



'''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

lowerCAmelCase:					List[Any]						       =						logging.get_logger(__name__)


lowerCAmelCase:					str						       =						Dict[str, Any]
lowerCAmelCase:					Optional[Any]						       =						List[Prediction]

@add_end_docstrings(lowerCAmelCase_	)
class 							a__(					lowerCAmelCase_	):







    def __init__(      self :  Optional[Any]						,			*__snake_case :  str						,			**__snake_case :  str	):
      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 			lowercase_       (      self :  Union[str, Any]						,			**__snake_case :  int	):
      a						:					int        =     {}
      if "threshold" in kwargs:
        a						:					Any        =     kwargs['threshold']
      return {}, {}, postprocess_kwargs







    def __call__(      self :  List[str]						,			*__snake_case :  List[str]						,			**__snake_case :  List[Any]	):
      return super().__call__(*lowerCamelCase__						,			**lowerCamelCase__	)







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







    def 			lowercase_       (      self :  Tuple						,			__snake_case :  str	):
      a						:					Any        =     model_inputs.pop('target_size'	)
      a						:					List[str]        =     self.model(**lowerCamelCase__	)
      a						:					Tuple        =     outputs.__class__({'target_size': target_size, **outputs}	)
      if self.tokenizer is not None:
        a						:					Tuple        =     model_inputs['bbox']
      return model_outputs







    def 			lowercase_       (      self :  List[Any]						,			__snake_case :  Tuple						,			__snake_case :  int=0.9	):
      a						:					Dict        =     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.
        a   ,	a						:					Optional[Any]        =     target_size[0].tolist()

        def unnormalize(__snake_case :  int	):
          return self._get_bounding_box(
              torch.Tensor(
                  [
                      (width * bbox[0] / 10_00),
                      (height * bbox[1] / 10_00),
                      (width * bbox[2] / 10_00),
                      (height * bbox[3] / 10_00),
                  ]	)	)

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

        a						:					Tuple        =     scores.tolist()
        a						:					Optional[int]        =     [self.model.config.idalabel[label.item()] for label in labels]
        a						:					str        =     [self._get_bounding_box(lowerCamelCase__	) for box in boxes]

        # {"scores": [...], ...} --> [{"score":x, ...}, ...]
        a						:					Any        =     ['score', 'label', 'box']
        a						:					Tuple        =     [
            dict(zip(lowerCamelCase__						,			lowerCamelCase__	)	)
            for vals in zip(raw_annotation['scores']						,			raw_annotation['labels']						,			raw_annotation['boxes']	)
        ]

      return annotation







    def 			lowercase_       (      self :  Optional[Any]						,			__snake_case :  "torch.Tensor"	):


      if self.framework != "pt":
        raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'	)
      a   ,	a   ,	a   ,	a						:					Optional[int]        =     box.int().tolist()
      a						:					Any        =     {
          'xmin': xmin,
          'ymin': ymin,
          'xmax': xmax,
          'ymax': ymax,
      }
      return bbox






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			"git_vision_model"






     def __init__(							self						:    List[Any]						,lowerCamelCase__						:    Dict=768						,lowerCamelCase__						:    Union[str, Any]=3072						,lowerCamelCase__						:    Optional[int]=12						,lowerCamelCase__						:    Tuple=12						,lowerCamelCase__						:    Tuple=3						,lowerCamelCase__						:    Optional[Any]=224						,lowerCamelCase__						:    Union[str, Any]=16						,lowerCamelCase__						:    List[Any]="quick_gelu"						,lowerCamelCase__						:    Optional[Any]=1e-5						,lowerCamelCase__						:    str=0.0						,lowerCamelCase__						:    Optional[int]=0.02						,**lowerCamelCase__						:    Union[str, Any]						,)     ->		Optional[int]:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						hidden_size
           SCREAMING_SNAKE_CASE		=						intermediate_size
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						hidden_act






     @classmethod
     def 				SCREAMING_SNAKE_CASE__  (							cls						:    Tuple						,lowerCamelCase__						:    Union[str, os.PathLike]						,**lowerCamelCase__						:    int       )     ->		"PretrainedConfig":




           '''simple docstring'''


           cls._set_token_in_kwargs(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						cls.get_config_dict(lowerCamelCase__						,**lowerCamelCase__       )

           # get the vision config dict if we are loading from GITConfig
           if config_dict.get("""model_type"""       ) == "git":
                 SCREAMING_SNAKE_CASE		=						config_dict["""vision_config"""]

           if "model_type" in config_dict and hasattr(cls						,"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                     F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

           return cls.from_dict(lowerCamelCase__						,**lowerCamelCase__       )

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			"git"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    int=None						,lowerCamelCase__						:    str=30522						,lowerCamelCase__						:    Tuple=768						,lowerCamelCase__						:    Union[str, Any]=6						,lowerCamelCase__						:    str=12						,lowerCamelCase__						:    List[str]=3072						,lowerCamelCase__						:    Dict="gelu"						,lowerCamelCase__						:    Tuple=0.1						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    List[str]=1024						,lowerCamelCase__						:    List[str]=0.02						,lowerCamelCase__						:    str=1e-1_2						,lowerCamelCase__						:    Optional[int]=0						,lowerCamelCase__						:    Optional[int]="absolute"						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    str=False						,lowerCamelCase__						:    int=101						,lowerCamelCase__						:    int=102						,lowerCamelCase__						:    Dict=None						,**lowerCamelCase__						:    List[Any]						,)     ->		Optional[Any]:




           '''simple docstring'''


           super().__init__(bos_token_id=lowerCamelCase__						,eos_token_id=lowerCamelCase__						,pad_token_id=lowerCamelCase__						,**lowerCamelCase__       )

           if vision_config is None:
                 SCREAMING_SNAKE_CASE		=						{}
                 logger.info("""vision_config is None. initializing the GitVisionConfig with default values."""       )

           SCREAMING_SNAKE_CASE		=						GitVisionConfig(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						vocab_size
           SCREAMING_SNAKE_CASE		=						hidden_size
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						intermediate_size
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						max_position_embeddings
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						position_embedding_type
           SCREAMING_SNAKE_CASE		=						use_cache
           SCREAMING_SNAKE_CASE		=						tie_word_embeddings
           SCREAMING_SNAKE_CASE		=						num_image_with_embedding

           SCREAMING_SNAKE_CASE		=						bos_token_id
           SCREAMING_SNAKE_CASE		=						eos_token_id






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						copy.deepcopy(self.__dict__       )
           SCREAMING_SNAKE_CASE		=						self.vision_config.to_dict()
           SCREAMING_SNAKE_CASE		=						self.__class__.model_type
           return output


