



"""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

__snake_case				:  List[str] =       logging.get_logger(__name__)


__snake_case				:  List[Any] =       Dict[str, Any]
__snake_case				:  List[str] =       List[Prediction]



@add_end_docstrings(__SCREAMING_SNAKE_CASE				)
class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[Any]			,     *_SCREAMING_SNAKE_CASE:						int			,     **_SCREAMING_SNAKE_CASE:						Any)							->			int:

				"""simple docstring"""

				super().__init__(*_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				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 				_SCREAMING_SNAKE_CASE  (	self:						int			,     **_SCREAMING_SNAKE_CASE:						Any)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			{}
				if "threshold" in kwargs:
							__lowerCAmelCase :    Any				=			kwargs["threshold"]
				return {}, {}, postprocess_kwargs

	def __call__(	self:						Optional[int]			,     *_SCREAMING_SNAKE_CASE:						Dict			,     **_SCREAMING_SNAKE_CASE:						int)							->			Union[Predictions, List[Prediction]]:

				"""simple docstring"""

				return super().__call__(*_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			load_image(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			torch.IntTensor([[image.height, image.width]])
				__lowerCAmelCase :    Any				=			self.image_processor(images=[image]			,     return_tensors="pt")
				if self.tokenizer is not None:
							__lowerCAmelCase :    Any				=			self.tokenizer(text=inputs["words"]			,     boxes=inputs["boxes"]			,     return_tensors="pt")
				__lowerCAmelCase :    str				=			target_size
				return inputs

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			model_inputs.pop("target_size")
				__lowerCAmelCase :    List[str]				=			self.model(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    int				=			outputs.__class__({"target_size": target_size, **outputs})
				if self.tokenizer is not None:
							__lowerCAmelCase :    Tuple				=			model_inputs["bbox"]
				return model_outputs

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.9)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    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.
							__lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			target_size[0].tolist()

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

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

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

							# {"scores": [...], ...} --> [{"score":x, ...}, ...]
							__lowerCAmelCase :    int				=			["score", "label", "box"]
							__lowerCAmelCase :    List[str]				=			[
							    dict(zip(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))
							    for vals in zip(raw_annotation["scores"]			,     raw_annotation["labels"]			,     raw_annotation["boxes"])
							]

				return annotation







	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						"torch.Tensor")							->			Dict[str, int]:

				"""simple docstring"""

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




"""simple docstring"""






import copy
import os
from typing import Union

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


__snake_case				:  str =       logging.get_logger(__name__)

__snake_case				:  Union[str, Any] =       {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'git_vision_model'

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

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Union[str, Any]				=			hidden_size
				__lowerCAmelCase :    List[str]				=			intermediate_size
				__lowerCAmelCase :    List[str]				=			num_hidden_layers
				__lowerCAmelCase :    Tuple				=			num_attention_heads
				__lowerCAmelCase :    List[Any]				=			num_channels
				__lowerCAmelCase :    Dict				=			patch_size
				__lowerCAmelCase :    Optional[int]				=			image_size
				__lowerCAmelCase :    Optional[Any]				=			initializer_range
				__lowerCAmelCase :    str				=			attention_dropout
				__lowerCAmelCase :    Optional[Any]				=			layer_norm_eps
				__lowerCAmelCase :    Tuple				=			hidden_act







	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Dict			,     _SCREAMING_SNAKE_CASE:						Union[str, os.PathLike]			,     **_SCREAMING_SNAKE_CASE:						Dict)							->			"PretrainedConfig":

				"""simple docstring"""

				cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			cls.get_config_dict(_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				# get the vision config dict if we are loading from GITConfig
				if config_dict.get("model_type") == "git":
							__lowerCAmelCase :    str				=			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(_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'git'

	def __init__(	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     _SCREAMING_SNAKE_CASE:						Dict=3_0522			,     _SCREAMING_SNAKE_CASE:						Any=768			,     _SCREAMING_SNAKE_CASE:						str=6			,     _SCREAMING_SNAKE_CASE:						int=12			,     _SCREAMING_SNAKE_CASE:						Dict=3072			,     _SCREAMING_SNAKE_CASE:						Optional[Any]="gelu"			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.1			,     _SCREAMING_SNAKE_CASE:						Any=0.1			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=1024			,     _SCREAMING_SNAKE_CASE:						str=0.02			,     _SCREAMING_SNAKE_CASE:						Tuple=1e-12			,     _SCREAMING_SNAKE_CASE:						List[str]=0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]="absolute"			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=True			,     _SCREAMING_SNAKE_CASE:						Tuple=False			,     _SCREAMING_SNAKE_CASE:						Dict=101			,     _SCREAMING_SNAKE_CASE:						Tuple=102			,     _SCREAMING_SNAKE_CASE:						Any=None			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[Any]:

				"""simple docstring"""

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

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

				__lowerCAmelCase :    List[str]				=			GitVisionConfig(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			vocab_size
				__lowerCAmelCase :    int				=			hidden_size
				__lowerCAmelCase :    Dict				=			num_hidden_layers
				__lowerCAmelCase :    Tuple				=			num_attention_heads
				__lowerCAmelCase :    Union[str, Any]				=			hidden_act
				__lowerCAmelCase :    Dict				=			intermediate_size
				__lowerCAmelCase :    List[str]				=			hidden_dropout_prob
				__lowerCAmelCase :    Union[str, Any]				=			attention_probs_dropout_prob
				__lowerCAmelCase :    Union[str, Any]				=			max_position_embeddings
				__lowerCAmelCase :    Optional[int]				=			initializer_range
				__lowerCAmelCase :    str				=			layer_norm_eps
				__lowerCAmelCase :    Optional[Any]				=			position_embedding_type
				__lowerCAmelCase :    str				=			use_cache
				__lowerCAmelCase :    str				=			tie_word_embeddings
				__lowerCAmelCase :    Optional[int]				=			num_image_with_embedding

				__lowerCAmelCase :    Optional[Any]				=			bos_token_id
				__lowerCAmelCase :    Any				=			eos_token_id







	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			copy.deepcopy(self.__dict__)
				__lowerCAmelCase :    List[Any]				=			self.vision_config.to_dict()
				__lowerCAmelCase :    Tuple				=			self.__class__.model_type
				return output