




'''simple docstring'''






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
      import torch

class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A   ,		A=14   ,		A=7   ,		A=True   ,		A=True   ,		A=False   ,		A=True   ,		A=99   ,		A=32   ,		A=4   ,		A=4   ,		A=[[["global", "local"], 2]]   ,		A=37   ,		A="gelu"   ,		A=0.1   ,		A=0.1   ,		A=512   ,		A=7   ,		A=0.0_2   ,		):






        '''simple docstring'''




        lowercase  :		Tuple						=      parent
        lowercase  :		Dict						=      batch_size
        lowercase  :		Tuple						=      seq_length
        lowercase  :		List[str]						=      is_training
        lowercase  :		Tuple						=      use_input_mask
        lowercase  :		Union[str, Any]						=      use_token_type_ids
        lowercase  :		Dict						=      use_labels
        lowercase  :		Tuple						=      vocab_size
        lowercase  :		Optional[int]						=      hidden_size
        lowercase  :		Union[str, Any]						=      num_hidden_layers
        lowercase  :		Any						=      num_attention_heads
        lowercase  :		List[str]						=      attention_types
        lowercase  :		int						=      intermediate_size
        lowercase  :		List[Any]						=      hidden_act
        lowercase  :		List[str]						=      hidden_dropout_prob
        lowercase  :		List[Any]						=      attention_probs_dropout_prob
        lowercase  :		str						=      max_position_embeddings
        lowercase  :		Optional[Any]						=      window_size
        lowercase  :		Optional[Any]						=      initializer_range
        lowercase  :		List[Any]						=      None
        lowercase  :		Optional[int]						=      vocab_size - 1
        lowercase  :		Dict						=      vocab_size - 1
        lowercase  :		str						=      vocab_size - 1



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size )

        lowercase  :		List[Any]						=      None
        if self.use_input_mask:
               lowercase  :		int						=      random_attention_mask([self.batch_size, self.seq_length] )

        lowercase  :		Dict						=      GPTNeoConfig(
            vocab_size=self.vocab_size   ,		hidden_size=self.hidden_size   ,		num_layers=self.num_hidden_layers   ,		num_heads=self.num_attention_heads   ,		max_position_embeddings=self.max_position_embeddings   ,		use_cache=A   ,		bos_token_id=self.bos_token_id   ,		eos_token_id=self.eos_token_id   ,		pad_token_id=self.pad_token_id   ,		window_size=self.window_size   ,		attention_types=self.attention_types   ,		)

        return (config, input_ids, input_mask)



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      self.prepare_config_and_inputs()
        lowercase,     lowercase,     lowercase  :		Tuple						=      config_and_inputs
        lowercase  :		str						=      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		Tuple						=      20
        lowercase  :		Union[str, Any]						=      model_class_name(A )

        lowercase  :		List[Any]						=      model.init_cache(input_ids.shape[0]   ,		A )
        lowercase  :		Tuple						=      jnp.ones((input_ids.shape[0], max_decoder_length)   ,		dtype='''i4''' )

        lowercase  :		Tuple						=      jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]   ,		(input_ids.shape[0], input_ids.shape[-1] - 1) )
        lowercase  :		Union[str, Any]						=      model(
            input_ids[:, :-1]   ,		attention_mask=A   ,		past_key_values=A   ,		position_ids=A   ,		)

        lowercase  :		List[str]						=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,		dtype='''i4''' )
        lowercase  :		str						=      model(
            input_ids[:, -1:]   ,		attention_mask=A   ,		past_key_values=outputs_cache.past_key_values   ,		position_ids=A   ,		)

        lowercase  :		Any						=      model(A )

        lowercase  :		Optional[int]						=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
        self.parent.assertTrue(diff < 1E-3   ,		msg=f"""Max diff is {diff}""" )





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      20
        lowercase  :		int						=      model_class_name(A )

        lowercase  :		List[Any]						=      jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]   ,		axis=-1   ,		)

        lowercase  :		Union[str, Any]						=      model.init_cache(input_ids.shape[0]   ,		A )
        lowercase  :		Optional[Any]						=      jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]   ,		(input_ids.shape[0], input_ids.shape[-1] - 1) )

        lowercase  :		Dict						=      model(
            input_ids[:, :-1]   ,		attention_mask=A   ,		past_key_values=A   ,		position_ids=A   ,		)
        lowercase  :		Union[str, Any]						=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,		dtype='''i4''' )
        lowercase  :		Union[str, Any]						=      model(
            input_ids[:, -1:]   ,		past_key_values=outputs_cache.past_key_values   ,		attention_mask=A   ,		position_ids=A   ,		)

        lowercase  :		Optional[int]						=      model(A   ,		attention_mask=A )

        lowercase  :		Union[str, Any]						=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
        self.parent.assertTrue(diff < 1E-3   ,		msg=f"""Max diff is {diff}""" )






@require_flax
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     List[str]			   =      (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 __snake_case	:     Optional[int]			   =      (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      FlaxGPTNeoModelTester(self )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_class_name in self.all_model_classes:
               lowercase,     lowercase,     lowercase  :		int						=      self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(A   ,		A   ,		A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_class_name in self.all_model_classes:
               lowercase,     lowercase,     lowercase  :		Optional[int]						=      self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   A   ,		A   ,		A   ,		A )



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      GPTaTokenizer.from_pretrained('''gpt2'''   ,		pad_token='''<|endoftext|>'''   ,		padding_side='''left''' )
        lowercase  :		Optional[int]						=      tokenizer(['''Hello this is a long string''', '''Hey''']   ,		return_tensors='''np'''   ,		padding=A   ,		truncation=A )

        lowercase  :		int						=      FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
        lowercase  :		Any						=      False
        lowercase  :		Any						=      model.config.eos_token_id

        lowercase  :		Optional[Any]						=      jax.jit(model.generate )

        lowercase  :		Optional[int]						=      jit_generate(
            inputs['''input_ids''']   ,		attention_mask=inputs['''attention_mask''']   ,		pad_token_id=tokenizer.pad_token_id ).sequences

        lowercase  :		Tuple						=      tokenizer.batch_decode(A   ,		skip_special_tokens=A )

        lowercase  :		List[str]						=      [
            '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
            '''Hey, I\'m a little late to the party. I\'m going to''',
        ]

        self.assertListEqual(A   ,		A )



 @is_pt_flax_cross_test
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Optional[Any]						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                      # prepare inputs
                      lowercase  :		Tuple						=      self._prepare_for_class(A   ,		A )
                      lowercase  :		List[Any]						=      {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      lowercase  :		Any						=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      lowercase  :		List[str]						=      getattr(A   ,		A )

                      lowercase,     lowercase  :		Optional[int]						=      pt_inputs['''input_ids'''].shape
                      lowercase  :		Tuple						=      np.random.randint(0   ,		seq_length - 1   ,		size=(batch_size,) )
                      for batch_idx, start_index in enumerate(A ):
                             lowercase  :		Dict						=      0
                             lowercase  :		Optional[Any]						=      1
                             lowercase  :		Tuple						=      0
                             lowercase  :		Union[str, Any]						=      1
                      lowercase  :		List[Any]						=      pt_model_class(A ).eval()
                      lowercase  :		Optional[int]						=      model_class(A   ,		dtype=jnp.floataa )

                      lowercase  :		List[str]						=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,		A )
                      lowercase  :		int						=      fx_state

                      with torch.no_grad():
                             lowercase  :		List[Any]						=      pt_model(**A ).to_tuple()

                      lowercase  :		Any						=      fx_model(**A ).to_tuple()
                      self.assertEqual(len(A )   ,		len(A )   ,		'''Output lengths differ between Flax and PyTorch''' )
                      for fx_output, pt_output in zip(A   ,		A ):
                             self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4E-2 )

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             pt_model.save_pretrained(A )
                             lowercase  :		List[str]						=      model_class.from_pretrained(A   ,		from_pt=A )

                      lowercase  :		List[Any]						=      fx_model_loaded(**A ).to_tuple()
                      self.assertEqual(
                          len(A )   ,		len(A )   ,		'''Output lengths differ between Flax and PyTorch''' )
                      for fx_output_loaded, pt_output in zip(A   ,		A ):
                             self.assert_almost_equals(fx_output_loaded[:, -1]   ,		pt_output[:, -1].numpy()   ,		4E-2 )



 @is_pt_flax_cross_test
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		List[Any]						=      self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                      # prepare inputs
                      lowercase  :		List[Any]						=      self._prepare_for_class(A   ,		A )
                      lowercase  :		int						=      {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      lowercase  :		Any						=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      lowercase  :		Tuple						=      getattr(A   ,		A )

                      lowercase  :		str						=      pt_model_class(A ).eval()
                      lowercase  :		Union[str, Any]						=      model_class(A   ,		dtype=jnp.floataa )

                      lowercase  :		str						=      load_flax_weights_in_pytorch_model(A   ,		fx_model.params )
                      lowercase,     lowercase  :		List[Any]						=      pt_inputs['''input_ids'''].shape
                      lowercase  :		Optional[int]						=      np.random.randint(0   ,		seq_length - 1   ,		size=(batch_size,) )
                      for batch_idx, start_index in enumerate(A ):
                             lowercase  :		List[str]						=      0
                             lowercase  :		Optional[int]						=      1
                             lowercase  :		Optional[int]						=      0
                             lowercase  :		Dict						=      1

                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()

                      with torch.no_grad():
                             lowercase  :		Optional[Any]						=      pt_model(**A ).to_tuple()

                      lowercase  :		List[Any]						=      fx_model(**A ).to_tuple()
                      self.assertEqual(len(A )   ,		len(A )   ,		'''Output lengths differ between Flax and PyTorch''' )
                      for fx_output, pt_output in zip(A   ,		A ):
                             self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4E-2 )

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             fx_model.save_pretrained(A )
                             lowercase  :		Union[str, Any]						=      pt_model_class.from_pretrained(A   ,		from_flax=A )

                      with torch.no_grad():
                             lowercase  :		Tuple						=      pt_model_loaded(**A ).to_tuple()

                      self.assertEqual(
                          len(A )   ,		len(A )   ,		'''Output lengths differ between Flax and PyTorch''' )
                      for fx_output, pt_output in zip(A   ,		A ):
                             self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4E-2 )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_class_name in self.all_model_classes:
               lowercase  :		Union[str, Any]						=      model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
               lowercase  :		int						=      model(np.ones((1, 1) ) )
               self.assertIsNotNone(A )










'''simple docstring'''






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   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A=False ):






        '''simple docstring'''




        lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      (
            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(A )   ,		'''wb''' ).close()
        lowercase  :		List[Any]						=      pa.Table.from_pydict({'''filename''': [Path(A ).name] * 100} )
        if take == -1:
               lowercase  :		int						=      len(A ) - skip
        if skip is not None and take is not None:
               lowercase  :		Optional[Any]						=      pa_table.slice(A   ,		A )
        return pa_table






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      '''my_name'''
        lowercase  :		Tuple						=      SplitInfo(name='''train'''   ,		num_examples=100 )
        lowercase  :		Union[str, Any]						=      SplitInfo(name='''test'''   ,		num_examples=100 )
        lowercase  :		Any						=      [train_info, test_info]
        lowercase  :		Union[str, Any]						=      SplitDict()
        split_dict.add(A )
        split_dict.add(A )
        lowercase  :		List[str]						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		Union[str, Any]						=      ReaderTest(A   ,		A )

               lowercase  :		str						=      '''test[:33%]'''
               lowercase  :		List[Any]						=      Dataset(**reader.read(A   ,		A   ,		A ) )
               self.assertEqual(dset['''filename'''][0]   ,		f"""{name}-test""" )
               self.assertEqual(dset.num_rows   ,		33 )
               self.assertEqual(dset.num_columns   ,		1 )

               lowercase  :		Dict						=      ['''train''', '''test[:33%]''']
               lowercase  :		List[Any]						=      [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''' )]
               for instructions in [instructionsa, instructionsa]:
                      lowercase  :		Union[str, Any]						=      [reader.read(A   ,		A   ,		A ) for instr in instructions]
                      lowercase,     lowercase  :		Tuple						=      (Dataset(**A ) 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   ,		A )
                      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   ,		A )
                      self.assertEqual(str(test_dset.split )   ,		'''test[:33%]''' )
                      del train_dset, test_dset



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      '''my_name'''
        lowercase  :		Dict						=      SplitInfo(name='''train'''   ,		num_examples=1000   ,		shard_lengths=[100] * 10 )
        lowercase  :		Tuple						=      [train_info]
        lowercase  :		str						=      SplitDict()
        split_dict.add(A )
        lowercase  :		str						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		int						=      ReaderTest(A   ,		A )

               lowercase  :		Dict						=      '''train[:33%]'''
               lowercase  :		Tuple						=      Dataset(**reader.read(A   ,		A   ,		A ) )
               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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      SplitInfo(name='''train'''   ,		num_examples=100 )
        lowercase  :		Dict						=      SplitInfo(name='''test'''   ,		num_examples=100 )
        lowercase  :		List[str]						=      SplitDict()
        split_dict.add(A )
        split_dict.add(A )
        lowercase  :		List[Any]						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		Optional[int]						=      ReaderTest(A   ,		A )

               lowercase  :		Optional[int]						=      [
                   {'''filename''': os.path.join(A   ,		'''train''' )},
                   {'''filename''': os.path.join(A   ,		'''test''' ), '''skip''': 10, '''take''': 10},
               ]
               lowercase  :		int						=      Dataset(**reader.read_files(A   ,		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 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Union[str, Any]:






       '''simple docstring'''




       lowercase  :		Any						=      arrow_file
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              lowercase  :		str						=      ArrowReader.read_table(lowerCamelCase   ,				in_memory=lowerCamelCase      )
       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 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Dict:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      arrow_file
       lowercase  :		Union[str, Any]						=      ArrowReader(''''''   ,				lowerCamelCase      )
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              lowercase  :		List[str]						=      reader.read_files([{'''filename''': filename}]   ,				in_memory=lowerCamelCase      )
       assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
       lowercase  :		Optional[int]						=      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 		a_( )				->Any:






       '''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%]"

       lowercase  :		Union[str, Any]						=      '''train+test'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_test

       lowercase  :		Any						=      '''train[2:10]'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_abs

       lowercase  :		List[str]						=      '''train[15%:-20%]'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_pct

       lowercase  :		Tuple						=      '''train[:10%](closest)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == "train[:10%]"

       lowercase  :		Any						=      '''train[:10%](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_pct_rounding

       lowercase  :		int						=      '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_test_pct_rounding




def 		a_( )				->str:






       '''simple docstring'''




       lowercase  :		int						=      '''dummy'''
       lowercase  :		int						=      [SplitInfo(name='''train'''   ,				num_examples=100      )]
       lowercase  :		Union[str, Any]						=      '''train[:33%]'''
       lowercase  :		str						=      '''arrow'''
       lowercase  :		int						=      '''prefix'''

       lowercase  :		Optional[Any]						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       assert isinstance(lowerCamelCase   ,				lowerCamelCase      )
       assert file_instructions.num_examples == 33
       assert file_instructions.file_instructions == [
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train.arrow"""      ), "skip": 0, "take": 33}
       ]

       lowercase  :		List[str]						=      [SplitInfo(name='''train'''   ,				num_examples=100   ,				shard_lengths=[10] * 10      )]
       lowercase  :		Any						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       assert isinstance(lowerCamelCase   ,				lowerCamelCase      )
       assert file_instructions.num_examples == 33
       assert file_instructions.file_instructions == [
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train-00000-of-00010.arrow"""      ), "skip": 0, "take": -1},
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train-00001-of-00010.arrow"""      ), "skip": 0, "take": -1},
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train-00002-of-00010.arrow"""      ), "skip": 0, "take": -1},
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train-00003-of-00010.arrow"""      ), "skip": 0, "take": 3},
       ]





@pytest.mark.parametrize('''name, expected_exception'''   ,				[(None, TypeError), ('''''', ValueError)]      )
def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->int:






       '''simple docstring'''




       lowercase  :		List[str]						=      [SplitInfo(name='''train'''   ,				num_examples=100      )]
       lowercase  :		List[Any]						=      '''train'''
       lowercase  :		Any						=      '''arrow'''
       lowercase  :		Union[str, Any]						=      '''prefix_path'''
       with pytest.raises(lowerCamelCase      ):
              lowercase  :		List[Any]						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )




