
import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


_lowerCamelCase        =datasets.utils.logging.get_logger(__name__)





@dataclass
class 							A__		(     datasets.BuilderConfig):
						_UpperCAmelCase    :   Optional[datasets.Features]   	=      None
						_UpperCAmelCase    :   str   	=      "utf-8"
						_UpperCAmelCase    :   Optional[str]   	=      None
						_UpperCAmelCase    :   int   	=      10 << 20  # 10MB
						_UpperCAmelCase    :   bool   	=      False
						_UpperCAmelCase    :   str   	=      "line"





class 							A__		(     datasets.ArrowBasedBuilder):
						_UpperCAmelCase    :   str   	=      TextConfig
						def 						UpperCamelCase__							(				self				):
							return datasets.DatasetInfo(features=self.config.features				)
						def 						UpperCamelCase__							(				self   ,  __magic_name__				):
							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}'''				)
							lowerCamelCase		:     Union[str, Any]          =				dl_manager.download_and_extract(self.config.data_files				)
							if isinstance(_A   ,  (str, list, tuple)				):
								lowerCamelCase		:     Union[str, Any]          =				data_files
								if isinstance(_A   ,  _A				):
									lowerCamelCase		:     Tuple          =				[files]
								lowerCamelCase		:     List[str]          =				[dl_manager.iter_files(_A				) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,  gen_kwargs={"""files""": files}				)]
							lowerCamelCase		:     Tuple          =				[]
							for split_name, files in data_files.items():
								if isinstance(_A   ,  _A				):
									lowerCamelCase		:     Dict          =				[files]
								lowerCamelCase		:     Optional[Any]          =				[dl_manager.iter_files(_A				) for file in files]
								splits.append(datasets.SplitGenerator(name=_A   ,  gen_kwargs={"""files""": files}				)				)
							return splits
						def 						UpperCamelCase__							(				self   ,  __magic_name__				):
							if self.config.features is not None:
								lowerCamelCase		:     Dict          =				self.config.features.arrow_schema
								if all(not require_storage_cast(_A				) for feature in self.config.features.values()				):
									# cheaper cast
									lowerCamelCase		:     Optional[Any]          =				pa_table.cast(_A				)
								else:
									# more expensive cast; allows str <-> int/float or str to Audio for example
									lowerCamelCase		:     Dict          =				table_cast(_A   ,  _A				)
								return pa_table
							else:
								return pa_table.cast(pa.schema({"""text""": pa.string()}				)				)






						def 						UpperCamelCase__							(				self   ,  __magic_name__				):
							lowerCamelCase		:     Any          =				list(self.config.features				) if self.config.features is not None else ['text']
							for file_idx, file in enumerate(itertools.chain.from_iterable(_A				)				):
								# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
								with open(_A   ,  encoding=self.config.encoding   ,  errors=self.config.errors				) as f:
									if self.config.sample_by == "line":
										lowerCamelCase		:     Tuple          =				0
										while True:
											lowerCamelCase		:     int          =				f.read(self.config.chunksize				)
											if not batch:
												break
											batch += f.readline()  # finish current line
											# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
											lowerCamelCase		:     Any          =				StringIO(_A				).readlines()
											if not self.config.keep_linebreaks:
												lowerCamelCase		:     List[str]          =				[line.rstrip("""\n"""				) for line in batch]
											lowerCamelCase		:     List[Any]          =				pa.Table.from_arrays([pa.array(_A				)]   ,  names=_A				)
											# 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 (file_idx, batch_idx), self._cast_table(_A				)
											batch_idx += 1
									elif self.config.sample_by == "paragraph":
										lowerCamelCase		:     Tuple          =				0
										lowerCamelCase		:     List[str]          =				''
										while True:
											lowerCamelCase		:     str          =				f.read(self.config.chunksize				)
											if not new_batch:
												break
											batch += new_batch
											batch += f.readline()  # finish current line
											lowerCamelCase		:     Dict          =				batch.split("""\n\n"""				)
											lowerCamelCase		:     Tuple          =				pa.Table.from_arrays(
											    [pa.array([example for example in batch[:-1] if example]				)]   ,  names=_A				)
											# 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 (file_idx, batch_idx), self._cast_table(_A				)
											batch_idx += 1
											lowerCamelCase		:     Union[str, Any]          =				batch[-1]
										if batch:
											lowerCamelCase		:     List[Any]          =				pa.Table.from_arrays([pa.array([batch]				)]   ,  names=_A				)
											yield (file_idx, batch_idx), self._cast_table(_A				)
									elif self.config.sample_by == "document":
										lowerCamelCase		:     Optional[Any]          =				f.read()
										lowerCamelCase		:     Dict          =				pa.Table.from_arrays([pa.array([text]				)]   ,  names=_A				)
										yield file_idx, self._cast_table(_A				)






from collections import defaultdict





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     defaultdict[str, int]          =				defaultdict(lowerCamelCase							)
 # Creating a dictionary containing count of each word
 for word in sentence.split():
  occurrence[word] += 1
 return occurrence


if __name__ == "__main__":
      for word, count in word_occurrence("""INPUT STRING""").items():
            print(f'''{word}: {count}''')




