




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,
      )


__SCREAMING_SNAKE_CASE    :   Union[str, Any]       =logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE    :   int       =Dict[str, Any]
__SCREAMING_SNAKE_CASE    :   Tuple       =List[Prediction]



@add_end_docstrings(A__		)
class 			A			(		A__		):
     """simple docstring"""

     def __init__(    self  :   List[str]	,  *A__  :   List[str]	,  **A__  :   Tuple			)				->       Dict:
           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_IMAGE_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)




     def  __snake_case   (    self  :   str	,  **A__  :   List[str]			)				->       Union[str, Any]:
           lowerCamelCase__        =				{}
           lowerCamelCase__        =				{}
           if "subtask" in kwargs:
                 lowerCamelCase__        =				kwargs['subtask']
                 lowerCamelCase__        =				kwargs['subtask']
           if "threshold" in kwargs:
                 lowerCamelCase__        =				kwargs['threshold']
           if "mask_threshold" in kwargs:
                 lowerCamelCase__        =				kwargs['mask_threshold']
           if "overlap_mask_area_threshold" in kwargs:
                 lowerCamelCase__        =				kwargs['overlap_mask_area_threshold']

           return preprocess_kwargs, {}, postprocess_kwargs




     def __call__(    self  :   str	,  A__  :   Dict	,  **A__  :   Tuple			)				->       List[Any]:
           return super().__call__(_lowerCAmelCase	,  **_lowerCAmelCase			)




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   str	,  A__  :   Dict=None			)				->       Optional[Any]:
           lowerCamelCase__        =				load_image(_lowerCAmelCase			)
           lowerCamelCase__        =				[(image.height, image.width)]
           if self.model.config.__class__.__name__ == "OneFormerConfig":
                 if subtask is None:
                       lowerCamelCase__        =				{}
                 else:
                       lowerCamelCase__        =				{'task_inputs': [subtask]}
                 lowerCamelCase__        =				self.image_processor(images=[image]	,  return_tensors='pt'	,  **_lowerCAmelCase			)
                 lowerCamelCase__        =				self.tokenizer(
                     inputs['task_inputs']	,  padding='max_length'	,  max_length=self.model.config.task_seq_len	,  return_tensors=self.framework	,  )['input_ids']
           else:
                 lowerCamelCase__        =				self.image_processor(images=[image]	,  return_tensors='pt'			)
           lowerCamelCase__        =				target_size
           return inputs




     def  __snake_case   (    self  :   int	,  A__  :   str			)				->       Optional[int]:
           lowerCamelCase__        =				model_inputs.pop('target_size'			)
           lowerCamelCase__        =				self.model(**_lowerCAmelCase			)
           lowerCamelCase__        =				target_size
           return model_outputs




     def  __snake_case   (    self  :   str	,  A__  :   Any	,  A__  :   Optional[Any]=None	,  A__  :   List[Any]=0.9	,  A__  :   Union[str, Any]=0.5	,  A__  :   List[str]=0.5			)				->       str:
           lowerCamelCase__        =				None
           if subtask in {"panoptic", None} and hasattr(self.image_processor	,  'post_process_panoptic_segmentation'			):
                 lowerCamelCase__        =				self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor	,  'post_process_instance_segmentation'			):
                 lowerCamelCase__        =				self.image_processor.post_process_instance_segmentation

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

                 lowerCamelCase__        =				[]
                 lowerCamelCase__        =				outputs['segmentation']

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

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

                 lowerCamelCase__        =				[]
                 lowerCamelCase__        =				outputs.numpy()
                 lowerCamelCase__        =				np.unique(_lowerCAmelCase			)

                 for label in labels:
                       lowerCamelCase__        =				(segmentation == label) * 2_5_5
                       lowerCamelCase__        =				Image.fromarray(mask.astype(np.uinta			)	,  mode='L'			)
                       lowerCamelCase__        =				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 argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE    :   List[str]       =logging.getLogger()

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowerCamelCase__        =				parser.parse_args()
      return args.f






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				logging.StreamHandler(sys.stdout			)
           logger.addHandler(A__			)

           lowerCamelCase__        =				self.get_auto_remove_tmp_dir()
           lowerCamelCase__        =				F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

           with patch.object(A__	,  'argv'	,  A__			):
                 lowerCamelCase__        =				run_glue_with_pabee.main()
                 for value in result.values():
                       self.assertGreaterEqual(A__	,  0.75			)