






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_mbartaa import MBartaaTokenizer
else:
							lowercase     :							Union[str, Any]	     =			None


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

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

lowercase     :							Union[str, Any]	     =			{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

lowercase     :							Any	     =			{
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
lowercase     :							Dict	     =			['''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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      Optional[int]						 =    VOCAB_FILES_NAMES
						A   :      Optional[int]						 =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A   :      Dict						 =    PRETRAINED_VOCAB_FILES_MAP
						A   :      List[str]						 =    ["input_ids", "attention_mask"]
						A   :      List[Any]						 =    MBartaaTokenizer

						A   :      List[int]						 =    []
						A   :      List[int]						 =    []






						def __init__(		self						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE="</s>"						, _SCREAMING_SNAKE_CASE="</s>"						, _SCREAMING_SNAKE_CASE="<s>"						, _SCREAMING_SNAKE_CASE="<unk>"						, _SCREAMING_SNAKE_CASE="<pad>"						, _SCREAMING_SNAKE_CASE="<mask>"						, **_SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							List[str]         =				AddedToken(lowercase__						, lstrip=lowercase__						, rstrip=lowercase__ ) if isinstance(lowercase__						, lowercase__ ) else mask_token

								snake_case_ :							Optional[int]         =				kwargs.get("additional_special_tokens"						, [] )
								kwargs["additional_special_tokens"] += [
								    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    lowercase__						, src_lang=lowercase__						, tgt_lang=lowercase__						, tokenizer_file=lowercase__						, eos_token=lowercase__						, sep_token=lowercase__						, cls_token=lowercase__						, unk_token=lowercase__						, pad_token=lowercase__						, mask_token=lowercase__						, **lowercase__						, )

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

								snake_case_ :							int         =				{
								    lang_code: self.convert_tokens_to_ids(lowercase__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								snake_case_ :							List[Any]         =				src_lang if src_lang is not None else "en_XX"
								snake_case_ :							Optional[int]         =				tgt_lang
								snake_case_ :							Optional[Any]         =				self.lang_code_to_id[self._src_lang]
								self.set_src_lang_special_tokens(self._src_lang )






						@property
						def 					_lowerCAmelCase				(		self )       -> str:
								return self._src_lang






						@src_lang.setter
						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> None:
								snake_case_ :							str         =				new_src_lang
								self.set_src_lang_special_tokens(self._src_lang )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None )       -> List[int]:







								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 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = "en_XX"						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = "ro_RO"						, **_SCREAMING_SNAKE_CASE						, )       -> BatchEncoding:
								snake_case_ :							Optional[int]         =				src_lang
								snake_case_ :							int         =				tgt_lang
								return super().prepare_seqaseq_batch(lowercase__						, lowercase__						, **lowercase__ )






						def 					_lowerCAmelCase				(		self )       -> Dict:
								return self.set_src_lang_special_tokens(self.src_lang )






						def 					_lowerCAmelCase				(		self )       -> Optional[int]:
								return self.set_tgt_lang_special_tokens(self.tgt_lang )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> None:
								snake_case_ :							Dict         =				self.convert_tokens_to_ids(lowercase__ )
								snake_case_ :							List[str]         =				[self.cur_lang_code_id]
								snake_case_ :							Union[str, Any]         =				[self.eos_token_id]

								snake_case_ :							Dict         =				self.convert_ids_to_tokens(self.prefix_tokens )
								snake_case_ :							Any         =				self.convert_ids_to_tokens(self.suffix_tokens )

								snake_case_ :							Dict         =				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 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> None:
								snake_case_ :							List[Any]         =				self.convert_tokens_to_ids(lowercase__ )
								snake_case_ :							Any         =				[self.cur_lang_code_id]
								snake_case_ :							Tuple         =				[self.eos_token_id]

								snake_case_ :							int         =				self.convert_ids_to_tokens(self.prefix_tokens )
								snake_case_ :							List[Any]         =				self.convert_ids_to_tokens(self.suffix_tokens )

								snake_case_ :							int         =				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 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )       -> Tuple:







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






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None )       -> Tuple[str]:







								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(lowercase__ ):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
										return
								snake_case_ :							Dict         =				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__ ):
										copyfile(self.vocab_file						, lowercase__ )

								return (out_vocab_file,)


"""simple docstring"""




from typing import Any, Dict, List, Union

import numpy as np

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 PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	from ..models.auto.modeling_auto import (
	    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
	    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
	    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
	    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
	)


snake_case    =		logging.get_logger(__name__)


snake_case    =		Dict[str, Any]
snake_case    =		List[Prediction]




@add_end_docstrings(__magic_name__      )
class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	*lowercase__		,	**lowercase__			)    ->    Dict:
									"""simple docstring"""







									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_IMAGE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)


				def 							A					(				self		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	{}
									SCREAMING_SNAKE_CASE				=	{}
									if "subtask" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['subtask']
														SCREAMING_SNAKE_CASE				=	kwargs['subtask']
									if "threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['threshold']
									if "mask_threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['mask_threshold']
									if "overlap_mask_area_threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['overlap_mask_area_threshold']

									return preprocess_kwargs, {}, postprocess_kwargs


				def __call__(				self		,	lowercase__		,	**lowercase__			)    ->    Union[Predictions, List[Prediction]]:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__		,	lowercase__=None			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	load_image(lowercase__			)
									SCREAMING_SNAKE_CASE				=	[(image.height, image.width)]
									if self.model.config.__class__.__name__ == "OneFormerConfig":
														if subtask is None:
																			SCREAMING_SNAKE_CASE				=	{}
														else:
																			SCREAMING_SNAKE_CASE				=	{'task_inputs': [subtask]}
														SCREAMING_SNAKE_CASE				=	self.image_processor(images=[image]		,	return_tensors='pt'		,	**lowercase__			)
														SCREAMING_SNAKE_CASE				=	self.tokenizer(
														    inputs['task_inputs']		,	padding='max_length'		,	max_length=self.model.config.task_seq_len		,	return_tensors=self.framework		,	)['input_ids']
									else:
														SCREAMING_SNAKE_CASE				=	self.image_processor(images=[image]		,	return_tensors='pt'			)
									SCREAMING_SNAKE_CASE				=	target_size
									return inputs


				def 							A					(				self		,	lowercase__			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	model_inputs.pop('target_size'			)
									SCREAMING_SNAKE_CASE				=	self.model(**lowercase__			)
									SCREAMING_SNAKE_CASE				=	target_size
									return model_outputs




				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=0.9		,	lowercase__=0.5		,	lowercase__=0.5			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	None
									if subtask in {"panoptic", None} and hasattr(self.image_processor		,	'post_process_panoptic_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_panoptic_segmentation
									elif subtask in {"instance", None} and hasattr(self.image_processor		,	'post_process_instance_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_instance_segmentation

									if fn is not None:
														SCREAMING_SNAKE_CASE				=	fn(
														    lowercase__		,	threshold=lowercase__		,	mask_threshold=lowercase__		,	overlap_mask_area_threshold=lowercase__		,	target_sizes=model_outputs['target_size']		,	)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs['segmentation']

														for segment in outputs["segments_info"]:
																			SCREAMING_SNAKE_CASE				=	(segmentation == segment['id']) * 255
																			SCREAMING_SNAKE_CASE				=	Image.fromarray(mask.numpy().astype(np.uinta			)		,	mode='L'			)
																			SCREAMING_SNAKE_CASE				=	self.model.config.idalabel[segment['label_id']]
																			SCREAMING_SNAKE_CASE				=	segment['score']
																			annotation.append({'score': score, 'label': label, 'mask': mask}			)

									elif subtask in {"semantic", None} and hasattr(self.image_processor		,	'post_process_semantic_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_semantic_segmentation(
														    lowercase__		,	target_sizes=model_outputs['target_size']			)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs.numpy()
														SCREAMING_SNAKE_CASE				=	np.unique(lowercase__			)

														for label in labels:
																			SCREAMING_SNAKE_CASE				=	(segmentation == label) * 255
																			SCREAMING_SNAKE_CASE				=	Image.fromarray(mask.astype(np.uinta			)		,	mode='L'			)
																			SCREAMING_SNAKE_CASE				=	self.model.config.idalabel[label]
																			annotation.append({'score': None, 'label': label, 'mask': mask}			)
									else:
														raise ValueError(f'''Subtask {subtask} is not supported for model {type(self.model			)}'''			)
									return annotation
