

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    ):





      """simple docstring"""





      super().__init__()
      self.register_modules(transformer=_UpperCamelCase      ,    vae=_UpperCamelCase      ,    scheduler=_UpperCamelCase  )

      # create a imagenet -> id dictionary for easier use
      _lowercase      :				List[Any]      					=       {}
      if idalabel is not None:
         for key, value in idalabel.items():
            for label in value.split(","  ):
               _lowercase      :				Dict      					=       int(_UpperCamelCase  )
         _lowercase      :				Optional[int]      					=       dict(sorted(self.labels.items()  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if not isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
         _lowercase      :				List[Any]      					=       list(_UpperCamelCase  )

      for l in label:
         if l not in self.labels:
            raise ValueError(
                f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''  )

      return [self.labels[l] for l in label]






   @torch.no_grad()
   def __call__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase = 4.0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 50      ,    _UpperCamelCase = "pil"      ,    _UpperCamelCase = True      ,    ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       len(_UpperCamelCase  )
      _lowercase      :				str      					=       self.transformer.config.sample_size
      _lowercase      :				List[str]      					=       self.transformer.config.in_channels

      _lowercase      :				Any      					=       randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)      ,    generator=_UpperCamelCase      ,    device=self.device      ,    dtype=self.transformer.dtype      ,    )
      _lowercase      :				Tuple      					=       torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

      _lowercase      :				List[Any]      					=       torch.tensor(_UpperCamelCase      ,    device=self.device  ).reshape(-1  )
      _lowercase      :				Any      					=       torch.tensor([1000] * batch_size      ,    device=self.device  )
      _lowercase      :				Optional[Any]      					=       torch.cat([class_labels, class_null]      ,    0  ) if guidance_scale > 1 else class_labels

      # set step values
      self.scheduler.set_timesteps(_UpperCamelCase  )

      for t in self.progress_bar(self.scheduler.timesteps  ):
         if guidance_scale > 1:
            _lowercase      :				Optional[Any]      					=       latent_model_input[: len(_UpperCamelCase  ) // 2]
            _lowercase      :				List[str]      					=       torch.cat([half, half]      ,    dim=0  )
         _lowercase      :				Tuple      					=       self.scheduler.scale_model_input(_UpperCamelCase      ,    _UpperCamelCase  )

         _lowercase      :				str      					=       t
         if not torch.is_tensor(_UpperCamelCase  ):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            _lowercase      :				Dict      					=       latent_model_input.device.type == "mps"
            if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
               _lowercase      :				List[str]      					=       torch.floataa if is_mps else torch.floataa
            else:
               _lowercase      :				Dict      					=       torch.intaa if is_mps else torch.intaa
            _lowercase      :				Any      					=       torch.tensor([timesteps]      ,    dtype=_UpperCamelCase      ,    device=latent_model_input.device  )
         elif len(timesteps.shape  ) == 0:
            _lowercase      :				Any      					=       timesteps[None].to(latent_model_input.device  )
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         _lowercase      :				int      					=       timesteps.expand(latent_model_input.shape[0]  )
         # predict noise model_output
         _lowercase      :				Optional[Any]      					=       self.transformer(
             _UpperCamelCase      ,    timestep=_UpperCamelCase      ,    class_labels=_UpperCamelCase  ).sample

         # perform guidance
         if guidance_scale > 1:
            _lowercase      :				Tuple      					=       noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
            _lowercase      :				List[str]      					=       torch.split(_UpperCamelCase      ,    len(_UpperCamelCase  ) // 2      ,    dim=0  )

            _lowercase      :				str      					=       uncond_eps + guidance_scale * (cond_eps - uncond_eps)
            _lowercase      :				Optional[Any]      					=       torch.cat([half_eps, half_eps]      ,    dim=0  )

            _lowercase      :				Optional[Any]      					=       torch.cat([eps, rest]      ,    dim=1  )

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
            _lowercase      :				Any      					=       torch.split(_UpperCamelCase      ,    _UpperCamelCase      ,    dim=1  )
         else:
            _lowercase      :				List[Any]      					=       noise_pred

         # compute previous image: x_t -> x_t-1
         _lowercase      :				List[Any]      					=       self.scheduler.step(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ).prev_sample

      if guidance_scale > 1:
         _lowercase      :				Optional[int]      					=       latent_model_input.chunk(2      ,    dim=0  )
      else:
         _lowercase      :				str      					=       latent_model_input

      _lowercase      :				Optional[int]      					=       1 / self.vae.config.scaling_factor * latents
      _lowercase      :				List[Any]      					=       self.vae.decode(_UpperCamelCase  ).sample

      _lowercase      :				Union[str, Any]      					=       (samples / 2 + 0.5).clamp(0      ,    1  )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      _lowercase      :				str      					=       samples.cpu().permute(0      ,    2      ,    3      ,    1  ).float().numpy()

      if output_type == "pil":
         _lowercase      :				Any      					=       self.numpy_to_pil(_UpperCamelCase  )

      if not return_dict:
         return (samples,)

      return ImagePipelineOutput(images=_UpperCamelCase  )









'''simple docstring'''
import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					str     	=							'OwlViTImageProcessor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							('CLIPTokenizer', 'CLIPTokenizerFast')


   def __init__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."      ,    _UpperCamelCase      ,    )
         _lowercase      :				Optional[int]      					=       kwargs.pop("feature_extractor"  )

      _lowercase      :				str      					=       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."  )
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."  )

      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )


   def __call__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase="max_length"      ,    _UpperCamelCase="np"      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if text is None and query_images is None and images is None:
         raise ValueError(
             "You have to specify at least one text or query image or image. All three cannot be none."  )

      if text is not None:
         if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) or (isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and not isinstance(text[0]      ,    _UpperCamelCase  )):
            _lowercase      :				int      					=       [self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )]

         elif isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and isinstance(text[0]      ,    _UpperCamelCase  ):
            _lowercase      :				str      					=       []

            # Maximum number of queries across batch
            _lowercase      :				str      					=       max([len(_UpperCamelCase  ) for t in text]  )

            # Pad all batch samples to max number of text queries
            for t in text:
               if len(_UpperCamelCase  ) != max_num_queries:
                  _lowercase      :				List[Any]      					=       t + [" "] * (max_num_queries - len(_UpperCamelCase  ))

               _lowercase      :				Tuple      					=       self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )
               encodings.append(_UpperCamelCase  )
         else:
            raise TypeError("Input text should be a string, a list of strings or a nested list of strings"  )

         if return_tensors == "np":
            _lowercase      :				List[Any]      					=       np.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				Optional[Any]      					=       np.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "jax" and is_flax_available():
            import jax.numpy as jnp

            _lowercase      :				Union[str, Any]      					=       jnp.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				int      					=       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "pt" and is_torch_available():
            import torch

            _lowercase      :				int      					=       torch.cat([encoding["input_ids"] for encoding in encodings]      ,    dim=0  )
            _lowercase      :				Dict      					=       torch.cat([encoding["attention_mask"] for encoding in encodings]      ,    dim=0  )

         elif return_tensors == "tf" and is_tf_available():
            import tensorflow as tf

            _lowercase      :				Optional[int]      					=       tf.stack([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				List[str]      					=       tf.stack([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         else:
            raise ValueError("Target return tensor type could not be returned"  )

         _lowercase      :				Optional[int]      					=       BatchEncoding()
         _lowercase      :				List[Any]      					=       input_ids
         _lowercase      :				Dict      					=       attention_mask

      if query_images is not None:
         _lowercase      :				int      					=       BatchEncoding()
         _lowercase      :				Any      					=       self.image_processor(
             _UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  ).pixel_values
         _lowercase      :				Any      					=       query_pixel_values

      if images is not None:
         _lowercase      :				str      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

      if text is not None and images is not None:
         _lowercase      :				List[Any]      					=       image_features.pixel_values
         return encoding
      elif query_images is not None and images is not None:
         _lowercase      :				Optional[Any]      					=       image_features.pixel_values
         return encoding
      elif text is not None or query_images is not None:
         return encoding
      else:
         return BatchEncoding(data=dict(**_UpperCamelCase  )      ,    tensor_type=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_object_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor_class






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor






