



"""simple docstring"""




import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.bert.modeling_flax_bert import (
     FlaxBertForMaskedLM,
     FlaxBertForMultipleChoice,
     FlaxBertForNextSentencePrediction,
     FlaxBertForPreTraining,
     FlaxBertForQuestionAnswering,
     FlaxBertForSequenceClassification,
     FlaxBertForTokenClassification,
     FlaxBertModel,
 )






class       UpperCAmelCase_    (							unittest.TestCase		):



      def __init__(	self	:      str	,      A	:      Dict	,      A	:      Optional[Any]=1_3	,      A	:      Tuple=7	,      A	:      int=True	,      A	:      Dict=True	,      A	:      Dict=True	,      A	:      List[Any]=True	,      A	:      str=9_9	,      A	:      Union[str, Any]=3_2	,      A	:      Optional[Any]=5	,      A	:      Optional[int]=4	,      A	:      Tuple=3_7	,      A	:      Optional[Any]="gelu"	,      A	:      str=0.1	,      A	:      Tuple=0.1	,      A	:      List[Any]=5_1_2	,      A	:      Optional[int]=1_6	,      A	:      Optional[int]=2	,      A	:      int=0.02	,      A	:      Any=4	,      ):
           _UpperCAmelCase     :      List[Any]														=       parent
           _UpperCAmelCase     :      Tuple														=       batch_size
           _UpperCAmelCase     :      Any														=       seq_length
           _UpperCAmelCase     :      Union[str, Any]														=       is_training
           _UpperCAmelCase     :      List[str]														=       use_attention_mask
           _UpperCAmelCase     :      Optional[int]														=       use_token_type_ids
           _UpperCAmelCase     :      str														=       use_labels
           _UpperCAmelCase     :      Union[str, Any]														=       vocab_size
           _UpperCAmelCase     :      str														=       hidden_size
           _UpperCAmelCase     :      int														=       num_hidden_layers
           _UpperCAmelCase     :      Any														=       num_attention_heads
           _UpperCAmelCase     :      Dict														=       intermediate_size
           _UpperCAmelCase     :      List[Any]														=       hidden_act
           _UpperCAmelCase     :      int														=       hidden_dropout_prob
           _UpperCAmelCase     :      int														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Dict														=       max_position_embeddings
           _UpperCAmelCase     :      int														=       type_vocab_size
           _UpperCAmelCase     :      Optional[int]														=       type_sequence_label_size
           _UpperCAmelCase     :      Optional[int]														=       initializer_range
           _UpperCAmelCase     :      Dict														=       num_choices



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      str														=       ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size		)

           _UpperCAmelCase     :      Union[str, Any]														=       None
           if self.use_attention_mask:
                _UpperCAmelCase     :      Optional[Any]														=       random_attention_mask([self.batch_size, self.seq_length]		)

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

           _UpperCAmelCase     :      Optional[int]														=       BertConfig(
               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=A	,      initializer_range=self.initializer_range	,      )

           return config, input_ids, token_type_ids, attention_mask



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      List[Any]														=       self.prepare_config_and_inputs()
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       config_and_inputs
           _UpperCAmelCase     :      Union[str, Any]														=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
           return config, inputs_dict



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Optional[int]														=       self.prepare_config_and_inputs()
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       config_and_inputs

           _UpperCAmelCase     :      List[Any]														=       True
           _UpperCAmelCase     :      str														=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
           _UpperCAmelCase     :      Tuple														=       ids_tensor([self.batch_size, self.seq_length]	,      vocab_size=2		)

           return (
               config,
               input_ids,
               attention_mask,
               encoder_hidden_states,
               encoder_attention_mask,
           )







@require_flax
class       UpperCAmelCase_    (							_UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					True

      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					(
          (
              FlaxBertModel,
              FlaxBertForPreTraining,
              FlaxBertForMaskedLM,
              FlaxBertForMultipleChoice,
              FlaxBertForQuestionAnswering,
              FlaxBertForNextSentencePrediction,
              FlaxBertForSequenceClassification,
              FlaxBertForTokenClassification,
              FlaxBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Tuple														=       FlaxBertModelTester(self		)



      @slow
      def 		snake_case_   (	self	:      Optional[int]		):
           # Only check this for base model, not necessary for all model classes.
           # This will also help speed-up tests.
           _UpperCAmelCase     :      Union[str, Any]														=       FlaxBertModel.from_pretrained("bert-base-cased"		)
           _UpperCAmelCase     :      Any														=       model(np.ones((1, 1)		)		)
           self.assertIsNotNone(A		)





"""simple docstring"""




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       tmp_path / "file.csv"
     _UpperCAmelCase     :      Union[str, Any]														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       tmp_path / "malformed_file.csv"
     _UpperCAmelCase     :      Dict														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20,\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       tmp_path / "csv_with_image.csv"
     _UpperCAmelCase     :      List[str]														=       textwrap.dedent(
         f'\\n        image\n        {image_file}\n        '							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       tmp_path / "csv_with_label.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        label\n        good\n        bad\n        good\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       tmp_path / "csv_with_int_list.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       Csv()
     _UpperCAmelCase     :      Optional[int]														=       csv._generate_tables([[csv_file, malformed_csv_file]]							)
     with pytest.raises(SCREAMING_SNAKE_CASE__		,    match="Error tokenizing data"							):
          for _ in generator:
               pass
     assert any(
         record.levelname == "ERROR"
         and "Failed to read file" in record.message
         and os.path.basename(SCREAMING_SNAKE_CASE__							) in record.message
         for record in caplog.records							)

@require_pil
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Union[str, Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Union[str, Any]														=       f.read().splitlines()[1]
     _UpperCAmelCase     :      Tuple														=       Csv(encoding="utf-8"		,    features=Features({"image": Image()}							)							)
     _UpperCAmelCase     :      Tuple														=       csv._generate_tables([[csv_file_with_image]]							)
     _UpperCAmelCase     :      List[str]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("image"							).type == Image()()
     _UpperCAmelCase     :      Optional[Any]														=       pa_table.to_pydict()["image"]
     assert generated_content == [{"path": image_file, "bytes": None}]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Tuple														=       f.read().splitlines()[1:]
     _UpperCAmelCase     :      Optional[Any]														=       Csv(encoding="utf-8"		,    features=Features({"label": ClassLabel(names=["good", "bad"]							)}							)							)
     _UpperCAmelCase     :      int														=       csv._generate_tables([[csv_file_with_label]]							)
     _UpperCAmelCase     :      Dict														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("label"							).type == ClassLabel(names=["good", "bad"]							)()
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["label"]
     assert generated_content == [ClassLabel(names=["good", "bad"]							).straint(SCREAMING_SNAKE_CASE__							) for label in labels]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       Csv(encoding="utf-8"		,    sep=","		,    converters={"int_list": lambda SCREAMING_SNAKE_CASE__							: [int(SCREAMING_SNAKE_CASE__							) for i in x.split()]}							)
     _UpperCAmelCase     :      Dict														=       csv._generate_tables([[csv_file_with_int_list]]							)
     _UpperCAmelCase     :      List[Any]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa.types.is_list(pa_table.schema.field("int_list"							).type							)
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["int_list"]
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
