



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class 	snake_case_			(UpperCAmelCase_	):




       """simple docstring"""






       def __init__(  self  ,lowercase  ,lowercase = None  ,lowercase = None  ,lowercase = None  ,lowercase = False  ,lowercase = False  ,lowercase = None  ,**lowercase  ,):

             """simple docstring"""





             super().__init__(
                 a__  ,split=a__  ,features=a__  ,cache_dir=a__  ,keep_in_memory=a__  ,streaming=a__  ,num_proc=a__  ,**a__  ,)
             UpperCAmelCase_	:						Tuple					   =		path_or_paths if isinstance(a__  ,a__) else {self.split: path_or_paths}
             UpperCAmelCase_	:						Any					   =		Csv(
                 cache_dir=a__  ,data_files=a__  ,features=a__  ,**a__  ,)



       def 			A_ (  self):

             """simple docstring"""





             if self.streaming:
                   UpperCAmelCase_	:						List[Any]					   =		self.builder.as_streaming_dataset(split=self.split)
             # Build regular (map-style) dataset
             else:
                   UpperCAmelCase_	:						Tuple					   =		None
                   UpperCAmelCase_	:						Dict					   =		None
                   UpperCAmelCase_	:						Tuple					   =		None
                   UpperCAmelCase_	:						Union[str, Any]					   =		None

                   self.builder.download_and_prepare(
                       download_config=a__  ,download_mode=a__  ,verification_mode=a__  ,base_path=a__  ,num_proc=self.num_proc  ,)
                   UpperCAmelCase_	:						Optional[Any]					   =		self.builder.as_dataset(
                       split=self.split  ,verification_mode=a__  ,in_memory=self.keep_in_memory)
             return dataset


class 	snake_case_			:




       """simple docstring"""






       def __init__(  self  ,lowercase  ,lowercase  ,lowercase = None  ,lowercase = None  ,**lowercase  ,):

             """simple docstring"""





             if num_proc is not None and num_proc <= 0:
                   raise ValueError(F"""num_proc {num_proc} must be an integer > 0.""")

             UpperCAmelCase_	:						Any					   =		dataset
             UpperCAmelCase_	:						str					   =		path_or_buf
             UpperCAmelCase_	:						Any					   =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
             UpperCAmelCase_	:						int					   =		num_proc
             UpperCAmelCase_	:						int					   =		'''utf-8'''
             UpperCAmelCase_	:						Tuple					   =		to_csv_kwargs



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Dict					   =		self.to_csv_kwargs.pop("path_or_buf"  ,a__)
             UpperCAmelCase_	:						List[Any]					   =		self.to_csv_kwargs.pop("header"  ,a__)
             UpperCAmelCase_	:						List[Any]					   =		self.to_csv_kwargs.pop("index"  ,a__)

             if isinstance(self.path_or_buf  ,(str, bytes, os.PathLike)):
                   with open(self.path_or_buf  ,"wb+") as buffer:
                         UpperCAmelCase_	:						Tuple					   =		self._write(file_obj=a__  ,header=a__  ,index=a__  ,**self.to_csv_kwargs)
             else:
                   UpperCAmelCase_	:						Union[str, Any]					   =		self._write(file_obj=self.path_or_buf  ,header=a__  ,index=a__  ,**self.to_csv_kwargs)
             return written



       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						Dict					   =		args

             UpperCAmelCase_	:						int					   =		query_table(
                 table=self.dataset.data  ,key=slice(a__  ,offset + self.batch_size)  ,indices=self.dataset._indices  ,)
             UpperCAmelCase_	:						Tuple					   =		batch.to_pandas().to_csv(
                 path_or_buf=a__  ,header=header if (offset == 0) else False  ,index=a__  ,**a__)
             return csv_str.encode(self.encoding)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase  ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						List[Any]					   =		0

             if self.num_proc is None or self.num_proc == 1:
                   for offset in logging.tqdm(
                       range(0  ,len(self.dataset)  ,self.batch_size)  ,unit="ba"  ,disable=not logging.is_progress_bar_enabled()  ,desc="Creating CSV from Arrow format"  ,):
                         UpperCAmelCase_	:						Tuple					   =		self._batch_csv((offset, header, index, to_csv_kwargs))
                         written += file_obj.write(a__)

             else:
                   UpperCAmelCase_	:						int					   =		len(self.dataset), self.batch_size
                   with multiprocessing.Pool(self.num_proc) as pool:
                         for csv_str in logging.tqdm(
                             pool.imap(
                                 self._batch_csv  ,[(offset, header, index, to_csv_kwargs) for offset in range(0  ,a__  ,a__)]  ,)  ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,unit="ba"  ,disable=not logging.is_progress_bar_enabled()  ,desc="Creating CSV from Arrow format"  ,):
                               written += file_obj.write(a__)

             return written






'''simple docstring'''





from typing import List

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


if is_torch_available():
       import torch






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''OneFormerImageProcessor'''
      __lowercase					=	('''CLIPTokenizer''', '''CLIPTokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	a__ = 7_7			,	a__ = 7_7			,	**a__       )							-> str:
         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`.'''       )

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			max_seq_length
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			task_seq_length

         super().__init__(a__			,	a__       )






      def  A__(self			,	a__=None			,	a__=7_7       )							-> str:
         if text_list is None:
            raise ValueError('''tokens cannot be None.'''       )

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(a__			,	padding='''max_length'''			,	max_length=a__			,	truncation=a__       )

         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			tokens['''attention_mask'''], tokens['''input_ids''']

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         for attn_mask, input_id in zip(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.tensor(a__       ) * torch.tensor(a__       )
            token_inputs.append(token.unsqueeze(0       )       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.cat(a__			,	dim=0       )
         return token_inputs






      def __call__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> 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.'''       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.image_processor(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[task_inputs]

         if isinstance(a__			,	a__       ) and all(isinstance(a__			,	a__       ) for task_input in task_inputs       ):
            SCREAMING_SNAKE_CASE__      :					Any							    =			[]
            for task in task_inputs:
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			f"""the task is {task}"""
               task_token_inputs.append(a__       )
            SCREAMING_SNAKE_CASE__      :					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'''       ):
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					int							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> Any:

         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.'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.image_processor.encode_inputs(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[task_inputs]

         if isinstance(a__			,	a__       ) and all(isinstance(a__			,	a__       ) for task_input in task_inputs       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for task in task_inputs:
               SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			f"""the task is {task}"""
               task_token_inputs.append(a__       )
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			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'''       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.image_processor.post_process_semantic_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.image_processor.post_process_instance_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Tuple:
         return self.image_processor.post_process_panoptic_segmentation(*a__			,	**a__       )

