


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,
)


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

SCREAMING_SNAKE_CASE__					:     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__    (		snake_case__					:							Tuple						,   snake_case__					:							Tuple						,   snake_case__					:							int=8     ):
  _UpperCAmelCase		:							Union[str, Any]							=     height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _UpperCAmelCase		:							int							=     width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





class 		_SCREAMING_SNAKE_CASE     (						A     ):


     def __init__(  self							,					A_							,					A_							,					A_							,					):
       super().__init__()

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


     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_							,					A_							,					A_    ):
       if latents is None:
         _UpperCAmelCase		:							Optional[int]							=     randn_tensor(A_							,					generator=A_							,					device=A_							,					dtype=A_    )
       else:
         if latents.shape != shape:
           raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
         _UpperCAmelCase		:							Any							=     latents.to(A_    )

       _UpperCAmelCase		:							List[str]							=     latents * scheduler.init_noise_sigma
       return latents


     def        __snake_case(  self							,					A_=0    ):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

       _UpperCAmelCase		:							int							=     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(A_							,					A_    )


     def        __snake_case(  self							,					A_=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."""    )

       _UpperCAmelCase		:							List[Any]							=     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		:							Tuple							=     None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _UpperCAmelCase,_UpperCAmelCase		:							Dict							=     cpu_offload_with_hook(A_							,					A_							,					prev_module_hook=A_    )

       # We'll offload the last model manually.
       _UpperCAmelCase		:							Any							=     hook


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def        __snake_case(  self    ):
       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_ = 5_12							,					A_ = 5_12							,					A_ = 1_00							,					A_ = 4.0							,					A_ = 1							,					A_ = None							,					A_ = None							,					A_ = "pil"							,					A_ = True							,					):
       _UpperCAmelCase		:							Dict							=     self._execution_device

       _UpperCAmelCase		:							List[str]							=     guidance_scale > 1.0

       if isinstance(A_							,					A_    ):
         _UpperCAmelCase		:							List[str]							=     torch.cat(A_							,					dim=0    )
       if isinstance(A_							,					A_    ):
         _UpperCAmelCase		:							List[Any]							=     torch.cat(A_							,					dim=0    )
       if isinstance(A_							,					A_    ):
         _UpperCAmelCase		:							Optional[Any]							=     torch.cat(A_							,					dim=0    )

       _UpperCAmelCase		:							List[Any]							=     image_embeds.shape[0] * num_images_per_prompt

       if do_classifier_free_guidance:
         _UpperCAmelCase		:							int							=     image_embeds.repeat_interleave(A_							,					dim=0    )
         _UpperCAmelCase		:							Optional[int]							=     negative_image_embeds.repeat_interleave(A_							,					dim=0    )
         _UpperCAmelCase		:							List[Any]							=     hint.repeat_interleave(A_							,					dim=0    )

       _UpperCAmelCase		:							str							=     torch.cat([negative_image_embeds, image_embeds]							,					dim=0    ).to(dtype=self.unet.dtype							,					device=A_    )
       _UpperCAmelCase		:							Optional[Any]							=     torch.cat([hint, hint]							,					dim=0    ).to(dtype=self.unet.dtype							,					device=A_    )
       self.scheduler.set_timesteps(A_							,					device=A_    )
       _UpperCAmelCase		:							Tuple							=     self.scheduler.timesteps

       _UpperCAmelCase		:							str							=     self.movq.config.latent_channels

       _UpperCAmelCase,_UpperCAmelCase		:							Tuple							=     downscale_height_and_width(A_							,					A_							,					self.movq_scale_factor    )

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

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

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

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

       if output_type == "pil":
         _UpperCAmelCase		:							List[Any]							=     self.numpy_to_pil(A_    )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=A_    )









import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''conditional_detr'''
     __SCREAMING_SNAKE_CASE     					=    ['''past_key_values''']
     __SCREAMING_SNAKE_CASE     					=    {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(  self							,					A_=True							,					A_=None							,					A_=3							,					A_=3_00							,					A_=6							,					A_=20_48							,					A_=8							,					A_=6							,					A_=20_48							,					A_=8							,					A_=0.0							,					A_=0.0							,					A_=True							,					A_="relu"							,					A_=2_56							,					A_=0.1							,					A_=0.0							,					A_=0.0							,					A_=0.0_2							,					A_=1.0							,					A_=False							,					A_="sine"							,					A_="resnet50"							,					A_=True							,					A_=False							,					A_=2							,					A_=5							,					A_=2							,					A_=1							,					A_=1							,					A_=2							,					A_=5							,					A_=2							,					A_=0.2_5							,					**A_							,					):
       if backbone_config is not None and use_timm_backbone:
         raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""    )

       if not use_timm_backbone:
         if backbone_config is None:
           logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
           _UpperCAmelCase		:							int							=     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
         elif isinstance(A_							,					A_    ):
           _UpperCAmelCase		:							Tuple							=     backbone_config.get("""model_type"""    )
           _UpperCAmelCase		:							Union[str, Any]							=     CONFIG_MAPPING[backbone_model_type]
           _UpperCAmelCase		:							Optional[Any]							=     config_class.from_dict(A_    )

       _UpperCAmelCase		:							Tuple							=     use_timm_backbone
       _UpperCAmelCase		:							List[str]							=     backbone_config
       _UpperCAmelCase		:							Any							=     num_channels
       _UpperCAmelCase		:							Tuple							=     num_queries
       _UpperCAmelCase		:							Dict							=     d_model
       _UpperCAmelCase		:							Dict							=     encoder_ffn_dim
       _UpperCAmelCase		:							Tuple							=     encoder_layers
       _UpperCAmelCase		:							List[Any]							=     encoder_attention_heads
       _UpperCAmelCase		:							Union[str, Any]							=     decoder_ffn_dim
       _UpperCAmelCase		:							Optional[int]							=     decoder_layers
       _UpperCAmelCase		:							List[str]							=     decoder_attention_heads
       _UpperCAmelCase		:							Optional[Any]							=     dropout
       _UpperCAmelCase		:							str							=     attention_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     activation_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     activation_function
       _UpperCAmelCase		:							int							=     init_std
       _UpperCAmelCase		:							Union[str, Any]							=     init_xavier_std
       _UpperCAmelCase		:							Tuple							=     encoder_layerdrop
       _UpperCAmelCase		:							Tuple							=     decoder_layerdrop
       _UpperCAmelCase		:							Tuple							=     encoder_layers
       _UpperCAmelCase		:							Any							=     auxiliary_loss
       _UpperCAmelCase		:							List[str]							=     position_embedding_type
       _UpperCAmelCase		:							int							=     backbone
       _UpperCAmelCase		:							Any							=     use_pretrained_backbone
       _UpperCAmelCase		:							int							=     dilation
       # Hungarian matcher
       _UpperCAmelCase		:							Dict							=     class_cost
       _UpperCAmelCase		:							Optional[Any]							=     bbox_cost
       _UpperCAmelCase		:							Tuple							=     giou_cost
       # Loss coefficients
       _UpperCAmelCase		:							Union[str, Any]							=     mask_loss_coefficient
       _UpperCAmelCase		:							int							=     dice_loss_coefficient
       _UpperCAmelCase		:							List[Any]							=     cls_loss_coefficient
       _UpperCAmelCase		:							List[Any]							=     bbox_loss_coefficient
       _UpperCAmelCase		:							int							=     giou_loss_coefficient
       _UpperCAmelCase		:							Optional[int]							=     focal_alpha
       super().__init__(is_encoder_decoder=A_							,					**A_    )


     @property
     def        __snake_case(  self    ):
       return self.encoder_attention_heads


     @property
     def        __snake_case(  self    ):
       return self.d_model




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     copy.deepcopy(self.__dict__    )
       if self.backbone_config is not None:
         _UpperCAmelCase		:							Optional[int]							=     self.backbone_config.to_dict()
       _UpperCAmelCase		:							Dict							=     self.__class__.model_type
       return output


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    version.parse('''1.11'''     )


     @property
     def        __snake_case(  self    ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]    )


     @property
     def        __snake_case(  self    ):
       return 1e-5




     @property
     def        __snake_case(  self    ):
       return 12





