





'''simple docstring'''




class 			SCREAMING_SNAKE_CASE   :  # Public class to implement a graph


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	row
          __A						:			List[str]								=	col
          __A						:			Union[str, Any]								=	graph


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          return (
              0 <= i < self.ROW
              and 0 <= j < self.COL
              and not visited[i][j]
              and self.graph[i][j]
          )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			List[str]								=	[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
          __A						:			Dict								=	[-1, 0, 1, -1, 1, -1, 0, 1]
          __A						:			Optional[Any]								=	True  # Make those cells visited
          for k in range(8):
               if self.is_safe(i + row_nbr[k]						,     j + col_nbr[k]						,     _UpperCAmelCase):
                    self.diffs(i + row_nbr[k]						,     j + col_nbr[k]						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):  # And finally, count all islands.

          '''simple docstring'''






          __A						:			Optional[Any]								=	[[False for j in range(self.COL)] for i in range(self.ROW)]
          __A						:			Tuple								=	0
          for i in range(self.ROW):
               for j in range(self.COL):
                    if visited[i][j] is False and self.graph[i][j] == 1:
                         self.diffs(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase)
                         count += 1
          return count






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''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__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

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