




'''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


__UpperCamelCase			:      int			=					1000_0000_0000
__UpperCamelCase			:      Dict			=					1_0000

__UpperCamelCase			:      List[str]			=					os.path.split(__file__)
__UpperCamelCase			:      Dict			=					os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     snake_case :			Tuple					   = pa.Table.from_pydict({"""col""": [0] * chunk_size}							)
     snake_case :			str					   = pa.concat_tables([table] * (num_examples // chunk_size)							)
     return datasets.Dataset(__lowercase    ,      fingerprint="""table_100B"""							)







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    int







     def  snake_case_			(							self       )			->							str:
          snake_case :			int					   = np.random.default_rng(self.seed       )
          snake_case :			Dict					   = rng.integers(low=self.low       ,high=self.high       ,size=self.size       ).tolist()







     def __iter__(							self       )			->							Optional[int]:
          return iter(self._sampled_values       )







     def __len__(							self       )			->							List[Any]:
          return self.size



@get_duration
def 					lowerCamelCase__							(			__lowercase							):
     snake_case :			Optional[Any]					   = dataset[0]



@get_duration
def 					lowerCamelCase__							(			__lowercase							):
     snake_case :			str					   = dataset[-1]



@get_duration
def 					lowerCamelCase__							(			__lowercase							):
     snake_case :			List[str]					   = dataset[range(len(__lowercase							) // 2    ,      len(__lowercase							) // 2 + 1_024							)]



@get_duration
def 					lowerCamelCase__							(			__lowercase							):
     snake_case :			str					   = dataset[RandIter(0    ,      len(__lowercase							)    ,      1_024    ,      seed=42							)]



def 					lowerCamelCase__							(			):
     snake_case :			List[Any]					   = {"""num examples""": SPEED_TEST_N_EXAMPLES}
     snake_case :			Any					   = (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
     print("""generating dataset"""							)
     snake_case :			Any					   = generate_100B_dataset(num_examples=__lowercase    ,      chunk_size=__lowercase							)
     print("""Functions"""							)
     for func in functions:
          print(func.__name__							)
          snake_case :			int					   = func(__lowercase							)

     with open(__lowercase    ,      """wb"""							) as f:
          f.write(json.dumps(__lowercase							).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 				_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       )



