




'''simple docstring'''







import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase			:      Any			=					datasets.utils.logging.get_logger(__name__)







@dataclass
class 				_a   (datasets.BuilderConfig  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[datasets.Features]									=				None







class 				_a   (datasets.ArrowBasedBuilder  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[int]									=				ArrowConfig







     def  snake_case_			(							self       )			->							Tuple:
          return datasets.DatasetInfo(features=self.config.features       )







     def  snake_case_			(							self       ,__a       )			->							int:
          if not self.config.data_files:
               raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''       )
          snake_case :			Tuple					   = dl_manager.download_and_extract(self.config.data_files       )
          if isinstance(__a       ,(str, list, tuple)       ):
               snake_case :			List[str]					   = data_files
               if isinstance(__a       ,__a       ):
                    snake_case :			Optional[Any]					   = [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               snake_case :			Dict					   = [dl_manager.iter_files(__a       ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,gen_kwargs={"""files""": files}       )]
          snake_case :			str					   = []
          for split_name, files in data_files.items():
               if isinstance(__a       ,__a       ):
                    snake_case :			Optional[Any]					   = [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               snake_case :			Optional[int]					   = [dl_manager.iter_files(__a       ) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                    for file in itertools.chain.from_iterable(__a       ):
                         with open(__a       ,"""rb"""       ) as f:
                              snake_case :			Optional[int]					   = datasets.Features.from_arrow_schema(pa.ipc.open_stream(__a       ).schema       )
                         break
               splits.append(datasets.SplitGenerator(name=__a       ,gen_kwargs={"""files""": files}       )       )
          return splits







     def  snake_case_			(							self       ,__a       )			->							pa.Table:
          if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               snake_case :			List[str]					   = table_cast(__a       ,self.info.features.arrow_schema       )
          return pa_table







     def  snake_case_			(							self       ,__a       )			->							List[str]:
          for file_idx, file in enumerate(itertools.chain.from_iterable(__a       )       ):
               with open(__a       ,"""rb"""       ) as f:
                    try:
                         for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__a       )       ):
                              snake_case :			str					   = pa.Table.from_batches([record_batch]       )
                              # Uncomment for debugging (will print the Arrow table size and elements)
                              # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                              # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                              yield F'''{file_idx}_{batch_idx}''', self._cast_table(__a       )
                    except ValueError as e:
                         logger.error(F'''Failed to read file \'{file}\' with error {type(__a       )}: {e}'''       )
                         raise









'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
      import torch







class 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Tuple									=				["""image_processor""", """tokenizer"""]
     lowerCAmelCase_     :    Optional[Any]									=				"""OneFormerImageProcessor"""
     lowerCAmelCase_     :    int									=				("""CLIPTokenizer""", """CLIPTokenizerFast""")







     def __init__(							self       ,__a=None       ,__a=None       ,__a = 77       ,__a = 77       ,**__a       )			->							Optional[Any]:
          if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""       )
          if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""       )

          snake_case :			str					   = max_seq_length
          snake_case :			Any					   = task_seq_length

          super().__init__(__a       ,__a       )







     def  snake_case_			(							self       ,__a=None       ,__a=77       )			->							List[str]:
          if text_list is None:
               raise ValueError("""tokens cannot be None."""       )

          snake_case :			int					   = self.tokenizer(__a       ,padding="""max_length"""       ,max_length=__a       ,truncation=__a       )

          snake_case   ,		snake_case :			int					   = tokens["""attention_mask"""], tokens["""input_ids"""]

          snake_case :			Tuple					   = []
          for attn_mask, input_id in zip(__a       ,__a       ):
               snake_case :			Dict					   = torch.tensor(__a       ) * torch.tensor(__a       )
               token_inputs.append(token.unsqueeze(0       )       )

          snake_case :			Optional[int]					   = torch.cat(__a       ,dim=0       )
          return token_inputs







     def __call__(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							Optional[int]:

          if task_inputs is None:
               raise ValueError("""You have to specify the task_input. Found None."""       )
          elif images is None:
               raise ValueError("""You have to specify the image. Found None."""       )

          if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs       ):
               raise ValueError("""task_inputs must be semantic, instance, or panoptic."""       )

          snake_case :			Union[str, Any]					   = self.image_processor(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Tuple					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Optional[Any]					   = []
               for task in task_inputs:
                    snake_case :			Any					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Optional[int]					   = self._preprocess_text(__a       ,max_length=self.task_seq_length       )
          else:
               raise TypeError("""Task Inputs should be a string or a list of strings."""       )

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Optional[int]					   = encoded_inputs.text_inputs

               snake_case :			Dict					   = []
               for texts in texts_list:
                    snake_case :			Optional[Any]					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Tuple					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							List[str]:

          if task_inputs is None:
               raise ValueError("""You have to specify the task_input. Found None."""       )
          elif images is None:
               raise ValueError("""You have to specify the image. Found None."""       )

          if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs       ):
               raise ValueError("""task_inputs must be semantic, instance, or panoptic."""       )

          snake_case :			Any					   = self.image_processor.encode_inputs(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Optional[int]					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Dict					   = []
               for task in task_inputs:
                    snake_case :			str					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Any					   = self._preprocess_text(__a       ,max_length=self.task_seq_length       )
          else:
               raise TypeError("""Task Inputs should be a string or a list of strings."""       )

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Dict					   = encoded_inputs.text_inputs

               snake_case :			Tuple					   = []
               for texts in texts_list:
                    snake_case :			Tuple					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[str]:
          return self.image_processor.post_process_semantic_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[Any]:
          return self.image_processor.post_process_instance_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							Optional[Any]:
          return self.image_processor.post_process_panoptic_segmentation(*__a       ,**__a       )



