import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
   import torch

   from transformers import (
       MraForMaskedLM,
       MraForMultipleChoice,
       MraForQuestionAnswering,
       MraForSequenceClassification,
       MraForTokenClassification,
       MraModel,
   )
   from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST





class 		_lowercase						:

 def __init__( self	:							Dict   ,      snake_case	:							Optional[int]   ,      snake_case	:							Any=2   ,      snake_case	:							List[Any]=8   ,      snake_case	:							Union[str, Any]=True   ,      snake_case	:							Tuple=True   ,      snake_case	:							Optional[int]=True   ,      snake_case	:							Optional[Any]=True   ,      snake_case	:							List[str]=9_9   ,      snake_case	:							int=1_6   ,      snake_case	:							List[str]=5   ,      snake_case	:							Dict=2   ,      snake_case	:							int=3_6   ,      snake_case	:							List[str]="gelu"   ,      snake_case	:							Any=0.0   ,      snake_case	:							Optional[Any]=0.0   ,      snake_case	:							Any=5_1_2   ,      snake_case	:							Optional[int]=1_6   ,      snake_case	:							Tuple=2   ,      snake_case	:							Dict=0.02   ,      snake_case	:							List[Any]=3   ,      snake_case	:							Dict=4   ,      snake_case	:							List[str]=None   ,      )   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					parent
  UpperCamelCase_					:						Tuple =					batch_size
  UpperCamelCase_					:						Optional[Any] =					seq_length
  UpperCamelCase_					:						Dict =					is_training
  UpperCamelCase_					:						str =					use_input_mask
  UpperCamelCase_					:						Dict =					use_token_type_ids
  UpperCamelCase_					:						Any =					use_labels
  UpperCamelCase_					:						Optional[int] =					vocab_size
  UpperCamelCase_					:						Optional[Any] =					hidden_size
  UpperCamelCase_					:						Tuple =					num_hidden_layers
  UpperCamelCase_					:						Any =					num_attention_heads
  UpperCamelCase_					:						Dict =					intermediate_size
  UpperCamelCase_					:						Dict =					hidden_act
  UpperCamelCase_					:						List[str] =					hidden_dropout_prob
  UpperCamelCase_					:						Any =					attention_probs_dropout_prob
  UpperCamelCase_					:						Optional[int] =					max_position_embeddings
  UpperCamelCase_					:						Dict =					type_vocab_size
  UpperCamelCase_					:						List[str] =					type_sequence_label_size
  UpperCamelCase_					:						Union[str, Any] =					initializer_range
  UpperCamelCase_					:						Any =					num_labels
  UpperCamelCase_					:						int =					num_choices
  UpperCamelCase_					:						Tuple =					scope

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)

  UpperCamelCase_					:						Union[str, Any] =					None
  if self.use_input_mask:
   UpperCamelCase_					:						int =					random_attention_mask([self.batch_size, self.seq_length]			)

  UpperCamelCase_					:						str =					None
  if self.use_token_type_ids:
   UpperCamelCase_					:						int =					ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size			)

  UpperCamelCase_					:						int =					None
  UpperCamelCase_					:						Optional[Any] =					None
  UpperCamelCase_					:						Optional[int] =					None
  if self.use_labels:
   UpperCamelCase_					:						str =					ids_tensor([self.batch_size]   ,      self.type_sequence_label_size			)
   UpperCamelCase_					:						Any =					ids_tensor([self.batch_size, self.seq_length]   ,      self.num_labels			)
   UpperCamelCase_					:						Optional[int] =					ids_tensor([self.batch_size]   ,      self.num_choices			)

  UpperCamelCase_					:						Tuple =					self.get_config()

  return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Optional[Any]:

  """simple docstring"""





  return MraConfig(
      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   ,      )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					self.get_config()
  UpperCamelCase_					:						Dict =					3_0_0
  return config

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						List[str]:

  """simple docstring"""





  (
      UpperCamelCase_				
  )					:						Any =					self.prepare_config_and_inputs()

  UpperCamelCase_					:						Dict =					True
  UpperCamelCase_					:						str =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
  UpperCamelCase_					:						Union[str, Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      vocab_size=2			)

  return (
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
      encoder_hidden_states,
      encoder_attention_mask,
  )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							List[str]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Tuple   ,      snake_case	:							Any			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					MraModel(config=__a			)
  model.to(__a			)
  model.eval()
  UpperCamelCase_					:						Optional[Any] =					model(__a   ,      attention_mask=__a   ,      token_type_ids=__a			)
  UpperCamelCase_					:						int =					model(__a   ,      token_type_ids=__a			)
  UpperCamelCase_					:						List[Any] =					model(__a			)
  self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							List[Any]   ,      snake_case	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Any   ,      snake_case	:							Tuple   ,      snake_case	:							List[str]   ,      snake_case	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							List[Any]   ,      )   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						str =					True
  UpperCamelCase_					:						Union[str, Any] =					MraModel(__a			)
  model.to(__a			)
  model.eval()
  UpperCamelCase_					:						Optional[Any] =					model(
      __a   ,      attention_mask=__a   ,      token_type_ids=__a   ,      encoder_hidden_states=__a   ,      encoder_attention_mask=__a   ,      )
  UpperCamelCase_					:						Union[str, Any] =					model(
      __a   ,      attention_mask=__a   ,      token_type_ids=__a   ,      encoder_hidden_states=__a   ,      )
  UpperCamelCase_					:						Optional[int] =					model(__a   ,      attention_mask=__a   ,      token_type_ids=__a			)
  self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							List[str]   ,      snake_case	:							List[Any]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							List[str]   ,      snake_case	:							List[Any]   ,      snake_case	:							str			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						int =					MraForMaskedLM(config=__a			)
  model.to(__a			)
  model.eval()
  UpperCamelCase_					:						Union[str, Any] =					model(__a   ,      attention_mask=__a   ,      token_type_ids=__a   ,      labels=__a			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							int   ,      snake_case	:							Optional[Any]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							List[str]   ,      snake_case	:							List[Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							List[str]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					MraForQuestionAnswering(config=__a			)
  model.to(__a			)
  model.eval()
  UpperCamelCase_					:						List[str] =					model(
      __a   ,      attention_mask=__a   ,      token_type_ids=__a   ,      start_positions=__a   ,      end_positions=__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 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any   ,      snake_case	:							List[str]   ,      snake_case	:							Any   ,      snake_case	:							Tuple   ,      snake_case	:							Any   ,      snake_case	:							List[Any]   ,      snake_case	:							Optional[int]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.num_labels
  UpperCamelCase_					:						Optional[Any] =					MraForSequenceClassification(__a			)
  model.to(__a			)
  model.eval()
  UpperCamelCase_					:						Any =					model(__a   ,      attention_mask=__a   ,      token_type_ids=__a   ,      labels=__a			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							str   ,      snake_case	:							List[Any]   ,      snake_case	:							int   ,      snake_case	:							int   ,      snake_case	:							int   ,      snake_case	:							Any   ,      snake_case	:							str			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					self.num_labels
  UpperCamelCase_					:						Union[str, Any] =					MraForTokenClassification(config=__a			)
  model.to(__a			)
  model.eval()
  UpperCamelCase_					:						Optional[int] =					model(__a   ,      attention_mask=__a   ,      token_type_ids=__a   ,      labels=__a			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.num_labels)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							List[Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							List[Any]   ,      snake_case	:							Any   ,      snake_case	:							str   ,      snake_case	:							int			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.num_choices
  UpperCamelCase_					:						str =					MraForMultipleChoice(config=__a			)
  model.to(__a			)
  model.eval()
  UpperCamelCase_					:						Union[str, Any] =					input_ids.unsqueeze(1			).expand(-1   ,      self.num_choices   ,      -1			).contiguous()
  UpperCamelCase_					:						Tuple =					token_type_ids.unsqueeze(1			).expand(-1   ,      self.num_choices   ,      -1			).contiguous()
  UpperCamelCase_					:						Union[str, Any] =					input_mask.unsqueeze(1			).expand(-1   ,      self.num_choices   ,      -1			).contiguous()
  UpperCamelCase_					:						Dict =					model(
      __a   ,      attention_mask=__a   ,      token_type_ids=__a   ,      labels=__a   ,      )
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_choices)			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					self.prepare_config_and_inputs()
  (
      UpperCamelCase_				
  )					:						Dict =					config_and_inputs
  UpperCamelCase_					:						int =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
  return config, inputs_dict





@require_torch
class 		_lowercase						(					lowerCAmelCase_						,      unittest.TestCase       ):
 lowercase				     =   (
     (
         MraModel,
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 lowercase				     =   False
 lowercase				     =   False
 lowercase				     =   False
 lowercase				     =   False

 lowercase				     =   ()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						str =					MraModelTester(self			)
  UpperCamelCase_					:						Tuple =					ConfigTester(self   ,      config_class=__a   ,      hidden_size=3_7			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  self.config_tester.run_common_tests()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__a			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   UpperCamelCase_					:						List[str] =					type
   self.model_tester.create_and_check_model(*__a			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*__a			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_multiple_choice(*__a			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*__a			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*__a			)

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

  """simple docstring"""





  UpperCamelCase_					:						str =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*__a			)

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   UpperCamelCase_					:						int =					MraModel.from_pretrained(__a			)
   self.assertIsNotNone(__a			)


 @unittest.skip(reason='MRA does not output attentions'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						List[str]:

  """simple docstring"""





  return





@require_torch
class 		_lowercase						(					unittest.TestCase       ):

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					MraModel.from_pretrained('uw-madison/mra-base-512-4'			)
  UpperCamelCase_					:						List[Any] =					torch.arange(2_5_6			).unsqueeze(0			)

  with torch.no_grad():
   UpperCamelCase_					:						str =					model(__a			)[0]

  UpperCamelCase_					:						int =					torch.Size((1, 2_5_6, 7_6_8)			)
  self.assertEqual(output.shape   ,      __a			)

  UpperCamelCase_					:						Any =					torch.tensor(
      [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]			)

  self.assertTrue(torch.allclose(output[:, :3, :3]   ,      __a   ,      atol=1e-4			)			)

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'			)
  UpperCamelCase_					:						Union[str, Any] =					torch.arange(2_5_6			).unsqueeze(0			)

  with torch.no_grad():
   UpperCamelCase_					:						int =					model(__a			)[0]

  UpperCamelCase_					:						Any =					5_0_2_6_5

  UpperCamelCase_					:						Dict =					torch.Size((1, 2_5_6, vocab_size)			)
  self.assertEqual(output.shape   ,      __a			)

  UpperCamelCase_					:						List[str] =					torch.tensor(
      [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]			)

  self.assertTrue(torch.allclose(output[:, :3, :3]   ,      __a   ,      atol=1e-4			)			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						int =					MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'			)
  UpperCamelCase_					:						int =					torch.arange(4_0_9_6			).unsqueeze(0			)

  with torch.no_grad():
   UpperCamelCase_					:						Union[str, Any] =					model(__a			)[0]

  UpperCamelCase_					:						Dict =					5_0_2_6_5

  UpperCamelCase_					:						Tuple =					torch.Size((1, 4_0_9_6, vocab_size)			)
  self.assertEqual(output.shape   ,      __a			)

  UpperCamelCase_					:						List[Any] =					torch.tensor(
      [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]			)

  self.assertTrue(torch.allclose(output[:, :3, :3]   ,      __a   ,      atol=1e-4			)			)







'''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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					__a			:       Optional[Any]					    =  tmp_path / 'file.csv'
					__a			:       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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       str					    =  tmp_path / 'malformed_file.csv'
					__a			:       int					    =  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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  tmp_path / 'csv_with_image.csv'
					__a			:       Dict					    =  textwrap.dedent(
					    F"""\
        image
        {image_file}
        """   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Union[str, Any]					    =  tmp_path / 'csv_with_label.csv'
					__a			:       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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Dict					    =  tmp_path / 'csv_with_int_list.csv'
					__a			:       Tuple					    =  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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       int					    =  Csv()
					__a			:       str					    =  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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					with open(_SCREAMING_SNAKE_CASE						,      encoding='utf-8'   ) as f:
										__a			:       Tuple					    =  f.read().splitlines()[1]
					__a			:       Tuple					    =  Csv(encoding='utf-8'						,      features=Features({'image': Image()}   )   )
					__a			:       Any					    =  csv._generate_tables([[csv_file_with_image]]   )
					__a			:       int					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('image'   ).type == Image()()
					__a			:       Any					    =  pa_table.to_pydict()['image']
					assert generated_content == [{"path": image_file, "bytes": None}]






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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  Csv(encoding='utf-8'						,      sep=','						,      converters={'int_list': lambda _SCREAMING_SNAKE_CASE   : [int(_SCREAMING_SNAKE_CASE   ) for i in x.split()]}   )
					__a			:       Any					    =  csv._generate_tables([[csv_file_with_int_list]]   )
					__a			:       Any					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa.types.is_list(pa_table.schema.field('int_list'   ).type   )
					__a			:       Tuple					    =  pa_table.to_pydict()['int_list']
					assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



