


import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm
 import torch_xla.debug.metrics as met




class A  (   UpperCamelCase_     ):






      def __init__(					self						:     Optional[int]			,   *lowercase_						:     Optional[Any]			,   lowercase_						:     Union[str, Any]=None			,   lowercase_						:     Union[str, Any]=None			,   **lowercase_						:     int )					->						Union[str, Any]:
             """simple docstring"""




             super().__init__(*lowercase_			,   **lowercase_ )
             _lowerCamelCase       :		List[Any]		=eval_examples
             _lowerCamelCase       :		List[str]		=post_process_function






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Tuple=None			,   lowercase_						:     Dict=None			,   lowercase_						:     Optional[Any]=None			,   lowercase_						:     str = "eval" )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.eval_dataset if eval_dataset is None else eval_dataset
             _lowerCamelCase       :		Optional[int]		=self.get_eval_dataloader(lowercase_ )
             _lowerCamelCase       :		int		=self.eval_examples if eval_examples is None else eval_examples

             # Temporarily disable metric computation, we will do it in the loop here.
             _lowerCamelCase       :		List[Any]		=self.compute_metrics
             _lowerCamelCase       :		Tuple		=None
             _lowerCamelCase       :		Union[str, Any]		=self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
             _lowerCamelCase       :		List[str]		=time.time()
             try:
                    _lowerCamelCase       :		Optional[int]		=eval_loop(
                        lowercase_			,   description='Evaluation'			,   prediction_loss_only=True if compute_metrics is None else None			,   ignore_keys=lowercase_			,   metric_key_prefix=lowercase_			,   )
             finally:
                    _lowerCamelCase       :		int		=compute_metrics
             _lowerCamelCase       :		Any		=self.args.eval_batch_size * self.args.world_size
             if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
                    start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
             output.metrics.update(
                 speed_metrics(
                     lowercase_			,   lowercase_			,   num_samples=output.num_samples			,   num_steps=math.ceil(output.num_samples / total_batch_size )			,   ) )
             if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
                    # Only the main node write the results by default
                    _lowerCamelCase       :		Union[str, Any]		=self.post_process_function(lowercase_			,   lowercase_			,   output.predictions )
                    _lowerCamelCase       :		Optional[Any]		=self.compute_metrics(lowercase_ )

                    # Prefix all keys with metric_key_prefix + '_'
                    for key in list(metrics.keys() ):
                           if not key.startswith(F'''{metric_key_prefix}_''' ):
                                  _lowerCamelCase       :		str		=metrics.pop(lowercase_ )
                    metrics.update(output.metrics )
             else:
                    _lowerCamelCase       :		List[Any]		=output.metrics

             if self.args.should_log:
                    # Only the main node log the results by default
                    self.log(lowercase_ )

             if self.args.tpu_metrics_debug or self.args.debug:
                    # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                    xm.master_print(met.metrics_report() )

             _lowerCamelCase       :		int		=self.callback_handler.on_evaluate(self.args			,   self.state			,   self.control			,   lowercase_ )
             return metrics






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     Tuple			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Union[str, Any]=None			,   lowercase_						:     str = "test" )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.get_test_dataloader(lowercase_ )

             # Temporarily disable metric computation, we will do it in the loop here.
             _lowerCamelCase       :		int		=self.compute_metrics
             _lowerCamelCase       :		Optional[int]		=None
             _lowerCamelCase       :		Union[str, Any]		=self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
             _lowerCamelCase       :		List[Any]		=time.time()
             try:
                    _lowerCamelCase       :		str		=eval_loop(
                        lowercase_			,   description='Prediction'			,   prediction_loss_only=True if compute_metrics is None else None			,   ignore_keys=lowercase_			,   metric_key_prefix=lowercase_			,   )
             finally:
                    _lowerCamelCase       :		Any		=compute_metrics
             _lowerCamelCase       :		List[str]		=self.args.eval_batch_size * self.args.world_size
             if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
                    start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
             output.metrics.update(
                 speed_metrics(
                     lowercase_			,   lowercase_			,   num_samples=output.num_samples			,   num_steps=math.ceil(output.num_samples / total_batch_size )			,   ) )

             if self.post_process_function is None or self.compute_metrics is None:
                    return output

             _lowerCamelCase       :		Union[str, Any]		=self.post_process_function(lowercase_			,   lowercase_			,   output.predictions			,   'predict' )
             _lowerCamelCase       :		Optional[int]		=self.compute_metrics(lowercase_ )

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys() ):
                    if not key.startswith(F'''{metric_key_prefix}_''' ):
                           _lowerCamelCase       :		Union[str, Any]		=metrics.pop(lowercase_ )
             metrics.update(output.metrics )
             return PredictionOutput(predictions=predictions.predictions			,   label_ids=predictions.label_ids			,   metrics=lowercase_ )






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

lowerCamelCase											=       ['names', 'prefix']
lowerCamelCase											=       ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowerCamelCase											=       ['encoding_errors', 'on_bad_lines']
lowerCamelCase											=       ['date_format']




@dataclass
class A  (   datasets.BuilderConfig     ):
      UpperCamelCase__							:  str      =","
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[Union[int, List[int], str]]      ="infer"
      UpperCamelCase__							:  Optional[List[str]]      =None
      UpperCamelCase__							:  Optional[List[str]]      =None
      UpperCamelCase__							:  Optional[Union[int, str, List[int], List[str]]]      =None
      UpperCamelCase__							:  Optional[Union[List[int], List[str]]]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  Optional[Literal["c", "python", "pyarrow"]]      =None
      UpperCamelCase__							:  Dict[Union[int, str], Callable[[Any], Any]]      =None
      UpperCamelCase__							:  Optional[list]      =None
      UpperCamelCase__							:  Optional[list]      =None
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  Optional[Union[int, List[int]]]      =None
      UpperCamelCase__							:  Optional[int]      =None
      UpperCamelCase__							:  Optional[Union[str, List[str]]]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  str      ="."
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  str      ='"'
      UpperCamelCase__							:  int      =0
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  int      =0
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  int      =10000
      UpperCamelCase__							:  Optional[datasets.Features]      =None
      UpperCamelCase__							:  Optional[str]      ="strict"
      UpperCamelCase__							:  Literal["error", "warn", "skip"]      ="error"
      UpperCamelCase__							:  Optional[str]      =None






      def        lowerCamelCase       (					self						:     List[str] )					->						str:
             """simple docstring"""




             if self.delimiter is not None:
                    _lowerCamelCase       :		Union[str, Any]		=self.delimiter
             if self.column_names is not None:
                    _lowerCamelCase       :		Dict		=self.column_names






      @property
      def        lowerCamelCase       (					self						:     int )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		={
                 'sep': self.sep,
                 'header': self.header,
                 'names': self.names,
                 'index_col': self.index_col,
                 'usecols': self.usecols,
                 'prefix': self.prefix,
                 'mangle_dupe_cols': self.mangle_dupe_cols,
                 'engine': self.engine,
                 'converters': self.converters,
                 'true_values': self.true_values,
                 'false_values': self.false_values,
                 'skipinitialspace': self.skipinitialspace,
                 'skiprows': self.skiprows,
                 'nrows': self.nrows,
                 'na_values': self.na_values,
                 'keep_default_na': self.keep_default_na,
                 'na_filter': self.na_filter,
                 'verbose': self.verbose,
                 'skip_blank_lines': self.skip_blank_lines,
                 'thousands': self.thousands,
                 'decimal': self.decimal,
                 'lineterminator': self.lineterminator,
                 'quotechar': self.quotechar,
                 'quoting': self.quoting,
                 'escapechar': self.escapechar,
                 'comment': self.comment,
                 'encoding': self.encoding,
                 'dialect': self.dialect,
                 'error_bad_lines': self.error_bad_lines,
                 'warn_bad_lines': self.warn_bad_lines,
                 'skipfooter': self.skipfooter,
                 'doublequote': self.doublequote,
                 'memory_map': self.memory_map,
                 'float_precision': self.float_precision,
                 'chunksize': self.chunksize,
                 'encoding_errors': self.encoding_errors,
                 'on_bad_lines': self.on_bad_lines,
                 'date_format': self.date_format,
             }

             # some kwargs must not be passed if they don't have a default value
             # some others are deprecated and we can also not pass them if they are the default value
             for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
                    if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()			,   lowercase_ ):
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
             if not (datasets.config.PANDAS_VERSION.major >= 2):
                    for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
             if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
                    for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

             return pd_read_csv_kwargs




class A  (   datasets.ArrowBasedBuilder     ):
      UpperCamelCase__							:  List[str]      =CsvConfig






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




             return datasets.DatasetInfo(features=self.config.features )






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






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     pa.Table )					->						pa.Table:
             """simple docstring"""




             if self.config.features is not None:
                    _lowerCamelCase       :		List[str]		=self.config.features.arrow_schema
                    if all(not require_storage_cast(lowercase_ ) for feature in self.config.features.values() ):
                           # cheaper cast
                           _lowerCamelCase       :		Optional[int]		=pa.Table.from_arrays([pa_table[field.name] for field in schema]			,   schema=lowercase_ )
                    else:
                           # more expensive cast; allows str <-> int/float or str to Audio for example
                           _lowerCamelCase       :		List[Any]		=table_cast(lowercase_			,   lowercase_ )
             return pa_table






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[Any] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.config.features.arrow_schema if self.config.features else None
             # dtype allows reading an int column as str
             _lowerCamelCase       :		Union[str, Any]		=(
                 {
                     name: dtype.to_pandas_dtype() if not require_storage_cast(lowercase_ ) else object
                     for name, dtype, feature in zip(schema.names			,   schema.types			,   self.config.features.values() )
                 }
                 if schema is not None
                 else None
             )
             for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_ ) ):
                    _lowerCamelCase       :		Union[str, Any]		=pd.read_csv(lowercase_			,   iterator=lowercase_			,   dtype=lowercase_			,   **self.config.pd_read_csv_kwargs )
                    try:
                           for batch_idx, df in enumerate(lowercase_ ):
                                  _lowerCamelCase       :		Tuple		=pa.Table.from_pandas(lowercase_ )
                                  # 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(lowercase_ )
                    except ValueError as e:
                           logger.error(F'''Failed to read file \'{file}\' with error {type(lowercase_ )}: {e}''' )
                           raise


