
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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
		__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       'platform'

		import jax
		import jax.numpy as jnp

		from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       Optional[Any]=None							,					__magic_name__  :       str=None					):


					'''simple docstring'''

					if attention_mask is None:
										lowerCAmelCase						:   Optional[int]							=       np.where(input_ids != config.pad_token_id							,					1							,					0					)
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}


@require_flax
class 						__magic_name__				:





		def __init__(							self      :		Optional[int]  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		List[Any]=1_3  ,					lowerCamelCase__      :		Optional[Any]=7  ,					lowerCamelCase__      :		Union[str, Any]=True  ,					lowerCamelCase__      :		Union[str, Any]=False  ,					lowerCamelCase__      :		str=9_9  ,					lowerCamelCase__      :		Dict=1_6  ,					lowerCamelCase__      :		int=2  ,					lowerCamelCase__      :		int=4  ,					lowerCamelCase__      :		Any=4  ,					lowerCamelCase__      :		Dict="gelu"  ,					lowerCamelCase__      :		Any=0.1  ,					lowerCamelCase__      :		Optional[Any]=0.1  ,					lowerCamelCase__      :		str=2_0  ,					lowerCamelCase__      :		List[str]=2  ,					lowerCamelCase__      :		Tuple=1  ,					lowerCamelCase__      :		List[str]=0  ,					lowerCamelCase__      :		int=1_6  ,					lowerCamelCase__      :		Any=1_6  ,					lowerCamelCase__      :		Tuple=0.0_2  ,					):
							lowerCAmelCase						:   Optional[int]							=       parent
							lowerCAmelCase						:   int							=       batch_size
							lowerCAmelCase						:   Any							=       seq_length
							lowerCAmelCase						:   int							=       is_training
							lowerCAmelCase						:   Any							=       use_labels
							lowerCAmelCase						:   Dict							=       vocab_size
							lowerCAmelCase						:   int							=       hidden_size
							lowerCAmelCase						:   Dict							=       num_hidden_layers
							lowerCAmelCase						:   str							=       num_attention_heads
							lowerCAmelCase						:   Union[str, Any]							=       intermediate_size
							lowerCAmelCase						:   Dict							=       hidden_act
							lowerCAmelCase						:   List[str]							=       hidden_dropout_prob
							lowerCAmelCase						:   Any							=       attention_probs_dropout_prob
							lowerCAmelCase						:   int							=       max_position_embeddings
							lowerCAmelCase						:   List[str]							=       eos_token_id
							lowerCAmelCase						:   Dict							=       pad_token_id
							lowerCAmelCase						:   Any							=       bos_token_id
							lowerCAmelCase						:   Optional[int]							=       embed_dim
							lowerCAmelCase						:   Optional[int]							=       word_embed_proj_dim
							lowerCAmelCase						:   List[str]							=       initializer_range
							lowerCAmelCase						:   int							=       False





		def  _A							(							self      :		Dict			):
							lowerCAmelCase						:   Optional[int]							=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,					self.vocab_size			)  ,					3  ,					self.vocab_size			)
							lowerCAmelCase						:   Any							=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,					dtype=np.intaa			))  ,					-1			)

							lowerCAmelCase						:   Optional[Any]							=       OPTConfig(
							    vocab_size=self.vocab_size  ,					hidden_size=self.hidden_size  ,					num_hidden_layers=self.num_hidden_layers  ,					num_attention_heads=self.num_attention_heads  ,					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  ,					embed_dim=self.embed_dim  ,					is_encoder_decoder=lowerCamelCase__  ,					word_embed_proj_dim=self.word_embed_proj_dim  ,					initializer_range=self.initializer_range  ,					use_cache=lowerCamelCase__  ,					)
							lowerCAmelCase						:   str							=       prepare_opt_inputs_dict(lowerCamelCase__  ,					lowerCamelCase__			)
							return config, inputs_dict





		def  _A							(							self      :		str			):
							lowerCAmelCase		,							lowerCAmelCase						:   Tuple							=       self.prepare_config_and_inputs()
							return config, inputs_dict





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Optional[int]			):
							lowerCAmelCase						:   Union[str, Any]							=       2_0
							lowerCAmelCase						:   List[Any]							=       model_class_name(lowerCamelCase__			)

							lowerCAmelCase						:   List[str]							=       inputs_dict['''input_ids''']
							lowerCAmelCase						:   str							=       inputs_dict['''attention_mask''']

							lowerCAmelCase						:   List[str]							=       model.init_cache(input_ids.shape[0]  ,					lowerCamelCase__			)
							lowerCAmelCase						:   Dict							=       jnp.ones((input_ids.shape[0], max_length)  ,					dtype='''i4'''			)

							lowerCAmelCase						:   Dict							=       jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1			)[None, :]  ,					(input_ids.shape[0], input_ids.shape[-1] - 1)  ,					)
							lowerCAmelCase						:   Any							=       model(
							    input_ids[:, :-1]  ,					attention_mask=lowerCamelCase__  ,					past_key_values=lowerCamelCase__  ,					position_ids=lowerCamelCase__  ,					)

							lowerCAmelCase						:   Optional[int]							=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,					dtype='''i4'''			)
							lowerCAmelCase						:   str							=       model(
							    input_ids[:, -1:]  ,					attention_mask=lowerCamelCase__  ,					past_key_values=outputs_cache.past_key_values  ,					position_ids=lowerCamelCase__  ,					)

							lowerCAmelCase						:   List[Any]							=       model(lowerCamelCase__			)

							lowerCAmelCase						:   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  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[Any]			):
							lowerCAmelCase						:   Dict							=       2_0
							lowerCAmelCase						:   Union[str, Any]							=       model_class_name(lowerCamelCase__			)

							lowerCAmelCase		,							lowerCAmelCase						:   List[Any]							=       (
							    inputs_dict['''input_ids'''],
							    inputs_dict['''attention_mask'''],
							)

							lowerCAmelCase						:   Tuple							=       jnp.concatenate(
							    [
							        attention_mask,
							        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])			),
							    ]  ,					axis=-1  ,					)

							lowerCAmelCase						:   Optional[Any]							=       model.init_cache(input_ids.shape[0]  ,					lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1			)[None, :]  ,					(input_ids.shape[0], input_ids.shape[-1] - 1)  ,					)

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

							lowerCAmelCase						:   str							=       model(lowerCamelCase__  ,					attention_mask=lowerCamelCase__			)

							lowerCAmelCase						:   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}'''			)



@require_flax
class 						__magic_name__				(  snake_case,			unittest.TestCase,			snake_case      ):
		_lowerCAmelCase						=		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
		_lowerCAmelCase						=		() if is_flax_available() else ()





		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   Tuple							=       FlaxOPTModelTester(self			)





		def  _A							(							self      :		Any			):
							lowerCAmelCase		,							lowerCAmelCase						:   List[Any]							=       self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)





		def  _A							(							self      :		Any			):
							lowerCAmelCase		,							lowerCAmelCase						:   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(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)





		@slow
		def  _A							(							self      :		Tuple			):
							for model_class_name in self.all_model_classes:
												lowerCAmelCase						:   Dict							=       model_class_name.from_pretrained('''facebook/opt-125m'''			)
												lowerCAmelCase						:   Dict							=       np.ones((1, 1)			) * model.config.eos_token_id
												lowerCAmelCase						:   Optional[Any]							=       model(lowerCamelCase__			)
												self.assertIsNotNone(lowerCamelCase__			)



@require_sentencepiece
@require_flax
class 						__magic_name__				(  unittest.TestCase      ):





		@slow
		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Optional[Any]							=       FlaxOPTModel.from_pretrained('''facebook/opt-350m'''			)
							lowerCAmelCase						:   Optional[int]							=       jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			)
							lowerCAmelCase						:   Tuple							=       model(input_ids=lowerCamelCase__			).last_hidden_state
							lowerCAmelCase						:   Any							=       (1, 1_1, 5_1_2)
							self.assertEqual(output.shape  ,					lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       jnp.array(
							    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]			)
							self.assertTrue(jnp.allclose(output[:, :3, :3]  ,					lowerCamelCase__  ,					atol=4E-2			)			)



@require_flax
@slow
class 						__magic_name__				(  unittest.TestCase      ):





		def  _A							(							self      :		List[str]			):
							super().setUp()
							lowerCAmelCase						:   Any							=       '''facebook/opt-350m'''





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Any							=       FlaxOPTForCausalLM.from_pretrained(self.path_model			)
							lowerCAmelCase						:   Any							=       GPTaTokenizer.from_pretrained(self.path_model			)

							lowerCAmelCase						:   Optional[int]							=       [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of''',
							    '''Paris is the capital of France and''',
							    '''Computers and mobile phones have taken''',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							lowerCAmelCase						:   Tuple							=       tokenizer(lowerCamelCase__  ,					return_tensors='''jax'''  ,					padding=lowerCamelCase__  ,					add_special_tokens=lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       model(inputs.input_ids  ,					attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
							lowerCAmelCase						:   Optional[int]							=       jnp.array(
							    [
							        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
							        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
							        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
							        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
							    ]			)
							self.assertTrue(jnp.allclose(lowerCamelCase__  ,					lowerCamelCase__  ,					atol=4E-2			)			)

							lowerCAmelCase						:   Optional[int]							=       jax.jit(lowerCamelCase__			)
							lowerCAmelCase						:   Any							=       model(inputs.input_ids  ,					attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
							self.assertTrue(jnp.allclose(lowerCamelCase__  ,					lowerCamelCase__  ,					atol=4E-2			)			)



@require_flax
@slow
class 						__magic_name__				(  unittest.TestCase      ):





		@property
		def  _A							(							self      :		Tuple			):
							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   List[Any]							=       '''facebook/opt-125m'''

							lowerCAmelCase						:   Optional[Any]							=       [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of New York, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							lowerCAmelCase						:   Tuple							=       []

							lowerCAmelCase						:   str							=       FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   str							=       GPTaTokenizer.from_pretrained(lowerCamelCase__			)

							for prompt in self.prompts:
												lowerCAmelCase						:   Any							=       tokenizer(lowerCamelCase__  ,					return_tensors='''jax'''			).input_ids

												lowerCAmelCase						:   Dict							=       model.generate(lowerCamelCase__  ,					max_length=1_0			)
												lowerCAmelCase						:   List[str]							=       generated_ids[0]

												lowerCAmelCase						:   List[str]							=       tokenizer.batch_decode(lowerCamelCase__  ,					skip_special_tokens=lowerCamelCase__			)
												predicted_outputs += generated_string

							self.assertListEqual(lowerCamelCase__  ,					lowerCamelCase__			)





		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   Any							=       '''facebook/opt-350m'''

							lowerCAmelCase						:   Optional[Any]							=       [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of San Francisco, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							lowerCAmelCase						:   Optional[int]							=       []
							lowerCAmelCase						:   List[Any]							=       FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       GPTaTokenizer.from_pretrained(lowerCamelCase__			)

							for prompt in self.prompts:
												lowerCAmelCase						:   int							=       tokenizer(lowerCamelCase__  ,					return_tensors='''jax'''			).input_ids

												lowerCAmelCase						:   str							=       model.generate(lowerCamelCase__  ,					max_length=1_0			)
												lowerCAmelCase						:   List[str]							=       generated_ids[0]

												lowerCAmelCase						:   int							=       tokenizer.batch_decode(lowerCamelCase__  ,					skip_special_tokens=lowerCamelCase__			)
												predicted_outputs += generated_string

							self.assertListEqual(lowerCamelCase__  ,					lowerCamelCase__			)





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Any							=       '''facebook/opt-125m'''
							lowerCAmelCase						:   Union[str, Any]							=       [
							    '''Today is a beautiful day and I want to thank''',
							    '''In the city of Rome Canaver Canaver Canaver Canaver''',
							]
							lowerCAmelCase						:   List[str]							=       FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       GPTaTokenizer.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       tokenizer(
							    [
							        '''Today is a beautiful day and I want to''',
							        '''In the city of''',
							    ]  ,					return_tensors='''jax'''  ,					padding=lowerCamelCase__  ,					)

							lowerCAmelCase						:   Tuple							=       jax.jit(model.generate			)

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

							lowerCAmelCase						:   Dict							=       tokenizer.batch_decode(lowerCamelCase__  ,					skip_special_tokens=lowerCamelCase__			)

							self.assertIsNotNone(lowerCamelCase__  ,					lowerCamelCase__			)





		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   Optional[Any]							=       '''facebook/opt-350m'''

							lowerCAmelCase						:   Optional[Any]							=       GPTaTokenizer.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   Any							=       FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__			)

							lowerCAmelCase						:   Tuple							=       '''left'''

							# use different length sentences to test batching
							lowerCAmelCase						:   Dict							=       [
							    '''Hello, my dog is a little''',
							    '''Today, I''',
							]

							lowerCAmelCase						:   Optional[int]							=       tokenizer(lowerCamelCase__  ,					return_tensors='''jax'''  ,					padding=lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       inputs['''input_ids''']

							lowerCAmelCase						:   Union[str, Any]							=       model.generate(input_ids=lowerCamelCase__  ,					attention_mask=inputs['''attention_mask''']  ,					trace=lowerCamelCase__			)

							lowerCAmelCase						:   str							=       tokenizer(sentences[0]  ,					return_tensors='''jax'''			).input_ids
							lowerCAmelCase						:   Union[str, Any]							=       model.generate(input_ids=lowerCamelCase__			)

							lowerCAmelCase						:   Optional[int]							=       inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
							lowerCAmelCase						:   Optional[int]							=       tokenizer(sentences[1]  ,					return_tensors='''jax'''			).input_ids
							lowerCAmelCase						:   str							=       model.generate(input_ids=lowerCamelCase__  ,					max_length=model.config.max_length - num_paddings			)

							lowerCAmelCase						:   List[Any]							=       tokenizer.batch_decode(outputs[0]  ,					skip_special_tokens=lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       tokenizer.decode(output_non_padded[0][0]  ,					skip_special_tokens=lowerCamelCase__			)
							lowerCAmelCase						:   Optional[int]							=       tokenizer.decode(output_padded[0][0]  ,					skip_special_tokens=lowerCamelCase__			)

							lowerCAmelCase						:   Tuple							=       [
							    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
							    '''Today, I was in the middle of a conversation with a friend about the''',
							]
							self.assertListEqual(lowerCamelCase__  ,					lowerCamelCase__			)
							self.assertListEqual(lowerCamelCase__  ,					[non_padded_sentence, padded_sentence]			)


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 						__magic_name__				(  snake_case      ):





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		List[Any]=False			):
							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   List[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(lowerCamelCase__			)  ,					'''wb'''			).close()
							lowerCAmelCase						:   int							=       pa.Table.from_pydict({'''filename''': [Path(lowerCamelCase__			).name] * 1_0_0}			)
							if take == -1:
												lowerCAmelCase						:   Optional[int]							=       len(lowerCamelCase__			) - skip
							if skip is not None and take is not None:
												lowerCAmelCase						:   List[str]							=       pa_table.slice(lowerCamelCase__  ,					lowerCamelCase__			)
							return pa_table



class 						__magic_name__				(  snake_case      ):





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   List[str]							=       '''my_name'''
							lowerCAmelCase						:   List[str]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   str							=       SplitInfo(name='''test'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   int							=       [train_info, test_info]
							lowerCAmelCase						:   Union[str, Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       DatasetInfo(splits=lowerCamelCase__			)

							with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCAmelCase						:   Tuple							=       ReaderTest(lowerCamelCase__  ,					lowerCamelCase__			)

												lowerCAmelCase						:   int							=       '''test[:33%]'''
												lowerCAmelCase						:   Dict							=       Dataset(**reader.read(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)			)
												self.assertEqual(dset['''filename'''][0]  ,					f'''{name}-test'''			)
												self.assertEqual(dset.num_rows  ,					3_3			)
												self.assertEqual(dset.num_columns  ,					1			)

												lowerCAmelCase						:   str							=       ['''train''', '''test[:33%]''']
												lowerCAmelCase						:   Tuple							=       [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''			)]
												for instructions in [instructionsa, instructionsa]:
																	lowerCAmelCase						:   Optional[Any]							=       [reader.read(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			) for instr in instructions]
																	lowerCAmelCase		,							lowerCAmelCase						:   Tuple							=       (Dataset(**lowerCamelCase__			) for dataset_kwargs in datasets_kwargs)
																	self.assertEqual(train_dset['''filename'''][0]  ,					f'''{name}-train'''			)
																	self.assertEqual(train_dset.num_rows  ,					1_0_0			)
																	self.assertEqual(train_dset.num_columns  ,					1			)
																	self.assertIsInstance(train_dset.split  ,					lowerCamelCase__			)
																	self.assertEqual(str(train_dset.split			)  ,					'''train'''			)
																	self.assertEqual(test_dset['''filename'''][0]  ,					f'''{name}-test'''			)
																	self.assertEqual(test_dset.num_rows  ,					3_3			)
																	self.assertEqual(test_dset.num_columns  ,					1			)
																	self.assertIsInstance(test_dset.split  ,					lowerCamelCase__			)
																	self.assertEqual(str(test_dset.split			)  ,					'''test[:33%]'''			)
																	del train_dset, test_dset





		def  _A							(							self      :		str			):
							lowerCAmelCase						:   Optional[Any]							=       '''my_name'''
							lowerCAmelCase						:   List[Any]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0_0  ,					shard_lengths=[1_0_0] * 1_0			)
							lowerCAmelCase						:   Optional[int]							=       [train_info]
							lowerCAmelCase						:   Union[str, Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   Optional[Any]							=       DatasetInfo(splits=lowerCamelCase__			)

							with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCAmelCase						:   Tuple							=       ReaderTest(lowerCamelCase__  ,					lowerCamelCase__			)

												lowerCAmelCase						:   Dict							=       '''train[:33%]'''
												lowerCAmelCase						:   int							=       Dataset(**reader.read(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)			)
												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_3_0			)
												self.assertEqual(dset.num_columns  ,					1			)





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Union[str, Any]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   int							=       SplitInfo(name='''test'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   Optional[Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       DatasetInfo(splits=lowerCamelCase__			)

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

												lowerCAmelCase						:   int							=       [
												    {'''filename''': os.path.join(lowerCamelCase__  ,					'''train'''			)},
												    {'''filename''': os.path.join(lowerCamelCase__  ,					'''test'''			), '''skip''': 1_0, '''take''': 1_0},
												]
												lowerCAmelCase						:   Optional[int]							=       Dataset(**reader.read_files(lowerCamelCase__  ,					original_instructions='''train+test[10:20]'''			)			)
												self.assertEqual(dset.num_rows  ,					1_1_0			)
												self.assertEqual(dset.num_columns  ,					1			)
												del dset



@pytest.mark.parametrize('''in_memory'''							,					[False, True]					)
def       UpperCAmelCase__							(    __magic_name__  :       Any							,					__magic_name__  :       Dict							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					lowerCAmelCase						:   Dict							=       arrow_file
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										lowerCAmelCase						:   Any							=       ArrowReader.read_table(__magic_name__							,					in_memory=__magic_name__					)
					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       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       arrow_file
					lowerCAmelCase						:   Union[str, Any]							=       ArrowReader(''''''							,					__magic_name__					)
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										lowerCAmelCase						:   Union[str, Any]							=       reader.read_files([{'''filename''': filename}]							,					in_memory=__magic_name__					)
					assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
					lowerCAmelCase						:   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       UpperCAmelCase__							(    ):


					'''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						:   Tuple							=       '''train+test'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_test

					lowerCAmelCase						:   List[Any]							=       '''train[2:10]'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_abs

					lowerCAmelCase						:   Optional[Any]							=       '''train[15%:-20%]'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_pct

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

					lowerCAmelCase						:   Optional[int]							=       '''train[:10%](pct1_dropremainder)'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_pct_rounding

					lowerCAmelCase						:   Optional[int]							=       '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_test_pct_rounding
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       '''dummy'''
					lowerCAmelCase						:   Optional[Any]							=       [SplitInfo(name='''train'''							,					num_examples=1_00					)]
					lowerCAmelCase						:   Optional[int]							=       '''train[:33%]'''
					lowerCAmelCase						:   Dict							=       '''arrow'''
					lowerCAmelCase						:   Optional[int]							=       '''prefix'''

					lowerCAmelCase						:   Union[str, Any]							=       make_file_instructions(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)
					assert isinstance(__magic_name__							,					__magic_name__					)
					assert file_instructions.num_examples == 33
					assert file_instructions.file_instructions == [
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train.arrow'''					), "skip": 0, "take": 33}
					]

					lowerCAmelCase						:   Dict							=       [SplitInfo(name='''train'''							,					num_examples=1_00							,					shard_lengths=[10] * 10					)]
					lowerCAmelCase						:   str							=       make_file_instructions(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)
					assert isinstance(__magic_name__							,					__magic_name__					)
					assert file_instructions.num_examples == 33
					assert file_instructions.file_instructions == [
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00000-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00001-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00002-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00003-of-00010.arrow'''					), "skip": 0, "take": 3},
					]
@pytest.mark.parametrize('''name, expected_exception'''							,					[(None, TypeError), ('''''', ValueError)]					)
def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       [SplitInfo(name='''train'''							,					num_examples=1_00					)]
					lowerCAmelCase						:   List[Any]							=       '''train'''
					lowerCAmelCase						:   Tuple							=       '''arrow'''
					lowerCAmelCase						:   List[Any]							=       '''prefix_path'''
					with pytest.raises(__magic_name__					):
										lowerCAmelCase						:   Optional[Any]							=       make_file_instructions(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)
