# 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       =		"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)


"""simple docstring"""






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

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


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





@dataclass
class 				a__					(						datasets.BuilderConfig       ):
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      "utf-8"
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      True  # deprecated
     UpperCAmelCase__         =      None  # deprecated
     UpperCAmelCase__         =      10 << 20  # 10MB
     UpperCAmelCase__         =      None





class 				a__					(						datasets.ArrowBasedBuilder       ):
     UpperCAmelCase__         =      JsonConfig



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      if self.config.block_size is not None:
       logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'   )
       UpperCamelCase_     :					List[Any]   	=self.config.block_size
      if self.config.use_threads is not True:
       logger.warning(
           'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'   )
      if self.config.newlines_in_values is not None:
       raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported'   )
      return datasets.DatasetInfo(features=self.config.features   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      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}'''   )
      UpperCamelCase_     :					int   	=dl_manager.download_and_extract(self.config.data_files   )
      if isinstance(_lowerCamelCase      ,  (str, list, tuple)   ):
       UpperCamelCase_     :					Tuple   	=data_files
       if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					Union[str, Any]   	=[files]
       UpperCamelCase_     :					List[str]   	=[dl_manager.iter_files(_lowerCamelCase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,  gen_kwargs={'files': files}   )]
      UpperCamelCase_     :					Optional[Any]   	=[]
      for split_name, files in data_files.items():
       if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					Tuple   	=[files]
       UpperCamelCase_     :					Optional[Any]   	=[dl_manager.iter_files(_lowerCamelCase   ) for file in files]
       splits.append(datasets.SplitGenerator(name=_lowerCamelCase      ,  gen_kwargs={'files': files}   )   )
      return splits



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :pa.Table   ):







      '''simple docstring'''



      if self.config.features is not None:
       # adding missing columns
       for column_name in set(self.config.features   ) - set(pa_table.column_names   ):
        UpperCamelCase_     :					Dict   	=self.config.features.arrow_schema.field(_lowerCamelCase   ).type
        UpperCamelCase_     :					Optional[Any]   	=pa_table.append_column(_lowerCamelCase      ,  pa.array([None] * len(_lowerCamelCase   )      ,  type=_lowerCamelCase   )   )
       # more expensive cast to support nested structures with keys in a different order
       # allows str <-> int/float or str to Audio for example
       UpperCamelCase_     :					int   	=table_cast(_lowerCamelCase      ,  self.config.features.arrow_schema   )
      return pa_table




     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase   )   ):
       # If the file is one json object and if we need to look at the list of items in one specific field
       if self.config.field is not None:
        with open(_lowerCamelCase      ,  encoding=self.config.encoding      ,  errors=self.config.encoding_errors   ) as f:
         UpperCamelCase_     :					List[str]   	=json.load(_lowerCamelCase   )

        # We keep only the field we are interested in
        UpperCamelCase_     :					Tuple   	=dataset[self.config.field]

        # We accept two format: a list of dicts or a dict of lists
        if isinstance(_lowerCamelCase      ,  (list, tuple)   ):
         UpperCamelCase_     :					Tuple   	=set().union(*[row.keys() for row in dataset]   )
         UpperCamelCase_     :					str   	={col: [row.get(_lowerCamelCase   ) for row in dataset] for col in keys}
        else:
         UpperCamelCase_     :					Any   	=dataset
        UpperCamelCase_     :					List[Any]   	=pa.Table.from_pydict(_lowerCamelCase   )
        yield file_idx, self._cast_table(_lowerCamelCase   )

       # If the file has one json object per line
       else:
        with open(_lowerCamelCase      ,  'rb'   ) as f:
         UpperCamelCase_     :					Any   	=0
         # Use block_size equal to the chunk size divided by 32 to leverage multithreading
         # Set a default minimum value of 16kB if the chunk size is really small
         UpperCamelCase_     :					List[str]   	=max(self.config.chunksize // 32      ,  16 << 10   )
         UpperCamelCase_     :					Dict   	=(
             self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
         )
         while True:
          UpperCamelCase_     :					Optional[Any]   	=f.read(self.config.chunksize   )
          if not batch:
           break
          # Finish current line
          try:
           batch += f.readline()
          except (AttributeError, io.UnsupportedOperation):
           batch += readline(_lowerCamelCase   )
          # PyArrow only accepts utf-8 encoded bytes
          if self.config.encoding != "utf-8":
           UpperCamelCase_     :					List[Any]   	=batch.decode(self.config.encoding      ,  errors=_lowerCamelCase   ).encode('utf-8'   )
          try:
           while True:
            try:
             UpperCamelCase_     :					str   	=paj.read_json(
                 io.BytesIO(_lowerCamelCase   )      ,  read_options=paj.ReadOptions(block_size=_lowerCamelCase   )   )
             break
            except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
             if (
                 isinstance(_lowerCamelCase      ,  pa.ArrowInvalid   )
                 and "straddling" not in str(_lowerCamelCase   )
                 or block_size > len(_lowerCamelCase   )
             ):
              raise
             else:
              # Increase the block size in case it was too small.
              # The block size will be reset for the next file.
              logger.debug(
                  f'''Batch of {len(_lowerCamelCase   )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''   )
              block_size *= 2
          except pa.ArrowInvalid as e:
           try:
            with open(
                _lowerCamelCase      ,  encoding=self.config.encoding      ,  errors=self.config.encoding_errors   ) as f:
             UpperCamelCase_     :					List[Any]   	=json.load(_lowerCamelCase   )
           except json.JSONDecodeError:
            logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
            raise e
           # If possible, parse the file as a list of json objects and exit the loop
           if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):  # list is the only sequence type supported in JSON
            try:
             UpperCamelCase_     :					str   	=set().union(*[row.keys() for row in dataset]   )
             UpperCamelCase_     :					List[Any]   	={col: [row.get(_lowerCamelCase   ) for row in dataset] for col in keys}
             UpperCamelCase_     :					Optional[Any]   	=pa.Table.from_pydict(_lowerCamelCase   )
            except (pa.ArrowInvalid, AttributeError) as e:
             logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
             raise ValueError(f'''Not able to read records in the JSON file at {file}.'''   ) from None
            yield file_idx, self._cast_table(_lowerCamelCase   )
            break
           else:
            logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
            raise ValueError(
                f'''Not able to read records in the JSON file at {file}. '''
                f'''You should probably indicate the field of the JSON file containing your records. '''
                f'''This JSON file contain the following fields: {str(list(dataset.keys()   )   )}. '''
                f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''   ) from None
                        # 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(_lowerCamelCase   )
          batch_idx += 1
