






'''simple docstring'''

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

UpperCAmelCase__						:Any          =			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

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

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""
def     __lowercase  (_lowercase,						_lowercase,						_lowercase=8     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     __lowerCamelCase						:       int				   =				width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor
def     __lowercase  (_lowercase,						_lowercase=512,						_lowercase=512     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				pil_image.resize((w, h),						resample=Image.BICUBIC,						reducing_gap=1     )
     __lowerCamelCase						:       List[Any]				   =				np.array(pil_image.convert("""RGB"""     )     )
     __lowerCamelCase						:       int				   =				arr.astype(np.floataa     ) / 127.5 - 1
     __lowerCamelCase						:       Any				   =				np.transpose(_lowercase,						[2, 0, 1]     )
     __lowerCamelCase						:       Any				   =				torch.from_numpy(_lowercase     ).unsqueeze(0     )
     return image







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : Tuple	,    A__      : UNetaDConditionModel	,    A__      : DDPMScheduler	,    A__      : VQModel	,    ):


            """simple docstring"""







            super().__init__()

            self.register_modules(
                unet=A__	,    scheduler=A__	,    movq=A__	,    )
            __lowerCamelCase						:       int				   =				2 ** (len(self.movq.config.block_out_channels					) - 1)




       def 					a_					(					self      : List[str]	,    A__      : List[Any]	,    A__      : Union[str, Any]	,    A__      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				min(int(num_inference_steps * strength					)	,    A__					)

            __lowerCamelCase						:       int				   =				max(num_inference_steps - init_timestep	,    0					)
            __lowerCamelCase						:       List[Any]				   =				self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start




       def 					a_					(					self      : List[str]	,    A__      : str	,    A__      : Dict	,    A__      : str	,    A__      : Optional[Any]	,    A__      : Dict	,    A__      : List[Any]	,    A__      : List[Any]=None					):


            """simple docstring"""







            if not isinstance(A__	,    (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(A__					)}"					)

            __lowerCamelCase						:       Optional[Any]				   =				image.to(device=A__	,    dtype=A__					)

            __lowerCamelCase						:       List[str]				   =				batch_size * num_images_per_prompt

            if image.shape[1] == 4:
                 __lowerCamelCase						:       int				   =				image

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

                 elif isinstance(A__	,    A__					):
                      __lowerCamelCase						:       Optional[int]				   =				[
                          self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(A__					)
                      ]
                      __lowerCamelCase						:       str				   =				torch.cat(A__	,    dim=0					)
                 else:
                      __lowerCamelCase						:       Optional[Any]				   =				self.movq.encode(A__					).latent_dist.sample(A__					)

                 __lowerCamelCase						:       Optional[int]				   =				self.movq.config.scaling_factor * init_latents

            __lowerCamelCase						:       List[Any]				   =				torch.cat([init_latents]	,    dim=0					)

            __lowerCamelCase						:       List[str]				   =				init_latents.shape
            __lowerCamelCase						:       Dict				   =				randn_tensor(A__	,    generator=A__	,    device=A__	,    dtype=A__					)

            # get latents
            __lowerCamelCase						:       Any				   =				self.scheduler.add_noise(A__	,    A__	,    A__					)

            __lowerCamelCase						:       Optional[int]				   =				init_latents

            return latents




       def 					a_					(					self      : List[Any]	,    A__      : Tuple=0					):


            """simple docstring"""







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

            __lowerCamelCase						:       Tuple				   =				torch.device(f"cuda:{gpu_id}"					)

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




       def 					a_					(					self      : Tuple	,    A__      : 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."""					)

            __lowerCamelCase						:       Tuple				   =				torch.device(f"cuda:{gpu_id}"					)

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

            __lowerCamelCase						:       List[Any]				   =				None
            for cpu_offloaded_model in [self.unet, self.movq]:
                 __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				cpu_offload_with_hook(A__	,    A__	,    prev_module_hook=A__					)

            # We'll offload the last model manually.
            __lowerCamelCase						:       Union[str, Any]				   =				hook




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


            """simple docstring"""







            if not hasattr(self.unet	,    """_hf_hook"""					):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(A__	,    """_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(A__					)
       def __call__(					self      : Tuple	,    A__      : Union[torch.FloatTensor, List[torch.FloatTensor]]	,    A__      : Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]	,    A__      : Union[torch.FloatTensor, List[torch.FloatTensor]]	,    A__      : int = 512	,    A__      : int = 512	,    A__      : int = 100	,    A__      : float = 4.0	,    A__      : float = 0.3	,    A__      : int = 1	,    A__      : Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    A__      : Optional[str] = "pil"	,    A__      : bool = True	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self._execution_device

            __lowerCamelCase						:       List[Any]				   =				guidance_scale > 1.0

            if isinstance(A__	,    A__					):
                 __lowerCamelCase						:       str				   =				torch.cat(A__	,    dim=0					)
            __lowerCamelCase						:       Union[str, Any]				   =				image_embeds.shape[0]
            if isinstance(A__	,    A__					):
                 __lowerCamelCase						:       Optional[int]				   =				torch.cat(A__	,    dim=0					)

            if do_classifier_free_guidance:
                 __lowerCamelCase						:       str				   =				image_embeds.repeat_interleave(A__	,    dim=0					)
                 __lowerCamelCase						:       Optional[int]				   =				negative_image_embeds.repeat_interleave(A__	,    dim=0					)

            __lowerCamelCase						:       Union[str, Any]				   =				torch.cat([negative_image_embeds, image_embeds]	,    dim=0					).to(dtype=self.unet.dtype	,    device=A__					)

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

            __lowerCamelCase						:       Tuple				   =				torch.cat([prepare_image(A__	,    A__	,    A__					) for i in image]	,    dim=0					)
            __lowerCamelCase						:       Tuple				   =				image.to(dtype=image_embeds.dtype	,    device=A__					)

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

                 __lowerCamelCase						:       int				   =				{"""image_embeds""": image_embeds}
                 __lowerCamelCase						:       int				   =				self.unet(
                     sample=A__	,    timestep=A__	,    encoder_hidden_states=A__	,    added_cond_kwargs=A__	,    return_dict=A__	,    )[0]

                 if do_classifier_free_guidance:
                      __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				noise_pred.split(latents.shape[1]	,    dim=1					)
                      __lowerCamelCase   ,      __lowerCamelCase						:       Any				   =				noise_pred.chunk(2					)
                      __lowerCamelCase   ,      __lowerCamelCase						:       Optional[Any]				   =				variance_pred.chunk(2					)
                      __lowerCamelCase						:       List[str]				   =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      __lowerCamelCase						:       List[str]				   =				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						:       Optional[int]				   =				noise_pred.split(latents.shape[1]	,    dim=1					)

                 # compute the previous noisy sample x_t -> x_t-1
                 __lowerCamelCase						:       Dict				   =				self.scheduler.step(
                     A__	,    A__	,    A__	,    generator=A__	,    )[0]

            # post-processing
            __lowerCamelCase						:       List[str]				   =				self.movq.decode(A__	,    force_not_quantize=A__					)["""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						:       int				   =				image * 0.5 + 0.5
                 __lowerCamelCase						:       Optional[Any]				   =				image.clamp(0	,    1					)
                 __lowerCamelCase						:       Any				   =				image.cpu().permute(0	,    2	,    3	,    1					).float().numpy()

            if output_type == "pil":
                 __lowerCamelCase						:       List[str]				   =				self.numpy_to_pil(A__					)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=A__					)











'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowerCAmelCase_  )
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     str   		=			field(default='automatic-speech-recognition'    ,   metadata={'include_in_asdict_even_if_is_default': True}  )
       snake_case__    :     ClassVar[Features]   		=			Features({'audio': Audio()}  )
       snake_case__    :     ClassVar[Features]   		=			Features({'transcription': Value('string'  )}  )
       snake_case__    :     str   		=			"audio"
       snake_case__    :     str   		=			"transcription"




       def 					a_					(					self      : Any	,    A__      : Any					):


            """simple docstring"""







            if self.audio_column not in features:
                 raise ValueError(f"Column {self.audio_column} is not present in features."					)
            if not isinstance(features[self.audio_column]	,    A__					):
                 raise ValueError(f"Column {self.audio_column} is not an Audio type."					)
            __lowerCamelCase						:       Dict				   =				copy.deepcopy(self					)
            __lowerCamelCase						:       List[Any]				   =				self.input_schema.copy()
            __lowerCamelCase						:       Optional[int]				   =				features[self.audio_column]
            __lowerCamelCase						:       Any				   =				input_schema
            return task_template




       @property
       def 					a_					(					self      : Any					):


            """simple docstring"""







            return {self.audio_column: "audio", self.transcription_column: "transcription"}



