




'''simple docstring'''



from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowerCamelCase_			=						logging.get_logger(__name__)







@add_end_docstrings(A       )
class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     int  ,				**__lowerCamelCase :     Dict   ):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )
      requires_backends(self  ,				"vision"   )
      requires_backends(self  ,				"torch"   )

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

      self.check_model_type(__lowerCamelCase   )



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{}
      _SCREAMING_SNAKE_CASE	=				{}
      _SCREAMING_SNAKE_CASE	=				{}
      # preprocess args
      if "points_per_batch" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["points_per_batch"]
      if "points_per_crop" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["points_per_crop"]
      if "crops_n_layers" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["crops_n_layers"]
      if "crop_overlap_ratio" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["crop_overlap_ratio"]
      if "crop_n_points_downscale_factor" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["crop_n_points_downscale_factor"]
      # postprocess args
      if "pred_iou_thresh" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["pred_iou_thresh"]
      if "stability_score_offset" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["stability_score_offset"]
      if "mask_threshold" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["mask_threshold"]
      if "stability_score_thresh" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["stability_score_thresh"]
      if "crops_nms_thresh" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["crops_nms_thresh"]
      if "output_rle_mask" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["output_rle_mask"]
      if "output_bboxes_mask" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["output_bboxes_mask"]
      return preprocess_kwargs, forward_params, postprocess_kwargs



   def __call__(   self :     Optional[Any]  ,				__lowerCamelCase :     List[Any]  ,				*__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Optional[int]=None  ,				__lowerCamelCase :     List[Any]=None  ,				**__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return super().__call__(__lowerCamelCase  ,				*__lowerCamelCase  ,				num_workers=__lowerCamelCase  ,				batch_size=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[Any]=6_4  ,				__lowerCamelCase :     int = 0  ,				__lowerCamelCase :     float = 5_1_2 / 1_5_0_0  ,				__lowerCamelCase :     Optional[int] = 3_2  ,				__lowerCamelCase :     Optional[int] = 1  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				load_image(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.image_processor.size["longest_edge"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.image_processor.generate_crop_boxes(
          __lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.image_processor(images=__lowerCamelCase  ,				return_tensors="pt"   )

      with self.device_placement():
         if self.framework == "pt":
            _SCREAMING_SNAKE_CASE	=				self.get_inference_context()
            with inference_context():
               _SCREAMING_SNAKE_CASE	=				self._ensure_tensor_on_device(__lowerCamelCase  ,				device=self.device   )
               _SCREAMING_SNAKE_CASE	=				self.model.get_image_embeddings(model_inputs.pop("pixel_values"   )   )
               _SCREAMING_SNAKE_CASE	=				image_embeddings

      _SCREAMING_SNAKE_CASE	=				grid_points.shape[1]
      _SCREAMING_SNAKE_CASE	=				points_per_batch if points_per_batch is not None else n_points

      if points_per_batch <= 0:
         raise ValueError(
             "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
             "To return all points at once, set points_per_batch to None"   )

      for i in range(0  ,				__lowerCamelCase  ,				__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				grid_points[:, i : i + points_per_batch, :, :]
         _SCREAMING_SNAKE_CASE	=				input_labels[:, i : i + points_per_batch]
         _SCREAMING_SNAKE_CASE	=				i == n_points - points_per_batch
         yield {
             "input_points": batched_points,
             "input_labels": labels,
             "input_boxes": crop_boxes,
             "is_last": is_last,
             **model_inputs,
         }



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[Any]=0.8_8  ,				__lowerCamelCase :     Tuple=0.9_5  ,				__lowerCamelCase :     Any=0  ,				__lowerCamelCase :     Tuple=1  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("input_boxes"   )
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("is_last"   )
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("original_sizes"   ).tolist()
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("reshaped_input_sizes"   ).tolist()

      _SCREAMING_SNAKE_CASE	=				self.model(**__lowerCamelCase   )

      # post processing happens here in order to avoid CPU GPU copies of ALL the masks
      _SCREAMING_SNAKE_CASE	=				model_outputs["pred_masks"]
      _SCREAMING_SNAKE_CASE	=				self.image_processor.post_process_masks(
          __lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				binarize=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				model_outputs["iou_scores"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.image_processor.filter_masks(
          masks[0]  ,				iou_scores[0]  ,				original_sizes[0]  ,				input_boxes[0]  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				)
      return {
          "masks": masks,
          "is_last": is_last,
          "boxes": boxes,
          "iou_scores": iou_scores,
      }



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     str=False  ,				__lowerCamelCase :     Any=False  ,				__lowerCamelCase :     str=0.7  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				[]
      for model_output in model_outputs:
         all_scores.append(model_output.pop("iou_scores"   )   )
         all_masks.extend(model_output.pop("masks"   )   )
         all_boxes.append(model_output.pop("boxes"   )   )

      _SCREAMING_SNAKE_CASE	=				torch.cat(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.cat(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.image_processor.post_process_for_mask_generation(
          __lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				defaultdict(__lowerCamelCase   )
      for output in model_outputs:
         for k, v in output.items():
            extra[k].append(__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				{}
      if output_rle_mask:
         _SCREAMING_SNAKE_CASE	=				rle_mask

      if output_bboxes_mask:
         _SCREAMING_SNAKE_CASE	=				bounding_boxes

      return {"masks": output_masks, "scores": iou_scores, **optional, **extra}







'''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]	,					),
       ]  )

