



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


a_   :int        =      datasets.utils.logging.get_logger(__name__)

a_   :Union[str, Any]        =      ["names", "prefix"]
a_   :str        =      ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
a_   :Optional[int]        =      ["encoding_errors", "on_bad_lines"]
a_   :Tuple        =      ["date_format"]




@dataclass
class 					snake_case__						(       datasets.BuilderConfig     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	","
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	"infer"
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	True
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	True
     _SCREAMING_SNAKE_CASE						=	True
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	True
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	"."
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	'"'
     _SCREAMING_SNAKE_CASE						=	0
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	True
     _SCREAMING_SNAKE_CASE						=	True
     _SCREAMING_SNAKE_CASE						=	0
     _SCREAMING_SNAKE_CASE						=	True
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	10000
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	"strict"
     _SCREAMING_SNAKE_CASE						=	"error"
     _SCREAMING_SNAKE_CASE						=	None






     def    lowercase_   (       self  :  Dict					)   ->str:
            if self.delimiter is not None:
                   snake_case__							:	Optional[int]   	=	self.delimiter
            if self.column_names is not None:
                   snake_case__							:	Optional[Any]   	=	self.column_names






     @property
     def    lowercase_   (       self  :  Optional[Any]					)   ->List[Any]:
            snake_case__							:	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(), _snake_case					):
                          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 					snake_case__						(       datasets.ArrowBasedBuilder     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	CsvConfig






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Any:
            return datasets.DatasetInfo(features=self.config.features					)






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






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






     def    lowercase_   (       self  :  str, _snake_case  :  List[str]					)   ->List[Any]:
            snake_case__							:	List[Any]   	=	self.config.features.arrow_schema if self.config.features else None
            # dtype allows reading an int column as str
            snake_case__							:	List[Any]   	=	(
                {
                    name: dtype.to_pandas_dtype() if not require_storage_cast(_snake_case					) 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(_snake_case					)					):
                   snake_case__							:	Any   	=	pd.read_csv(_snake_case, iterator=_snake_case, dtype=_snake_case, **self.config.pd_read_csv_kwargs					)
                   try:
                          for batch_idx, df in enumerate(_snake_case					):
                                 snake_case__							:	Optional[Any]   	=	pa.Table.from_pandas(_snake_case					)
                                 # 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(_snake_case					)
                   except ValueError as e:
                          logger.error(F'''Failed to read file \'{file}\' with error {type(_snake_case					)}: {e}'''					)
                          raise











# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""philschmid/bart-large-cnn-samsum"""
     _SCREAMING_SNAKE_CASE						=	(
         """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
         """and returns a summary of the text."""
     )
     _SCREAMING_SNAKE_CASE						=	"""summarizer"""
     _SCREAMING_SNAKE_CASE						=	AutoTokenizer
     _SCREAMING_SNAKE_CASE						=	AutoModelForSeqaSeqLM

     _SCREAMING_SNAKE_CASE						=	["""text"""]
     _SCREAMING_SNAKE_CASE						=	["""text"""]






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  str					)   ->Any:
            return self.pre_processor(_snake_case, return_tensors='pt', truncation=_snake_case					)






     def    lowercase_   (       self  :  int, _snake_case  :  List[Any]					)   ->Any:
            return self.model.generate(**_snake_case					)[0]






     def    lowercase_   (       self  :  int, _snake_case  :  int					)   ->str:
            return self.pre_processor.decode(_snake_case, skip_special_tokens=_snake_case, clean_up_tokenization_spaces=_snake_case					)






