






"""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            =	logging.get_logger(__name__)


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


@add_end_docstrings(__snake_case     )
class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :int			, *__magic_name__  :Tuple			, **__magic_name__  :int				)      ->Optional[int]:
										super().__init__(*__magic_name__			, **__magic_name__				)

										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  :Dict			, **__magic_name__  :int				)      ->Optional[int]:
										lowercase    :      Union[str, Any]       = {}
										if "threshold" in kwargs:
														lowercase    :      Optional[Any]       = kwargs["""threshold"""]
										return {}, {}, postprocess_kwargs






						def __call__(		self  :Union[str, Any]			, *__magic_name__  :List[Any]			, **__magic_name__  :Optional[Any]				)      ->Union[Predictions, List[Prediction]]:
										return super().__call__(*__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Optional[Any]				)      ->Tuple:
										lowercase    :      int       = load_image(__magic_name__				)
										lowercase    :      Optional[Any]       = torch.IntTensor([[image.height, image.width]]				)
										lowercase    :      List[Any]       = self.image_processor(images=[image]			, return_tensors="""pt"""				)
										if self.tokenizer is not None:
														lowercase    :      str       = self.tokenizer(text=inputs["""words"""]			, boxes=inputs["""boxes"""]			, return_tensors="""pt"""				)
										lowercase    :      Union[str, Any]       = target_size
										return inputs






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :List[str]				)      ->Optional[int]:
										lowercase    :      str       = model_inputs.pop("""target_size"""				)
										lowercase    :      Optional[Any]       = self.model(**__magic_name__				)
										lowercase    :      Any       = outputs.__class__({"""target_size""": target_size, **outputs}				)
										if self.tokenizer is not None:
														lowercase    :      str       = model_inputs["""bbox"""]
										return model_outputs






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Optional[int]			, __magic_name__  :Union[str, Any]=0.9				)      ->Optional[int]:
										lowercase    :      int       = 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.
														lowercase      ,      lowercase    :      Union[str, Any]       = target_size[0].tolist()

														def unnormalize(__magic_name__  :Any				):
																		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),
																		        ]				)				)

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

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

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

										return annotation






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








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase            =	{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				_lowerCAmelCase            =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
