




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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

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







@pytest.fixture
def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

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







@pytest.fixture
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

       A__   				=					tmp_path / '''csv_with_image.csv'''
       A__   				=					textwrap.dedent(
           f"""\
        image
        {image_file}
        """		)
       with open(lowercase_				,       '''w'''		) as f:
              f.write(lowercase_		)
       return str(lowercase_		)







@pytest.fixture
def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path / '''csv_with_label.csv'''
       A__   				=					textwrap.dedent(
           '''\
        label
        good
        bad
        good
        '''		)
       with open(lowercase_				,       '''w'''		) as f:
              f.write(lowercase_		)
       return str(lowercase_		)







@pytest.fixture
def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[Any]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					Csv()
       A__   				=					csv._generate_tables([[csv_file, malformed_csv_file]]		)
       with pytest.raises(lowercase_				,       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(lowercase_		) in record.message
           for record in caplog.records		)







@require_pil
def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[str]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase			:	List[Any]							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Dict							 =     {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''xlm'''
    UpperCAmelCase__													=   {
        '''hidden_size''': '''emb_dim''',
        '''num_attention_heads''': '''n_heads''',
        '''num_hidden_layers''': '''n_layers''',
        '''n_words''': '''vocab_size''',  # For backward compatibility
    }


    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			Optional[Any]=30_145      ,				UpperCAmelCase__   :			List[str]=2_048      ,				UpperCAmelCase__   :			str=12      ,				UpperCAmelCase__   :			List[Any]=16      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			str=True      ,				UpperCAmelCase__   :			Union[str, Any]=False      ,				UpperCAmelCase__   :			str=False      ,				UpperCAmelCase__   :			Optional[int]=False      ,				UpperCAmelCase__   :			List[str]=1      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Union[str, Any]=512      ,				UpperCAmelCase__   :			List[str]=2_048**-0.5      ,				UpperCAmelCase__   :			List[Any]=1e-12      ,				UpperCAmelCase__   :			Any=0.02      ,				UpperCAmelCase__   :			str=0      ,				UpperCAmelCase__   :			int=1      ,				UpperCAmelCase__   :			Union[str, Any]=2      ,				UpperCAmelCase__   :			List[Any]=3      ,				UpperCAmelCase__   :			int=5      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Tuple="first"      ,				UpperCAmelCase__   :			Optional[int]=True      ,				UpperCAmelCase__   :			List[str]=None      ,				UpperCAmelCase__   :			Tuple=True      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Any=5      ,				UpperCAmelCase__   :			List[Any]=5      ,				UpperCAmelCase__   :			int=0      ,				UpperCAmelCase__   :			Dict=0      ,				UpperCAmelCase__   :			Dict=2      ,				UpperCAmelCase__   :			str=0      ,				**UpperCAmelCase__   :			List[str]      ,				)		->str:





           '''simple docstring'''

           A__   				=					vocab_size
           A__   				=					emb_dim
           A__   				=					n_layers
           A__   				=					n_heads
           A__   				=					dropout
           A__   				=					attention_dropout
           A__   				=					gelu_activation
           A__   				=					sinusoidal_embeddings
           A__   				=					causal
           A__   				=					asm
           A__   				=					n_langs
           A__   				=					use_lang_emb
           A__   				=					layer_norm_eps
           A__   				=					bos_index
           A__   				=					eos_index
           A__   				=					pad_index
           A__   				=					unk_index
           A__   				=					mask_index
           A__   				=					is_encoder
           A__   				=					max_position_embeddings
           A__   				=					embed_init_std
           A__   				=					init_std
           A__   				=					summary_type
           A__   				=					summary_use_proj
           A__   				=					summary_activation
           A__   				=					summary_proj_to_labels
           A__   				=					summary_first_dropout
           A__   				=					start_n_top
           A__   				=					end_n_top
           A__   				=					mask_token_id
           A__   				=					lang_id

           if "n_words" in kwargs:
                  A__   				=					kwargs['''n_words''']

           super().__init__(pad_token_id=UpperCAmelCase__      ,				bos_token_id=UpperCAmelCase__      ,				**UpperCAmelCase__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           if self.task == "multiple-choice":
                  A__   				=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
                  A__   				=					{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
                   ('''token_type_ids''', dynamic_axis),
               ])


