

"""simple docstring"""



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_       :						Any					=    'platform'

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Dict=None       , UpperCAmelCase_	:       Tuple=None      ):
     if attention_mask is None:
          A__				      =	np.where(input_ids != config.pad_token_id       , 1       , 0      )
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }



@require_flax
class        a    :



    """simple docstring"""



    def __init__(  self:			List[str]     ,    UpperCamelCase:			str     ,    UpperCamelCase:			Tuple=13     ,    UpperCamelCase:			Optional[Any]=7     ,    UpperCamelCase:			int=True     ,    UpperCamelCase:			List[Any]=False     ,    UpperCamelCase:			Union[str, Any]=99     ,    UpperCamelCase:			Optional[Any]=16     ,    UpperCamelCase:			Dict=2     ,    UpperCamelCase:			Any=4     ,    UpperCamelCase:			List[str]=4     ,    UpperCamelCase:			str="gelu"     ,    UpperCamelCase:			Any=0.1     ,    UpperCamelCase:			List[Any]=0.1     ,    UpperCamelCase:			List[Any]=20     ,    UpperCamelCase:			List[Any]=2     ,    UpperCamelCase:			int=1     ,    UpperCamelCase:			str=0     ,    UpperCamelCase:			Optional[Any]=16     ,    UpperCamelCase:			List[Any]=16     ,    UpperCamelCase:			Union[str, Any]=0.02     ,    ):






         """simple docstring"""


         A__				      =	parent
         A__				      =	batch_size
         A__				      =	seq_length
         A__				      =	is_training
         A__				      =	use_labels
         A__				      =	vocab_size
         A__				      =	hidden_size
         A__				      =	num_hidden_layers
         A__				      =	num_attention_heads
         A__				      =	intermediate_size
         A__				      =	hidden_act
         A__				      =	hidden_dropout_prob
         A__				      =	attention_probs_dropout_prob
         A__				      =	max_position_embeddings
         A__				      =	eos_token_id
         A__				      =	pad_token_id
         A__				      =	bos_token_id
         A__				      =	embed_dim
         A__				      =	word_embed_proj_dim
         A__				      =	initializer_range
         A__				      =	False

    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


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

         A__				      =	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=UpperCamelCase     ,    word_embed_proj_dim=self.word_embed_proj_dim     ,    initializer_range=self.initializer_range     ,    use_cache=UpperCamelCase     ,    )
         A__				      =	prepare_opt_inputs_dict(UpperCamelCase     ,    UpperCamelCase							)
         return config, inputs_dict

    def 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         A__      ,					A__				      =	self.prepare_config_and_inputs()
         return config, inputs_dict

    def 					UpperCamelCase						(  self:			List[Any]     ,    UpperCamelCase:			Union[str, Any]     ,    UpperCamelCase:			List[str]     ,    UpperCamelCase:			Union[str, Any]							):






         """simple docstring"""


         A__				      =	20
         A__				      =	model_class_name(UpperCamelCase							)

         A__				      =	inputs_dict["""input_ids"""]
         A__				      =	inputs_dict["""attention_mask"""]

         A__				      =	model.init_cache(input_ids.shape[0]     ,    UpperCamelCase							)
         A__				      =	jnp.ones((input_ids.shape[0], max_length)     ,    dtype="""i4"""							)

         A__				      =	jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1							)[None, :]     ,    (input_ids.shape[0], input_ids.shape[-1] - 1)     ,    )
         A__				      =	model(
             input_ids[:, :-1]     ,    attention_mask=UpperCamelCase     ,    past_key_values=UpperCamelCase     ,    position_ids=UpperCamelCase     ,    )

         A__				      =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,    dtype="""i4"""							)
         A__				      =	model(
             input_ids[:, -1:]     ,    attention_mask=UpperCamelCase     ,    past_key_values=outputs_cache.past_key_values     ,    position_ids=UpperCamelCase     ,    )

         A__				      =	model(UpperCamelCase							)

         A__				      =	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:			Tuple     ,    UpperCamelCase:			List[str]     ,    UpperCamelCase:			Optional[int]     ,    UpperCamelCase:			Any							):






         """simple docstring"""


         A__				      =	20
         A__				      =	model_class_name(UpperCamelCase							)

         A__      ,					A__				      =	(
             inputs_dict["""input_ids"""],
             inputs_dict["""attention_mask"""],
         )

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

         A__				      =	model.init_cache(input_ids.shape[0]     ,    UpperCamelCase							)
         A__				      =	jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1							)[None, :]     ,    (input_ids.shape[0], input_ids.shape[-1] - 1)     ,    )

         A__				      =	model(
             input_ids[:, :-1]     ,    attention_mask=UpperCamelCase     ,    past_key_values=UpperCamelCase     ,    position_ids=UpperCamelCase     ,    )
         A__				      =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,    dtype="""i4"""							)
         A__				      =	model(
             input_ids[:, -1:]     ,    past_key_values=outputs_cache.past_key_values     ,    attention_mask=UpperCamelCase     ,    position_ids=UpperCamelCase     ,    )

         A__				      =	model(UpperCamelCase     ,    attention_mask=UpperCamelCase							)

         A__				      =	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        a    (							_lowerCamelCase,						unittest.TestCase,						_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    UpperCAmelCase    			=       () if is_flax_available() else ()

    def 					UpperCamelCase						(  self:			str							):






         """simple docstring"""


         A__				      =	FlaxOPTModelTester(self							)

    def 					UpperCamelCase						(  self:			List[str]							):






         """simple docstring"""


         A__      ,					A__				      =	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:			Optional[Any]							):






         """simple docstring"""


         A__      ,					A__				      =	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							)





    @slow
    def 					UpperCamelCase						(  self:			Dict							):






         """simple docstring"""


         for model_class_name in self.all_model_classes:
              A__				      =	model_class_name.from_pretrained("""facebook/opt-125m"""							)
              A__				      =	np.ones((1, 1)							) * model.config.eos_token_id
              A__				      =	model(UpperCamelCase							)
              self.assertIsNotNone(UpperCamelCase							)



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



    """simple docstring"""



    @slow
    def 					UpperCamelCase						(  self:			str							):






         """simple docstring"""


         A__				      =	FlaxOPTModel.from_pretrained("""facebook/opt-350m"""							)
         A__				      =	jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
         A__				      =	model(input_ids=UpperCamelCase							).last_hidden_state
         A__				      =	(1, 11, 5_12)
         self.assertEqual(output.shape     ,    UpperCamelCase							)
         A__				      =	jnp.array(
             [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]							)
         self.assertTrue(jnp.allclose(output[:, :3, :3]     ,    UpperCamelCase     ,    atol=4e-2							)							)



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



    """simple docstring"""



    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         super().setUp()
         A__				      =	"""facebook/opt-350m"""





    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         A__				      =	FlaxOPTForCausalLM.from_pretrained(self.path_model							)
         A__				      =	GPTaTokenizer.from_pretrained(self.path_model							)

         A__				      =	[
             """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
         A__				      =	tokenizer(UpperCamelCase     ,    return_tensors="""jax"""     ,    padding=UpperCamelCase     ,    add_special_tokens=UpperCamelCase							)
         A__				      =	model(inputs.input_ids     ,    attention_mask=inputs.attention_mask							)[0].mean(axis=-1							)
         A__				      =	jnp.array(
             [
                 [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
                 [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
                 [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                 [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
             ]							)
         self.assertTrue(jnp.allclose(UpperCamelCase     ,    UpperCamelCase     ,    atol=4e-2							)							)

         A__				      =	jax.jit(UpperCamelCase							)
         A__				      =	model(inputs.input_ids     ,    attention_mask=inputs.attention_mask							)[0].mean(axis=-1							)
         self.assertTrue(jnp.allclose(UpperCamelCase     ,    UpperCamelCase     ,    atol=4e-2							)							)



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



    """simple docstring"""



    @property
    def 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         A__				      =	"""facebook/opt-125m"""

         A__				      =	[
             """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""",
         ]

         A__				      =	[]

         A__				      =	FlaxOPTForCausalLM.from_pretrained(UpperCamelCase							)
         A__				      =	GPTaTokenizer.from_pretrained(UpperCamelCase							)

         for prompt in self.prompts:
              A__				      =	tokenizer(UpperCamelCase     ,    return_tensors="""jax"""							).input_ids

              A__				      =	model.generate(UpperCamelCase     ,    max_length=10							)
              A__				      =	generated_ids[0]

              A__				      =	tokenizer.batch_decode(UpperCamelCase     ,    skip_special_tokens=UpperCamelCase							)
              predicted_outputs += generated_string

         self.assertListEqual(UpperCamelCase     ,    UpperCamelCase							)

    def 					UpperCamelCase						(  self:			List[str]							):






         """simple docstring"""


         A__				      =	"""facebook/opt-350m"""

         A__				      =	[
             """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""",
         ]

         A__				      =	[]
         A__				      =	FlaxOPTForCausalLM.from_pretrained(UpperCamelCase							)
         A__				      =	GPTaTokenizer.from_pretrained(UpperCamelCase							)

         for prompt in self.prompts:
              A__				      =	tokenizer(UpperCamelCase     ,    return_tensors="""jax"""							).input_ids

              A__				      =	model.generate(UpperCamelCase     ,    max_length=10							)
              A__				      =	generated_ids[0]

              A__				      =	tokenizer.batch_decode(UpperCamelCase     ,    skip_special_tokens=UpperCamelCase							)
              predicted_outputs += generated_string

         self.assertListEqual(UpperCamelCase     ,    UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Optional[int]							):






         """simple docstring"""


         A__				      =	"""facebook/opt-125m"""
         A__				      =	[
             """Today is a beautiful day and I want to thank""",
             """In the city of Rome Canaver Canaver Canaver Canaver""",
         ]
         A__				      =	FlaxOPTForCausalLM.from_pretrained(UpperCamelCase							)
         A__				      =	GPTaTokenizer.from_pretrained(UpperCamelCase							)
         A__				      =	tokenizer(
             [
                 """Today is a beautiful day and I want to""",
                 """In the city of""",
             ]     ,    return_tensors="""jax"""     ,    padding=UpperCamelCase     ,    )

         A__				      =	jax.jit(model.generate							)

         A__				      =	jit_generate(inputs["""input_ids"""]     ,    attention_mask=inputs["""attention_mask"""]							).sequences

         A__				      =	tokenizer.batch_decode(UpperCamelCase     ,    skip_special_tokens=UpperCamelCase							)

         self.assertIsNotNone(UpperCamelCase     ,    UpperCamelCase							)





    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         A__				      =	"""facebook/opt-350m"""

         A__				      =	GPTaTokenizer.from_pretrained(UpperCamelCase							)
         A__				      =	FlaxOPTForCausalLM.from_pretrained(UpperCamelCase							)

         A__				      =	"""left"""

         # use different length sentences to test batching
         A__				      =	[
             """Hello, my dog is a little""",
             """Today, I""",
         ]

         A__				      =	tokenizer(UpperCamelCase     ,    return_tensors="""jax"""     ,    padding=UpperCamelCase							)
         A__				      =	inputs["""input_ids"""]

         A__				      =	model.generate(input_ids=UpperCamelCase     ,    attention_mask=inputs["""attention_mask"""]     ,    trace=UpperCamelCase							)

         A__				      =	tokenizer(sentences[0]     ,    return_tensors="""jax"""							).input_ids
         A__				      =	model.generate(input_ids=UpperCamelCase							)

         A__				      =	inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
         A__				      =	tokenizer(sentences[1]     ,    return_tensors="""jax"""							).input_ids
         A__				      =	model.generate(input_ids=UpperCamelCase     ,    max_length=model.config.max_length - num_paddings							)

         A__				      =	tokenizer.batch_decode(outputs[0]     ,    skip_special_tokens=UpperCamelCase							)
         A__				      =	tokenizer.decode(output_non_padded[0][0]     ,    skip_special_tokens=UpperCamelCase							)
         A__				      =	tokenizer.decode(output_padded[0][0]     ,    skip_special_tokens=UpperCamelCase							)

         A__				      =	[
             """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(UpperCamelCase     ,    UpperCamelCase							)
         self.assertListEqual(UpperCamelCase     ,    [non_padded_sentence, padded_sentence]							)









"""simple docstring"""



import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     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 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Optional[Any]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





@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 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Dict      ):
     A__				      =	tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	features.copy() if features else default_expected_features
     A__				      =	(
         Features({feature: Value(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""      ), """train""", """test"""]      )
def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , split=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )
     assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""       , [str, list]      )
def 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Dict      ):
     if issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	csv_path
     elif issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	[csv_path]
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       int=("train",)      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     for split in splits:
          A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       List[str]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_      )





@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 			_snake_case  (     UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Optional[Any]      ):
     A__				      =	tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	features.copy() if features else default_expected_features
     A__				      =	(
         Features({feature: Value(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_      )





@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""      ), """train""", """test"""]      )
def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Optional[int]      ):
     if split:
          A__				      =	{split: csv_path}
     else:
          A__				      =	{"""train""": csv_path, """test""": csv_path}
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_       , splits=list(path.keys()      )      )
     assert all(dataset[split].split == split for split in path.keys()      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]      ):
     with open(UpperCAmelCase_       , encoding="""utf-8"""      ) as csvfile:
          yield from csv.reader(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=1      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

     for rowa, rowa in zip(UpperCAmelCase_       , UpperCAmelCase_      ):
          assert rowa == rowa





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[Any]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=2      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

     for rowa, rowa in zip(UpperCAmelCase_       , UpperCAmelCase_      ):
          assert rowa == rowa





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     with pytest.raises(UpperCAmelCase_      ):
          CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=0      )






