snake_case__     )
							lowerCAmelCase__        =   torch.cat([hint, hint]  ,						dim=0     ).to(dtype=self.unet.dtype  ,						device=snake_case__     )
							self.scheduler.set_timesteps(snake_case__  ,						device=snake_case__     )
							lowerCAmelCase__        =   self.scheduler.timesteps

							lowerCAmelCase__        =   self.movq.config.latent_channels

							lowerCAmelCase__   ,					lowerCAmelCase__        =   downscale_height_and_width(snake_case__  ,						snake_case__  ,						self.movq_scale_factor     )

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

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

									lowerCAmelCase__        =   {"""image_embeds""": image_embeds, """hint""": hint}
									lowerCAmelCase__        =   self.unet(
									    sample=snake_case__  ,						timestep=snake_case__  ,						encoder_hidden_states=snake_case__  ,						added_cond_kwargs=snake_case__  ,						return_dict=snake_case__  ,						)[0]

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

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

							if output_type == "pil":
									lowerCAmelCase__        =   self.numpy_to_pil(snake_case__     )

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=snake_case__     )





"""simple docstring"""




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       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    LayoutLMTokenizer
					UpperCamelCase_ :	List[Any]			=    LayoutLMTokenizerFast
					UpperCamelCase_ :	Dict			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							lowerCAmelCase__        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase__        =   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						:     int  ,						**snake_case__						:     Union[str, Any]     ):
							return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   """UNwant\u00E9d,running"""
							lowerCAmelCase__        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.tokenizer_class(self.vocab_file     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							pass




