




'''simple docstring'''



from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase_			=						logging.get_logger(__name__)







@add_end_docstrings(A       )
class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     int  ,				**__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      if self.framework == "tf":
         raise ValueError(F"""The {self.__class__} is only available in PyTorch."""   )

      requires_backends(self  ,				"vision"   )
      self.check_model_type(__lowerCamelCase   )



   def __call__(   self :     List[str]  ,				__lowerCamelCase :     Union[str, "Image.Image", List[Dict[str, Any]]]  ,				__lowerCamelCase :     Union[str, List[str]] = None  ,				**__lowerCamelCase :     List[str]  ,				):

      """simple docstring"""
      if "text_queries" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs.pop("text_queries"   )

      if isinstance(__lowerCamelCase  ,				(str, Image.Image)   ):
         _SCREAMING_SNAKE_CASE	=				{"image": image, "candidate_labels": candidate_labels}
      else:
         _SCREAMING_SNAKE_CASE	=				image
      _SCREAMING_SNAKE_CASE	=				super().__call__(__lowerCamelCase  ,				**__lowerCamelCase   )
      return results



   def 							lowerCAmelCase_      (   self :     Any  ,				**__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{}
      if "threshold" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["threshold"]
      if "top_k" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["top_k"]
      return {}, {}, postprocess_params



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				load_image(inputs["image"]   )
      _SCREAMING_SNAKE_CASE	=				inputs["candidate_labels"]
      if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				candidate_labels.split(","   )

      _SCREAMING_SNAKE_CASE	=				torch.tensor([[image.height, image.width]]  ,				dtype=torch.intaa   )
      for i, candidate_label in enumerate(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				self.tokenizer(__lowerCamelCase  ,				return_tensors=self.framework   )
         _SCREAMING_SNAKE_CASE	=				self.image_processor(__lowerCamelCase  ,				return_tensors=self.framework   )
         yield {
             "is_last": i == len(__lowerCamelCase   ) - 1,
             "target_size": target_size,
             "candidate_label": candidate_label,
             **text_inputs,
             **image_features,
         }



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("target_size"   )
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("candidate_label"   )
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("is_last"   )

      _SCREAMING_SNAKE_CASE	=				self.model(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
      return model_outputs



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Tuple=0.1  ,				__lowerCamelCase :     Tuple=None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[]
      for model_output in model_outputs:
         _SCREAMING_SNAKE_CASE	=				model_output["candidate_label"]
         _SCREAMING_SNAKE_CASE	=				BaseModelOutput(__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				self.image_processor.post_process_object_detection(
             outputs=__lowerCamelCase  ,				threshold=__lowerCamelCase  ,				target_sizes=model_output["target_size"]   )[0]

         for index in outputs["scores"].nonzero():
            _SCREAMING_SNAKE_CASE	=				outputs["scores"][index].item()
            _SCREAMING_SNAKE_CASE	=				self._get_bounding_box(outputs["boxes"][index][0]   )

            _SCREAMING_SNAKE_CASE	=				{"score": score, "label": label, "box": box}
            results.append(__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				sorted(__lowerCamelCase  ,				key=lambda __lowerCamelCase   : x["score"]  ,				reverse=__lowerCamelCase   )
      if top_k:
         _SCREAMING_SNAKE_CASE	=				results[:top_k]

      return results



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     "torch.Tensor"   ):

      """simple docstring"""
      if self.framework != "pt":
         raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."   )
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				box.int().tolist()
      _SCREAMING_SNAKE_CASE	=				{
          "xmin": xmin,
          "ymin": ymin,
          "xmax": xmax,
          "ymax": ymax,
      }
      return bbox







'''simple docstring'''



import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowerCamelCase_			=						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}







class 		lowercase_   (			nn.Module       ):


   """simple docstring"""

   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int   ):

      """simple docstring"""
      super().__init__()
      _SCREAMING_SNAKE_CASE	=				torchvision.models.resnetaaa(pretrained=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(model.children()   )[:-2]
      _SCREAMING_SNAKE_CASE	=				nn.Sequential(*__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      _SCREAMING_SNAKE_CASE	=				self.pool(self.model(__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.flatten(__lowerCamelCase  ,				start_dim=2   )
      _SCREAMING_SNAKE_CASE	=				out.transpose(1  ,				2   ).contiguous()
      return out  # BxNx2048







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[json.loads(__lowerCamelCase   ) for l in open(__lowerCamelCase   )]
      _SCREAMING_SNAKE_CASE	=				os.path.dirname(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer
      _SCREAMING_SNAKE_CASE	=				labels
      _SCREAMING_SNAKE_CASE	=				len(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				max_seq_length

      _SCREAMING_SNAKE_CASE	=				transforms



   def __len__(   self :     int   ):

      """simple docstring"""
      return len(self.data   )



   def __getitem__(   self :     str  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,				add_special_tokens=__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				sentence[0], sentence[1:-1], sentence[-1]
      _SCREAMING_SNAKE_CASE	=				sentence[: self.max_seq_length]

      _SCREAMING_SNAKE_CASE	=				torch.zeros(self.n_classes   )
      _SCREAMING_SNAKE_CASE	=				1

      _SCREAMING_SNAKE_CASE	=				Image.open(os.path.join(self.data_dir  ,				self.data[index]["img"]   )   ).convert("RGB"   )
      _SCREAMING_SNAKE_CASE	=				self.transforms(__lowerCamelCase   )

      return {
          "image_start_token": start_token,
          "image_end_token": end_token,
          "sentence": sentence,
          "image": image,
          "label": label,
      }



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				Counter()
      for row in self.data:
         label_freqs.update(row["label"]   )
      return label_freqs


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				[len(row["sentence"]  ) for row in batch]
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				len(__A  ), max(__A  )

   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )
   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )

   for i_batch, (input_row, length) in enumerate(zip(__A	,					__A  )  ):
      _SCREAMING_SNAKE_CASE	=				input_row["sentence"]
      _SCREAMING_SNAKE_CASE	=				1

   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["label"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image_start_token"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image_end_token"] for row in batch]  )

   return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   return [
       "Crime",
       "Drama",
       "Thriller",
       "Action",
       "Comedy",
       "Romance",
       "Documentary",
       "Short",
       "Mystery",
       "History",
       "Family",
       "Adventure",
       "Fantasy",
       "Sci-Fi",
       "Western",
       "Horror",
       "Sport",
       "War",
       "Music",
       "Musical",
       "Animation",
       "Biography",
       "Film-Noir",
   ]






def 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   return transforms.Compose(
       [
           transforms.Resize(2_56  ),
           transforms.CenterCrop(2_24  ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]	,					std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]	,					),
       ]  )

