





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[Any] 							=						Dict[str, Any]
UpperCAmelCase_					:   int 							=						List[Prediction]





@add_end_docstrings(__A  )
class      lowercase__    (							__A  ):







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

       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 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       lowerCAmelCase_					:       List[Any]    =		{}
       if "threshold" in kwargs:
            lowerCAmelCase_					:       List[str]    =		kwargs["""threshold"""]
       return {}, {}, postprocess_kwargs







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







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







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       int    =		model_inputs.pop("""target_size"""						)
       lowerCAmelCase_					:       Any    =		self.model(**_lowercase						)
       lowerCAmelCase_					:       List[str]    =		outputs.__class__({"""target_size""": target_size, **outputs}						)
       if self.tokenizer is not None:
            lowerCAmelCase_					:       Optional[int]    =		model_inputs["""bbox"""]
       return model_outputs







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase=0.9						):
       lowerCAmelCase_					:       Optional[int]    =		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_					:       Tuple    =		target_size[0].tolist()

            def unnormalize(_lowercase						):
                 return self._get_bounding_box(
                     torch.Tensor(
                         [
                             (width * bbox[0] / 1_000),
                             (height * bbox[1] / 1_000),
                             (width * bbox[2] / 1_000),
                             (height * bbox[3] / 1_000),
                         ]						)						)

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

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

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

       return annotation







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       if self.framework != "pt":
            raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""						)
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       List[Any]    =		box.int().tolist()
       lowerCAmelCase_					:       Union[str, Any]    =		{
           """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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_					:   Optional[Any] 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   str 							=						"""▁"""

UpperCAmelCase_					:   int 							=						{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_					:   Optional[Any] 							=						{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

UpperCAmelCase_					:   Union[str, Any] 							=						{
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
UpperCAmelCase_					:   Tuple 							=						["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  VOCAB_FILES_NAMES
  __UpperCamelCase	     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCamelCase	     =  PRETRAINED_VOCAB_FILES_MAP
  __UpperCamelCase	     =  ["""input_ids""", """attention_mask"""]

  __UpperCamelCase	     =  []
  __UpperCamelCase	     =  []







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

       lowerCAmelCase_					:       Optional[Any]    =		{} if sp_model_kwargs is None else sp_model_kwargs
       lowerCAmelCase_					:       str    =		legacy_behaviour

       super().__init__(
           bos_token=_lowercase    ,						eos_token=_lowercase    ,						unk_token=_lowercase    ,						sep_token=_lowercase    ,						cls_token=_lowercase    ,						pad_token=_lowercase    ,						mask_token=_lowercase    ,						tokenizer_file=_lowercase    ,						src_lang=_lowercase    ,						tgt_lang=_lowercase    ,						additional_special_tokens=_lowercase    ,						sp_model_kwargs=self.sp_model_kwargs    ,						legacy_behaviour=_lowercase    ,						**_lowercase    ,						)

       lowerCAmelCase_					:       Tuple    =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(str(_lowercase						)						)
       lowerCAmelCase_					:       int    =		vocab_file
       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
       # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
       # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

       # Mimic fairseq token-to-id alignment for the first 4 token
       lowerCAmelCase_					:       Optional[Any]    =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       lowerCAmelCase_					:       Dict    =		1

       lowerCAmelCase_					:       Tuple    =		len(self.sp_model						)
       lowerCAmelCase_					:       Any    =		{
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowercase						)
       }
       lowerCAmelCase_					:       Optional[int]    =		{v: k for k, v in self.lang_code_to_id.items()}
       lowerCAmelCase_					:       List[str]    =		len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id						)
       lowerCAmelCase_					:       Optional[Any]    =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
       lowerCAmelCase_					:       Tuple    =		list(self.lang_code_to_id.keys()						)

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

       lowerCAmelCase_					:       str    =		src_lang if src_lang is not None else """eng_Latn"""
       lowerCAmelCase_					:       List[str]    =		self.lang_code_to_id[self._src_lang]
       lowerCAmelCase_					:       Union[str, Any]    =		tgt_lang
       self.set_src_lang_special_tokens(self._src_lang						)







  def __getstate__(	self						):
       lowerCAmelCase_					:       int    =		self.__dict__.copy()
       lowerCAmelCase_					:       List[Any]    =		None
       lowerCAmelCase_					:       Any    =		self.sp_model.serialized_model_proto()
       return state







  def __setstate__(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		d

       # for backward compatibility
       if not hasattr(self    ,						"""sp_model_kwargs"""						):
            lowerCAmelCase_					:       Optional[Any]    =		{}

       lowerCAmelCase_					:       Union[str, Any]    =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token







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







  @src_lang.setter
  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[Any]    =		new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = False						):

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

       lowerCAmelCase_					:       Optional[int]    =		[1] * len(self.prefix_tokens						)
       lowerCAmelCase_					:       str    =		[1] * len(self.suffix_tokens						)
       if token_ids_a is None:
            return prefix_ones + ([0] * len(_lowercase						)) + suffix_ones
       return prefix_ones + ([0] * len(_lowercase						)) + ([0] * len(_lowercase						)) + suffix_ones







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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		{self.convert_ids_to_tokens(_lowercase						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab







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







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       lowerCAmelCase_					:       Dict    =		self.sp_model.PieceToId(_lowercase						)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       List[Any]    =		"""""".join(_lowercase						).replace(_lowercase    ,						""" """						).strip()
       return out_string







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       if not os.path.isdir(_lowercase						):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
            return
       lowerCAmelCase_					:       List[str]    =		os.path.join(
           _lowercase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""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:
                 lowerCAmelCase_					:       int    =		self.sp_model.serialized_model_proto()
                 fi.write(_lowercase						)

       return (out_vocab_file,)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = "eng_Latn"    ,						_lowercase = None    ,						_lowercase = "fra_Latn"    ,						**_lowercase    ,						):
       lowerCAmelCase_					:       Optional[int]    =		src_lang
       lowerCAmelCase_					:       Dict    =		tgt_lang
       return super().prepare_seqaseq_batch(_lowercase    ,						_lowercase    ,						**_lowercase						)







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







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







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Dict    =		self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
            lowerCAmelCase_					:       Any    =		[]
            lowerCAmelCase_					:       Dict    =		[self.eos_token_id, self.cur_lang_code]
       else:
            lowerCAmelCase_					:       Any    =		[self.cur_lang_code]
            lowerCAmelCase_					:       List[str]    =		[self.eos_token_id]







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Any    =		self.lang_code_to_id[lang]
       if self.legacy_behaviour:
            lowerCAmelCase_					:       Union[str, Any]    =		[]
            lowerCAmelCase_					:       Dict    =		[self.eos_token_id, self.cur_lang_code]
       else:
            lowerCAmelCase_					:       List[str]    =		[self.cur_lang_code]
            lowerCAmelCase_					:       List[Any]    =		[self.eos_token_id]
