


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


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: str				   =					{
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      str       					=     """xglm"""
       lowercase_					:      Dict       					=     ["""past_key_values"""]

       lowercase_					:      str       					=     {
           """num_attention_heads""": """attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """num_layers""",
       }
       def __init__(   self, lowerCamelCase=25_60_08, lowerCamelCase=20_48, lowerCamelCase=10_24, lowerCamelCase=40_96, lowerCamelCase=24, lowerCamelCase=16, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0_2, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=0, lowerCamelCase=2, **lowerCamelCase, )			->  Tuple:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						vocab_size
           _lowercase			:				Union[str, Any] 						=						max_position_embeddings
           _lowercase			:				Tuple 						=						d_model
           _lowercase			:				Union[str, Any] 						=						ffn_dim
           _lowercase			:				str 						=						num_layers
           _lowercase			:				int 						=						attention_heads
           _lowercase			:				List[str] 						=						activation_function
           _lowercase			:				Tuple 						=						dropout
           _lowercase			:				int 						=						attention_dropout
           _lowercase			:				Optional[int] 						=						activation_dropout
           _lowercase			:				List[str] 						=						layerdrop
           _lowercase			:				Optional[Any] 						=						init_std
           _lowercase			:				Any 						=						scale_embedding  # scale factor will be sqrt(d_model) if True
           _lowercase			:				Union[str, Any] 						=						use_cache

           super().__init__(
               pad_token_id=lowerCamelCase, bos_token_id=lowerCamelCase, eos_token_id=lowerCamelCase, decoder_start_token_id=lowerCamelCase, **lowerCamelCase, )






import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

SCREAMING_SNAKE_CASE			: int				   =					list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

SCREAMING_SNAKE_CASE			: Dict				   =					[file for file in filepaths if file != file.lower()]
if upper_files:
 print(F"{len(upper_files)} files contain uppercase characters:")
 print("\n".join(upper_files) + "\n")

SCREAMING_SNAKE_CASE			: List[Any]				   =					[file for file in filepaths if " " in file]
if space_files:
 print(F"{len(space_files)} files contain space characters:")
 print("\n".join(space_files) + "\n")

SCREAMING_SNAKE_CASE			: Any				   =					[file for file in filepaths if "-" in file]
if hyphen_files:
 print(F"{len(hyphen_files)} files contain hyphen characters:")
 print("\n".join(hyphen_files) + "\n")

SCREAMING_SNAKE_CASE			: str				   =					[file for file in filepaths if os.sep not in file]
if nodir_files:
 print(F"{len(nodir_files)} files are not in a directory:")
 print("\n".join(nodir_files) + "\n")

SCREAMING_SNAKE_CASE			: Tuple				   =					len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)


