


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Tuple=1_3  ,							SCREAMING_SNAKE_CASE__       : Tuple=7  ,							SCREAMING_SNAKE_CASE__       : Any=True  ,							SCREAMING_SNAKE_CASE__       : int=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=False  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : Dict=9_9  ,							SCREAMING_SNAKE_CASE__       : int=3_2  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=5  ,							SCREAMING_SNAKE_CASE__       : Dict=4  ,							SCREAMING_SNAKE_CASE__       : str=3_7  ,							SCREAMING_SNAKE_CASE__       : List[Any]="gelu"  ,							SCREAMING_SNAKE_CASE__       : Tuple=0.1  ,							SCREAMING_SNAKE_CASE__       : List[str]=0.1  ,							SCREAMING_SNAKE_CASE__       : Tuple=5_1_2  ,							SCREAMING_SNAKE_CASE__       : Any=1_6  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=0.02  ,							SCREAMING_SNAKE_CASE__       : str=3  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=4  ,							SCREAMING_SNAKE_CASE__       : Any=None  ,							)	->    str:
             a_      :	List[str]							=						parent
             a_      :	List[Any]							=						batch_size
             a_      :	int							=						seq_length
             a_      :	Dict							=						is_training
             a_      :	str							=						use_input_mask
             a_      :	Tuple							=						use_token_type_ids
             a_      :	List[Any]							=						use_labels
             a_      :	Dict							=						vocab_size
             a_      :	Union[str, Any]							=						hidden_size
             a_      :	Tuple							=						num_hidden_layers
             a_      :	Tuple							=						num_attention_heads
             a_      :	str							=						intermediate_size
             a_      :	Tuple							=						hidden_act
             a_      :	Dict							=						hidden_dropout_prob
             a_      :	Optional[int]							=						attention_probs_dropout_prob
             a_      :	Any							=						max_position_embeddings
             a_      :	Tuple							=						type_vocab_size
             a_      :	List[str]							=						type_sequence_label_size
             a_      :	List[str]							=						initializer_range
             a_      :	Optional[Any]							=						num_labels
             a_      :	Optional[Any]							=						num_choices
             a_      :	Any							=						scope
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Dict:
             a_      :	Any							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )

             a_      :	Tuple							=						None
             if self.use_input_mask:
                    a_      :	Dict							=						random_attention_mask([self.batch_size, self.seq_length]  )

             a_      :	Tuple							=						None
             if self.use_token_type_ids:
                    a_      :	Union[str, Any]							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.type_vocab_size  )

             a_      :	List[Any]							=						None
             a_      :	Dict							=						None
             a_      :	List[Any]							=						None
             if self.use_labels:
                    a_      :	Dict							=						ids_tensor([self.batch_size]  ,							self.type_sequence_label_size  )
                    a_      :	Tuple							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.num_labels  )
                    a_      :	Optional[Any]							=						ids_tensor([self.batch_size]  ,							self.num_choices  )

             a_      :	List[str]							=						self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             return OpenLlamaConfig(
                 vocab_size=self.vocab_size  ,							hidden_size=self.hidden_size  ,							num_hidden_layers=self.num_hidden_layers  ,							num_attention_heads=self.num_attention_heads  ,							intermediate_size=self.intermediate_size  ,							hidden_act=self.hidden_act  ,							hidden_dropout_prob=self.hidden_dropout_prob  ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,							max_position_embeddings=self.max_position_embeddings  ,							type_vocab_size=self.type_vocab_size  ,							is_decoder=SCREAMING_SNAKE_CASE__  ,							initializer_range=self.initializer_range  ,							use_stable_embedding=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str  )	->    Dict:
             a_      :	Optional[Any]							=						OpenLlamaModel(config=SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  )
             self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size)  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							)	->    Optional[Any]:
             a_      :	Optional[int]							=						True
             a_      :	int							=						OpenLlamaModel(SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Union[str, Any]							=						model(
                 SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							encoder_attention_mask=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	List[str]							=						model(
                 SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	Optional[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )
             self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size)  )
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							)	->    Dict:
             a_      :	Any							=						OpenLlamaForCausalLM(config=SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  )
             self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.vocab_size)  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							)	->    Dict:
             a_      :	Tuple							=						True
             a_      :	Optional[Any]							=						True
             a_      :	Dict							=						OpenLlamaForCausalLM(config=SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()

             # first forward pass
             a_      :	Any							=						model(
                 SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							encoder_attention_mask=SCREAMING_SNAKE_CASE__  ,							use_cache=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	Tuple							=						outputs.past_key_values

             # create hypothetical multiple next token and extent to next_input_ids
             a_      :	str							=						ids_tensor((self.batch_size, 3)  ,							config.vocab_size  )
             a_      :	str							=						ids_tensor((self.batch_size, 3)  ,							vocab_size=2  )

             # append to next input_ids and
             a_      :	Optional[int]							=						torch.cat([input_ids, next_tokens]  ,							dim=-1  )
             a_      :	Dict							=						torch.cat([input_mask, next_mask]  ,							dim=-1  )

             a_      :	str							=						model(
                 SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							encoder_attention_mask=SCREAMING_SNAKE_CASE__  ,							output_hidden_states=SCREAMING_SNAKE_CASE__  ,							)['hidden_states'][0]
             a_      :	Tuple							=						model(
                 SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							encoder_attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  ,							output_hidden_states=SCREAMING_SNAKE_CASE__  ,							)['hidden_states'][0]

             # select random slice
             a_      :	Optional[Any]							=						ids_tensor((1,)  ,							output_from_past.shape[-1]  ).item()
             a_      :	Optional[int]							=						output_from_no_past[:, -3:, random_slice_idx].detach()
             a_      :	int							=						output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )  )

      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[Any]:
             a_      :	List[str]							=						self.prepare_config_and_inputs()
             (
                 (
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	
             )      :	Optional[int]							=						config_and_inputs
             a_      :	List[str]							=						{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Dict       =		(
          (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
      )
      snake_case__       :  Optional[Any]       =		(OpenLlamaForCausalLM,) if is_torch_available() else ()
      snake_case__       :  Optional[Any]       =		(
          {
              '''feature-extraction''': OpenLlamaModel,
              '''text-classification''': OpenLlamaForSequenceClassification,
              '''text-generation''': OpenLlamaForCausalLM,
              '''zero-shot''': OpenLlamaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__       :  List[str]       =		False
      snake_case__       :  str       =		False
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Optional[Any]:
             a_      :	Optional[int]							=						OpenLlamaModelTester(self  )
             a_      :	List[str]							=						ConfigTester(self  ,							config_class=SCREAMING_SNAKE_CASE__  ,							hidden_size=3_7  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Optional[int]:
             self.config_tester.run_common_tests()
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    List[str]:
             a_      :	List[Any]							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             a_      :	int							=						self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    a_      :	Optional[Any]							=						type
                    self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    int:
             a_							,	a_      :	Dict							=						self.model_tester.prepare_config_and_inputs_for_common()
             a_      :	List[Any]							=						3
             a_      :	Tuple							=						input_dict['input_ids']
             a_      :	Dict							=						input_ids.ne(1  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						ids_tensor([self.model_tester.batch_size]  ,							self.model_tester.type_sequence_label_size  )
             a_      :	int							=						OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Union[str, Any]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  )
             self.assertEqual(result.logits.shape  ,							(self.model_tester.batch_size, self.model_tester.num_labels)  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Any:
             a_							,	a_      :	Optional[int]							=						self.model_tester.prepare_config_and_inputs_for_common()
             a_      :	Optional[Any]							=						3
             a_      :	Optional[Any]							=						'single_label_classification'
             a_      :	List[Any]							=						input_dict['input_ids']
             a_      :	Tuple							=						input_ids.ne(1  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						ids_tensor([self.model_tester.batch_size]  ,							self.model_tester.type_sequence_label_size  )
             a_      :	Any							=						OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  )
             self.assertEqual(result.logits.shape  ,							(self.model_tester.batch_size, self.model_tester.num_labels)  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Optional[int]:
             a_							,	a_      :	Any							=						self.model_tester.prepare_config_and_inputs_for_common()
             a_      :	Tuple							=						3
             a_      :	Tuple							=						'multi_label_classification'
             a_      :	Optional[Any]							=						input_dict['input_ids']
             a_      :	str							=						input_ids.ne(1  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]  ,							self.model_tester.type_sequence_label_size  ).to(torch.float  )
             a_      :	str							=						OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	List[str]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  )
             self.assertEqual(result.logits.shape  ,							(self.model_tester.batch_size, self.model_tester.num_labels)  )
      @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    str:
             pass

      @parameterized.expand([('linear',), ('dynamic',)]  )
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : Tuple  )	->    Optional[int]:
             a_							,	a_      :	str							=						self.model_tester.prepare_config_and_inputs_for_common()
             a_      :	Any							=						ids_tensor([1, 1_0]  ,							config.vocab_size  )
             a_      :	Optional[Any]							=						ids_tensor([1, int(config.max_position_embeddings * 1.5  )]  ,							config.vocab_size  )

             set_seed(4_2  )  # Fixed seed at init time so the two models get the same random weights
             a_      :	Optional[int]							=						OpenLlamaModel(SCREAMING_SNAKE_CASE__  )
             original_model.to(SCREAMING_SNAKE_CASE__  )
             original_model.eval()
             a_      :	Dict							=						original_model(SCREAMING_SNAKE_CASE__  ).last_hidden_state
             a_      :	List[Any]							=						original_model(SCREAMING_SNAKE_CASE__  ).last_hidden_state

             set_seed(4_2  )  # Fixed seed at init time so the two models get the same random weights
             a_      :	Optional[int]							=						{'type': scaling_type, 'factor': 10.0}
             a_      :	Optional[Any]							=						OpenLlamaModel(SCREAMING_SNAKE_CASE__  )
             scaled_model.to(SCREAMING_SNAKE_CASE__  )
             scaled_model.eval()
             a_      :	int							=						scaled_model(SCREAMING_SNAKE_CASE__  ).last_hidden_state
             a_      :	str							=						scaled_model(SCREAMING_SNAKE_CASE__  ).last_hidden_state

             # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
             # maximum sequence length, so the outputs for the short input should match.
             if scaling_type == "dynamic":
                    self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-5  )  )
             else:
                    self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-5  )  )

             # The output should be different for long inputs
             self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-5  )  )






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Tuple       )			->	Any:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    Any						,					__A				:    Tuple       )			->	Tuple:

       """simple docstring"""

       a_      :	Dict							=						tmp_path / 'cache'
       a_      :	int							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Union[str, Any]							=						ParquetDatasetReader(__A						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Dict						,					__A				:    Union[str, Any]       )			->	Dict:

       """simple docstring"""

       a_      :	Tuple							=						tmp_path / 'cache'
       a_      :	str							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	List[str]							=						features.copy() if features else default_expected_features
       a_      :	int							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	List[str]							=						ParquetDatasetReader(__A						,					features=__A						,					cache_dir=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    List[str]						,					__A				:    int       )			->	List[str]:

       """simple docstring"""

       a_      :	int							=						tmp_path / 'cache'
       a_      :	str							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	List[str]							=						ParquetDatasetReader(__A						,					cache_dir=__A						,					split=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('path_type'						,					[str, list]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    str						,					__A				:    Optional[int]       )			->	Any:

       """simple docstring"""

       if issubclass(__A						,					__A       ):
              a_      :	Tuple							=						parquet_path
       elif issubclass(__A						,					__A       ):
              a_      :	str							=						[parquet_path]
       a_      :	int							=						tmp_path / 'cache'
       a_      :	int							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	str							=						ParquetDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Dict						,					__A				:    Optional[Any]=("train",)       )			->	Optional[int]:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       for split in splits:
              a_      :	str							=						dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Union[str, Any]							=						tmp_path / 'cache'
       a_      :	Any							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Tuple							=						ParquetDatasetReader(
                  {'train': parquet_path}						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_parquet_datasetdict(__A						,					__A       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Optional[int]						,					__A				:    Tuple       )			->	List[Any]:

       """simple docstring"""

       a_      :	Optional[Any]							=						tmp_path / 'cache'
       a_      :	List[str]							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	Optional[int]							=						features.copy() if features else default_expected_features
       a_      :	Tuple							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	Optional[Any]							=						ParquetDatasetReader({'train': parquet_path}						,					features=__A						,					cache_dir=__A       ).read()
       _check_parquet_datasetdict(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[Any]						,					__A				:    Optional[Any]       )			->	Any:

       """simple docstring"""

       if split:
              a_      :	Any							=						{split: parquet_path}
       else:
              a_      :	Dict							=						'train'
              a_      :	int							=						{'train': parquet_path, 'test': parquet_path}
       a_      :	int							=						tmp_path / 'cache'
       a_      :	List[Any]							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	Tuple							=						ParquetDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_parquet_datasetdict(__A						,					__A						,					splits=list(path.keys()       )       )
       assert all(dataset[split].split == split for split in path.keys()       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Optional[int]       )			->	List[Any]:

       """simple docstring"""

       a_      :	List[str]							=						ParquetDatasetWriter(__A						,					tmp_path / 'foo.parquet'       )
       assert writer.write() > 0
       a_      :	List[str]							=						pq.ParquetFile(tmp_path / 'foo.parquet'       )
       a_      :	Dict							=						pf.read()
       assert dataset.data.table == output_table







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any       )			->	Optional[int]:

       """simple docstring"""

       a_      :	str							=						str(shared_datadir / 'test_image_rgb.jpg'       )
       a_      :	List[Any]							=						{'image': [image_path]}
       a_      :	int							=						Features({'image': Image()}       )
       a_      :	List[Any]							=						Dataset.from_dict(__A						,					features=__A       )
       a_      :	str							=						ParquetDatasetWriter(__A						,					tmp_path / 'foo.parquet'       )
       assert writer.write() > 0

       a_      :	str							=						Dataset.from_parquet(str(tmp_path / 'foo.parquet'       )       )
       assert dataset.features == reloaded_dataset.features

       a_      :	str							=						ParquetDatasetReader(str(tmp_path / 'foo.parquet'       )						,					streaming=__A       ).read()
       assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    'feature, expected'						,					[
        (Features({'foo': Value('int32'       )}       ), None),
        (Features({'image': Image(), 'foo': Value('int32'       )}       ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()       )}       ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[str]       )			->	List[str]:

       """simple docstring"""

       assert get_writer_batch_size(__A       ) == expected


