





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


__UpperCAmelCase       :   Optional[int]				       =							{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     __snake_case       (							nn.Module			):





       '''simple docstring'''







       def __init__(  self :    Dict							,				A :    int		):
         super().__init__()
         __snake_case:						List[str]   =						torchvision.models.resnetaaa(pretrained=A		)
         __snake_case:						Optional[Any]   =						list(model.children()		)[:-2]
         __snake_case:						Optional[int]   =						nn.Sequential(*A		)
         __snake_case:						Any   =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)






       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    Any		):
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         __snake_case:						Optional[Any]   =						self.pool(self.model(A		)		)
         __snake_case:						Dict   =						torch.flatten(A							,				start_dim=2		)
         __snake_case:						List[Any]   =						out.transpose(1							,				2		).contiguous()
         return out  # BxNx2048







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Optional[Any]							,				A :    Optional[Any]							,				A :    Optional[Any]							,				A :    List[Any]							,				A :    str							,				A :    Union[str, Any]		):
         __snake_case:						Dict   =						[json.loads(A		) for l in open(A		)]
         __snake_case:						int   =						os.path.dirname(A		)
         __snake_case:						List[Any]   =						tokenizer
         __snake_case:						List[Any]   =						labels
         __snake_case:						Union[str, Any]   =						len(A		)
         __snake_case:						Any   =						max_seq_length

         __snake_case:						Any   =						transforms




       def __len__(  self :    Dict		):
         return len(self.data		)




       def __getitem__(  self :    Optional[int]							,				A :    List[str]		):
         __snake_case:						Optional[int]   =						torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]							,				add_special_tokens=A		)		)
         __snake_case  ,   __snake_case  ,   __snake_case:						Union[str, Any]   =						sentence[0], sentence[1:-1], sentence[-1]
         __snake_case:						Dict   =						sentence[: self.max_seq_length]

         __snake_case:						Any   =						torch.zeros(self.n_classes		)
         __snake_case:						Optional[int]   =						1

         __snake_case:						Optional[Any]   =						Image.open(os.path.join(self.data_dir							,				self.data[index]["""img"""]		)		).convert("""RGB"""		)
         __snake_case:						Tuple   =						self.transforms(A		)

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






       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						int   =						Counter()
         for row in self.data:
           label_freqs.update(row["""label"""]		)
         return label_freqs







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Union[str, Any]:
  __snake_case:						str   =						[len(row["""sentence"""]) for row in batch]
  __snake_case  ,   __snake_case:						Tuple   =						len(SCREAMING_SNAKE_CASE__), max(SCREAMING_SNAKE_CASE__)

  __snake_case:						List[Any]   =						torch.zeros(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					dtype=torch.long)
  __snake_case:						Optional[Any]   =						torch.zeros(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					dtype=torch.long)

  for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)):
    __snake_case:						Optional[int]   =						input_row["""sentence"""]
    __snake_case:						Optional[Any]   =						1

  __snake_case:						List[Any]   =						torch.stack([row["""image"""] for row in batch])
  __snake_case:						List[str]   =						torch.stack([row["""label"""] for row in batch])
  __snake_case:						Union[str, Any]   =						torch.stack([row["""image_start_token"""] for row in batch])
  __snake_case:						int   =						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 			A__							(    )   ->    Optional[Any]:
  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 			A__							(    )   ->    List[Any]:
  return transforms.Compose(
      [
          transforms.Resize(256),
          transforms.CenterCrop(224),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]					,					std=[0.12_221_994, 0.12_145_835, 0.14_380_469]					,					),
      ])













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, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__UpperCAmelCase       :   Any				       =							logging.get_logger(__name__)


__UpperCAmelCase       :   str				       =							Dict[str, Any]
__UpperCAmelCase       :   int				       =							List[Prediction]







@add_end_docstrings(__lowerCamelCase			)
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    int							,				*A :    Optional[int]							,				**A :    Optional[int]		):
         super().__init__(*A							,				**A		)

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

         requires_backends(self							,				"""vision"""		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				**A :    Tuple		):
         __snake_case:						List[str]   =						{}
         if "threshold" in kwargs:
           __snake_case:						Optional[Any]   =						kwargs["""threshold"""]
         return {}, {}, postprocess_kwargs




       def __call__(  self :    int							,				*A :    Optional[Any]							,				**A :    Tuple		):
         return super().__call__(*A							,				**A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    str		):
         __snake_case:						Optional[Any]   =						load_image(A		)
         __snake_case:						Dict   =						torch.IntTensor([[image.height, image.width]]		)
         __snake_case:						str   =						self.image_processor(images=[image]							,				return_tensors="""pt"""		)
         if self.tokenizer is not None:
           __snake_case:						Optional[Any]   =						self.tokenizer(text=inputs["""words"""]							,				boxes=inputs["""boxes"""]							,				return_tensors="""pt"""		)
         __snake_case:						Any   =						target_size
         return inputs




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    Dict		):
         __snake_case:						int   =						model_inputs.pop("""target_size"""		)
         __snake_case:						int   =						self.model(**A		)
         __snake_case:						Any   =						outputs.__class__({"""target_size""": target_size, **outputs}		)
         if self.tokenizer is not None:
           __snake_case:						Optional[int]   =						model_inputs["""bbox"""]
         return model_outputs




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Optional[int]							,				A :    Union[str, Any]=0.9		):
         __snake_case:						Optional[Any]   =						model_outputs["""target_size"""]
         if self.tokenizer is not None:
           # This is a LayoutLMForTokenClassification variant.
           # The OCR got the boxes and the model classified the words.
           __snake_case  ,   __snake_case:						Union[str, Any]   =						target_size[0].tolist()

           def unnormalize(A :    Tuple		):
             return self._get_bounding_box(
                 torch.Tensor(
                     [
                         (width * bbox[0] / 1_000),
                         (height * bbox[1] / 1_000),
                         (width * bbox[2] / 1_000),
                         (height * bbox[3] / 1_000),
                     ]		)		)

           __snake_case  ,   __snake_case:						Optional[int]   =						model_outputs["""logits"""].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
           __snake_case:						List[Any]   =						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
           __snake_case:						int   =						[unnormalize(A		) for bbox in model_outputs["""bbox"""].squeeze(0		)]
           __snake_case:						int   =						["""score""", """label""", """box"""]
           __snake_case:						List[Any]   =						[dict(zip(A							,				A		)		) for vals in zip(scores.tolist()							,				A							,				A		) if vals[0] > threshold]
         else:
           # This is a regular ForObjectDetectionModel
           __snake_case:						Tuple   =						self.image_processor.post_process_object_detection(A							,				A							,				A		)
           __snake_case:						Optional[Any]   =						raw_annotations[0]
           __snake_case:						int   =						raw_annotation["""scores"""]
           __snake_case:						int   =						raw_annotation["""labels"""]
           __snake_case:						Optional[Any]   =						raw_annotation["""boxes"""]

           __snake_case:						Union[str, Any]   =						scores.tolist()
           __snake_case:						List[str]   =						[self.model.config.idalabel[label.item()] for label in labels]
           __snake_case:						List[str]   =						[self._get_bounding_box(A		) for box in boxes]

           # {"scores": [...], ...} --> [{"score":x, ...}, ...]
           __snake_case:						List[Any]   =						["""score""", """label""", """box"""]
           __snake_case:						Dict   =						[
               dict(zip(A							,				A		)		)
               for vals in zip(raw_annotation["""scores"""]							,				raw_annotation["""labels"""]							,				raw_annotation["""boxes"""]		)
           ]

         return annotation






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    "torch.Tensor"		):
         if self.framework != "pt":
           raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""		)
         __snake_case  ,   __snake_case  ,   __snake_case  ,   __snake_case:						Union[str, Any]   =						box.int().tolist()
         __snake_case:						Optional[Any]   =						{
             """xmin""": xmin,
             """ymin""": ymin,
             """xmax""": xmax,
             """ymax""": ymax,
         }
         return bbox






