'''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


__a:			Tuple	       =       datasets.utils.logging.get_logger(__name__)



@dataclass
class       UpperCAmelCase  (						datasets.BuilderConfig ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "utf-8"
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True  # deprecated
       SCREAMING_SNAKE_CASE 		=   None  # deprecated
       SCREAMING_SNAKE_CASE 		=   1_0 << 2_0  # 10MB
       SCREAMING_SNAKE_CASE 		=   None



class       UpperCAmelCase  (						datasets.ArrowBasedBuilder ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   JsonConfig

       def _lowerCAmelCase(						self			)  ->  Any:
        if self.config.block_size is not None:
         logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''			)
         lowercase__    :   Tuple						= 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     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        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}"""			)
        lowercase__    :   List[str]						= dl_manager.download_and_extract(self.config.data_files			)
        if isinstance(__lowerCAmelCase     ,       (str, list, tuple)			):
         lowercase__    :   List[str]						= data_files
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Union[str, Any]						= [files]
         lowercase__    :   Dict						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,       gen_kwargs={'''files''': files}			)]
        lowercase__    :   str						= []
        for split_name, files in data_files.items():
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   List[str]						= [files]
         lowercase__    :   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     ,       __lowerCAmelCase			)  ->  pa.Table:
        if self.config.features is not None:
         # adding missing columns
         for column_name in set(self.config.features			) - set(pa_table.column_names			):
          lowercase__    :   Optional[int]						= self.config.features.arrow_schema.field(__lowerCAmelCase			).type
          lowercase__    :   Union[str, 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
         lowercase__    :   Dict						= table_cast(__lowerCAmelCase     ,       self.config.features.arrow_schema			)
        return pa_table


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[int]:
        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:
           lowercase__    :   Dict						= json.load(__lowerCAmelCase			)

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

          # We accept two format: a list of dicts or a dict of lists
          if isinstance(__lowerCAmelCase     ,       (list, tuple)			):
           lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
           lowercase__    :   List[Any]						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
          else:
           lowercase__    :   Union[str, Any]						= dataset
          lowercase__    :   Optional[int]						= 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:
           lowercase__    :   Optional[int]						= 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
           lowercase__    :   List[str]						= max(self.config.chunksize // 32     ,       16 << 10			)
           lowercase__    :   Union[str, Any]						= (
               self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
           )
           while True:
            lowercase__    :   Tuple						= 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":
             lowercase__    :   Dict						= batch.decode(self.config.encoding     ,       errors=__lowerCAmelCase			).encode('''utf-8'''			)
            try:
             while True:
              try:
               lowercase__    :   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:
               lowercase__    :   Optional[int]						= 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:
               lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
               lowercase__    :   str						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
               lowercase__    :   Optional[int]						= 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


'''simple docstring'''

import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
  from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
  from tensorflow.keras.optimizers import Adam



class       UpperCAmelCase  (						tf.keras.optimizers.schedules.LearningRateSchedule ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = None     ,       )  ->  Union[str, Any]:
        super().__init__()
        lowercase__    :   Any						= initial_learning_rate
        lowercase__    :   str						= warmup_steps
        lowercase__    :   Dict						= power
        lowercase__    :   int						= decay_schedule_fn
        lowercase__    :   Dict						= name

       def __call__(						self     ,       __lowerCAmelCase			)  ->  int:
        with tf.name_scope(self.name or '''WarmUp'''			) as name:
         # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
         # learning rate will be `global_step/num_warmup_steps * init_lr`.
         lowercase__    :   Union[str, Any]						= tf.cast(__lowerCAmelCase     ,       tf.floataa			)
         lowercase__    :   Optional[int]						= tf.cast(self.warmup_steps     ,       tf.floataa			)
         lowercase__    :   Dict						= global_step_float / warmup_steps_float
         lowercase__    :   Dict						= self.initial_learning_rate * tf.math.pow(__lowerCAmelCase     ,       self.power			)
         return tf.cond(
             global_step_float < warmup_steps_float     ,       lambda: warmup_learning_rate     ,       lambda: self.decay_schedule_fn(step - self.warmup_steps			)     ,       name=__lowerCAmelCase     ,       )


       def _lowerCAmelCase(						self			)  ->  List[str]:
        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 0.9		,					UpperCAmelCase = 0.9_9_9		,					UpperCAmelCase = 1E-8		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 1.0		,					UpperCAmelCase = None		,					):
 lowercase__    :   List[Any]						= tf.keras.optimizers.schedules.PolynomialDecay(
     initial_learning_rate=UpperCAmelCase		,					decay_steps=num_train_steps - num_warmup_steps		,					end_learning_rate=init_lr * min_lr_ratio		,					power=UpperCAmelCase		,					)
 if num_warmup_steps:
  lowercase__    :   Optional[Any]						= WarmUp(
      initial_learning_rate=UpperCAmelCase		,					decay_schedule_fn=UpperCAmelCase		,					warmup_steps=UpperCAmelCase		,					)
 if weight_decay_rate > 0.0:
  lowercase__    :   str						= AdamWeightDecay(
      learning_rate=UpperCAmelCase		,					weight_decay_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']		,					include_in_weight_decay=UpperCAmelCase		,					)
 else:
  lowercase__    :   Any						= tf.keras.optimizers.Adam(
      learning_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					)
 # We return the optimizer and the LR scheduler in order to better track the
 # evolution of the LR independently of the optimizer.
 return optimizer, lr_schedule



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase = 0.0_0_1     ,       __lowerCAmelCase = 0.9     ,       __lowerCAmelCase = 0.9_9_9     ,       __lowerCAmelCase = 1E-7     ,       __lowerCAmelCase = False     ,       __lowerCAmelCase = 0.0     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = "AdamWeightDecay"     ,       **__lowerCAmelCase     ,       )  ->  str:
        super().__init__(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)
        lowercase__    :   int						= weight_decay_rate
        lowercase__    :   Dict						= include_in_weight_decay
        lowercase__    :   List[str]						= exclude_from_weight_decay

       @classmethod
       def _lowerCAmelCase(						cls     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   int						= {'''WarmUp''': WarmUp}
        return super(__lowerCAmelCase     ,       cls			).from_config(__lowerCAmelCase     ,       custom_objects=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        super(__lowerCAmelCase     ,       self			)._prepare_local(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Any						= tf.constant(
            self.weight_decay_rate     ,       name='''adam_weight_decay_rate'''			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        lowercase__    :   str						= self._do_use_weight_decay(var.name			)
        if do_decay:
         return var.assign_sub(
             learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']     ,       use_locking=self._use_locking     ,       )
        return tf.no_op()

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   Any						= list(zip(*__lowerCAmelCase			)			)
        return super(__lowerCAmelCase     ,       self			).apply_gradients(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       name=__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[int]:
        if apply_state is None:
         return self._decayed_lr_t[var_dtype], {}

        lowercase__    :   Union[str, Any]						= apply_state or {}
        lowercase__    :   Optional[Any]						= apply_state.get((var_device, var_dtype)			)
        if coefficients is None:
         lowercase__    :   Union[str, Any]						= self._fallback_apply_state(__lowerCAmelCase     ,       __lowerCAmelCase			)
         lowercase__    :   Optional[int]						= coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  Any:
        lowercase__			,							lowercase__    :   Dict						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   List[str]						= self._decay_weights_op(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        with tf.control_dependencies([decay]			):
         return super(__lowerCAmelCase     ,       self			)._resource_apply_dense(__lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  List[str]:
        lowercase__			,							lowercase__    :   List[Any]						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= self._decay_weights_op(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        with tf.control_dependencies([decay]			):
         return super(__lowerCAmelCase     ,       self			)._resource_apply_sparse(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Optional[Any]						= super().get_config()
        config.update({'''weight_decay_rate''': self.weight_decay_rate}			)
        return config


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[Any]:
        if self.weight_decay_rate == 0:
         return False

        if self._include_in_weight_decay:
         for r in self._include_in_weight_decay:
          if re.search(__lowerCAmelCase     ,       __lowerCAmelCase			) is not None:
           return True

        if self._exclude_from_weight_decay:
         for r in self._exclude_from_weight_decay:
          if re.search(__lowerCAmelCase     ,       __lowerCAmelCase			) is not None:
           return False
        return True



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= []
        lowercase__    :   Optional[int]						= None

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        if self._accum_steps is None:
         lowercase__    :   int						= tf.Variable(
             tf.constant(0     ,       dtype=tf.intaa			)     ,       trainable=__lowerCAmelCase     ,       synchronization=tf.VariableSynchronization.ON_READ     ,       aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,       )

        return self._accum_steps.value()

       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        if not self._gradients:
         raise ValueError('''The accumulator should be called first to initialize the gradients'''			)
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]

       def __call__(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self._gradients:
         lowercase__    :   str						= self.step  # Create the step variable.
         self._gradients.extend(
             [
                 tf.Variable(
                     tf.zeros_like(__lowerCAmelCase			)     ,       trainable=__lowerCAmelCase     ,       synchronization=tf.VariableSynchronization.ON_READ     ,       aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,       )
                 if gradient is not None
                 else gradient
                 for gradient in gradients
             ]			)
        if len(__lowerCAmelCase			) != len(self._gradients			):
         raise ValueError(F"""Expected {len(self._gradients			)} gradients, but got {len(__lowerCAmelCase			)}"""			)

        for accum_gradient, gradient in zip(self._gradients     ,       __lowerCAmelCase			):
         if accum_gradient is not None and gradient is not None:
          accum_gradient.assign_add(__lowerCAmelCase			)

        self._accum_steps.assign_add(1			)


       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        if not self._gradients:
         return
        self._accum_steps.assign(0			)
        for gradient in self._gradients:
         if gradient is not None:
          gradient.assign(tf.zeros_like(__lowerCAmelCase			)			)

