





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   _UpperCamelCase  (       __UpperCAmelCase  ):







					'''simple docstring'''

					def __init__(						self    :  List[str] ,					*__lowercase    :  Any ,					**__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







						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        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					**__lowercase    :  Dict		):






						'''simple docstring'''







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





					def __call__(						self    :  str ,					*__lowercase    :  int ,					**__lowercase    :  Dict		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  Dict		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[Any] ,					__lowercase    :  int=0.9		):






						'''simple docstring'''







						UpperCAmelCase_								=     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_								=     target_size[0].tolist()

							def unnormalize(__lowercase    :  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),
								        ]		)		)

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

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

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

						return annotation





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  "torch.Tensor"		):






						'''simple docstring'''







						if self.framework != "pt":
							raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""		)
						UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     box.int().tolist()
						UpperCAmelCase_								=     {
						    """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


UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)

UpperCamelCase__   : int					  = """▁"""

UpperCamelCase__   : Dict					  = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

UpperCamelCase__   : Dict					  = {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

UpperCamelCase__   : Optional[int]					  = {"""vinai/bartpho-syllable""": 10_24}







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     str			    =       VOCAB_FILES_NAMES
					lowerCamelCase     :     Optional[int]			    =       PRETRAINED_VOCAB_FILES_MAP
					lowerCamelCase     :     List[str]			    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCamelCase     :     Optional[Any]			    =       ['input_ids', 'attention_mask']





					def __init__(						self    :  List[str] ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[Any] ,					__lowercase    :  List[str]="<s>" ,					__lowercase    :  str="</s>" ,					__lowercase    :  Optional[Any]="</s>" ,					__lowercase    :  Any="<s>" ,					__lowercase    :  Dict="<unk>" ,					__lowercase    :  int="<pad>" ,					__lowercase    :  str="<mask>" ,					__lowercase    :  Optional[Dict[str, Any]] = None ,					**__lowercase    :  Optional[Any] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     AddedToken(__lowercase ,					lstrip=__lowercase ,					rstrip=__lowercase		) if isinstance(__lowercase ,					__lowercase		) else mask_token

						UpperCAmelCase_								=     {} if sp_model_kwargs is None else sp_model_kwargs

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

						UpperCAmelCase_								=     vocab_file
						UpperCAmelCase_								=     monolingual_vocab_file
						UpperCAmelCase_								=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(str(__lowercase		)		)

						# Load the reduced vocab

						# Keep order of special tokens for backward compatibility
						UpperCAmelCase_								=     {}
						UpperCAmelCase_								=     0
						for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
							if str(__lowercase		) not in self.fairseq_tokens_to_ids:
								UpperCAmelCase_								=     cnt
								cnt += 1
						with open(__lowercase ,					"""r""" ,					encoding="""utf-8"""		) as f:
							for line in f.readlines():
								UpperCAmelCase_								=     line.strip().split()[0]
								UpperCAmelCase_								=     len(self.fairseq_tokens_to_ids		)
						if str(__lowercase		) not in self.fairseq_tokens_to_ids:
							UpperCAmelCase_								=     len(self.fairseq_tokens_to_ids		)

						UpperCAmelCase_								=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}





					def __getstate__(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.__dict__.copy()
						UpperCAmelCase_								=     None
						UpperCAmelCase_								=     self.sp_model.serialized_model_proto()
						return state





					def __setstate__(						self    :  List[str] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     d

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

						UpperCAmelCase_								=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None ,					__lowercase    :  bool = False		):






						'''simple docstring'''







						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__lowercase ,					token_ids_a=__lowercase ,					already_has_special_tokens=__lowercase		)

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





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None		):






						'''simple docstring'''







						UpperCAmelCase_								=     [self.sep_token_id]
						UpperCAmelCase_								=     [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        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						return len(self.fairseq_ids_to_tokens		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     {self.convert_ids_to_tokens(__lowercase		): i for i in range(self.vocab_size		)}
						vocab.update(self.added_tokens_encoder		)
						return vocab





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  str		):






						'''simple docstring'''







						return self.sp_model.encode(__lowercase ,					out_type=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  Tuple		):






						'''simple docstring'''







						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						else:
							return self.unk_token_id





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return self.fairseq_ids_to_tokens[index]





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     """""".join(__lowercase		).replace(__lowercase ,					""" """		).strip()
						return out_string





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  str ,					__lowercase    :  Optional[str] = None		):






						'''simple docstring'''







						if not os.path.isdir(__lowercase		):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
						UpperCAmelCase_								=     os.path.join(
						    __lowercase ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
						UpperCAmelCase_								=     os.path.join(
						    __lowercase ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] ,					)

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

						if os.path.abspath(self.monolingual_vocab_file		) != os.path.abspath(
						    __lowercase		) and os.path.isfile(self.monolingual_vocab_file		):
							copyfile(self.monolingual_vocab_file ,					__lowercase		)
						elif not os.path.isfile(self.monolingual_vocab_file		):
							with open(__lowercase ,					"""w""" ,					encoding="""utf-8"""		) as fp:
								for token in self.fairseq_tokens_to_ids:
									if token not in self.all_special_tokens:
										fp.write(F"""{str(__lowercase		)} \n"""		)

						return out_vocab_file, out_monolingual_vocab_file


