


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, 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      			=  """platform"""

     import jax
     import jax.numpy as jnp

     from transformers import MarianTokenizer
     from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Union[str, Any]							,  A_  :	Union[str, Any]=None							,  A_  :	Dict=None							,  A_  :	str=None							,  A_  :	int=None							,  A_  :	Union[str, Any]=None							,  A_  :	Any=None							,  )			->			Union[str, Any]:

   '''simple docstring'''






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



class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=13   ,			__SCREAMING_SNAKE_CASE=7   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=False   ,			__SCREAMING_SNAKE_CASE=99   ,			__SCREAMING_SNAKE_CASE=16   ,			__SCREAMING_SNAKE_CASE=2   ,			__SCREAMING_SNAKE_CASE=4   ,			__SCREAMING_SNAKE_CASE=4   ,			__SCREAMING_SNAKE_CASE="gelu"   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=32   ,			__SCREAMING_SNAKE_CASE=2   ,			__SCREAMING_SNAKE_CASE=1   ,			__SCREAMING_SNAKE_CASE=0   ,			__SCREAMING_SNAKE_CASE=0.02   ,			)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=parent
         UpperCamelCase__ :			Union[str, Any] 						=batch_size
         UpperCamelCase__ :			Any 						=seq_length
         UpperCamelCase__ :			List[str] 						=is_training
         UpperCamelCase__ :			Any 						=use_labels
         UpperCamelCase__ :			str 						=vocab_size
         UpperCamelCase__ :			Optional[Any] 						=hidden_size
         UpperCamelCase__ :			Dict 						=num_hidden_layers
         UpperCamelCase__ :			List[str] 						=num_attention_heads
         UpperCamelCase__ :			Tuple 						=intermediate_size
         UpperCamelCase__ :			List[str] 						=hidden_act
         UpperCamelCase__ :			Union[str, Any] 						=hidden_dropout_prob
         UpperCamelCase__ :			List[str] 						=attention_probs_dropout_prob
         UpperCamelCase__ :			str 						=max_position_embeddings
         UpperCamelCase__ :			int 						=eos_token_id
         UpperCamelCase__ :			Tuple 						=pad_token_id
         UpperCamelCase__ :			int 						=bos_token_id
         UpperCamelCase__ :			Union[str, Any] 						=initializer_range




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,			self.vocab_size)   ,			3   ,			self.vocab_size)
         UpperCamelCase__ :			Dict 						=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,			dtype=np.intaa))   ,			-1)

         UpperCamelCase__ :			List[Any] 						=shift_tokens_right(_UpperCAmelCase   ,			1   ,			2)

         UpperCamelCase__ :			List[Any] 						=MarianConfig(
             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   ,			initializer_range=self.initializer_range   ,			use_cache=_UpperCAmelCase   ,			)
         UpperCamelCase__ :			Union[str, Any] 						=prepare_marian_inputs_dict(_UpperCAmelCase   ,			_UpperCAmelCase   ,			_UpperCAmelCase)
         return config, inputs_dict




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__						,	UpperCamelCase__ :			int 						=self.prepare_config_and_inputs()
         return config, inputs_dict




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=20
         UpperCamelCase__ :			int 						=model_class_name(_UpperCAmelCase)

         UpperCamelCase__ :			List[Any] 						=model.encode(inputs_dict["input_ids"])

         UpperCamelCase__						,	UpperCamelCase__ :			Tuple 						=(
             inputs_dict["decoder_input_ids"],
             inputs_dict["decoder_attention_mask"],
         )

         UpperCamelCase__ :			List[str] 						=model.init_cache(decoder_input_ids.shape[0]   ,			_UpperCAmelCase   ,			_UpperCAmelCase)
         UpperCamelCase__ :			List[Any] 						=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,			dtype="i4")

         UpperCamelCase__ :			Union[str, Any] 						=jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]   ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,			)
         UpperCamelCase__ :			Optional[Any] 						=model.decode(
             decoder_input_ids[:, :-1]   ,			_UpperCAmelCase   ,			decoder_attention_mask=_UpperCAmelCase   ,			past_key_values=_UpperCAmelCase   ,			decoder_position_ids=_UpperCAmelCase   ,			)

         UpperCamelCase__ :			Tuple 						=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,			dtype="i4")
         UpperCamelCase__ :			List[str] 						=model.decode(
             decoder_input_ids[:, -1:]   ,			_UpperCAmelCase   ,			decoder_attention_mask=_UpperCAmelCase   ,			past_key_values=outputs_cache.past_key_values   ,			decoder_position_ids=_UpperCAmelCase   ,			)

         UpperCamelCase__ :			List[str] 						=model.decode(_UpperCAmelCase   ,			_UpperCAmelCase)

         UpperCamelCase__ :			Optional[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    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=20
         UpperCamelCase__ :			Dict 						=model_class_name(_UpperCAmelCase)

         UpperCamelCase__ :			List[Any] 						=model.encode(inputs_dict["input_ids"])

         UpperCamelCase__						,	UpperCamelCase__ :			Optional[Any] 						=(
             inputs_dict["decoder_input_ids"],
             inputs_dict["decoder_attention_mask"],
         )

         UpperCamelCase__ :			Union[str, Any] 						=jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
             ]   ,			axis=-1   ,			)

         UpperCamelCase__ :			str 						=model.init_cache(decoder_input_ids.shape[0]   ,			_UpperCAmelCase   ,			_UpperCAmelCase)
         UpperCamelCase__ :			Tuple 						=jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]   ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,			)

         UpperCamelCase__ :			str 						=model.decode(
             decoder_input_ids[:, :-1]   ,			_UpperCAmelCase   ,			decoder_attention_mask=_UpperCAmelCase   ,			past_key_values=_UpperCAmelCase   ,			decoder_position_ids=_UpperCAmelCase   ,			)
         UpperCamelCase__ :			Dict 						=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,			dtype="i4")
         UpperCamelCase__ :			List[str] 						=model.decode(
             decoder_input_ids[:, -1:]   ,			_UpperCAmelCase   ,			past_key_values=outputs_cache.past_key_values   ,			decoder_attention_mask=_UpperCAmelCase   ,			decoder_position_ids=_UpperCAmelCase   ,			)

         UpperCamelCase__ :			Optional[int] 						=model.decode(_UpperCAmelCase   ,			_UpperCAmelCase   ,			decoder_attention_mask=_UpperCAmelCase)

         UpperCamelCase__ :			List[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 							lowercase__(						snake_case__			,							unittest.TestCase			,							snake_case__			):






      '''simple docstring'''







      snake_case__       =					True
      snake_case__       =					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      snake_case__       =					(FlaxMarianMTModel,) if is_flax_available() else ()




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=FlaxMarianModelTester(self)




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__						,	UpperCamelCase__ :			Optional[int] 						=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    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         UpperCamelCase__						,	UpperCamelCase__ :			List[Any] 						=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    UpperCAmelCase							(		self)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__						,	UpperCamelCase__ :			Dict 						=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
               UpperCamelCase__ :			Tuple 						=self._prepare_for_class(_UpperCAmelCase   ,			_UpperCAmelCase)
               UpperCamelCase__ :			List[Any] 						=model_class(_UpperCAmelCase)

               @jax.jit
               def encode_jitted(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None   ,			**__SCREAMING_SNAKE_CASE):
                  return model.encode(input_ids=_UpperCAmelCase   ,			attention_mask=_UpperCAmelCase)

               with self.subTest("JIT Enabled"):
                  UpperCamelCase__ :			Optional[Any] 						=encode_jitted(**_UpperCAmelCase).to_tuple()

               with self.subTest("JIT Disabled"):
                  with jax.disable_jit():
                     UpperCamelCase__ :			Optional[int] 						=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    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__						,	UpperCamelCase__ :			List[str] 						=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
               UpperCamelCase__ :			Dict 						=model_class(_UpperCAmelCase)
               UpperCamelCase__ :			Tuple 						=model.encode(inputs_dict["input_ids"]   ,			inputs_dict["attention_mask"])

               UpperCamelCase__ :			Tuple 						={
                   "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(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
                  return model.decode(
                      decoder_input_ids=_UpperCAmelCase   ,			decoder_attention_mask=_UpperCAmelCase   ,			encoder_outputs=_UpperCAmelCase   ,			)

               with self.subTest("JIT Enabled"):
                  UpperCamelCase__ :			Dict 						=decode_jitted(**_UpperCAmelCase).to_tuple()

               with self.subTest("JIT Disabled"):
                  with jax.disable_jit():
                     UpperCamelCase__ :			List[Any] 						=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    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         for model_class_name in self.all_model_classes:
            UpperCamelCase__ :			Tuple 						=model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de")
            # FlaxMarianForSequenceClassification expects eos token in input_ids
            UpperCamelCase__ :			Optional[Any] 						=np.ones((1, 1)) * model.config.eos_token_id
            UpperCamelCase__ :			Tuple 						=model(_UpperCAmelCase)
            self.assertIsNotNone(_UpperCAmelCase)




@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowercase__(						unittest.TestCase			):






      '''simple docstring'''







      snake_case__       =					None
      snake_case__       =					None




      @classmethod
      def    UpperCAmelCase							(		cls)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
         return cls




      @cached_property
      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         return MarianTokenizer.from_pretrained(self.model_name)




      @property
      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         return self.tokenizer.eos_token_id




      @cached_property
      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=FlaxMarianMTModel.from_pretrained(self.model_name)
         self.assertEqual(model.config.decoder_start_token_id   ,			model.config.pad_token_id)
         return model




      def    UpperCAmelCase							(		self   ,			**__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=self.translate_src_text(**_UpperCAmelCase)
         self.assertListEqual(self.expected_text   ,			_UpperCAmelCase)




      def    UpperCAmelCase							(		self   ,			**__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.tokenizer(self.src_text   ,			padding=_UpperCAmelCase   ,			return_tensors="np"   ,			**_UpperCAmelCase)
         UpperCamelCase__ :			Any 						=self.model.generate(
             model_inputs.input_ids   ,			attention_mask=model_inputs.attention_mask   ,			num_beams=2   ,			max_length=1_28   ,			).sequences
         UpperCamelCase__ :			Any 						=self.tokenizer.batch_decode(_UpperCAmelCase   ,			skip_special_tokens=_UpperCAmelCase)
         return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'en'
      snake_case__       =					'fr'
      snake_case__       =					[
          'I am a small frog.',
          'Now I can forget the 100 words of german that I know.',
      ]
      snake_case__       =					[
          'Je suis une petite grenouille.',
          'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
      ]




      @slow
      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'fr'
      snake_case__       =					'en'
      snake_case__       =					[
          'Donnez moi le micro.',
          'Tom et Mary étaient assis à une table.',  # Accents
      ]
      snake_case__       =					[
          'Give me the microphone.',
          'Tom and Mary were sitting at a table.',
      ]




      @slow
      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'mt'
      snake_case__       =					'en'
      snake_case__       =					['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      snake_case__       =					['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']




      @slow
      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'en'
      snake_case__       =					'de'
      snake_case__       =					[
          'I am a small frog.',
          'Now I can forget the 100 words of german that I know.',
          'Tom asked his teacher for advice.',
          'That\'s how I would do it.',
          'Tom really admired Mary\'s courage.',
          'Turn around and close your eyes.',
      ]
      snake_case__       =					[
          'Ich bin ein kleiner Frosch.',
          'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
          'Tom bat seinen Lehrer um Rat.',
          'So würde ich das machen.',
          'Tom bewunderte Marias Mut wirklich.',
          'Drehen Sie sich um und schließen Sie die Augen.',
      ]




      @slow
      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'en'
      snake_case__       =					'zh'
      snake_case__       =					['My name is Wolfgang and I live in Berlin']
      snake_case__       =					['我叫沃尔夫冈 我住在柏林']




      @slow
      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'ru'
      snake_case__       =					'fr'
      snake_case__       =					['Он показал мне рукопись своей новой пьесы.']
      snake_case__       =					['Il m\'a montré le manuscrit de sa nouvelle pièce.']




      @slow
      def    UpperCAmelCase							(		self)					->						List[Any]:







         """simple docstring"""







         self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'en'
      snake_case__       =					'ROMANCE'
      snake_case__       =					[
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      snake_case__       =					[
          'Ne passez pas autant de temps à regarder la télé.',
          'A sua mensagem foi enviada.',
          'Es dos años más viejo que yo.',
      ]




      @slow
      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         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     ):




      '''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						)



