


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,
)


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

__UpperCAmelCase : Optional[int] 			=				"\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      a	(							SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		str=8       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       UpperCamelCase    :	List[str]   	=		width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__()

              self.register_modules(
                  unet=__SCREAMING_SNAKE_CASE ,					scheduler=__SCREAMING_SNAKE_CASE ,					movq=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	Any   	=		2 ** (len(self.movq.config.block_out_channels						) - 1)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if latents is None:
                     UpperCamelCase    :	List[str]   	=		randn_tensor(__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					device=__SCREAMING_SNAKE_CASE ,					dtype=__SCREAMING_SNAKE_CASE						)
              else:
                     if latents.shape != shape:
                            raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""						)
                     UpperCamelCase    :	Optional[Any]   	=		latents.to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[Any]   	=		latents * scheduler.init_noise_sigma
              return latents






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE=0						):




              """simple docstring"""




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

              UpperCamelCase    :	List[str]   	=		torch.device(f"""cuda:{gpu_id}"""						)

              UpperCamelCase    :	str   	=		[
                  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 ,					__SCREAMING_SNAKE_CASE=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.'''						)

              UpperCamelCase    :	Dict   	=		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    :	Optional[int]   	=		None
              for cpu_offloaded_model in [self.unet, self.movq]:
                     UpperCamelCase	, UpperCamelCase    :	Dict   	=		cpu_offload_with_hook(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					prev_module_hook=__SCREAMING_SNAKE_CASE						)

              # We'll offload the last model manually.
              UpperCamelCase    :	Tuple   	=		hook






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




              """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 ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 512 ,					__SCREAMING_SNAKE_CASE = 512 ,					__SCREAMING_SNAKE_CASE = 100 ,					__SCREAMING_SNAKE_CASE = 4.0 ,					__SCREAMING_SNAKE_CASE = 1 ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "pil" ,					__SCREAMING_SNAKE_CASE = True ,					):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		self._execution_device

              UpperCamelCase    :	str   	=		guidance_scale > 1.0

              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	List[Any]   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=0						)
              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Optional[int]   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=0						)
              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	int   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=0						)

              UpperCamelCase    :	List[str]   	=		image_embeds.shape[0] * num_images_per_prompt

              if do_classifier_free_guidance:
                     UpperCamelCase    :	Union[str, Any]   	=		image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE ,					dim=0						)
                     UpperCamelCase    :	Tuple   	=		negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE ,					dim=0						)
                     UpperCamelCase    :	Any   	=		hint.repeat_interleave(__SCREAMING_SNAKE_CASE ,					dim=0						)

              UpperCamelCase    :	List[Any]   	=		torch.cat([negative_image_embeds, image_embeds] ,					dim=0						).to(dtype=self.unet.dtype ,					device=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		torch.cat([hint, hint] ,					dim=0						).to(dtype=self.unet.dtype ,					device=__SCREAMING_SNAKE_CASE						)
              self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE ,					device=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		self.scheduler.timesteps

              UpperCamelCase    :	List[Any]   	=		self.movq.config.latent_channels

              UpperCamelCase	, UpperCamelCase    :	Any   	=		downscale_height_and_width(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					self.movq_scale_factor						)

              # create initial latent
              UpperCamelCase    :	str   	=		self.prepare_latents(
                  (batch_size, num_channels_latents, height, width) ,					image_embeds.dtype ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					self.scheduler ,					)

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

                     UpperCamelCase    :	Union[str, Any]   	=		{'''image_embeds''': image_embeds, '''hint''': hint}
                     UpperCamelCase    :	Optional[int]   	=		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    :	int   	=		noise_pred.split(latents.shape[1] ,					dim=1						)
                            UpperCamelCase	, UpperCamelCase    :	List[Any]   	=		noise_pred.chunk(2						)
                            UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		variance_pred.chunk(2						)
                            UpperCamelCase    :	int   	=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                            UpperCamelCase    :	Dict   	=		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    :	Dict   	=		noise_pred.split(latents.shape[1] ,					dim=1						)

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

              if output_type == "pil":
                     UpperCamelCase    :	List[Any]   	=		self.numpy_to_pil(__SCREAMING_SNAKE_CASE						)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE						)





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_SNAKE_CASE_       )

              # Activate later when tokenizer support batched inputs
              # with dataset.formatted_as(type='numpy'):
              #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

       with open(SCREAMING_SNAKE_CASE_							,			'''wb'''       ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()

