
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, ChunkPipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							import torch

							from transformers.modeling_outputs import BaseModelOutput

							from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowercase__		:Any			  =   logging.get_logger(__name__)







@add_end_docstrings(SCREAMING_SNAKE_CASE__		)
class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,**A__):
												super().__init__(**A__)

												if self.framework == "tf":
																	raise ValueError(f'The {self.__class__} is only available in PyTorch.')

												requires_backends(self       ,'''vision''')
												self.check_model_type(A__)




							def __call__(    self       ,A__       ,A__ = None       ,**A__       ,):
												if "text_queries" in kwargs:
																	lowercase						     =						kwargs.pop('''text_queries''')

												if isinstance(A__       ,(str, Image.Image)):
																	lowercase						     =						{'''image''': image, '''candidate_labels''': candidate_labels}
												else:
																	lowercase						     =						image
												lowercase						     =						super().__call__(A__       ,**A__)
												return results




							def 			A__				(    self       ,**A__):
												lowercase						     =						{}
												if "threshold" in kwargs:
																	lowercase						     =						kwargs['''threshold''']
												if "top_k" in kwargs:
																	lowercase						     =						kwargs['''top_k''']
												return {}, {}, postprocess_params




							def 			A__				(    self       ,A__):
												lowercase						     =						load_image(inputs['''image'''])
												lowercase						     =						inputs['''candidate_labels''']
												if isinstance(A__       ,A__):
																	lowercase						     =						candidate_labels.split(''',''')

												lowercase						     =						torch.tensor([[image.height, image.width]]       ,dtype=torch.intaa)
												for i, candidate_label in enumerate(A__):
																	lowercase						     =						self.tokenizer(A__       ,return_tensors=self.framework)
																	lowercase						     =						self.image_processor(A__       ,return_tensors=self.framework)
																	yield {
																	    "is_last": i == len(A__) - 1,
																	    "target_size": target_size,
																	    "candidate_label": candidate_label,
																	    **text_inputs,
																	    **image_features,
																	}




							def 			A__				(    self       ,A__):
												lowercase						     =						model_inputs.pop('''target_size''')
												lowercase						     =						model_inputs.pop('''candidate_label''')
												lowercase						     =						model_inputs.pop('''is_last''')

												lowercase						     =						self.model(**A__)

												lowercase						     =						{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
												return model_outputs




							def 			A__				(    self       ,A__       ,A__=0.1       ,A__=None):
												lowercase						     =						[]
												for model_output in model_outputs:
																	lowercase						     =						model_output['''candidate_label''']
																	lowercase						     =						BaseModelOutput(A__)
																	lowercase						     =						self.image_processor.post_process_object_detection(
																	    outputs=A__       ,threshold=A__       ,target_sizes=model_output['''target_size'''])[0]

																	for index in outputs["scores"].nonzero():
																						lowercase						     =						outputs['''scores'''][index].item()
																						lowercase						     =						self._get_bounding_box(outputs['''boxes'''][index][0])

																						lowercase						     =						{'''score''': score, '''label''': label, '''box''': box}
																						results.append(A__)

												lowercase						     =						sorted(A__       ,key=lambda A__: x["score"]       ,reverse=A__)
												if top_k:
																	lowercase						     =						results[:top_k]

												return results





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






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__		:str			  =   logging.get_logger(__name__)

lowercase__		:Any			  =   {"vocab_file": "sentencepiece.bpe.model"}

lowercase__		:Tuple			  =   {
    "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"
        ),
    },
}

lowercase__		:str			  =   {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

lowercase__		:int			  =   "▁"







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Union[str, Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	List[Any]	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Tuple	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




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

												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A__       ,eos_token=A__       ,unk_token=A__       ,sep_token=A__       ,cls_token=A__       ,pad_token=A__       ,mask_token=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												lowercase						     =						vocab_file
												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))

												lowercase						     =						{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

												lowercase						     =						len(self.sp_model) - 1
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.cls_token_id]
												lowercase						     =						[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):
												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__)

												if token_ids_a is None:
																	return [1] + ([0] * len(A__)) + [1]
												return [1] + ([0] * len(A__)) + [1, 1] + ([0] * len(A__)) + [1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[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]




							@property
							def 			A__				(    self):
												return len(self.sp_model)




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												return spm_id if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						[]
												lowercase						     =						''''''
												lowercase						     =						False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(A__) + token
																						lowercase						     =						True
																						lowercase						     =						[]
																	else:
																						current_sub_tokens.append(A__)
																						lowercase						     =						False
												out_string += self.sp_model.decode(A__)
												return out_string.strip()




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d

												# for backward compatibility
												if not hasattr(self       ,'''sp_model_kwargs'''):
																	lowercase						     =						{}

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)





							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						os.path.join(
												    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file       ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__       ,'''wb''') as fi:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)




