'''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

__UpperCAmelCase     							=logging.get_logger(__name__)


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




@add_end_docstrings(UpperCAmelCase__ )
class     a__      (   UpperCAmelCase__ ):






     def __init__( self		:		Optional[int]  ,				*a		:		int  ,				**a		:		List[Any]    ):





         """simple docstring"""



         super().__init__(*a  ,				**a    )

         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		:		Optional[Any]  ,				**a		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  {}
         if "threshold" in kwargs:
             __lowerCamelCase		=  kwargs['''threshold''']
         return {}, {}, postprocess_kwargs






     def __call__( self		:		str  ,				*a		:		str  ,				**a		:		Union[str, Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]  ,				a		:		int    ):





         """simple docstring"""



         __lowerCamelCase		=  load_image(a    )
         __lowerCamelCase		=  torch.IntTensor([[image.height, image.width]]    )
         __lowerCamelCase		=  self.image_processor(images=[image]  ,				return_tensors='''pt'''    )
         if self.tokenizer is not None:
             __lowerCamelCase		=  self.tokenizer(text=inputs['''words''']  ,				boxes=inputs['''boxes''']  ,				return_tensors='''pt'''    )
         __lowerCamelCase		=  target_size
         return inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		Optional[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  model_inputs.pop('''target_size'''    )
         __lowerCamelCase		=  self.model(**a    )
         __lowerCamelCase		=  outputs.__class__({'''target_size''': target_size, **outputs}    )
         if self.tokenizer is not None:
             __lowerCamelCase		=  model_inputs['''bbox''']
         return model_outputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				a		:		int  ,				a		:		List[str]=0.9    ):





         """simple docstring"""



         __lowerCamelCase		=  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		=  target_size[0].tolist()

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

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

             __lowerCamelCase		=  scores.tolist()
             __lowerCamelCase		=  [self.model.config.idalabel[label.item()] for label in labels]
             __lowerCamelCase		=  [self._get_bounding_box(a    ) for box in boxes]

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

         return annotation






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]  ,				a		:		"torch.Tensor"    ):





         """simple docstring"""



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


import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Dict				       =LxmertTokenizer
	A__    :   List[Any]				       =LxmertTokenizerFast
	A__    :   Any				       =True
	A__    :   List[Any]				       =True
	def      A_				(			self							:   Optional[Any]							):
						super().setUp()

						SCREAMING_SNAKE_CASE__													= [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file']							)
						with open(self.vocab_file       ,   'w'       ,   encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   int							):
						SCREAMING_SNAKE_CASE__													= 'UNwant\u00E9d,running'
						SCREAMING_SNAKE_CASE__													= 'unwanted, running'
						return input_text, output_text
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class(self.vocab_file							)

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('UNwant\u00E9d,running'							)
						self.assertListEqual(UpperCAmelCase_       ,   ['un', '##want', '##ed', ',', 'runn', '##ing']							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)       ,   [7, 4, 5, 10, 8, 9]							)





	def      A_				(			self							:   List[str]							):
						if not self.test_rust_tokenizer:
											return

						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__													= 'I was born in 92000, and this is falsé.'

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.tokenize(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)





