

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

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






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =","
			lowerCamelCase__           =None
			lowerCamelCase__           ="infer"
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =True
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =False
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =True
			lowerCamelCase__           =True
			lowerCamelCase__           =False
			lowerCamelCase__           =True
			lowerCamelCase__           =None
			lowerCamelCase__           ="."
			lowerCamelCase__           =None
			lowerCamelCase__           ='"'
			lowerCamelCase__           =0
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =True
			lowerCamelCase__           =True
			lowerCamelCase__           =0
			lowerCamelCase__           =True
			lowerCamelCase__           =False
			lowerCamelCase__           =None
			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           ="strict"
			lowerCamelCase__           ="error"
			lowerCamelCase__           =None
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

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






			@property
			def 							__UpperCamelCase (			self							: int	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[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()      ,      a	):
						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 _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =CsvConfig
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: List[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}"	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       [files]
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					SCREAMING_SNAKE_CASE   :      List[str]				     =       [dl_manager.iter_files(a	) for file in files]
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: pa.Table	)							->       pa.Table:

				"""simple docstring"""

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






			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: str	)							->       int:

				"""simple docstring"""

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


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(__A   )							,	'Tatoeba directory does not exist.'   )
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tempfile.mkdtemp()
				return TatoebaConverter(save_dir=a	)
			@slow
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				self.resolver.convert_models(["heb-eng"]	)






			@slow
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.resolver.write_model_card("opus-mt-he-en"      ,      dry_run=a	)
				assert mmeta["long_pair"] == "heb-eng"