


'''simple docstring'''



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

from tokenizers import processors

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


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		__A	=None


__A	=logging.get_logger(__name__)


__A	={'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A	={
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

__A	={
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
__A	=['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']


class 		_snake_case					(     a__	):
					lowerCAmelCase      :List[str]   =					VOCAB_FILES_NAMES
					lowerCAmelCase      :int   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase      :Optional[int]   =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase      :Optional[Any]   =					['''input_ids''', '''attention_mask''']
					lowerCAmelCase      :int   =					MBartTokenizer

					lowerCAmelCase      :List[int]   =					[]
					lowerCAmelCase      :List[int]   =					[]




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

								super().__init__(
								    vocab_file=_lowerCamelCase							,							tokenizer_file=_lowerCamelCase							,							bos_token=_lowerCamelCase							,							eos_token=_lowerCamelCase							,							sep_token=_lowerCamelCase							,							cls_token=_lowerCamelCase							,							unk_token=_lowerCamelCase							,							pad_token=_lowerCamelCase							,							mask_token=_lowerCamelCase							,							src_lang=_lowerCamelCase							,							tgt_lang=_lowerCamelCase							,							additional_special_tokens=_lowerCamelCase							,							**_lowerCamelCase							,							)

								UpperCAmelCase__  :					List[str]        =     vocab_file
								UpperCAmelCase__  :					Optional[Any]        =     False if not self.vocab_file else True

								UpperCAmelCase__  :					Optional[Any]        =     FAIRSEQ_LANGUAGE_CODES.copy()

								if additional_special_tokens is not None:
											# Only add those special tokens if they are not already there.
											_additional_special_tokens.extend(
											    [t for t in additional_special_tokens if t not in _additional_special_tokens])

								self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens})
								UpperCAmelCase__  :					List[str]        =     {
								    lang_code: self.convert_tokens_to_ids(_lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								UpperCAmelCase__  :					Optional[int]        =     src_lang if src_lang is not None else """en_XX"""
								UpperCAmelCase__  :					Optional[Any]        =     self.convert_tokens_to_ids(self._src_lang)
								UpperCAmelCase__  :					Tuple        =     tgt_lang
								self.set_src_lang_special_tokens(self._src_lang)




					@property
					def 				snake_case__						(      self):
								return self._src_lang




					@src_lang.setter
					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     new_src_lang
								self.set_src_lang_special_tokens(self._src_lang)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if token_ids_a is None:
											return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					int        =     [self.sep_token_id]
								UpperCAmelCase__  :					Dict        =     [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							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								if src_lang is None or tgt_lang is None:
											raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""")
								UpperCAmelCase__  :					Optional[int]        =     src_lang
								UpperCAmelCase__  :					Union[str, Any]        =     self(_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     self.convert_tokens_to_ids(_lowerCamelCase)
								UpperCAmelCase__  :					Optional[Any]        =     tgt_lang_id
								return inputs




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = "en_XX"							,							_lowerCamelCase = None							,							_lowerCamelCase = "ro_RO"							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Optional[int]        =     src_lang
								UpperCAmelCase__  :					Union[str, Any]        =     tgt_lang
								return super().prepare_seqaseq_batch(_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase)




					def 				snake_case__						(      self):
								return self.set_src_lang_special_tokens(self.src_lang)




					def 				snake_case__						(      self):
								return self.set_tgt_lang_special_tokens(self.tgt_lang)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     self.convert_tokens_to_ids(_lowerCamelCase)
								UpperCAmelCase__  :					str        =     []
								UpperCAmelCase__  :					Union[str, Any]        =     [self.eos_token_id, self.cur_lang_code]

								UpperCAmelCase__  :					List[Any]        =     self.convert_ids_to_tokens(self.prefix_tokens)
								UpperCAmelCase__  :					List[str]        =     self.convert_ids_to_tokens(self.suffix_tokens)

								UpperCAmelCase__  :					Optional[Any]        =     processors.TemplateProcessing(
								    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str							,							pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str							,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,							self.prefix_tokens + self.suffix_tokens))							,							)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[Any]        =     self.convert_tokens_to_ids(_lowerCamelCase)
								UpperCAmelCase__  :					Optional[Any]        =     []
								UpperCAmelCase__  :					List[str]        =     [self.eos_token_id, self.cur_lang_code]

								UpperCAmelCase__  :					Optional[int]        =     self.convert_ids_to_tokens(self.prefix_tokens)
								UpperCAmelCase__  :					Union[str, Any]        =     self.convert_ids_to_tokens(self.suffix_tokens)

								UpperCAmelCase__  :					List[str]        =     processors.TemplateProcessing(
								    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str							,							pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str							,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,							self.prefix_tokens + self.suffix_tokens))							,							)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = 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(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''')
											return
								UpperCAmelCase__  :					str        =     os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

								return (out_vocab_file,)



'''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