




import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType

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


lowerCamelCase	:	Union[str, Any]				=   logging.get_logger(__name__)

lowerCamelCase	:	Union[str, Any]				=   {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}




class 						__lowercase		(__lowercase							):




      """simple docstring"""





      _snake_case												=						"owlvit_text_model"

      def __init__(				self    ,				A=4_9_4_0_8    ,				A=5_1_2    ,				A=2_0_4_8    ,				A=1_2    ,				A=8    ,				A=1_6    ,				A="quick_gelu"    ,				A=1e-5    ,				A=0.0    ,				A=0.02    ,				A=1.0    ,				A=0    ,				A=4_9_4_0_6    ,				A=4_9_4_0_7    ,				**A    ,				)		->    Dict:
         super().__init__(pad_token_id=_a    ,				bos_token_id=_a    ,				eos_token_id=_a    ,				**_a     )

         snake_case				:    int           =	vocab_size
         snake_case				:    Any           =	hidden_size
         snake_case				:    Optional[Any]           =	intermediate_size
         snake_case				:    Optional[Any]           =	num_hidden_layers
         snake_case				:    Optional[int]           =	num_attention_heads
         snake_case				:    Tuple           =	max_position_embeddings
         snake_case				:    Any           =	hidden_act
         snake_case				:    str           =	layer_norm_eps
         snake_case				:    int           =	attention_dropout
         snake_case				:    Tuple           =	initializer_range
         snake_case				:    Optional[Any]           =	initializer_factor

      @classmethod
      def      UpperCAmelCase				(				cls    ,				A    ,				**A     )		->    "PretrainedConfig":
         cls._set_token_in_kwargs(_a     )

         snake_case				:    Tuple           =	cls.get_config_dict(_a    ,				**_a     )

         # get the text config dict if we are loading from OwlViTConfig
         if config_dict.get("""model_type"""     ) == "owlvit":
            snake_case				:    List[Any]           =	config_dict['''text_config''']

         if "model_type" in config_dict and hasattr(cls    ,				"""model_type"""     ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

         return cls.from_dict(_a    ,				**_a     )




class 						__lowercase		(__lowercase							):




      """simple docstring"""





      _snake_case												=						"owlvit_vision_model"

      def __init__(				self    ,				A=7_6_8    ,				A=3_0_7_2    ,				A=1_2    ,				A=1_2    ,				A=3    ,				A=7_6_8    ,				A=3_2    ,				A="quick_gelu"    ,				A=1e-5    ,				A=0.0    ,				A=0.02    ,				A=1.0    ,				**A    ,				)		->    str:
         super().__init__(**_a     )

         snake_case				:    Optional[int]           =	hidden_size
         snake_case				:    Union[str, Any]           =	intermediate_size
         snake_case				:    Dict           =	num_hidden_layers
         snake_case				:    Optional[Any]           =	num_attention_heads
         snake_case				:    str           =	num_channels
         snake_case				:    Optional[int]           =	image_size
         snake_case				:    Optional[Any]           =	patch_size
         snake_case				:    Optional[int]           =	hidden_act
         snake_case				:    Optional[int]           =	layer_norm_eps
         snake_case				:    int           =	attention_dropout
         snake_case				:    Optional[Any]           =	initializer_range
         snake_case				:    Any           =	initializer_factor

      @classmethod
      def      UpperCAmelCase				(				cls    ,				A    ,				**A     )		->    "PretrainedConfig":
         cls._set_token_in_kwargs(_a     )

         snake_case				:    Tuple           =	cls.get_config_dict(_a    ,				**_a     )

         # get the vision config dict if we are loading from OwlViTConfig
         if config_dict.get("""model_type"""     ) == "owlvit":
            snake_case				:    Optional[int]           =	config_dict['''vision_config''']

         if "model_type" in config_dict and hasattr(cls    ,				"""model_type"""     ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

         return cls.from_dict(_a    ,				**_a     )




class 						__lowercase		(__lowercase							):




      """simple docstring"""





      _snake_case												=						"owlvit"
      _snake_case												=						True

      def __init__(				self    ,				A=None    ,				A=None    ,				A=5_1_2    ,				A=2.65_92    ,				A=True    ,				**A    ,				)		->    Tuple:
         super().__init__(**_a     )

         if text_config is None:
            snake_case				:    str           =	{}
            logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values."""     )

         if vision_config is None:
            snake_case				:    List[Any]           =	{}
            logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values."""     )

         snake_case				:    int           =	OwlViTTextConfig(**_a     )
         snake_case				:    Union[str, Any]           =	OwlViTVisionConfig(**_a     )

         snake_case				:    List[Any]           =	projection_dim
         snake_case				:    Optional[Any]           =	logit_scale_init_value
         snake_case				:    List[str]           =	return_dict
         snake_case				:    Union[str, Any]           =	1.0

      @classmethod
      def      UpperCAmelCase				(				cls    ,				A    ,				**A     )		->    "PretrainedConfig":
         cls._set_token_in_kwargs(_a     )

         snake_case				:    Optional[Any]           =	cls.get_config_dict(_a    ,				**_a     )

         if "model_type" in config_dict and hasattr(cls    ,				"""model_type"""     ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

         return cls.from_dict(_a    ,				**_a     )

      @classmethod
      def      UpperCAmelCase				(				cls    ,				A    ,				A    ,				**A     )		->    Union[str, Any]:
         snake_case				:    Dict           =	{}
         snake_case				:    Tuple           =	text_config
         snake_case				:    Optional[Any]           =	vision_config

         return cls.from_dict(_a    ,				**_a     )

      def      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case				:    Tuple           =	copy.deepcopy(self.__dict__     )
         snake_case				:    Tuple           =	self.text_config.to_dict()
         snake_case				:    Any           =	self.vision_config.to_dict()
         snake_case				:    Tuple           =	self.__class__.model_type
         return output




class 						__lowercase		(__lowercase							):




      """simple docstring"""





      @property
      def      UpperCAmelCase				(				self     )		->    Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                 ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
             ]     )

      @property
      def      UpperCAmelCase				(				self     )		->    Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("""logits_per_image""", {0: """batch"""}),
                 ("""logits_per_text""", {0: """batch"""}),
                 ("""text_embeds""", {0: """batch"""}),
                 ("""image_embeds""", {0: """batch"""}),
             ]     )

      @property
      def      UpperCAmelCase				(				self     )		->    float:
         return 1e-4

      def      UpperCAmelCase				(				self    ,				A    ,				A = -1    ,				A = -1    ,				A = None    ,				)		->    Mapping[str, Any]:
         snake_case				:    int           =	super().generate_dummy_inputs(
             processor.tokenizer    ,				batch_size=_a    ,				seq_length=_a    ,				framework=_a     )
         snake_case				:    int           =	super().generate_dummy_inputs(
             processor.image_processor    ,				batch_size=_a    ,				framework=_a     )
         return {**text_input_dict, **image_input_dict}

      @property
      def      UpperCAmelCase				(				self     )		->    int:
         return 1_4











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,
)


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

lowerCamelCase	:	Dict				=   '\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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=8					)  ->       Optional[int]:
   snake_case				:    Dict           =	height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   snake_case				:    int           =	width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





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

         self.register_modules(
             unet=A    ,				scheduler=A    ,				movq=A    ,				)
         snake_case				:    Tuple           =	2 ** (len(self.movq.config.block_out_channels     ) - 1)

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A     )		->    str:
         if latents is None:
            snake_case				:    List[str]           =	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}"""     )
            snake_case				:    Union[str, Any]           =	latents.to(A     )

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

      def      UpperCAmelCase				(				self    ,				A=0     )		->    List[str]:
         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

         snake_case				:    Dict           =	torch.device(f"""cuda:{gpu_id}"""     )

         snake_case				:    Tuple           =	[
             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     )		->    Optional[Any]:
         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."""     )

         snake_case				:    int           =	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)

         snake_case				:    Tuple           =	None
         for cpu_offloaded_model in [self.unet, self.movq]:
            snake_case     , snake_case				:    Tuple           =	cpu_offload_with_hook(A    ,				A    ,				prev_module_hook=A     )

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

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def      UpperCAmelCase				(				self     )		->    str:
         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_1_2    ,				A = 5_1_2    ,				A = 1_0_0    ,				A = 4.0    ,				A = 1    ,				A = None    ,				A = None    ,				A = "pil"    ,				A = True    ,				)		->    Any:
         snake_case				:    int           =	self._execution_device

         snake_case				:    Tuple           =	guidance_scale > 1.0

         if isinstance(A    ,				A     ):
            snake_case				:    str           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    int           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    Tuple           =	torch.cat(A    ,				dim=0     )

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

         if do_classifier_free_guidance:
            snake_case				:    Optional[int]           =	image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Dict           =	negative_image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Optional[Any]           =	hint.repeat_interleave(A    ,				dim=0     )

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

         snake_case				:    Tuple           =	self.movq.config.latent_channels

         snake_case     , snake_case				:    Optional[int]           =	downscale_height_and_width(A    ,				A    ,				self.movq_scale_factor     )

         # create initial latent
         snake_case				:    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
            snake_case				:    str           =	torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

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

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

         if output_type == "pil":
            snake_case				:    Tuple           =	self.numpy_to_pil(A     )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=A     )





