

'''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 		UpperCamelCase			(					a					) ->      Tuple:






     '''simple docstring'''
     __magic_name__		  =							tmp_path / 'file.csv'
     __magic_name__		  =							textwrap.dedent(
         '''\\n        header1,header2\n        1,2\n        10,20\n        '''					)
     with open(__lowerCAmelCase						,		'''w'''					) as f:
          f.write(__lowerCAmelCase					)
     return str(__lowerCAmelCase					)




@pytest.fixture
def 		UpperCamelCase			(					a					) ->      List[str]:






     '''simple docstring'''
     __magic_name__		  =							tmp_path / 'malformed_file.csv'
     __magic_name__		  =							textwrap.dedent(
         '''\\n        header1,header2\n        1,2\n        10,20,\n        '''					)
     with open(__lowerCAmelCase						,		'''w'''					) as f:
          f.write(__lowerCAmelCase					)
     return str(__lowerCAmelCase					)




@pytest.fixture
def 		UpperCamelCase			(					a						,		a					) ->      Optional[int]:






     '''simple docstring'''
     __magic_name__		  =							tmp_path / 'csv_with_image.csv'
     __magic_name__		  =							textwrap.dedent(
         F'''\
        image
        {image_file}
        '''					)
     with open(__lowerCAmelCase						,		'''w'''					) as f:
          f.write(__lowerCAmelCase					)
     return str(__lowerCAmelCase					)




@pytest.fixture
def 		UpperCamelCase			(					a					) ->      Dict:






     '''simple docstring'''
     __magic_name__		  =							tmp_path / 'csv_with_label.csv'
     __magic_name__		  =							textwrap.dedent(
         '''\\n        label\n        good\n        bad\n        good\n        '''					)
     with open(__lowerCAmelCase						,		'''w'''					) as f:
          f.write(__lowerCAmelCase					)
     return str(__lowerCAmelCase					)




@pytest.fixture
def 		UpperCamelCase			(					a					) ->      int:






     '''simple docstring'''
     __magic_name__		  =							tmp_path / 'csv_with_int_list.csv'
     __magic_name__		  =							textwrap.dedent(
         '''\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '''					)
     with open(__lowerCAmelCase						,		'''w'''					) as f:
          f.write(__lowerCAmelCase					)
     return str(__lowerCAmelCase					)




def 		UpperCamelCase			(					a						,		a						,		a					) ->      Tuple:






     '''simple docstring'''
     __magic_name__		  =							Csv()
     __magic_name__		  =							csv._generate_tables([[csv_file, malformed_csv_file]]					)
     with pytest.raises(__lowerCAmelCase						,		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(__lowerCAmelCase					) in record.message
         for record in caplog.records					)




@require_pil
def 		UpperCamelCase			(					a					) ->      Union[str, Any]:






     '''simple docstring'''
     with open(__lowerCAmelCase						,		encoding='''utf-8'''					) as f:
          __magic_name__		  =							f.read().splitlines()[1]
     __magic_name__		  =							Csv(encoding='''utf-8'''						,		features=Features({'''image''': Image()}					)					)
     __magic_name__		  =							csv._generate_tables([[csv_file_with_image]]					)
     __magic_name__		  =							pa.concat_tables([table for _, table in generator]					)
     assert pa_table.schema.field('''image'''					).type == Image()()
     __magic_name__		  =							pa_table.to_pydict()['image']
     assert generated_content == [{"path": image_file, "bytes": None}]




def 		UpperCamelCase			(					a					) ->      str:






     '''simple docstring'''
     with open(__lowerCAmelCase						,		encoding='''utf-8'''					) as f:
          __magic_name__		  =							f.read().splitlines()[1:]
     __magic_name__		  =							Csv(encoding='''utf-8'''						,		features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']					)}					)					)
     __magic_name__		  =							csv._generate_tables([[csv_file_with_label]]					)
     __magic_name__		  =							pa.concat_tables([table for _, table in generator]					)
     assert pa_table.schema.field('''label'''					).type == ClassLabel(names=['''good''', '''bad''']					)()
     __magic_name__		  =							pa_table.to_pydict()['label']
     assert generated_content == [ClassLabel(names=['''good''', '''bad''']					).straint(__lowerCAmelCase					) for label in labels]




def 		UpperCamelCase			(					a					) ->      Union[str, Any]:






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











from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase    :		int		      =    logging.get_logger(__name__)

_lowerCamelCase    :		Union[str, Any]		      =    {
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Tuple		=		'xlm-prophetnet'
						UpperCAmelCase			:		int		=		['past_key_values']
						UpperCAmelCase			:		List[Any]		=		{
						    'num_attention_heads': 'num_encoder_attention_heads',
						}







						def __init__(  self    :				List[Any]       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[Union[str, Callable]] = "gelu"       ,	snake_case    :				Optional[int] = 30522       ,	snake_case    :				Optional[int] = 1024       ,	snake_case    :				Optional[int] = 4096       ,	snake_case    :				Optional[int] = 12       ,	snake_case    :				Optional[int] = 16       ,	snake_case    :				Optional[int] = 4096       ,	snake_case    :				Optional[int] = 12       ,	snake_case    :				Optional[int] = 16       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[int] = 512       ,	snake_case    :				Optional[float] = 0.02       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[int] = 0       ,	snake_case    :				Optional[int] = 2       ,	snake_case    :				Optional[int] = 32       ,	snake_case    :				Optional[int] = 128       ,	snake_case    :				Optional[bool] = False       ,	snake_case    :				Optional[float] = 0.0       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[int] = 0       ,	snake_case    :				Optional[int] = 1       ,	snake_case    :				Optional[int] = 2       ,	**snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     int						=				hidden_size
											SCREAMING_SNAKE_CASE							:     Dict						=				encoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				num_encoder_layers
											SCREAMING_SNAKE_CASE							:     str						=				num_encoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				decoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Any						=				num_decoder_layers
											SCREAMING_SNAKE_CASE							:     List[str]						=				num_decoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     Any						=				init_std  # Normal(0, this parameter)
											SCREAMING_SNAKE_CASE							:     List[str]						=				activation_function

											# parameters for xlmprophetnet
											SCREAMING_SNAKE_CASE							:     Tuple						=				ngram
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				num_buckets
											SCREAMING_SNAKE_CASE							:     Any						=				relative_max_distance
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				disable_ngram_loss
											SCREAMING_SNAKE_CASE							:     List[str]						=				eps

											# 3 Types of Dropout
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				attention_dropout
											SCREAMING_SNAKE_CASE							:     Any						=				activation_dropout
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				dropout

											SCREAMING_SNAKE_CASE							:     Tuple						=				use_cache

											super().__init__(
											    pad_token_id=snake_case       ,	bos_token_id=snake_case       ,	eos_token_id=snake_case       ,	is_encoder_decoder=snake_case       ,	add_cross_attention=snake_case       ,	decoder_start_token_id=snake_case       ,	**snake_case       ,	)







						@property
						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											return self.num_encoder_layers + self.num_decoder_layers







						@num_hidden_layers.setter
						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											raise NotImplementedError(
											    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
											    ' `num_decoder_layers`.'	)