






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


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		{
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[str]								=						'''transfo-xl'''
     UpperCamelCase_				:		List[str]								=						['''mems''']
     UpperCamelCase_				:		Optional[Any]								=						{
         '''n_token''': '''vocab_size''',
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }


     def __init__(					self							:						Tuple       ,			UpperCAmelCase_							:						List[str]=26_7735       ,			UpperCAmelCase_							:						str=[2_0000, 4_0000, 20_0000]       ,			UpperCAmelCase_							:						Optional[int]=1024       ,			UpperCAmelCase_							:						int=1024       ,			UpperCAmelCase_							:						str=16       ,			UpperCAmelCase_							:						int=64       ,			UpperCAmelCase_							:						Union[str, Any]=4096       ,			UpperCAmelCase_							:						Optional[int]=4       ,			UpperCAmelCase_							:						Any=False       ,			UpperCAmelCase_							:						List[str]=18       ,			UpperCAmelCase_							:						List[str]=1600       ,			UpperCAmelCase_							:						Optional[Any]=1000       ,			UpperCAmelCase_							:						Tuple=True       ,			UpperCAmelCase_							:						Any=True       ,			UpperCAmelCase_							:						List[str]=0       ,			UpperCAmelCase_							:						int=-1       ,			UpperCAmelCase_							:						str=True       ,			UpperCAmelCase_							:						List[str]=0.1       ,			UpperCAmelCase_							:						Union[str, Any]=0.0       ,			UpperCAmelCase_							:						Optional[Any]=True       ,			UpperCAmelCase_							:						Union[str, Any]="normal"       ,			UpperCAmelCase_							:						Optional[Any]=0.01       ,			UpperCAmelCase_							:						Any=0.01       ,			UpperCAmelCase_							:						int=0.02       ,			UpperCAmelCase_							:						Dict=1E-5       ,			UpperCAmelCase_							:						int=0       ,			**UpperCAmelCase_							:						List[Any]       ,			):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     vocab_size
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     []
       self.cutoffs.extend(UpperCAmelCase_   )
       if proj_share_all_but_first:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     [False] + [True] * len(self.cutoffs   )
       else:
         SCREAMING_SNAKE_CASE							:   Dict						    =     [False] + [False] * len(self.cutoffs   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     d_model
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     d_embed
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     d_head
       SCREAMING_SNAKE_CASE							:   Any						    =     d_inner
       SCREAMING_SNAKE_CASE							:   str						    =     div_val
       SCREAMING_SNAKE_CASE							:   str						    =     pre_lnorm
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     n_layer
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     n_head
       SCREAMING_SNAKE_CASE							:   List[str]						    =     mem_len
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     same_length
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     attn_type
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     clamp_len
       SCREAMING_SNAKE_CASE							:   Dict						    =     sample_softmax
       SCREAMING_SNAKE_CASE							:   List[str]						    =     adaptive
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     dropout
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     dropatt
       SCREAMING_SNAKE_CASE							:   str						    =     untie_r
       SCREAMING_SNAKE_CASE							:   str						    =     init
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     init_range
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     proj_init_std
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     init_std
       SCREAMING_SNAKE_CASE							:   Any						    =     layer_norm_epsilon
       super().__init__(eos_token_id=UpperCAmelCase_       ,			**UpperCAmelCase_   )


     @property
     def 					_A							(					self							:						Any   ):
       # Message copied from Transformer-XL documentation
       logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
       return -1






     @max_position_embeddings.setter
     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[Any]   ):
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )









from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase_   							=  HfArgumentParser(InitializationArguments)
lowercase_   							=  parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase_   							=  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase_   							=  {
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowercase_   							=  AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase_   							=  AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)


