




'''simple docstring'''





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


lowercase__								=						1_0_0_0_0_0_0_0_0_0_0_0
lowercase__								=						1_0_0_0_0

lowercase__						,  lowercase__								=						os.path.split(__file__)
lowercase__								=						os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Dict							    =			pa.Table.from_pydict({'''col''': [0] * chunk_size} )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			pa.concat_tables([table] * (num_examples // chunk_size) )
   return datasets.Dataset(snake_case						,  fingerprint='''table_100B''' )






@dataclass
class     lowercase  :
      __lowercase					=	42
      __lowercase					=	42
      __lowercase					=	42
      __lowercase					=	42






      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			np.random.default_rng(self.seed       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			rng.integers(low=self.low			,	high=self.high			,	size=self.size       ).tolist()






      def __iter__(self       )							-> Dict:
         return iter(self._sampled_values       )






      def __len__(self       )							-> int:
         return self.size


@get_duration
def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			dataset[0]


@get_duration
def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			dataset[-1]


@get_duration
def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			dataset[range(len(snake_case ) // 2						,  len(snake_case ) // 2 + 10_24 )]


@get_duration
def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			dataset[RandIter(0						,  len(snake_case )						,  10_24						,  seed=42 )]


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Tuple							    =			{'''num examples''': SPEED_TEST_N_EXAMPLES}
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
   print('''generating dataset''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			generate_100B_dataset(num_examples=snake_case						,  chunk_size=snake_case )
   print('''Functions''' )
   for func in functions:
      print(func.__name__ )
      SCREAMING_SNAKE_CASE__      :					Dict							    =			func(snake_case )

   with open(snake_case						,  '''wb''' ) as f:
      f.write(json.dumps(snake_case ).encode('''utf-8''' ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_table_100B()







'''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__       )

