
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 						lowercase_			(   A__ ,		A__		)							->							Tuple:
    """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 						lowercase_			(   A__ ,		A__ ,		A__		)							->							int:
    """simple docstring"""

    snake_case      =					tmp_path / "cache"
    snake_case      =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        snake_case      =					CsvDatasetReader(A__ ,		cache_dir=A__ ,		keep_in_memory=A__		).read()
    _check_csv_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 						lowercase_			(   A__ ,		A__ ,		A__		)							->							Optional[int]:
    """simple docstring"""

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



@pytest.mark.parametrize("split" ,		[None, NamedSplit("train"		), "train", "test"]		)
def 						lowercase_			(   A__ ,		A__ ,		A__		)							->							int:
    """simple docstring"""

    snake_case      =					tmp_path / "cache"
    snake_case      =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case      =					CsvDatasetReader(A__ ,		cache_dir=A__ ,		split=A__		).read()
    _check_csv_dataset(A__ ,		A__		)
    assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type" ,		[str, list]		)
def 						lowercase_			(   A__ ,		A__ ,		A__		)							->							Any:
    """simple docstring"""

    if issubclass(A__ ,		A__		):
        snake_case      =					csv_path
    elif issubclass(A__ ,		A__		):
        snake_case      =					[csv_path]
    snake_case      =					tmp_path / "cache"
    snake_case      =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case      =					CsvDatasetReader(A__ ,		cache_dir=A__		).read()
    _check_csv_dataset(A__ ,		A__		)



def 						lowercase_			(   A__ ,		A__ ,		A__=("train",)		)							->							int:
    """simple docstring"""

    assert isinstance(A__ ,		A__		)
    for split in splits:
        snake_case      =					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 						lowercase_			(   A__ ,		A__ ,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case      =					tmp_path / "cache"
    snake_case      =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        snake_case      =					CsvDatasetReader({"train": csv_path} ,		cache_dir=A__ ,		keep_in_memory=A__		).read()
    _check_csv_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 						lowercase_			(   A__ ,		A__ ,		A__		)							->							int:
    """simple docstring"""

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



@pytest.mark.parametrize("split" ,		[None, NamedSplit("train"		), "train", "test"]		)
def 						lowercase_			(   A__ ,		A__ ,		A__		)							->							Optional[Any]:
    """simple docstring"""

    if split:
        snake_case      =					{split: csv_path}
    else:
        snake_case      =					{"train": csv_path, "test": csv_path}
    snake_case      =					tmp_path / "cache"
    snake_case      =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case      =					CsvDatasetReader(A__ ,		cache_dir=A__		).read()
    _check_csv_datasetdict(A__ ,		A__ ,		splits=list(path.keys()		)		)
    assert all(dataset[split].split == split for split in path.keys()		)



def 						lowercase_			(   A__		)							->							Optional[Any]:
    """simple docstring"""

    with open(A__ ,		encoding="utf-8"		) as csvfile:
        yield from csv.reader(A__		)



def 						lowercase_			(   A__ ,		A__		)							->							List[str]:
    """simple docstring"""

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

    snake_case      =					iter_csv_file(A__		)
    snake_case      =					iter_csv_file(A__		)

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



def 						lowercase_			(   A__ ,		A__		)							->							Union[str, Any]:
    """simple docstring"""

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

    snake_case      =					iter_csv_file(A__		)
    snake_case      =					iter_csv_file(A__		)

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





def 						lowercase_			(   A__ ,		A__		)							->							str:
    """simple docstring"""

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



from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.mpnet.modeling_tf_mpnet import (
       TFMPNetForMaskedLM,
       TFMPNetForMultipleChoice,
       TFMPNetForQuestionAnswering,
       TFMPNetForSequenceClassification,
       TFMPNetForTokenClassification,
       TFMPNetModel,
   )







class      lowerCamelCase		:





     def __init__(self      :    Optional[Any]				,    _A      :    int				,    _A      :    Tuple=1_3				,    _A      :    List[str]=7				,    _A      :    Any=True				,    _A      :    Union[str, Any]=True				,    _A      :    Optional[Any]=False				,    _A      :    Tuple=True				,    _A      :    str=9_9				,    _A      :    Optional[Any]=6_4				,    _A      :    Optional[int]=2				,    _A      :    Union[str, Any]=4				,    _A      :    Any=6_4				,    _A      :    Any="gelu"				,    _A      :    str=0.1				,    _A      :    Union[str, Any]=0.1				,    _A      :    Optional[Any]=5_1_2				,    _A      :    int=1_6				,    _A      :    List[str]=2				,    _A      :    Optional[int]=0.02				,    _A      :    Optional[Any]=3				,    _A      :    List[str]=4				,    _A      :    int=None				,    )						->						List[Any]:
         snake_case      =					parent
         snake_case      =					batch_size
         snake_case      =					seq_length
         snake_case      =					is_training
         snake_case      =					use_input_mask
         snake_case      =					use_token_type_ids
         snake_case      =					use_labels
         snake_case      =					vocab_size
         snake_case      =					hidden_size
         snake_case      =					num_hidden_layers
         snake_case      =					num_attention_heads
         snake_case      =					intermediate_size
         snake_case      =					hidden_act
         snake_case      =					hidden_dropout_prob
         snake_case      =					attention_probs_dropout_prob
         snake_case      =					max_position_embeddings
         snake_case      =					type_vocab_size
         snake_case      =					type_sequence_label_size
         snake_case      =					initializer_range
         snake_case      =					num_labels
         snake_case      =					num_choices
         snake_case      =					scope





     def 		UpperCAmelCase(self      :    List[str]   )						->						Dict:
         snake_case      =					ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size   )

         snake_case      =					None
         if self.use_input_mask:
             snake_case      =					random_attention_mask([self.batch_size, self.seq_length]   )

         snake_case      =					None
         snake_case      =					None
         snake_case      =					None
         if self.use_labels:
             snake_case      =					ids_tensor([self.batch_size]				,    self.type_sequence_label_size   )
             snake_case      =					ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels   )
             snake_case      =					ids_tensor([self.batch_size]				,    self.num_choices   )

         snake_case      =					MPNetConfig(
             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				,    initializer_range=self.initializer_range				,    )
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    str				,    _A      :    Dict				,    _A      :    Optional[Any]				,    _A      :    Optional[Any]				,    _A      :    List[str]				,    _A      :    int   )						->						List[Any]:
         snake_case      =					TFMPNetModel(config=_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         snake_case      =					[input_ids, input_mask]
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)   )





     def 		UpperCAmelCase(self      :    int				,    _A      :    str				,    _A      :    Optional[Any]				,    _A      :    Optional[Any]				,    _A      :    Union[str, Any]				,    _A      :    Optional[Any]				,    _A      :    Any   )						->						int:
         snake_case      =					TFMPNetForMaskedLM(config=_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)   )





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    Optional[int]				,    _A      :    int				,    _A      :    int				,    _A      :    List[str]				,    _A      :    Union[str, Any]				,    _A      :    Union[str, Any]   )						->						int:
         snake_case      =					TFMPNetForQuestionAnswering(config=_A   )
         snake_case      =					{
             "input_ids": input_ids,
             "attention_mask": input_mask,
         }
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.start_logits.shape				,    (self.batch_size, self.seq_length)   )
         self.parent.assertEqual(result.end_logits.shape				,    (self.batch_size, self.seq_length)   )





     def 		UpperCAmelCase(self      :    Optional[int]				,    _A      :    List[Any]				,    _A      :    Dict				,    _A      :    Dict				,    _A      :    Optional[int]				,    _A      :    str				,    _A      :    Optional[Any]   )						->						Dict:
         snake_case      =					self.num_labels
         snake_case      =					TFMPNetForSequenceClassification(_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)   )





     def 		UpperCAmelCase(self      :    Union[str, Any]				,    _A      :    List[str]				,    _A      :    int				,    _A      :    Optional[int]				,    _A      :    Union[str, Any]				,    _A      :    Any				,    _A      :    int   )						->						Optional[Any]:
         snake_case      =					self.num_choices
         snake_case      =					TFMPNetForMultipleChoice(_A   )
         snake_case      =					tf.tile(tf.expand_dims(_A				,    1   )				,    (1, self.num_choices, 1)   )
         snake_case      =					tf.tile(tf.expand_dims(_A				,    1   )				,    (1, self.num_choices, 1)   )
         snake_case      =					{
             "input_ids": multiple_choice_inputs_ids,
             "attention_mask": multiple_choice_input_mask,
         }
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_choices)   )





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    Union[str, Any]				,    _A      :    Dict				,    _A      :    Optional[Any]				,    _A      :    int				,    _A      :    int				,    _A      :    Tuple   )						->						Tuple:
         snake_case      =					self.num_labels
         snake_case      =					TFMPNetForTokenClassification(_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)   )





     def 		UpperCAmelCase(self      :    List[Any]   )						->						List[str]:
         snake_case      =					self.prepare_config_and_inputs()
         ((snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case))      =					config_and_inputs
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_tf
class      lowerCamelCase		(  A_							, A_							, unittest.TestCase  ):
     UpperCAmelCase__				:							List[Any]           =		(
         (
             TFMPNetForMaskedLM,
             TFMPNetForMultipleChoice,
             TFMPNetForQuestionAnswering,
             TFMPNetForSequenceClassification,
             TFMPNetForTokenClassification,
             TFMPNetModel,
         )
         if is_tf_available()
         else ()
     )
     UpperCAmelCase__				:							Optional[int]           =		(
         {
             "feature-extraction": TFMPNetModel,
             "fill-mask": TFMPNetForMaskedLM,
             "question-answering": TFMPNetForQuestionAnswering,
             "text-classification": TFMPNetForSequenceClassification,
             "token-classification": TFMPNetForTokenClassification,
             "zero-shot": TFMPNetForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase__				:							int           =		False
     UpperCAmelCase__				:							str           =		False





     def 		UpperCAmelCase(self      :    List[Any]   )						->						str:
         snake_case      =					TFMPNetModelTester(self   )
         snake_case      =					ConfigTester(self				,    config_class=_A				,    hidden_size=3_7   )





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						int:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self      :    Optional[int]   )						->						str:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*_A   )





     def 		UpperCAmelCase(self      :    List[str]   )						->						List[str]:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_masked_lm(*_A   )





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						str:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*_A   )





     def 		UpperCAmelCase(self      :    Dict   )						->						Tuple:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*_A   )





     def 		UpperCAmelCase(self      :    int   )						->						List[Any]:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*_A   )





     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						int:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*_A   )





     @slow
     def 		UpperCAmelCase(self      :    Optional[int]   )						->						Union[str, Any]:
         for model_name in ["microsoft/mpnet-base"]:
             snake_case      =					TFMPNetModel.from_pretrained(_A   )
             self.assertIsNotNone(_A   )







@require_tf
class      lowerCamelCase		(  unittest.TestCase  ):





     @slow
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						List[Any]:
         snake_case      =					TFMPNetModel.from_pretrained("microsoft/mpnet-base"   )
         snake_case      =					tf.constant([[0, 1, 2, 3, 4, 5]]   )
         snake_case      =					model(_A   )[0]

         snake_case      =					[1, 6, 7_6_8]
         self.assertEqual(output.shape				,    _A   )

         snake_case      =					tf.constant(
             [
                 [
                     [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
                     [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
                     [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
                 ]
             ]   )
         tf.debugging.assert_near(output[:, :3, :3]				,    _A				,    atol=1E-4   )

