



import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

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


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




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


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)









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


A_		:     Any        =     logging.get_logger(__name__)

A_		:     Optional[int]        =     {
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''lilt'''

   def __init__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]=3_0522							,   __lowerCAmelCase			:       str=768							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Optional[Any]=12							,   __lowerCAmelCase			:       List[Any]=3072							,   __lowerCAmelCase			:       Dict="gelu"							,   __lowerCAmelCase			:       int=0.1							,   __lowerCAmelCase			:       Union[str, Any]=0.1							,   __lowerCAmelCase			:       List[str]=512							,   __lowerCAmelCase			:       Optional[int]=2							,   __lowerCAmelCase			:       int=0.0_2							,   __lowerCAmelCase			:       Union[str, Any]=1E-12							,   __lowerCAmelCase			:       Tuple=0							,   __lowerCAmelCase			:       List[Any]="absolute"							,   __lowerCAmelCase			:       int=None							,   __lowerCAmelCase			:       Dict=4							,   __lowerCAmelCase			:       Dict=1024							,   **__lowerCAmelCase			:       Dict							,   )       ->				int:


    """simple docstring"""






    super().__init__(pad_token_id=__lowerCAmelCase							,   **__lowerCAmelCase							)

    a						=		vocab_size
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		hidden_act
    a						=		intermediate_size
    a						=		hidden_dropout_prob
    a						=		attention_probs_dropout_prob
    a						=		max_position_embeddings
    a						=		type_vocab_size
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		position_embedding_type
    a						=		classifier_dropout
    a						=		channel_shrink_ratio
    a						=		max_ad_position_embeddings

