


'''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


__A	={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	):




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




					def 				snake_case__						(      self							,							_lowerCamelCase):
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase__  :					List[Any]        =     self.pool(self.model(_lowerCamelCase))
								UpperCAmelCase__  :					Tuple        =     torch.flatten(_lowerCamelCase							,							start_dim=2)
								UpperCAmelCase__  :					Any        =     out.transpose(1							,							2).contiguous()
								return out  # BxNx2048


class 		_snake_case					(     a__	):




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

								UpperCAmelCase__  :					List[str]        =     transforms




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




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

								UpperCAmelCase__  :					List[str]        =     torch.zeros(self.n_classes)
								UpperCAmelCase__  :					str        =     1

								UpperCAmelCase__  :					int        =     Image.open(os.path.join(self.data_dir							,							self.data[index]["""img"""])).convert("""RGB""")
								UpperCAmelCase__  :					Dict        =     self.transforms(_lowerCamelCase)

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




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







def     _UpperCamelCase      (		UpperCamelCase__					)     ->   str:
			UpperCAmelCase__  :					str        =     [len(row["""sentence"""]					) for row in batch]
			UpperCAmelCase__  :					Any        =     len(UpperCamelCase__					), max(UpperCamelCase__					)

			UpperCAmelCase__  :					Dict        =     torch.zeros(UpperCamelCase__ ,      UpperCamelCase__ ,      dtype=torch.long					)
			UpperCAmelCase__  :					Tuple        =     torch.zeros(UpperCamelCase__ ,      UpperCamelCase__ ,      dtype=torch.long					)

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

			UpperCAmelCase__  :					str        =     torch.stack([row["""image"""] for row in batch]					)
			UpperCAmelCase__  :					str        =     torch.stack([row["""label"""] for row in batch]					)
			UpperCAmelCase__  :					int        =     torch.stack([row["""image_start_token"""] for row in batch]					)
			UpperCAmelCase__  :					List[str]        =     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     _UpperCamelCase      (		)     ->   List[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     _UpperCamelCase      (		)     ->   List[str]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6					),
			        transforms.CenterCrop(2_2_4					),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17] ,      std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69] ,      ),
			    ]					)



'''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, 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

__A	=logging.get_logger(__name__)


__A	=Dict[str, Any]
__A	=List[Prediction]


@add_end_docstrings(a__	)
class 		_snake_case					(     a__	):




					def __init__(      self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(*_lowerCamelCase							,							**_lowerCamelCase)

								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 				snake_case__						(      self							,							**_lowerCamelCase):
								UpperCAmelCase__  :					int        =     {}
								if "threshold" in kwargs:
											UpperCAmelCase__  :					List[str]        =     kwargs["""threshold"""]
								return {}, {}, postprocess_kwargs




					def __call__(      self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return super().__call__(*_lowerCamelCase							,							**_lowerCamelCase)




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




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




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=0.9):
								UpperCAmelCase__  :					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.
											UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     target_size[0].tolist()

											def unnormalize(_lowerCamelCase):
														return self._get_bounding_box(
														    torch.Tensor(
														        [
														            (width * bbox[0] / 1000),
														            (height * bbox[1] / 1000),
														            (width * bbox[2] / 1000),
														            (height * bbox[3] / 1000),
														        ]))

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

											UpperCAmelCase__  :					Tuple        =     scores.tolist()
											UpperCAmelCase__  :					int        =     [self.model.config.idalabel[label.item()] for label in labels]
											UpperCAmelCase__  :					int        =     [self._get_bounding_box(_lowerCamelCase) for box in boxes]

											# {"scores": [...], ...} --> [{"score":x, ...}, ...]
											UpperCAmelCase__  :					Dict        =     ["""score""", """label""", """box"""]
											UpperCAmelCase__  :					Optional[int]        =     [
											    dict(zip(_lowerCamelCase							,							_lowerCamelCase))
											    for vals in zip(raw_annotation["""scores"""]							,							raw_annotation["""labels"""]							,							raw_annotation["""boxes"""])
											]

								return annotation




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