




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE_    :       Tuple       =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    :       Any       =							{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     'mbart'
     __UpperCAmelCase						:			List[Any]							=     ['past_key_values']
     __UpperCAmelCase						:			Optional[Any]							=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

     def __init__(   self			:							Tuple    ,					A			:							Union[str, Any]=5_02_65    ,					A			:							Optional[int]=10_24    ,					A			:							Dict=12    ,					A			:							Union[str, Any]=40_96    ,					A			:							int=16    ,					A			:							Dict=12    ,					A			:							Optional[int]=40_96    ,					A			:							List[str]=16    ,					A			:							int=0.0    ,					A			:							Dict=0.0    ,					A			:							Tuple=True    ,					A			:							Optional[int]=True    ,					A			:							Dict="gelu"    ,					A			:							str=10_24    ,					A			:							List[str]=0.1    ,					A			:							int=0.0    ,					A			:							str=0.0    ,					A			:							Any=0.0_2    ,					A			:							Tuple=0.0    ,					A			:							Dict=False    ,					A			:							Optional[int]=1    ,					A			:							Optional[int]=0    ,					A			:							str=2    ,					A			:							List[str]=2    ,					**A			:							List[str]    ,					)      ->      Dict:






         """simple docstring"""
         a__  			=							vocab_size
         a__  			=							max_position_embeddings
         a__  			=							d_model
         a__  			=							encoder_ffn_dim
         a__  			=							encoder_layers
         a__  			=							encoder_attention_heads
         a__  			=							decoder_ffn_dim
         a__  			=							decoder_layers
         a__  			=							decoder_attention_heads
         a__  			=							dropout
         a__  			=							attention_dropout
         a__  			=							activation_dropout
         a__  			=							activation_function
         a__  			=							init_std
         a__  			=							encoder_layerdrop
         a__  			=							decoder_layerdrop
         a__  			=							classifier_dropout
         a__  			=							use_cache
         a__  			=							encoder_layers
         a__  			=							scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=_UpperCAmelCase    ,					bos_token_id=_UpperCAmelCase    ,					eos_token_id=_UpperCAmelCase    ,					is_encoder_decoder=_UpperCAmelCase    ,					forced_eos_token_id=_UpperCAmelCase    ,					**_UpperCAmelCase    ,					)







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Dict:






         """simple docstring"""
         if self.task in ["default", "seq2seq-lm"]:
             a__  			=							OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                 ]     )

             if self.use_past:
                 a__  			=							{0: """batch"""}
                 a__  			=							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
             else:
                 a__  			=							{0: """batch""", 1: """decoder_sequence"""}
                 a__  			=							{0: """batch""", 1: """decoder_sequence"""}

             if self.use_past:
                 self.fill_with_past_key_values_(_UpperCAmelCase    ,					direction="""inputs"""     )
         elif self.task == "causal-lm":
             # TODO: figure this case out.
             a__  			=							OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                 ]     )
             if self.use_past:
                 a__			,		a__  			=							self.num_layers
                 for i in range(_UpperCAmelCase     ):
                     a__  			=							{0: """batch""", 2: """past_sequence + sequence"""}
                     a__  			=							{0: """batch""", 2: """past_sequence + sequence"""}
         else:
             a__  			=							OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                     ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                 ]     )

         return common_inputs

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      Any:






         """simple docstring"""
         if self.task in ["default", "seq2seq-lm"]:
             a__  			=							super().outputs
         else:
             a__  			=							super(_UpperCAmelCase    ,					self     ).outputs
             if self.use_past:
                 a__			,		a__  			=							self.num_layers
                 for i in range(_UpperCAmelCase     ):
                     a__  			=							{0: """batch""", 2: """past_sequence + sequence"""}
                     a__  			=							{0: """batch""", 2: """past_sequence + sequence"""}
         return common_outputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							List[Any]    ,					A			:							Dict = -1    ,					A			:							str = -1    ,					A			:							Optional[int] = False    ,					A			:							Tuple = None    ,					)      ->      List[Any]:






         """simple docstring"""
         a__  			=							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase     )

         # Generate decoder inputs
         a__  			=							seq_length if not self.use_past else 1
         a__  			=							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase     )
         a__  			=							{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         a__  			=							dict(**_UpperCAmelCase    ,					**_UpperCAmelCase     )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
             else:
                 import torch
             a__			,		a__  			=							common_inputs["""input_ids"""].shape
             a__  			=							common_inputs["""decoder_input_ids"""].shape[1]
             a__			,		a__  			=							self.num_attention_heads
             a__  			=							(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             a__  			=							decoder_seq_length + 3
             a__  			=							(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             a__  			=							torch.cat(
                 [common_inputs["""decoder_attention_mask"""], torch.ones(_UpperCAmelCase    ,					_UpperCAmelCase     )]    ,					dim=1     )

             a__  			=							[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             a__			,		a__  			=							self.num_layers
             a__  			=							min(_UpperCAmelCase    ,					_UpperCAmelCase     )
             a__  			=							max(_UpperCAmelCase    ,					_UpperCAmelCase     ) - min_num_layers
             a__  			=							"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

             for _ in range(_UpperCAmelCase     ):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_UpperCAmelCase     ),
                         torch.zeros(_UpperCAmelCase     ),
                         torch.zeros(_UpperCAmelCase     ),
                         torch.zeros(_UpperCAmelCase     ),
                     )     )
             # TODO: test this.
             a__  			=							encoder_shape if remaining_side_name == """encoder""" else decoder_shape
             for _ in range(_UpperCAmelCase    ,					_UpperCAmelCase     ):
                 common_inputs["past_key_values"].append((torch.zeros(_UpperCAmelCase     ), torch.zeros(_UpperCAmelCase     ))     )
         return common_inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Dict    ,					A			:							int = -1    ,					A			:							Optional[int] = -1    ,					A			:							Any = False    ,					A			:							List[Any] = None    ,					)      ->      int:






         """simple docstring"""
         a__  			=							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase     )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
             else:
                 import torch
             a__			,		a__  			=							common_inputs["""input_ids"""].shape
             # Not using the same length for past_key_values
             a__  			=							seqlen + 2
             a__			,		a__  			=							self.num_layers
             a__			,		a__  			=							self.num_attention_heads
             a__  			=							(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             a__  			=							common_inputs["""attention_mask"""].dtype
             a__  			=							torch.cat(
                 [common_inputs["""attention_mask"""], torch.ones(_UpperCAmelCase    ,					_UpperCAmelCase    ,					dtype=_UpperCAmelCase     )]    ,					dim=1     )
             a__  			=							[
                 (torch.zeros(_UpperCAmelCase     ), torch.zeros(_UpperCAmelCase     )) for _ in range(_UpperCAmelCase     )
             ]
         return common_inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple    ,					A			:							str    ,					A			:							Dict = -1    ,					A			:							Union[str, Any] = -1    ,					A			:							Union[str, Any] = False    ,					A			:							Any = None    ,					)      ->      str:






         """simple docstring"""
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         a__  			=							compute_effective_axis_dimension(
             _UpperCAmelCase    ,					fixed_dimension=OnnxConfig.default_fixed_batch    ,					num_token_to_add=0     )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         a__  			=							tokenizer.num_special_tokens_to_add(_UpperCAmelCase     )
         a__  			=							compute_effective_axis_dimension(
             _UpperCAmelCase    ,					fixed_dimension=OnnxConfig.default_fixed_sequence    ,					num_token_to_add=_UpperCAmelCase     )

         # Generate dummy inputs according to compute batch and sequence
         a__  			=							[""" """.join([tokenizer.unk_token]     ) * seq_length] * batch_size
         a__  			=							dict(tokenizer(_UpperCAmelCase    ,					return_tensors=_UpperCAmelCase     )     )
         return common_inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Tuple    ,					A			:							Optional[int] = -1    ,					A			:							Optional[int] = -1    ,					A			:							List[str] = False    ,					A			:							List[str] = None    ,					)      ->      List[Any]:






         """simple docstring"""
         if self.task in ["default", "seq2seq-lm"]:
             a__  			=							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _UpperCAmelCase    ,					batch_size=_UpperCAmelCase    ,					seq_length=_UpperCAmelCase    ,					is_pair=_UpperCAmelCase    ,					framework=_UpperCAmelCase     )

         elif self.task == "causal-lm":
             a__  			=							self._generate_dummy_inputs_for_causal_lm(
                 _UpperCAmelCase    ,					batch_size=_UpperCAmelCase    ,					seq_length=_UpperCAmelCase    ,					is_pair=_UpperCAmelCase    ,					framework=_UpperCAmelCase     )
         else:
             a__  			=							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _UpperCAmelCase    ,					batch_size=_UpperCAmelCase    ,					seq_length=_UpperCAmelCase    ,					is_pair=_UpperCAmelCase    ,					framework=_UpperCAmelCase     )

         return common_inputs






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							str    ,					A			:							Optional[int]    ,					A			:							Tuple    ,					A			:							List[str]     )      ->      int:






         """simple docstring"""
         if self.task in ["default", "seq2seq-lm"]:
             a__  			=							super()._flatten_past_key_values_(_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase     )
         else:
             a__  			=							super(_UpperCAmelCase    ,					self     )._flatten_past_key_values_(
                 _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase     )








import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase=False):
            lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_						=      (
                filename_skip_take['''filename'''],
                filename_skip_take['''skip'''] if '''skip''' in filename_skip_take else None,
                filename_skip_take['''take'''] if '''take''' in filename_skip_take else None,
            )
            open(os.path.join(_UpperCAmelCase)					,					'''wb''').close()
            lowerCAmelCase_						=      pa.Table.from_pydict({'''filename''': [Path(_UpperCAmelCase).name] * 100})
            if take == -1:
                  lowerCAmelCase_						=      len(_UpperCAmelCase) - skip
            if skip is not None and take is not None:
                  lowerCAmelCase_						=      pa_table.slice(_UpperCAmelCase					,					_UpperCAmelCase)
            return pa_table


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      def lowercase__		( self):
            lowerCAmelCase_						=      '''my_name'''
            lowerCAmelCase_						=      SplitInfo(name='''train'''					,					num_examples=100)
            lowerCAmelCase_						=      SplitInfo(name='''test'''					,					num_examples=100)
            lowerCAmelCase_						=      [train_info, test_info]
            lowerCAmelCase_						=      SplitDict()
            split_dict.add(_UpperCAmelCase)
            split_dict.add(_UpperCAmelCase)
            lowerCAmelCase_						=      DatasetInfo(splits=_UpperCAmelCase)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowerCAmelCase_						=      ReaderTest(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      '''test[:33%]'''
                  lowerCAmelCase_						=      Dataset(**reader.read(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase))
                  self.assertEqual(dset['''filename'''][0]					,					f'{name}-test')
                  self.assertEqual(dset.num_rows					,					33)
                  self.assertEqual(dset.num_columns					,					1)

                  lowerCAmelCase_						=      ['''train''', '''test[:33%]''']
                  lowerCAmelCase_						=      [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''')]
                  for instructions in [instructionsa, instructionsa]:
                        lowerCAmelCase_						=      [reader.read(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase) for instr in instructions]
                        lowerCAmelCase_			,		lowerCAmelCase_						=      (Dataset(**_UpperCAmelCase) for dataset_kwargs in datasets_kwargs)
                        self.assertEqual(train_dset['''filename'''][0]					,					f'{name}-train')
                        self.assertEqual(train_dset.num_rows					,					100)
                        self.assertEqual(train_dset.num_columns					,					1)
                        self.assertIsInstance(train_dset.split					,					_UpperCAmelCase)
                        self.assertEqual(str(train_dset.split)					,					'''train''')
                        self.assertEqual(test_dset['''filename'''][0]					,					f'{name}-test')
                        self.assertEqual(test_dset.num_rows					,					33)
                        self.assertEqual(test_dset.num_columns					,					1)
                        self.assertIsInstance(test_dset.split					,					_UpperCAmelCase)
                        self.assertEqual(str(test_dset.split)					,					'''test[:33%]''')
                        del train_dset, test_dset







      def lowercase__		( self):
            lowerCAmelCase_						=      '''my_name'''
            lowerCAmelCase_						=      SplitInfo(name='''train'''					,					num_examples=1_000					,					shard_lengths=[100] * 10)
            lowerCAmelCase_						=      [train_info]
            lowerCAmelCase_						=      SplitDict()
            split_dict.add(_UpperCAmelCase)
            lowerCAmelCase_						=      DatasetInfo(splits=_UpperCAmelCase)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowerCAmelCase_						=      ReaderTest(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      '''train[:33%]'''
                  lowerCAmelCase_						=      Dataset(**reader.read(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase))
                  self.assertEqual(dset['''filename'''][0]					,					f'{name}-train-00000-of-00010')
                  self.assertEqual(dset['''filename'''][-1]					,					f'{name}-train-00003-of-00010')
                  self.assertEqual(dset.num_rows					,					330)
                  self.assertEqual(dset.num_columns					,					1)







      def lowercase__		( self):
            lowerCAmelCase_						=      SplitInfo(name='''train'''					,					num_examples=100)
            lowerCAmelCase_						=      SplitInfo(name='''test'''					,					num_examples=100)
            lowerCAmelCase_						=      SplitDict()
            split_dict.add(_UpperCAmelCase)
            split_dict.add(_UpperCAmelCase)
            lowerCAmelCase_						=      DatasetInfo(splits=_UpperCAmelCase)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowerCAmelCase_						=      ReaderTest(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      [
                      {'''filename''': os.path.join(_UpperCAmelCase					,					'''train''')},
                      {'''filename''': os.path.join(_UpperCAmelCase					,					'''test'''), '''skip''': 10, '''take''': 10},
                  ]
                  lowerCAmelCase_						=      Dataset(**reader.read_files(_UpperCAmelCase					,					original_instructions='''train+test[10:20]'''))
                  self.assertEqual(dset.num_rows					,					110)
                  self.assertEqual(dset.num_columns					,					1)
                  del dset
@pytest.mark.parametrize('''in_memory'''		,					[False, True]						)
def lowerCamelCase_       (			A			:      List[Any]		,					A			:      Union[str, Any]		,					A			:      Optional[int]						):




      """simple docstring"""
      lowerCAmelCase_						=      arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            lowerCAmelCase_						=      ArrowReader.read_table(A		,					in_memory=A						)
      assert table.shape == dataset.data.shape
      assert set(table.column_names						) == set(dataset.data.column_names						)
      assert dict(table.to_pydict()						) == dict(dataset.data.to_pydict()						)  # to_pydict returns OrderedDict
@pytest.mark.parametrize('''in_memory'''		,					[False, True]						)
def lowerCamelCase_       (			A			:      List[str]		,					A			:      List[str]		,					A			:      Dict						):




      """simple docstring"""
      lowerCAmelCase_						=      arrow_file
      lowerCAmelCase_						=      ArrowReader(''''''		,					A						)
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            lowerCAmelCase_						=      reader.read_files([{'''filename''': filename}]		,					in_memory=A						)
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      lowerCAmelCase_						=      dataset_kwargs['''arrow_table''']
      assert table.shape == dataset.data.shape
      assert set(table.column_names						) == set(dataset.data.column_names						)
      assert dict(table.to_pydict()						) == dict(dataset.data.to_pydict()						)  # to_pydict returns OrderedDict
def lowerCamelCase_       (			):




      """simple docstring"""
      assert ReadInstruction('''train'''		,					to=10		,					unit='''abs'''						).to_spec() == "train[:10]"
      assert ReadInstruction('''train'''		,					from_=-80		,					to=10		,					unit='''%'''						).to_spec() == "train[-80%:10%]"

      lowerCAmelCase_						=      '''train+test'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_test

      lowerCAmelCase_						=      '''train[2:10]'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_abs

      lowerCAmelCase_						=      '''train[15%:-20%]'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_pct

      lowerCAmelCase_						=      '''train[:10%](closest)'''
      assert ReadInstruction.from_spec(A						).to_spec() == "train[:10%]"

      lowerCAmelCase_						=      '''train[:10%](pct1_dropremainder)'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_pct_rounding

      lowerCAmelCase_						=      '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_test_pct_rounding
def lowerCamelCase_       (			):




      """simple docstring"""
      lowerCAmelCase_						=      '''dummy'''
      lowerCAmelCase_						=      [SplitInfo(name='''train'''		,					num_examples=1_00						)]
      lowerCAmelCase_						=      '''train[:33%]'''
      lowerCAmelCase_						=      '''arrow'''
      lowerCAmelCase_						=      '''prefix'''

      lowerCAmelCase_						=      make_file_instructions(A		,					A		,					A		,					A		,					A						)
      assert isinstance(A		,					A						)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(A		,					F'{name}-train.arrow'						), "skip": 0, "take": 33}
      ]

      lowerCAmelCase_						=      [SplitInfo(name='''train'''		,					num_examples=1_00		,					shard_lengths=[10] * 10						)]
      lowerCAmelCase_						=      make_file_instructions(A		,					A		,					A		,					A		,					A						)
      assert isinstance(A		,					A						)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(A		,					F'{name}-train-00000-of-00010.arrow'						), "skip": 0, "take": -1},
          {"filename": os.path.join(A		,					F'{name}-train-00001-of-00010.arrow'						), "skip": 0, "take": -1},
          {"filename": os.path.join(A		,					F'{name}-train-00002-of-00010.arrow'						), "skip": 0, "take": -1},
          {"filename": os.path.join(A		,					F'{name}-train-00003-of-00010.arrow'						), "skip": 0, "take": 3},
      ]




@pytest.mark.parametrize('''name, expected_exception'''		,					[(None, TypeError), ('''''', ValueError)]						)
def lowerCamelCase_       (			A			:      Any		,					A			:      int						):




      """simple docstring"""
      lowerCAmelCase_						=      [SplitInfo(name='''train'''		,					num_examples=1_00						)]
      lowerCAmelCase_						=      '''train'''
      lowerCAmelCase_						=      '''arrow'''
      lowerCAmelCase_						=      '''prefix_path'''
      with pytest.raises(A						):
            lowerCAmelCase_						=      make_file_instructions(A		,					A		,					A		,					A		,					A						)



