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

import fsspec

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







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       List[str] ,			_lowercase    :       NestedDataStructureLike[PathLike] ,			_lowercase    :       Optional[NamedSplit] = None ,			_lowercase    :       Optional[Features] = None ,			_lowercase    :       str = None ,			_lowercase    :       bool = False ,			_lowercase    :       bool = False ,			_lowercase    :       Optional[str] = None ,			_lowercase    :       Optional[int] = None ,			**_lowercase    :       Tuple ,			):



     """simple docstring"""






     super().__init__(
         _lowercase ,			split=_lowercase ,			features=_lowercase ,			cache_dir=_lowercase ,			keep_in_memory=_lowercase ,			streaming=_lowercase ,			num_proc=_lowercase ,			**_lowercase ,			)
     SCREAMING_SNAKE_CASE__    							=   field
     SCREAMING_SNAKE_CASE__    							=   path_or_paths if isinstance(_lowercase ,			_lowercase   ) else {self.split: path_or_paths}
     SCREAMING_SNAKE_CASE__    							=   Json(
         cache_dir=_lowercase ,			data_files=_lowercase ,			features=_lowercase ,			field=_lowercase ,			**_lowercase ,			)




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     if self.streaming:
       SCREAMING_SNAKE_CASE__    							=   self.builder.as_streaming_dataset(split=self.split   )
     # Build regular (map-style) dataset
     else:
       SCREAMING_SNAKE_CASE__    							=   None
       SCREAMING_SNAKE_CASE__    							=   None
       SCREAMING_SNAKE_CASE__    							=   None
       SCREAMING_SNAKE_CASE__    							=   None

       self.builder.download_and_prepare(
           download_config=_lowercase ,			download_mode=_lowercase ,			verification_mode=_lowercase ,			base_path=_lowercase ,			num_proc=self.num_proc ,			)
       SCREAMING_SNAKE_CASE__    							=   self.builder.as_dataset(
           split=self.split ,			verification_mode=_lowercase ,			in_memory=self.keep_in_memory   )
     return dataset







class __snake_case       :




   def __init__(					self    :       Tuple ,			_lowercase    :       Dataset ,			_lowercase    :       Union[PathLike, BinaryIO] ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       Optional[int] = None ,			**_lowercase    :       Union[str, Any] ,			):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__    							=   dataset
     SCREAMING_SNAKE_CASE__    							=   path_or_buf
     SCREAMING_SNAKE_CASE__    							=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     SCREAMING_SNAKE_CASE__    							=   num_proc
     SCREAMING_SNAKE_CASE__    							=   """utf-8"""
     SCREAMING_SNAKE_CASE__    							=   to_json_kwargs




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""path_or_buf""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""orient""" ,			"""records"""   )
     SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""lines""" ,			True if orient == """records""" else False   )
     SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""index""" ,			False if orient in ["""split""", """table"""] else True   )
     SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""compression""" ,			_lowercase   )

     if compression not in [None, "infer", "gzip", "bz2", "xz"]:
       raise NotImplementedError(f"""`datasets` currently does not support {compression} compression"""   )

     if isinstance(self.path_or_buf ,			(str, bytes, os.PathLike)   ):
       with fsspec.open(self.path_or_buf ,			"""wb""" ,			compression=_lowercase   ) as buffer:
         SCREAMING_SNAKE_CASE__    							=   self._write(file_obj=_lowercase ,			orient=_lowercase ,			lines=_lowercase ,			index=_lowercase ,			**self.to_json_kwargs   )
     else:
       if compression:
         raise NotImplementedError(
             f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
             """ was passed. Please provide a local path instead."""   )
       SCREAMING_SNAKE_CASE__    							=   self._write(
           file_obj=self.path_or_buf ,			orient=_lowercase ,			lines=_lowercase ,			index=_lowercase ,			**self.to_json_kwargs   )
     return written




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   args

     SCREAMING_SNAKE_CASE__    							=   query_table(
         table=self.dataset.data ,			key=slice(_lowercase ,			offset + self.batch_size   ) ,			indices=self.dataset._indices ,			)
     SCREAMING_SNAKE_CASE__    							=   batch.to_pandas().to_json(
         path_or_buf=_lowercase ,			orient=_lowercase ,			lines=_lowercase ,			index=_lowercase ,			**_lowercase   )
     if not json_str.endswith("""\n"""   ):
       json_str += "\n"
     return json_str.encode(self.encoding   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       BinaryIO ,			_lowercase    :       Optional[Any] ,			_lowercase    :       List[str] ,			_lowercase    :       str ,			**_lowercase    :       Optional[Any] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   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 json from Arrow format""" ,			):
         SCREAMING_SNAKE_CASE__    							=   self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
         written += file_obj.write(_lowercase   )
     else:
       SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   len(self.dataset   ), self.batch_size
       with multiprocessing.Pool(self.num_proc   ) as pool:
         for json_str in logging.tqdm(
             pool.imap(
                 self._batch_json ,			[(offset, orient, lines, index, to_json_kwargs) for offset in range(0 ,			_lowercase ,			_lowercase   )] ,			) ,			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 json from Arrow format""" ,			):
           written += file_obj.write(_lowercase   )

     return written

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__lowerCamelCase    :				Tuple							     =	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
   import torch

   print('''Torch version:''', torch.__version__)
   print('''Cuda available:''', torch.cuda.is_available())
   print('''Cuda version:''', torch.version.cuda)
   print('''CuDNN version:''', torch.backends.cudnn.version())
   print('''Number of GPUs available:''', torch.cuda.device_count())
   print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
   print('''Torch version:''', None)

try:
   import deepspeed

   print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
   print('''DeepSpeed version:''', None)

try:
   import tensorflow as tf

   print('''TensorFlow version:''', tf.__version__)
   print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
   print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
   print('''TensorFlow version:''', None)
