





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,
				)


__A       :					Tuple     					=     logging.get_logger(__name__)


__A       :					Tuple     					=     Dict[str, Any]
__A       :					Optional[Any]     					=     List[Prediction]

@add_end_docstrings(a_    )
class 			A_ (a_    ):
	def __init__(					self							,      *_A							,      **_A	):






				'''simple docstring'''







				super().__init__(*_A							,      **_A	)

				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   _lowercase       (					self							,      **_A	):






				'''simple docstring'''







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

				return preprocess_kwargs, {}, postprocess_kwargs
	def __call__(					self							,      _A							,      **_A	):






				'''simple docstring'''







				return super().__call__(_A							,      **_A	)
	def   _lowercase       (					self							,      _A							,      _A=None	):






				'''simple docstring'''







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






				'''simple docstring'''







				UpperCAmelCase     	=       model_inputs.pop('''target_size'''	)
				UpperCAmelCase     	=       self.model(**_A	)
				UpperCAmelCase     	=       target_size
				return model_outputs







	def   _lowercase       (					self							,      _A							,      _A=None							,      _A=0.9							,      _A=0.5							,      _A=0.5	):






				'''simple docstring'''







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

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

							UpperCAmelCase     	=       []
							UpperCAmelCase     	=       outputs['''segmentation''']

							for segment in outputs["segments_info"]:
										UpperCAmelCase     	=       (segmentation == segment['''id''']) * 2_5_5
										UpperCAmelCase     	=       Image.fromarray(mask.numpy().astype(np.uinta	)							,      mode='''L'''	)
										UpperCAmelCase     	=       self.model.config.idalabel[segment['''label_id''']]
										UpperCAmelCase     	=       segment['''score''']
										annotation.append({'''score''': score, '''label''': label, '''mask''': mask}	)

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

							UpperCAmelCase     	=       []
							UpperCAmelCase     	=       outputs.numpy()
							UpperCAmelCase     	=       np.unique(_A	)

							for label in labels:
										UpperCAmelCase     	=       (segmentation == label) * 2_5_5
										UpperCAmelCase     	=       Image.fromarray(mask.astype(np.uinta	)							,      mode='''L'''	)
										UpperCAmelCase     	=       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










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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
				from .tokenization_deberta_va import DebertaVaTokenizer
else:
				__A       :					Optional[int]     					=     None

__A       :					Dict     					=     logging.get_logger(__name__)

__A       :					Union[str, Any]     					=     {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

__A       :					Union[str, Any]     					=     {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

__A       :					List[Any]     					=     {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

__A       :					Any     					=     {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}

class 			A_ (a_    ):
	UpperCAmelCase__      =    VOCAB_FILES_NAMES
	UpperCAmelCase__      =    PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__      =    PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__      =    DebertaVaTokenizer
	def __init__(					self							,      _A=None							,      _A=None							,      _A=False							,      _A=False							,      _A="[CLS]"							,      _A="[SEP]"							,      _A="[UNK]"							,      _A="[SEP]"							,      _A="[PAD]"							,      _A="[CLS]"							,      _A="[MASK]"							,      **_A							,      ):






				'''simple docstring'''







				super().__init__(
				    _A							,      tokenizer_file=_A							,      do_lower_case=_A							,      bos_token=_A							,      eos_token=_A							,      unk_token=_A							,      sep_token=_A							,      pad_token=_A							,      cls_token=_A							,      mask_token=_A							,      split_by_punct=_A							,      **_A							,      )

				UpperCAmelCase     	=       do_lower_case
				UpperCAmelCase     	=       split_by_punct
				UpperCAmelCase     	=       vocab_file
				UpperCAmelCase     	=       False if not self.vocab_file else True
	def   _lowercase       (					self							,      _A							,      _A=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 + token_ids_a + sep
	def   _lowercase       (					self							,      _A							,      _A=None							,      _A=False	):






				'''simple docstring'''







				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 not None:
							return [1] + ([0] * len(_A	)) + [1] + ([0] * len(_A	)) + [1]
				return [1] + ([0] * len(_A	)) + [1]
	def   _lowercase       (					self							,      _A							,      _A=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	) * [0] + len(token_ids_a + sep	) * [1]







	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				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(_A	):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
							return
				UpperCAmelCase     	=       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	):
							copyfile(self.vocab_file							,      _A	)

				return (out_vocab_file,)



