





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,
)


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

_UpperCAmelCase										=					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=8	)  ->		Tuple:
       UpperCamelCase_ 							=	height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       UpperCamelCase_ 							=	width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_=512       ,		UpperCamelCase_=512	)  ->		Dict:
       UpperCamelCase_ 							=	pil_image.resize((w, h)       ,		resample=Image.BICUBIC       ,		reducing_gap=1	)
       UpperCamelCase_ 							=	np.array(pil_image.convert("RGB"	)	)
       UpperCamelCase_ 							=	arr.astype(np.floataa	) / 1_27.5 - 1
       UpperCamelCase_ 							=	np.transpose(UpperCamelCase_       ,		[2, 0, 1]	)
       UpperCamelCase_ 							=	torch.from_numpy(UpperCamelCase_	).unsqueeze(0	)
       return image







class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			UNetaDConditionModel       , _SCREAMING_SNAKE_CASE:			DDPMScheduler       , _SCREAMING_SNAKE_CASE:			VQModel       , )							->			Union[str, Any]:




              """simple docstring"""


              super().__init__()

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

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Any		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	min(int(num_inference_steps * strength		)       , _SCREAMING_SNAKE_CASE		)

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

              return timesteps, num_inference_steps - t_start

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[Any]=None		)							->			str:




              """simple docstring"""


              if not isinstance(_SCREAMING_SNAKE_CASE       , (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(_SCREAMING_SNAKE_CASE		)}'''		)

              UpperCamelCase_ 							=	image.to(device=_SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	batch_size * num_images_per_prompt

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

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

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

                     UpperCamelCase_ 							=	self.movq.config.scaling_factor * init_latents

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

              UpperCamelCase_ 							=	init_latents.shape
              UpperCamelCase_ 							=	randn_tensor(_SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , device=_SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)

              # get latents
              UpperCamelCase_ 							=	self.scheduler.add_noise(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	init_latents

              return latents

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Tuple=0		)							->			Any:




              """simple docstring"""


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

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

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

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Optional[Any]=0		)							->			Dict:




              """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."		)

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

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

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

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

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def lowercase			(		self:			Tuple		)							->			Dict:




              """simple docstring"""


              if not hasattr(self.unet       , "_hf_hook"		):
                     return self.device
              for module in self.unet.modules():
                     if (
                         hasattr(_SCREAMING_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(_SCREAMING_SNAKE_CASE		)
       def __call__(		self:			Dict       , _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:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			int = 512       , _SCREAMING_SNAKE_CASE:			int = 512       , _SCREAMING_SNAKE_CASE:			int = 100       , _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[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	self._execution_device

              UpperCamelCase_ 							=	guidance_scale > 1.0

              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	torch.cat(_SCREAMING_SNAKE_CASE       , dim=0		)
              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	torch.cat(_SCREAMING_SNAKE_CASE       , dim=0		)
              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	torch.cat(_SCREAMING_SNAKE_CASE       , dim=0		)

              UpperCamelCase_ 							=	image_embeds.shape[0]

              if do_classifier_free_guidance:
                     UpperCamelCase_ 							=	image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE       , dim=0		)
                     UpperCamelCase_ 							=	negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE       , dim=0		)
                     UpperCamelCase_ 							=	hint.repeat_interleave(_SCREAMING_SNAKE_CASE       , dim=0		)

              UpperCamelCase_ 							=	torch.cat([negative_image_embeds, image_embeds]       , dim=0		).to(dtype=self.unet.dtype       , device=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	torch.cat([hint, hint]       , dim=0		).to(dtype=self.unet.dtype       , device=_SCREAMING_SNAKE_CASE		)

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

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

              UpperCamelCase_ 							=	self.movq.encode(_SCREAMING_SNAKE_CASE		)["latents"]
              UpperCamelCase_ 							=	latents.repeat_interleave(_SCREAMING_SNAKE_CASE       , dim=0		)
              self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE       , device=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_							,			UpperCamelCase_ 							=	self.get_timesteps(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	timesteps[:1].repeat(batch_size * num_images_per_prompt		)
              UpperCamelCase_							,			UpperCamelCase_ 							=	downscale_height_and_width(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , self.movq_scale_factor		)
              UpperCamelCase_ 							=	self.prepare_latents(
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , image_embeds.dtype       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE		)		):
                     # expand the latents if we are doing classifier free guidance
                     UpperCamelCase_ 							=	torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

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

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

                     # compute the previous noisy sample x_t -> x_t-1

                     UpperCamelCase_ 							=	self.scheduler.step(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , )[0]

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

              if output_type == "pil":
                     UpperCamelCase_ 							=	self.numpy_to_pil(_SCREAMING_SNAKE_CASE		)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)









from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Tuple				=       DPRContextEncoderTokenizer
						_SCREAMING_SNAKE_CASE						:							Any				=       DPRContextEncoderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							str				=       True




@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       DPRQuestionEncoderTokenizer
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       DPRQuestionEncoderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       True




@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Any				=       DPRReaderTokenizer
						_SCREAMING_SNAKE_CASE						:							int				=       DPRReaderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							List[str]				=       True






						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer_class.from_pretrained('bert-base-uncased' )

												lowerCAmelCase__         =    tokenizer.encode('question sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('title sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('text sequence ' * 4  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    [[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
												lowerCAmelCase__         =    BatchEncoding({'input_ids': input_ids} )

												lowerCAmelCase__         =    [[0] * len(input_ids[0] )]
												lowerCAmelCase__         =    [[0] * len(input_ids[0] )]
												lowerCAmelCase__         =    [0]
												lowerCAmelCase__         =    DPRReaderOutput(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )

												lowerCAmelCase__    ,							lowerCAmelCase__         =    8, 9
												lowerCAmelCase__         =    10
												lowerCAmelCase__         =    10
												lowerCAmelCase__         =    tokenizer.decode_best_spans(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].start_index  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].end_index  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].doc_id  ,    0 )







						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer_class.from_pretrained('bert-base-uncased' )

												lowerCAmelCase__         =    tokenizer.encode('question sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('title sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('text sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
												lowerCAmelCase__         =    tokenizer(questions=['question sequence']  ,    titles=['title sequence']  ,    texts=['text sequence'] )
												self.assertIn('input_ids'  ,    _UpperCamelCase )
												self.assertIn('attention_mask'  ,    _UpperCamelCase )
												self.assertListEqual(encoded_input['input_ids'][0]  ,    _UpperCamelCase )





