





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        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'






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




   '''simple docstring'''

   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 				_UpperCAmelCase			(    A				,							A=512				,							A=512     ):




   '''simple docstring'''

   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(A				,							[2, 0, 1]     )
   UpperCAmelCase__						=torch.from_numpy(A     ).unsqueeze(0     )
   return image




class     snake_case_		(   a							):






 '''simple docstring'''





 def __init__(  self,		A_,		A_,		A_,		)	->       int:
    super().__init__()

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

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       int:
    # get the original timestep using init_timestep
    UpperCAmelCase__						=min(int(num_inference_steps * strength		),		A_		)

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

    return timesteps, num_inference_steps - t_start

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_,		A_,		A_=None		)	->       Union[str, Any]:
    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_		)}"""		)

    UpperCAmelCase__						=image.to(device=A_,		dtype=A_		)

    UpperCAmelCase__						=batch_size * num_images_per_prompt

    if image.shape[1] == 4:
       UpperCAmelCase__						=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_		):
          UpperCAmelCase__						=[
              self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(A_		)
          ]
          UpperCAmelCase__						=torch.cat(A_,		dim=0		)
       else:
          UpperCAmelCase__						=self.movq.encode(A_		).latent_dist.sample(A_		)

       UpperCAmelCase__						=self.movq.config.scaling_factor * init_latents

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

    UpperCAmelCase__						=init_latents.shape
    UpperCAmelCase__						=randn_tensor(A_,		generator=A_,		device=A_,		dtype=A_		)

    # get latents
    UpperCAmelCase__						=self.scheduler.add_noise(A_,		A_,		A_		)

    UpperCAmelCase__						=init_latents

    return latents

 def  __UpperCAmelCase						(  self,		A_=0		)	->       str:
    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(A_,		A_		)

 def  __UpperCAmelCase						(  self,		A_=0		)	->       Dict:
    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=A_		)
       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(A_,		A_,		prev_module_hook=A_		)

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

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    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,		A_,		A_,		A_,		A_ = 512,		A_ = 512,		A_ = 100,		A_ = 4.0,		A_ = 0.3,		A_ = 1,		A_ = None,		A_ = "pil",		A_ = True,		)	->       Optional[Any]:
    UpperCAmelCase__						=self._execution_device

    UpperCAmelCase__						=guidance_scale > 1.0

    if isinstance(A_,		A_		):
       UpperCAmelCase__						=torch.cat(A_,		dim=0		)
    UpperCAmelCase__						=image_embeds.shape[0]
    if isinstance(A_,		A_		):
       UpperCAmelCase__						=torch.cat(A_,		dim=0		)

    if do_classifier_free_guidance:
       UpperCAmelCase__						=image_embeds.repeat_interleave(A_,		dim=0		)
       UpperCAmelCase__						=negative_image_embeds.repeat_interleave(A_,		dim=0		)

    UpperCAmelCase__						=torch.cat([negative_image_embeds, image_embeds],		dim=0		).to(dtype=self.unet.dtype,		device=A_		)

    if not isinstance(A_,		A_		):
       UpperCAmelCase__						=[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"""		)

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

    UpperCAmelCase__						=self.movq.encode(A_		)["latents"]
    UpperCAmelCase__						=latents.repeat_interleave(A_,		dim=0		)
    self.scheduler.set_timesteps(A_,		device=A_		)
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_timesteps(A_,		A_,		A_		)
    UpperCAmelCase__						=timesteps[:1].repeat(batch_size * num_images_per_prompt		)
    UpperCAmelCase__			,							UpperCAmelCase__						=downscale_height_and_width(A_,		A_,		self.movq_scale_factor		)
    UpperCAmelCase__						=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
       UpperCAmelCase__						=torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

       UpperCAmelCase__						={"image_embeds": image_embeds}
       UpperCAmelCase__						=self.unet(
           sample=A_,		timestep=A_,		encoder_hidden_states=A_,		added_cond_kwargs=A_,		return_dict=A_,		)[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(
           A_,		A_,		A_,		generator=A_,		)[0]

    # post-processing
    UpperCAmelCase__						=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"]:
       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(A_		)

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=A_		)










import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   return 1.0 / (1.0 + np.exp(-_outputs     ))






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=np.max(_outputs				,							axis=-1				,							keepdims=A     )
   UpperCAmelCase__						=np.exp(_outputs - maxes     )
   return shifted_exp / shifted_exp.sum(axis=-1				,							keepdims=A     )

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'sigmoid'
 __UpperCamelCase       =      'softmax'
 __UpperCamelCase       =      'none'

@add_end_docstrings(
    a,		R'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    ',		)
class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      False
 __UpperCamelCase       =      ClassificationFunction.NONE

 def __init__(  self,		**A_		)	->       Optional[int]:
    super().__init__(**A_		)

    self.check_model_type(
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
        if self.framework == "tf"
        else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING		)

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_="",		**A_		)	->       Optional[int]:
    # Using "" as default argument because we're going to use `top_k=None` in user code to declare
    # "No top_k"
    UpperCAmelCase__						=tokenizer_kwargs

    UpperCAmelCase__						={}
    if hasattr(self.model.config,		"return_all_scores"		) and return_all_scores is None:
       UpperCAmelCase__						=self.model.config.return_all_scores

    if isinstance(A_,		A_		) or top_k is None:
       UpperCAmelCase__						=top_k
       UpperCAmelCase__						=False
    elif return_all_scores is not None:
       warnings.warn(
           "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
           " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.",		A_,		)
       if return_all_scores:
          UpperCAmelCase__						=None
       else:
          UpperCAmelCase__						=1

    if isinstance(A_,		A_		):
       UpperCAmelCase__						=ClassificationFunction[function_to_apply.upper()]

    if function_to_apply is not None:
       UpperCAmelCase__						=function_to_apply
    return preprocess_params, {}, postprocess_params

 def __call__(  self,		*A_,		**A_		)	->       str:
    UpperCAmelCase__						=super().__call__(*A_,		**A_		)
    # TODO try and retrieve it in a nicer way from _sanitize_parameters.
    UpperCAmelCase__						="top_k" not in kwargs
    if isinstance(args[0],		A_		) and _legacy:
       # This pipeline is odd, and return a list when single item is run
       return [result]
    else:
       return result

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       Dict[str, GenericTensor]:
    UpperCAmelCase__						=self.framework
    if isinstance(A_,		A_		):
       return self.tokenizer(**A_,		return_tensors=A_,		**A_		)
    elif isinstance(A_,		A_		) and len(A_		) == 1 and isinstance(inputs[0],		A_		) and len(inputs[0]		) == 2:
       # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
       return self.tokenizer(
           text=inputs[0][0],		text_pair=inputs[0][1],		return_tensors=A_,		**A_		)
    elif isinstance(A_,		A_		):
       # This is likely an invalid usage of the pipeline attempting to pass text pairs.
       raise ValueError(
           "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
           " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."		)
    return self.tokenizer(A_,		return_tensors=A_,		**A_		)

 def  __UpperCAmelCase						(  self,		A_		)	->       Dict:
    return self.model(**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_=None,		A_=1,		A_=True		)	->       Any:
    # `_legacy` is used to determine if we're running the naked pipeline and in backward
    # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
    # the more natural result containing the list.
    # Default value before `set_parameters`
    if function_to_apply is None:
       if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
          UpperCAmelCase__						=ClassificationFunction.SIGMOID
       elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
          UpperCAmelCase__						=ClassificationFunction.SOFTMAX
       elif hasattr(self.model.config,		"function_to_apply"		) and function_to_apply is None:
          UpperCAmelCase__						=self.model.config.function_to_apply
       else:
          UpperCAmelCase__						=ClassificationFunction.NONE

    UpperCAmelCase__						=model_outputs["logits"][0]
    UpperCAmelCase__						=outputs.numpy()

    if function_to_apply == ClassificationFunction.SIGMOID:
       UpperCAmelCase__						=sigmoid(A_		)
    elif function_to_apply == ClassificationFunction.SOFTMAX:
       UpperCAmelCase__						=softmax(A_		)
    elif function_to_apply == ClassificationFunction.NONE:
       UpperCAmelCase__						=outputs
    else:
       raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}"""		)

    if top_k == 1 and _legacy:
       return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

    UpperCAmelCase__						=[
        {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(A_		)
    ]
    if not _legacy:
       dict_scores.sort(key=lambda A_		: x["score"],		reverse=A_		)
       if top_k is not None:
          UpperCAmelCase__						=dict_scores[:top_k]
    return dict_scores



