



"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase

import pytest



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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    with open(_SCREAMING_SNAKE_CASE      ,     encoding="""utf-8"""						) as input_file:
     UpperCAmelCase	:					List[str]	=	re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""						)
     UpperCAmelCase	:					str	=	input_file.read()
     UpperCAmelCase	:					int	=	regexp.search(_SCREAMING_SNAKE_CASE						)

    return match

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




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

    UpperCAmelCase	:					Optional[int]	=	[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 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	Path("""./datasets"""						)
    UpperCAmelCase	:					Optional[int]	=	list(dataset_paths.absolute().glob("""**/*.py"""						)						)

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




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	Path("""./datasets"""						)
    UpperCAmelCase	:					Optional[Any]	=	list(dataset_paths.absolute().glob("""**/*.py"""						)						)

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


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


_UpperCAmelCase					: Union[str, Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: List[str]      =       {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				"gpt_neox"



		def __init__( self				,      A_=50_432				,      A_=6_144				,      A_=44				,      A_=64				,      A_=24_576				,      A_="gelu"				,      A_=0.25				,      A_=10_000				,      A_=0.0				,      A_=0.0				,      A_=0.1				,      A_=2_048				,      A_=0.02				,      A_=1e-5				,      A_=True				,      A_=0				,      A_=2				,      A_=False				,      A_=True				,      A_=None				,      **A_				,      )  ->					Tuple:
				"""simple docstring"""
				super().__init__(bos_token_id=A_				,      eos_token_id=A_				,      **A_       )
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     rotary_pct
				UpperCamelCase		=     rotary_emb_base
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     hidden_dropout
				UpperCamelCase		=     classifier_dropout
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     tie_word_embeddings
				UpperCamelCase		=     use_parallel_residual
				UpperCamelCase		=     rope_scaling
				self._rope_scaling_validation()

				if self.hidden_size % self.num_attention_heads != 0:
						raise ValueError(
						    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				if self.rope_scaling is None:
						return

				if not isinstance(self.rope_scaling				,      A_       ) or len(self.rope_scaling       ) != 2:
						raise ValueError(
						    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
						    F'''got {self.rope_scaling}'''       )
				UpperCamelCase		=     self.rope_scaling.get('type'				,      A_       )
				UpperCamelCase		=     self.rope_scaling.get('factor'				,      A_       )
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''       )
				if rope_scaling_factor is None or not isinstance(A_				,      A_       ) or rope_scaling_factor <= 1.0:
						raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''       )
