

import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


_lowerCamelCase							=get_logger(__name__)







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     return TestCommand(
         args.dataset,       args.name,       args.cache_dir,       args.data_dir,       args.all_configs,       args.save_info or args.save_infos,       args.ignore_verifications,       args.force_redownload,       args.clear_cache,       )



class 						a_			(				A__	):



      """simple docstring"""







      __UpperCAmelCase     						= False  # to tell pytest it's not a test class
      @staticmethod
      def 			_lowerCAmelCase	( snake_case	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		=parser.add_parser('test'     ,help='Test dataset implementation.'							)
           test_parser.add_argument('--name'     ,type=snake_case     ,default=snake_case     ,help='Dataset processing name'							)
           test_parser.add_argument(
               '--cache_dir'     ,type=snake_case     ,default=snake_case     ,help='Cache directory where the datasets are stored.'     ,)
           test_parser.add_argument(
               '--data_dir'     ,type=snake_case     ,default=snake_case     ,help='Can be used to specify a manual directory to get the files from.'     ,)
           test_parser.add_argument('--all_configs'     ,action='store_true'     ,help='Test all dataset configurations'							)
           test_parser.add_argument(
               '--save_info'     ,action='store_true'     ,help='Save the dataset infos in the dataset card (README.md)'							)
           test_parser.add_argument(
               '--ignore_verifications'     ,action='store_true'     ,help='Run the test without checksums and splits checks.'     ,)
           test_parser.add_argument('--force_redownload'     ,action='store_true'     ,help='Force dataset redownload'							)
           test_parser.add_argument(
               '--clear_cache'     ,action='store_true'     ,help='Remove downloaded files and cached datasets after each config test'     ,)
           # aliases
           test_parser.add_argument('--save_infos'     ,action='store_true'     ,help='alias to save_info'							)
           test_parser.add_argument('dataset'     ,type=snake_case     ,help='Name of the dataset to download'							)
           test_parser.set_defaults(func=_test_command_factory							)
      def __init__( self	:				Optional[Any]     ,snake_case	:				Dict     ,snake_case	:				str     ,snake_case	:				Optional[Any]     ,snake_case	:				List[Any]     ,snake_case	:				Tuple     ,snake_case	:				Union[str, Any]     ,snake_case	:				List[Any]     ,snake_case	:				Any     ,snake_case	:				Any     ,):
           SCREAMING_SNAKE_CASE 		=dataset
           SCREAMING_SNAKE_CASE 		=name
           SCREAMING_SNAKE_CASE 		=cache_dir
           SCREAMING_SNAKE_CASE 		=data_dir
           SCREAMING_SNAKE_CASE 		=all_configs
           SCREAMING_SNAKE_CASE 		=save_infos
           SCREAMING_SNAKE_CASE 		=ignore_verifications
           SCREAMING_SNAKE_CASE 		=force_redownload
           SCREAMING_SNAKE_CASE 		=clear_cache
           if clear_cache and not cache_dir:
                print(
                    'When --clear_cache is used, specifying a cache directory is mandatory.\n'
                    'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
                    'Please provide a --cache_dir that will be used to test the dataset script.'							)
                exit(1							)
           if save_infos:
                SCREAMING_SNAKE_CASE 		=True


      def 			_lowerCAmelCase	( self	:				Dict							):
           fl_logger().setLevel(snake_case							)
           if self._name is not None and self._all_configs:
                print('Both parameters `config` and `all_configs` can\'t be used at once.'							)
                exit(1							)
           SCREAMING_SNAKE_CASE 		=self._dataset, self._name
           SCREAMING_SNAKE_CASE 		=dataset_module_factory(snake_case							)
           SCREAMING_SNAKE_CASE 		=import_main_class(module.module_path							)
           SCREAMING_SNAKE_CASE 		=len(builder_cls.BUILDER_CONFIGS							) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

           def get_builders() -> Generator[DatasetBuilder, None, None]:
                if self._all_configs and builder_cls.BUILDER_CONFIGS:
                     for i, config in enumerate(builder_cls.BUILDER_CONFIGS							):
                          if "config_name" in module.builder_kwargs:
                               yield builder_cls(
                                   cache_dir=self._cache_dir     ,data_dir=self._data_dir     ,**module.builder_kwargs     ,)
                          else:
                               yield builder_cls(
                                   config_name=config.name     ,cache_dir=self._cache_dir     ,data_dir=self._data_dir     ,**module.builder_kwargs     ,)
                else:
                     if "config_name" in module.builder_kwargs:
                          yield builder_cls(cache_dir=self._cache_dir     ,data_dir=self._data_dir     ,**module.builder_kwargs							)
                     else:
                          yield builder_cls(
                              config_name=snake_case     ,cache_dir=self._cache_dir     ,data_dir=self._data_dir     ,**module.builder_kwargs     ,)

           for j, builder in enumerate(get_builders()							):
                print(f'Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'							)
                SCREAMING_SNAKE_CASE 		=os.path.exists(
                    os.path.join(builder.get_imported_module_dir()     ,datasets.config.DATASETDICT_INFOS_FILENAME							)							)  # record checksums only if we need to update a (deprecated) dataset_infos.json
                builder.download_and_prepare(
                    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                    if not self._force_redownload
                    else DownloadMode.FORCE_REDOWNLOAD     ,verification_mode=VerificationMode.NO_CHECKS
                    if self._ignore_verifications
                    else VerificationMode.ALL_CHECKS     ,try_from_hf_gcs=snake_case     ,)
                builder.as_dataset()
                if self._save_infos:
                     builder._save_infos()

                # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
                # The dataset_infos are saved in the YAML part of the README.md

                # Let's move it to the original directory of the dataset script, to allow the user to
                # upload them on S3 at the same time afterwards.
                if self._save_infos:
                     SCREAMING_SNAKE_CASE 		=os.path.join(builder_cls.get_imported_module_dir()     ,'README.md'							)
                     SCREAMING_SNAKE_CASE 		=Path(snake_case							).name + '''.py'''
                     SCREAMING_SNAKE_CASE 		=os.path.join(snake_case     ,snake_case							)
                     if os.path.isfile(snake_case							):
                          SCREAMING_SNAKE_CASE 		=os.path.dirname(snake_case							)
                     elif os.path.isfile(snake_case							):
                          SCREAMING_SNAKE_CASE 		=path
                     elif os.path.isdir(snake_case							):  # for local directories containing only data files
                          SCREAMING_SNAKE_CASE 		=path
                     else:  # in case of a remote dataset
                          SCREAMING_SNAKE_CASE 		=None
                          print(f'Dataset card saved at {dataset_readme_path}'							)

                     # Move dataset_info back to the user
                     if dataset_dir is not None:
                          SCREAMING_SNAKE_CASE 		=os.path.join(snake_case     ,'README.md'							)
                          copyfile(snake_case     ,snake_case							)
                          print(f'Dataset card saved at {user_dataset_readme_path}'							)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
                if self._clear_cache:
                     if os.path.isdir(builder._cache_dir							):
                          logger.warning(f'Clearing cache at {builder._cache_dir}'							)
                          rmtree(builder._cache_dir							)
                     SCREAMING_SNAKE_CASE 		=os.path.join(self._cache_dir     ,datasets.config.DOWNLOADED_DATASETS_DIR							)
                     if os.path.isdir(snake_case							):
                          logger.warning(f'Clearing cache at {download_dir}'							)
                          rmtree(snake_case							)

           print('Test successful.'							)


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,
    )


_lowerCAmelCase        =       logging.get_logger(__name__)


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






@add_end_docstrings(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        super().__init__(*__magic_name__ ,       **__magic_name__      )

        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  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        A_							:       str	      =				{}
        A_							:       List[Any]	      =				{}
        if "subtask" in kwargs:
               A_							:       Optional[int]	      =				kwargs['''subtask''']
               A_							:       Dict	      =				kwargs['''subtask''']
        if "threshold" in kwargs:
               A_							:       Tuple	      =				kwargs['''threshold''']
        if "mask_threshold" in kwargs:
               A_							:       int	      =				kwargs['''mask_threshold''']
        if "overlap_mask_area_threshold" in kwargs:
               A_							:       Optional[Any]	      =				kwargs['''overlap_mask_area_threshold''']

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

        """simple docstring"""

        return super().__call__(__magic_name__ ,       **__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

        """simple docstring"""

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

        """simple docstring"""

        A_							:       Dict	      =				model_inputs.pop('''target_size'''      )
        A_							:       Union[str, Any]	      =				self.model(**__magic_name__      )
        A_							:       Any	      =				target_size
        return model_outputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=0.9 ,       __magic_name__=0.5 ,       __magic_name__=0.5      ):

        """simple docstring"""

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

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

               A_							:       Optional[Any]	      =				[]
               A_							:       Optional[int]	      =				outputs['''segmentation''']

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

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

               A_							:       Tuple	      =				[]
               A_							:       Any	      =				outputs.numpy()
               A_							:       Optional[Any]	      =				np.unique(__magic_name__      )

               for label in labels:
                      A_							:       Any	      =				(segmentation == label) * 255
                      A_							:       str	      =				Image.fromarray(mask.astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       List[str]	      =				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


