


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


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

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
   import torch
class 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=1_4  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=7  ,							SCREAMING_SNAKE_CASE__       : List[Any]=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : List[str]=False  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=9_9  ,							SCREAMING_SNAKE_CASE__       : List[Any]=3_2  ,							SCREAMING_SNAKE_CASE__       : Any=4  ,							SCREAMING_SNAKE_CASE__       : Tuple=4  ,							SCREAMING_SNAKE_CASE__       : List[Any]=4  ,							SCREAMING_SNAKE_CASE__       : Tuple=3_7  ,							SCREAMING_SNAKE_CASE__       : Tuple="gelu"  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=5_1_2  ,							SCREAMING_SNAKE_CASE__       : Dict=0.02  ,							)	->    Dict:
             a_      :	Tuple							=						parent
             a_      :	Any							=						batch_size
             a_      :	Optional[Any]							=						seq_length
             a_      :	Optional[int]							=						is_training
             a_      :	Dict							=						use_input_mask
             a_      :	List[Any]							=						use_token_type_ids
             a_      :	List[Any]							=						use_labels
             a_      :	Optional[Any]							=						vocab_size
             a_      :	str							=						hidden_size
             a_      :	List[Any]							=						rotary_dim
             a_      :	int							=						num_hidden_layers
             a_      :	str							=						num_attention_heads
             a_      :	Any							=						intermediate_size
             a_      :	Optional[int]							=						hidden_act
             a_      :	List[Any]							=						hidden_dropout_prob
             a_      :	Optional[Any]							=						attention_probs_dropout_prob
             a_      :	int							=						max_position_embeddings
             a_      :	Tuple							=						initializer_range
             a_      :	List[str]							=						None
             a_      :	Union[str, Any]							=						vocab_size - 1
             a_      :	Tuple							=						vocab_size - 1
             a_      :	Optional[Any]							=						vocab_size - 1
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    str:
             a_      :	List[str]							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )

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

             a_      :	List[str]							=						GPTJConfig(
                 vocab_size=self.vocab_size  ,							n_embd=self.hidden_size  ,							n_layer=self.num_hidden_layers  ,							n_head=self.num_attention_heads  ,							n_positions=self.max_position_embeddings  ,							use_cache=SCREAMING_SNAKE_CASE__  ,							bos_token_id=self.bos_token_id  ,							eos_token_id=self.eos_token_id  ,							pad_token_id=self.pad_token_id  ,							rotary_dim=self.rotary_dim  ,							)

             return (config, input_ids, input_mask)
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Any:
             a_      :	Union[str, Any]							=						self.prepare_config_and_inputs()
             a_							,	a_							,	a_      :	Optional[Any]							=						config_and_inputs
             a_      :	int							=						{'input_ids': input_ids, 'attention_mask': attention_mask}
             return config, inputs_dict
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    List[Any]:
             a_      :	str							=						2_0
             a_      :	Any							=						model_class_name(SCREAMING_SNAKE_CASE__  )

             a_      :	str							=						model.init_cache(input_ids.shape[0]  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						jnp.ones((input_ids.shape[0], max_decoder_length)  ,							dtype='i4'  )

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

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

             a_      :	Union[str, Any]							=						model(SCREAMING_SNAKE_CASE__  )

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

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  )	->    str:
             a_      :	Optional[Any]							=						2_0
             a_      :	int							=						model_class_name(SCREAMING_SNAKE_CASE__  )

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

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

             a_      :	Tuple							=						model(
                 input_ids[:, :-1]  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  ,							position_ids=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	Tuple							=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,							dtype='i4'  )
             a_      :	List[Any]							=						model(
                 input_ids[:, -1:]  ,							past_key_values=outputs_cache.past_key_values  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							position_ids=SCREAMING_SNAKE_CASE__  ,							)

             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )

             a_      :	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 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  int       =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      snake_case__       :  Any       =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    str:
             a_      :	int							=						FlaxGPTJModelTester(self  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             for model_class_name in self.all_model_classes:
                    a_							,	a_							,	a_      :	List[str]							=						self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Tuple:
             for model_class_name in self.all_model_classes:
                    a_							,	a_							,	a_      :	Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward_with_attn_mask(
                        SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      @tooslow
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    List[str]:
             a_      :	str							=						GPTaTokenizer.from_pretrained('gpt2'  ,							pad_token='<|endoftext|>'  ,							padding_side='left'  )
             a_      :	Union[str, Any]							=						tokenizer(['Hello this is a long string', 'Hey']  ,							return_tensors='np'  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  )

             a_      :	List[Any]							=						FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'  )
             a_      :	List[str]							=						False
             a_      :	Optional[int]							=						model.config.eos_token_id

             a_      :	Optional[int]							=						jax.jit(model.generate  )

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

             a_      :	List[str]							=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )

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

             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      @is_pt_flax_cross_test
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[Any]:
             a_							,	a_      :	Tuple							=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__  ):
                           # prepare inputs
                           a_      :	Any							=						self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                           a_      :	Any							=						{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           a_      :	Dict							=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           a_      :	Optional[Any]							=						getattr(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

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

                           a_      :	Any							=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,							SCREAMING_SNAKE_CASE__  )
                           a_      :	Optional[int]							=						fx_state

                           with torch.no_grad():
                                  a_      :	Tuple							=						pt_model(**SCREAMING_SNAKE_CASE__  ).to_tuple()

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

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  pt_model.save_pretrained(SCREAMING_SNAKE_CASE__  )
                                  a_      :	Optional[int]							=						model_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							from_pt=SCREAMING_SNAKE_CASE__  )

                           a_      :	Dict							=						fx_model_loaded(**SCREAMING_SNAKE_CASE__  ).to_tuple()
                           self.assertEqual(
                               len(SCREAMING_SNAKE_CASE__  )  ,							len(SCREAMING_SNAKE_CASE__  )  ,							'Output lengths differ between Flax and PyTorch'  )
                           for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                                  self.assert_almost_equals(fx_output_loaded[:, -1]  ,							pt_output[:, -1].numpy()  ,							4E-2  )
      @is_pt_flax_cross_test
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             a_							,	a_      :	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__  ):
                           # prepare inputs
                           a_      :	Dict							=						self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                           a_      :	List[str]							=						{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

                           a_      :	int							=						pt_model_class(SCREAMING_SNAKE_CASE__  ).eval()
                           a_      :	List[str]							=						model_class(SCREAMING_SNAKE_CASE__  ,							dtype=jnp.floataa  )

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

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

                           with torch.no_grad():
                                  a_      :	List[Any]							=						pt_model(**SCREAMING_SNAKE_CASE__  ).to_tuple()

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

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  fx_model.save_pretrained(SCREAMING_SNAKE_CASE__  )
                                  a_      :	Optional[int]							=						pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							from_flax=SCREAMING_SNAKE_CASE__  )

                           with torch.no_grad():
                                  a_      :	int							=						pt_model_loaded(**SCREAMING_SNAKE_CASE__  ).to_tuple()

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

      @tooslow
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Optional[Any]:
             for model_class_name in self.all_model_classes:
                    a_      :	Union[str, Any]							=						model_class_name.from_pretrained('EleutherAI/gpt-j-6B'  )
                    a_      :	Tuple							=						model(np.ones((1, 1)  )  )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )






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


