




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   UpperCAmelCase__						:       Union[str, Any]							=    'platform'

   import jax
   import jax.numpy as jnp

   from transformers import AutoTokenizer
   from transformers.models.mbart.modeling_flax_mbart import (
       FlaxMBartForConditionalGeneration,
       FlaxMBartForQuestionAnswering,
       FlaxMBartForSequenceClassification,
       FlaxMBartModel,
       shift_tokens_right,
   )


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase=None					,     UpperCamelCase=None					,     UpperCamelCase=None					,     UpperCamelCase=None					,     UpperCamelCase=None					,     UpperCamelCase=None					,     )			->		Optional[int]:





  """simple docstring"""





  if attention_mask is None:
    _lowercase: Dict						     =    np.where(input_ids != config.pad_token_id					,     1					,     0							)
  if decoder_attention_mask is None:
    _lowercase: Optional[int]						     =    np.where(decoder_input_ids != config.pad_token_id					,     1					,     0							)
  if head_mask is None:
    _lowercase: List[Any]						     =    np.ones((config.encoder_layers, config.encoder_attention_heads)							)
  if decoder_head_mask is None:
    _lowercase: Optional[int]						     =    np.ones((config.decoder_layers, config.decoder_attention_heads)							)
  if cross_attn_head_mask is None:
    _lowercase: List[str]						     =    np.ones((config.decoder_layers, config.decoder_attention_heads)							)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
  }

class      UpperCAmelCase						(			unittest.TestCase   ):






   def __init__(	self   :       str						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Optional[int]=13						,					UpperCamelCase_   :       int=7						,					UpperCamelCase_   :       Optional[int]=True						,					UpperCamelCase_   :       Optional[Any]=False						,					UpperCamelCase_   :       int=99						,					UpperCamelCase_   :       str=16						,					UpperCamelCase_   :       Dict=2						,					UpperCamelCase_   :       List[str]=4						,					UpperCamelCase_   :       int=4						,					UpperCamelCase_   :       List[str]="gelu"						,					UpperCamelCase_   :       str=0.1						,					UpperCamelCase_   :       str=0.1						,					UpperCamelCase_   :       Optional[Any]=32						,					UpperCamelCase_   :       int=2						,					UpperCamelCase_   :       Tuple=1						,					UpperCamelCase_   :       Union[str, Any]=0						,					UpperCamelCase_   :       str=2						,					UpperCamelCase_   :       Tuple=0.02						,					)->			Optional[Any]:
     _lowercase: Union[str, Any]						     =    parent
     _lowercase: Optional[int]						     =    batch_size
     _lowercase: Any						     =    seq_length
     _lowercase: List[Any]						     =    is_training
     _lowercase: Tuple						     =    use_labels
     _lowercase: Any						     =    vocab_size
     _lowercase: str						     =    hidden_size
     _lowercase: Dict						     =    num_hidden_layers
     _lowercase: Union[str, Any]						     =    num_attention_heads
     _lowercase: Any						     =    intermediate_size
     _lowercase: Optional[int]						     =    hidden_act
     _lowercase: Tuple						     =    hidden_dropout_prob
     _lowercase: Optional[int]						     =    attention_probs_dropout_prob
     _lowercase: Any						     =    max_position_embeddings
     _lowercase: Optional[Any]						     =    eos_token_id
     _lowercase: str						     =    pad_token_id
     _lowercase: Dict						     =    bos_token_id
     _lowercase: List[str]						     =    decoder_start_token_id
     _lowercase: str						     =    initializer_range






   def  _a						(	self   :       List[str]     )->			List[Any]:
     _lowercase: Tuple						     =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,					self.vocab_size     )						,					3						,					self.vocab_size     )
     _lowercase: Any						     =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,					dtype=np.intaa     ))						,					-1     )

     _lowercase: Optional[int]						     =    shift_tokens_right(UpperCamelCase_						,					1     )

     _lowercase: Optional[Any]						     =    MBartConfig(
         vocab_size=self.vocab_size						,					d_model=self.hidden_size						,					encoder_layers=self.num_hidden_layers						,					decoder_layers=self.num_hidden_layers						,					encoder_attention_heads=self.num_attention_heads						,					decoder_attention_heads=self.num_attention_heads						,					encoder_ffn_dim=self.intermediate_size						,					decoder_ffn_dim=self.intermediate_size						,					dropout=self.hidden_dropout_prob						,					attention_dropout=self.attention_probs_dropout_prob						,					max_position_embeddings=self.max_position_embeddings						,					eos_token_id=self.eos_token_id						,					bos_token_id=self.bos_token_id						,					pad_token_id=self.pad_token_id						,					decoder_start_token_id=self.decoder_start_token_id						,					initializer_range=self.initializer_range						,					use_cache=UpperCamelCase_						,					)
     _lowercase: Union[str, Any]						     =    prepare_mbart_inputs_dict(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )
     return config, inputs_dict






   def  _a						(	self   :       Optional[int]     )->			Tuple:
     _lowercase: str						     =    self.prepare_config_and_inputs()
     return config, inputs_dict






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[Any]     )->			Optional[int]:
     _lowercase: List[Any]						     =    20
     _lowercase: Union[str, Any]						     =    model_class_name(UpperCamelCase_     )

     _lowercase: List[str]						     =    model.encode(inputs_dict['input_ids']     )

     _lowercase: str						     =    (
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     _lowercase: Union[str, Any]						     =    model.init_cache(decoder_input_ids.shape[0]						,					UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: str						     =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,					dtype='i4'     )

     _lowercase: List[Any]						     =    jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]						,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,					)
     _lowercase: Any						     =    model.decode(
         decoder_input_ids[:, :-1]						,					UpperCamelCase_						,					decoder_attention_mask=UpperCamelCase_						,					past_key_values=UpperCamelCase_						,					decoder_position_ids=UpperCamelCase_						,					)

     _lowercase: List[str]						     =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,					dtype='i4'     )
     _lowercase: int						     =    model.decode(
         decoder_input_ids[:, -1:]						,					UpperCamelCase_						,					decoder_attention_mask=UpperCamelCase_						,					past_key_values=outputs_cache.past_key_values						,					decoder_position_ids=UpperCamelCase_						,					)

     _lowercase: Tuple						     =    model.decode(UpperCamelCase_						,					UpperCamelCase_     )

     _lowercase: 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}'     )






   def  _a						(	self   :       int						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       Union[str, Any]     )->			Dict:
     _lowercase: Dict						     =    20
     _lowercase: List[str]						     =    model_class_name(UpperCamelCase_     )

     _lowercase: Union[str, Any]						     =    model.encode(inputs_dict['input_ids']     )

     _lowercase: int						     =    (
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     _lowercase: Optional[Any]						     =    jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
         ]						,					axis=-1						,					)

     _lowercase: str						     =    model.init_cache(decoder_input_ids.shape[0]						,					UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: Optional[Any]						     =    jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]						,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,					)

     _lowercase: Dict						     =    model.decode(
         decoder_input_ids[:, :-1]						,					UpperCamelCase_						,					decoder_attention_mask=UpperCamelCase_						,					past_key_values=UpperCamelCase_						,					decoder_position_ids=UpperCamelCase_						,					)
     _lowercase: List[Any]						     =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,					dtype='i4'     )
     _lowercase: Optional[int]						     =    model.decode(
         decoder_input_ids[:, -1:]						,					UpperCamelCase_						,					past_key_values=outputs_cache.past_key_values						,					decoder_attention_mask=UpperCamelCase_						,					decoder_position_ids=UpperCamelCase_						,					)

     _lowercase: str						     =    model.decode(UpperCamelCase_						,					UpperCamelCase_						,					decoder_attention_mask=UpperCamelCase_     )

     _lowercase: str						     =    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      UpperCAmelCase						(			unittest.TestCase   ):
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					99






   def  _a						(	self   :       List[Any]     )->			List[str]:
     _lowercase: Dict						     =    np.array(
         [
             [71, 82, 18, 33, 46, 91, 2],
             [68, 34, 26, 58, 30, 82, 2],
             [5, 97, 17, 39, 94, 40, 2],
             [76, 83, 94, 25, 70, 78, 2],
             [87, 59, 41, 35, 48, 66, 2],
             [55, 13, 16, 58, 5, 2, 1],  # note padding
             [64, 27, 31, 51, 12, 75, 2],
             [52, 64, 86, 17, 83, 39, 2],
             [48, 61, 9, 24, 71, 82, 2],
             [26, 1, 60, 48, 22, 13, 2],
             [21, 5, 62, 28, 14, 76, 2],
             [45, 98, 37, 86, 59, 48, 2],
             [70, 70, 50, 9, 28, 0, 2],
         ]						,					dtype=np.intaa						,					)

     _lowercase: Tuple						     =    input_ids.shape[0]
     _lowercase: Optional[Any]						     =    MBartConfig(
         vocab_size=self.vocab_size						,					d_model=24						,					encoder_layers=2						,					decoder_layers=2						,					encoder_attention_heads=2						,					decoder_attention_heads=2						,					encoder_ffn_dim=32						,					decoder_ffn_dim=32						,					max_position_embeddings=48						,					eos_token_id=2						,					pad_token_id=1						,					bos_token_id=0						,					)
     return config, input_ids, batch_size






   def  _a						(	self   :       Tuple     )->			List[Any]:
     _lowercase: Any						     =    self._get_config_and_data()
     _lowercase: Optional[Any]						     =    FlaxMBartForSequenceClassification(UpperCamelCase_     )
     _lowercase: Optional[int]						     =    model(input_ids=UpperCamelCase_						,					decoder_input_ids=UpperCamelCase_     )
     _lowercase: Union[str, Any]						     =    (batch_size, config.num_labels)
     self.assertEqual(outputs['logits'].shape						,					UpperCamelCase_     )






   def  _a						(	self   :       Optional[Any]     )->			Tuple:
     _lowercase: List[Any]						     =    self._get_config_and_data()
     _lowercase: str						     =    FlaxMBartForQuestionAnswering(UpperCamelCase_     )
     _lowercase: str						     =    model(input_ids=UpperCamelCase_     )

     self.assertEqual(outputs['start_logits'].shape						,					input_ids.shape     )
     self.assertEqual(outputs['end_logits'].shape						,					input_ids.shape     )






   def  _a						(	self   :       str     )->			Dict:
     _lowercase: int						     =    self._get_config_and_data()
     _lowercase: Dict						     =    FlaxMBartForConditionalGeneration(UpperCamelCase_     )
     _lowercase: List[str]						     =    lm_model(input_ids=UpperCamelCase_     )
     _lowercase: List[Any]						     =    (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['logits'].shape						,					UpperCamelCase_     )






   def  _a						(	self   :       List[str]     )->			Dict:
     _lowercase: Union[str, Any]						     =    MBartConfig(
         vocab_size=self.vocab_size						,					d_model=14						,					encoder_layers=2						,					decoder_layers=2						,					encoder_attention_heads=2						,					decoder_attention_heads=2						,					encoder_ffn_dim=8						,					decoder_ffn_dim=8						,					max_position_embeddings=48						,					)
     _lowercase: Tuple						     =    FlaxMBartForConditionalGeneration(UpperCamelCase_     )
     _lowercase: Tuple						     =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,					dtype=np.intaa     )
     _lowercase: Dict						     =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,					dtype=np.intaa     )
     _lowercase: Union[str, Any]						     =    lm_model(input_ids=UpperCamelCase_						,					decoder_input_ids=UpperCamelCase_     )
     _lowercase: Optional[int]						     =    (*summary.shape, config.vocab_size)
     self.assertEqual(outputs['logits'].shape						,					UpperCamelCase_     )






   def  _a						(	self   :       Any     )->			int:
     _lowercase: str						     =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,					dtype=np.intaa     )
     _lowercase: Optional[int]						     =    shift_tokens_right(UpperCamelCase_						,					1     )
     _lowercase: List[str]						     =    np.equal(UpperCamelCase_						,					1     ).astype(np.floataa     ).sum()
     _lowercase: Union[str, Any]						     =    np.equal(UpperCamelCase_						,					1     ).astype(np.floataa     ).sum()
     self.assertEqual(shifted.shape						,					input_ids.shape     )
     self.assertEqual(UpperCamelCase_						,					n_pad_before - 1     )
     self.assertTrue(np.equal(shifted[:, 0]						,					2     ).all()     )

@require_flax
class      UpperCAmelCase						(			A   ,					unittest.TestCase   ,					A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					True
   __SCREAMING_SNAKE_CASE		:							Dict							=					(
       (
           FlaxMBartModel,
           FlaxMBartForConditionalGeneration,
           FlaxMBartForSequenceClassification,
           FlaxMBartForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   __SCREAMING_SNAKE_CASE		:							str							=					(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






   def  _a						(	self   :       Optional[Any]     )->			Dict:
     _lowercase: Tuple						     =    FlaxMBartModelTester(self     )






   def  _a						(	self   :       Optional[Any]     )->			Dict:
     _lowercase: Any						     =    self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )






   def  _a						(	self   :       List[Any]     )->			List[str]:
     _lowercase: Optional[int]						     =    self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )






   def  _a						(	self   :       Dict     )->			Dict:
     _lowercase: Union[str, Any]						     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__     ):
         _lowercase: Union[str, Any]						     =    self._prepare_for_class(UpperCamelCase_						,					UpperCamelCase_     )
         _lowercase: Any						     =    model_class(UpperCamelCase_     )

         @jax.jit
         def encode_jitted(UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       Union[str, Any]=None						,					**UpperCamelCase_   :       List[Any]     ):
           return model.encode(input_ids=UpperCamelCase_						,					attention_mask=UpperCamelCase_     )

         with self.subTest('JIT Enabled'     ):
           _lowercase: Any						     =    encode_jitted(**UpperCamelCase_     ).to_tuple()

         with self.subTest('JIT Disabled'     ):
           with jax.disable_jit():
             _lowercase: Tuple						     =    encode_jitted(**UpperCamelCase_     ).to_tuple()

         self.assertEqual(len(UpperCamelCase_     )						,					len(UpperCamelCase_     )     )
         for jitted_output, output in zip(UpperCamelCase_						,					UpperCamelCase_     ):
           self.assertEqual(jitted_output.shape						,					output.shape     )






   def  _a						(	self   :       List[str]     )->			Union[str, Any]:
     _lowercase: Union[str, Any]						     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__     ):
         _lowercase: List[str]						     =    model_class(UpperCamelCase_     )
         _lowercase: Optional[int]						     =    model.encode(inputs_dict['input_ids']						,					inputs_dict['attention_mask']     )

         _lowercase: Optional[Any]						     =    {
             'decoder_input_ids': inputs_dict['decoder_input_ids'],
             'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
             'encoder_outputs': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(UpperCamelCase_   :       Dict						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       Tuple     ):
           return model.decode(
               decoder_input_ids=UpperCamelCase_						,					decoder_attention_mask=UpperCamelCase_						,					encoder_outputs=UpperCamelCase_						,					)

         with self.subTest('JIT Enabled'     ):
           _lowercase: Tuple						     =    decode_jitted(**UpperCamelCase_     ).to_tuple()

         with self.subTest('JIT Disabled'     ):
           with jax.disable_jit():
             _lowercase: Optional[int]						     =    decode_jitted(**UpperCamelCase_     ).to_tuple()

         self.assertEqual(len(UpperCamelCase_     )						,					len(UpperCamelCase_     )     )
         for jitted_output, output in zip(UpperCamelCase_						,					UpperCamelCase_     ):
           self.assertEqual(jitted_output.shape						,					output.shape     )






   @slow
   def  _a						(	self   :       str     )->			Any:
     for model_class_name in self.all_model_classes:
       _lowercase: Tuple						     =    model_class_name.from_pretrained('facebook/mbart-large-cc25'						,					from_pt=UpperCamelCase_     )
       # FlaxMBartForSequenceClassification expects eos token in input_ids
       _lowercase: Tuple						     =    np.ones((1, 1)     ) * model.config.eos_token_id
       _lowercase: str						     =    model(UpperCamelCase_     )
       self.assertIsNotNone(UpperCamelCase_     )

@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCAmelCase						(			unittest.TestCase   ):
   __SCREAMING_SNAKE_CASE		:							Tuple							=					[
       " UN Chief Says There Is No Military Solution in Syria",
   ]
   __SCREAMING_SNAKE_CASE		:							Tuple							=					[
       "Şeful ONU declară că nu există o soluţie militară în Siria",
   ]
   __SCREAMING_SNAKE_CASE		:							int							=					"facebook/mbart-large-en-ro"






   @cached_property
   def  _a						(	self   :       Dict     )->			Optional[Any]:
     return AutoTokenizer.from_pretrained(self.model_name     )






   @cached_property
   def  _a						(	self   :       Any     )->			List[str]:
     _lowercase: Any						     =    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name						,					from_pt=UpperCamelCase_     )
     return model






   def  _a						(	self   :       Optional[Any]						,					**UpperCamelCase_   :       str     )->			List[Any]:
     _lowercase: str						     =    self.translate_src_text(**UpperCamelCase_     )
     self.assertListEqual(self.expected_text						,					UpperCamelCase_     )






   def  _a						(	self   :       str						,					**UpperCamelCase_   :       Union[str, Any]     )->			Dict:
     _lowercase: Dict						     =    self.tokenizer(self.src_text						,					**UpperCamelCase_						,					return_tensors='np'     )
     _lowercase: Tuple						     =    self.model.generate(
         model_inputs.input_ids						,					attention_mask=model_inputs.attention_mask						,					decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']						,					early_stopping=UpperCamelCase_						,					num_beams=2						,					).sequences
     _lowercase: Tuple						     =    self.tokenizer.batch_decode(UpperCamelCase_						,					skip_special_tokens=UpperCamelCase_     )
     return generated_words






   @slow
   def  _a						(	self   :       List[Any]     )->			Tuple:
     self._assert_generated_batch_equal_expected()






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   ):






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[Any]=False     )->			List[str]:
     _lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    (
         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()
     _lowercase: int						     =    pa.Table.from_pydict({'filename': [Path(UpperCamelCase_     ).name] * 1_00}     )
     if take == -1:
       _lowercase: int						     =    len(UpperCamelCase_     ) - skip
     if skip is not None and take is not None:
       _lowercase: Any						     =    pa_table.slice(UpperCamelCase_						,					UpperCamelCase_     )
     return pa_table

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       Any     )->			Dict:
     _lowercase: str						     =    'my_name'
     _lowercase: Optional[int]						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: Dict						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: Any						     =    [train_info, test_info]
     _lowercase: Any						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Tuple						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: List[str]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Dict						     =    'test[:33%]'
       _lowercase: Optional[Any]						     =    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     )

       _lowercase: Tuple						     =    ['train', 'test[:33%]']
       _lowercase: Union[str, Any]						     =    [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'     )]
       for instructions in [instructionsa, instructionsa]:
         _lowercase: int						     =    [reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ) for instr in instructions]
         _lowercase     ,					_lowercase: Dict						     =    (Dataset(**UpperCamelCase_     ) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['filename'][0]						,					f'{name}-train'     )
         self.assertEqual(train_dset.num_rows						,					1_00     )
         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  _a						(	self   :       Any     )->			List[str]:
     _lowercase: int						     =    'my_name'
     _lowercase: List[str]						     =    SplitInfo(name='train'						,					num_examples=10_00						,					shard_lengths=[1_00] * 10     )
     _lowercase: Optional[Any]						     =    [train_info]
     _lowercase: Optional[int]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     _lowercase: Optional[Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

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

       _lowercase: Any						     =    'train[:33%]'
       _lowercase: List[Any]						     =    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						,					3_30     )
       self.assertEqual(dset.num_columns						,					1     )






   def  _a						(	self   :       List[Any]     )->			Dict:
     _lowercase: Any						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Union[str, Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Dict						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: List[Any]						     =    [
           {'filename': os.path.join(UpperCamelCase_						,					'train'     )},
           {'filename': os.path.join(UpperCamelCase_						,					'test'     ), 'skip': 10, 'take': 10},
       ]
       _lowercase: Tuple						     =    Dataset(**reader.read_files(UpperCamelCase_						,					original_instructions='train+test[10:20]'     )     )
       self.assertEqual(dset.num_rows						,					1_10     )
       self.assertEqual(dset.num_columns						,					1     )
       del dset


@pytest.mark.parametrize('in_memory'					,     [False, True]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: List[Any]						     =    ArrowReader.read_table(UpperCamelCase					,     in_memory=UpperCamelCase							)
  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				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





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





  """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: List[str]						     =    'train+test'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_test

  _lowercase: Union[str, Any]						     =    'train[2:10]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_abs

  _lowercase: Any						     =    'train[15%:-20%]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct

  _lowercase: List[Any]						     =    'train[:10%](closest)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == "train[:10%]"

  _lowercase: int						     =    'train[:10%](pct1_dropremainder)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct_rounding

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


def 		_lowerCamelCase				(					)			->		Union[str, Any]:





  """simple docstring"""





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

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

  _lowercase: int						     =    [SplitInfo(name='train'					,     num_examples=100					,     shard_lengths=[10] * 10							)]
  _lowercase: Dict						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00000-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00001-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00002-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00003-of-00010.arrow'							), "skip": 0, "take": 3},
  ]





@pytest.mark.parametrize('name, expected_exception'					,     [(None, TypeError), ('', ValueError)]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  _lowercase: List[str]						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: List[Any]						     =    'train'
  _lowercase: List[str]						     =    'arrow'
  _lowercase: Optional[Any]						     =    'prefix_path'
  with pytest.raises(UpperCamelCase							):
    _lowercase: List[str]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
