


'''simple docstring'''



from typing import TYPE_CHECKING

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


__A	={
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BertForMaskedLM',
		    'BertForMultipleChoice',
		    'BertForNextSentencePrediction',
		    'BertForPreTraining',
		    'BertForQuestionAnswering',
		    'BertForSequenceClassification',
		    'BertForTokenClassification',
		    'BertLayer',
		    'BertLMHeadModel',
		    'BertModel',
		    'BertPreTrainedModel',
		    'load_tf_weights_in_bert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBertEmbeddings',
		    'TFBertForMaskedLM',
		    'TFBertForMultipleChoice',
		    'TFBertForNextSentencePrediction',
		    'TFBertForPreTraining',
		    'TFBertForQuestionAnswering',
		    'TFBertForSequenceClassification',
		    'TFBertForTokenClassification',
		    'TFBertLMHeadModel',
		    'TFBertMainLayer',
		    'TFBertModel',
		    'TFBertPreTrainedModel',
		]
try:
		if not is_tensorflow_text_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['TFBertTokenizer']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'FlaxBertForCausalLM',
		    'FlaxBertForMaskedLM',
		    'FlaxBertForMultipleChoice',
		    'FlaxBertForNextSentencePrediction',
		    'FlaxBertForPreTraining',
		    'FlaxBertForQuestionAnswering',
		    'FlaxBertForSequenceClassification',
		    'FlaxBertForTokenClassification',
		    'FlaxBertModel',
		    'FlaxBertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
		from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_fast import BertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bert import (
				    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BertForMaskedLM,
				    BertForMultipleChoice,
				    BertForNextSentencePrediction,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    BertForTokenClassification,
				    BertLayer,
				    BertLMHeadModel,
				    BertModel,
				    BertPreTrainedModel,
				    load_tf_weights_in_bert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_bert import (
				    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBertEmbeddings,
				    TFBertForMaskedLM,
				    TFBertForMultipleChoice,
				    TFBertForNextSentencePrediction,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertForTokenClassification,
				    TFBertLMHeadModel,
				    TFBertMainLayer,
				    TFBertModel,
				    TFBertPreTrainedModel,
				)

		try:
				if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_tf import TFBertTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_bert import (
				    FlaxBertForCausalLM,
				    FlaxBertForMaskedLM,
				    FlaxBertForMultipleChoice,
				    FlaxBertForNextSentencePrediction,
				    FlaxBertForPreTraining,
				    FlaxBertForQuestionAnswering,
				    FlaxBertForSequenceClassification,
				    FlaxBertForTokenClassification,
				    FlaxBertModel,
				    FlaxBertPreTrainedModel,
				)

else:
		import sys

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



'''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