
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,
)


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

__snake_case								=  """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

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

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=8		)		->		Any:
					'''simple docstring'''


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




class 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   str       ,   UpperCAmelCase_							:   UNetaDConditionModel       ,   UpperCAmelCase_							:   DDPMScheduler       ,   UpperCAmelCase_							:   VQModel       ,   ):
						super().__init__()

						self.register_modules(
						    unet=UpperCAmelCase_       ,   scheduler=UpperCAmelCase_       ,   movq=UpperCAmelCase_       ,   )
						SCREAMING_SNAKE_CASE__													= 2 ** (len(self.movq.config.block_out_channels							) - 1)
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   Optional[int]							):
						if latents is None:
											SCREAMING_SNAKE_CASE__													= randn_tensor(UpperCAmelCase_       ,   generator=UpperCAmelCase_       ,   device=UpperCAmelCase_       ,   dtype=UpperCAmelCase_							)
						else:
											if latents.shape != shape:
																raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'							)
											SCREAMING_SNAKE_CASE__													= latents.to(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= latents * scheduler.init_noise_sigma
						return latents
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   Tuple=0							):
						if is_accelerate_available():
											from accelerate import cpu_offload
						else:
											raise ImportError('Please install accelerate via `pip install accelerate`'							)

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

						SCREAMING_SNAKE_CASE__													= [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
																cpu_offload(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   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.'							)

						SCREAMING_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)

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

						# We'll offload the last model manually.
						SCREAMING_SNAKE_CASE__													= hook
	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      A_				(			self							:   Dict							):
						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							:   Union[str, Any]       ,   UpperCAmelCase_							:   Union[torch.FloatTensor, List[torch.FloatTensor]]       ,   UpperCAmelCase_							:   Union[torch.FloatTensor, List[torch.FloatTensor]]       ,   UpperCAmelCase_							:   torch.FloatTensor       ,   UpperCAmelCase_							:   int = 512       ,   UpperCAmelCase_							:   int = 512       ,   UpperCAmelCase_							:   int = 100       ,   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       ,   ):
						SCREAMING_SNAKE_CASE__													= self._execution_device

						SCREAMING_SNAKE_CASE__													= guidance_scale > 1.0

						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							):
											SCREAMING_SNAKE_CASE__													= torch.cat(UpperCAmelCase_       ,   dim=0							)
						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							):
											SCREAMING_SNAKE_CASE__													= torch.cat(UpperCAmelCase_       ,   dim=0							)
						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							):
											SCREAMING_SNAKE_CASE__													= torch.cat(UpperCAmelCase_       ,   dim=0							)

						SCREAMING_SNAKE_CASE__													= image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
											SCREAMING_SNAKE_CASE__													= image_embeds.repeat_interleave(UpperCAmelCase_       ,   dim=0							)
											SCREAMING_SNAKE_CASE__													= negative_image_embeds.repeat_interleave(UpperCAmelCase_       ,   dim=0							)
											SCREAMING_SNAKE_CASE__													= hint.repeat_interleave(UpperCAmelCase_       ,   dim=0							)

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

						SCREAMING_SNAKE_CASE__													= self.movq.config.latent_channels

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= downscale_height_and_width(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.movq_scale_factor							)

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

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

											SCREAMING_SNAKE_CASE__													= {'image_embeds': image_embeds, 'hint': hint}
											SCREAMING_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:
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= noise_pred.split(latents.shape[1]       ,   dim=1							)
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= noise_pred.chunk(2							)
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= variance_pred.chunk(2							)
																SCREAMING_SNAKE_CASE__													= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																SCREAMING_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"]
											):
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= noise_pred.split(latents.shape[1]       ,   dim=1							)

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

						if output_type == "pil":
											SCREAMING_SNAKE_CASE__													= self.numpy_to_pil(UpperCAmelCase_							)

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=UpperCAmelCase_							)







import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Dict				       =LxmertTokenizer
	A__    :   List[Any]				       =LxmertTokenizerFast
	A__    :   Any				       =True
	A__    :   List[Any]				       =True
	def      A_				(			self							:   Optional[Any]							):
						super().setUp()

						SCREAMING_SNAKE_CASE__													= [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE__													= 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      A_				(			self							:   int       ,   UpperCAmelCase_							:   int							):
						SCREAMING_SNAKE_CASE__													= 'UNwant\u00E9d,running'
						SCREAMING_SNAKE_CASE__													= 'unwanted, running'
						return input_text, output_text
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class(self.vocab_file							)

						SCREAMING_SNAKE_CASE__													= 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      A_				(			self							:   List[str]							):
						if not self.test_rust_tokenizer:
											return

						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__													= 'I was born in 92000, and this is falsé.'

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.tokenize(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)





