




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


UpperCamelCase__ :   Optional[int]						=							Dict[str, Any]
UpperCamelCase__ :   Dict						=							List[Prediction]







@add_end_docstrings(lowerCamelCase_							)
class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							super().__init__(*snake_case__      ,**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 		snake_case	(			self      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      {}
							if "threshold" in kwargs:
										SCREAMING_SNAKE_CASE_   : int					  =      kwargs['threshold']
							return {}, {}, postprocess_kwargs






				def __call__(			self      ,*snake_case__      ,**snake_case__ ):
							return super().__call__(*snake_case__      ,**snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      load_image(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.IntTensor([[image.height, image.width]] )
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.image_processor(images=[image]      ,return_tensors='pt' )
							if self.tokenizer is not None:
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.tokenizer(text=inputs['words']      ,boxes=inputs['boxes']      ,return_tensors='pt' )
							SCREAMING_SNAKE_CASE_   : Dict					  =      target_size
							return inputs






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model_inputs.pop('target_size' )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.model(**snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      outputs.__class__({'target_size': target_size, **outputs} )
							if self.tokenizer is not None:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      model_inputs['bbox']
							return model_outputs






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=0.9 ):
							SCREAMING_SNAKE_CASE_   : Union[str, 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.
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Tuple					  =      target_size[0].tolist()

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

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

										SCREAMING_SNAKE_CASE_   : Tuple					  =      scores.tolist()
										SCREAMING_SNAKE_CASE_   : Dict					  =      [self.model.config.idalabel[label.item()] for label in labels]
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      [self._get_bounding_box(snake_case__ ) for box in boxes]

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

							return annotation






				def 		snake_case	(			self      ,snake_case__ ):
							if self.framework != "pt":
										raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.' )
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      box.int().tolist()
							SCREAMING_SNAKE_CASE_   : int					  =      {
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						UpperCamelCase__ :   Dict						=							None

UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)

UpperCamelCase__ :   Union[str, Any]						=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   Dict						=							{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase__ :   Dict						=							{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

UpperCamelCase__ :   Any						=							'''▁'''







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[str]						=     VOCAB_FILES_NAMES
				__a     :       int						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Optional[int]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       List[Any]						=     ["input_ids", "attention_mask"]
				__a     :       Dict						=     BarthezTokenizer






				def __init__(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__="<s>"      ,snake_case__="</s>"      ,snake_case__="</s>"      ,snake_case__="<s>"      ,snake_case__="<unk>"      ,snake_case__="<pad>"      ,snake_case__="<mask>"      ,**snake_case__      ,):
							# Mask token behave like a normal word, i.e. include the space before it
							SCREAMING_SNAKE_CASE_   : str					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else mask_token

							super().__init__(
							    snake_case__      ,tokenizer_file=snake_case__      ,bos_token=snake_case__      ,eos_token=snake_case__      ,unk_token=snake_case__      ,sep_token=snake_case__      ,cls_token=snake_case__      ,pad_token=snake_case__      ,mask_token=snake_case__      ,**snake_case__      ,)

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_file
							SCREAMING_SNAKE_CASE_   : int					  =      False if not self.vocab_file else True






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):

							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : List[str]					  =      [self.cls_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : Any					  =      [self.cls_token_id]

							if token_ids_a is None:
										return len(cls + token_ids_a + sep ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							if not self.can_save_slow_tokenizer:
										raise ValueError(
										    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
										    'tokenizer.' )

							if not os.path.isdir(snake_case__ ):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
										return
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      os.path.join(
							    snake_case__      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
										copyfile(self.vocab_file      ,snake_case__ )

							return (out_vocab_file,)



