


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)


lowerCamelCase						=		{
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}





class  _a							(    _lowercase):
	_a  :  Tuple				   =							'''codegen'''
	_a  :  Optional[Any]				   =							{
	    '''max_position_embeddings''': '''n_positions''',
	    '''hidden_size''': '''n_embd''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Dict=5_0400		, _SCREAMING_SNAKE_CASE						:		Dict=2048		, _SCREAMING_SNAKE_CASE						:		Tuple=2048		, _SCREAMING_SNAKE_CASE						:		Any=4096		, _SCREAMING_SNAKE_CASE						:		Dict=28		, _SCREAMING_SNAKE_CASE						:		str=16		, _SCREAMING_SNAKE_CASE						:		List[Any]=64		, _SCREAMING_SNAKE_CASE						:		List[str]=None		, _SCREAMING_SNAKE_CASE						:		str="gelu_new"		, _SCREAMING_SNAKE_CASE						:		str=0.0		, _SCREAMING_SNAKE_CASE						:		Any=0.0		, _SCREAMING_SNAKE_CASE						:		Any=0.0		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1E-5		, _SCREAMING_SNAKE_CASE						:		List[Any]=0.02		, _SCREAMING_SNAKE_CASE						:		str=True		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=5_0256		, _SCREAMING_SNAKE_CASE						:		Optional[int]=5_0256		, _SCREAMING_SNAKE_CASE						:		List[str]=False		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	Dict:
			lowerCAmelCase__       :      Tuple   					= vocab_size
			lowerCAmelCase__       :      Optional[int]   					= n_ctx
			lowerCAmelCase__       :      str   					= n_positions
			lowerCAmelCase__       :      Optional[int]   					= n_embd
			lowerCAmelCase__       :      int   					= n_layer
			lowerCAmelCase__       :      List[str]   					= n_head
			lowerCAmelCase__       :      str   					= n_inner
			lowerCAmelCase__       :      List[Any]   					= rotary_dim
			lowerCAmelCase__       :      List[Any]   					= activation_function
			lowerCAmelCase__       :      Optional[Any]   					= resid_pdrop
			lowerCAmelCase__       :      List[Any]   					= embd_pdrop
			lowerCAmelCase__       :      List[Any]   					= attn_pdrop
			lowerCAmelCase__       :      List[str]   					= layer_norm_epsilon
			lowerCAmelCase__       :      Union[str, Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= use_cache

			lowerCAmelCase__       :      Union[str, Any]   					= bos_token_id
			lowerCAmelCase__       :      List[str]   					= eos_token_id

			super().__init__(
			    bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, tie_word_embeddings=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)





class  _a							(    _lowercase):




	def __init__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		PretrainedConfig		, _SCREAMING_SNAKE_CASE						:		str = "default"		, _SCREAMING_SNAKE_CASE						:		List[PatchingSpec] = None		, _SCREAMING_SNAKE_CASE						:		bool = False		, )->	Union[str, Any]:
			super().__init__(_SCREAMING_SNAKE_CASE		, task=_SCREAMING_SNAKE_CASE		, patching_specs=_SCREAMING_SNAKE_CASE		, use_past=_SCREAMING_SNAKE_CASE		)
			if not getattr(self._config		, '''pad_token_id'''		, _SCREAMING_SNAKE_CASE		):
					# TODO: how to do that better?
					lowerCAmelCase__       :      Dict   					= 0




	@property
	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	Mapping[str, Mapping[int, str]]:
			lowerCAmelCase__       :      Dict   					= OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
			if self.use_past:
					self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE		, direction='''inputs'''		)
					lowerCAmelCase__       :      str   					= {0: '''batch''', 1: '''past_sequence + sequence'''}
			else:
					lowerCAmelCase__       :      List[str]   					= {0: '''batch''', 1: '''sequence'''}

			return common_inputs




	@property
	def 							UpperCAmelCase__( self						:		int		)->	int:
			return self._config.n_layer




	@property
	def 							UpperCAmelCase__( self						:		Any		)->	int:
			return self._config.n_head




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		PreTrainedTokenizer		, _SCREAMING_SNAKE_CASE						:		int = -1		, _SCREAMING_SNAKE_CASE						:		int = -1		, _SCREAMING_SNAKE_CASE						:		bool = False		, _SCREAMING_SNAKE_CASE						:		Optional[TensorType] = None		, )->	Mapping[str, Any]:
			lowerCAmelCase__       :      Optional[Any]   					= super(_SCREAMING_SNAKE_CASE		, self		).generate_dummy_inputs(
			    _SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE		, seq_length=_SCREAMING_SNAKE_CASE		, is_pair=_SCREAMING_SNAKE_CASE		, framework=_SCREAMING_SNAKE_CASE		)

			# We need to order the input in the way they appears in the forward()
			lowerCAmelCase__       :      Union[str, Any]   					= OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

			# Need to add the past_keys
			if self.use_past:
					if not is_torch_available():
							raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
					else:
							import torch

							lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= common_inputs['''input_ids'''].shape
							# Not using the same length for past_key_values
							lowerCAmelCase__       :      Dict   					= seqlen + 2
							lowerCAmelCase__       :      int   					= (
							    batch,
							    self.num_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // self.num_attention_heads,
							)
							lowerCAmelCase__       :      Any   					= [
							    (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(self.num_layers		)
							]

			lowerCAmelCase__       :      Optional[int]   					= common_inputs['''attention_mask''']
			if self.use_past:
					lowerCAmelCase__       :      Any   					= ordered_inputs['''attention_mask'''].dtype
					lowerCAmelCase__       :      List[Any]   					= torch.cat(
					    [ordered_inputs['''attention_mask'''], torch.ones(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, dtype=_SCREAMING_SNAKE_CASE		)]		, dim=1		)

			return ordered_inputs




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	int:
			return 13








import math





class  _a							:




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Any=0		)->	Optional[Any]:  # a graph with Node 0,1,...,N-1
			lowerCAmelCase__       :      Optional[int]   					= n
			lowerCAmelCase__       :      List[Any]   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # adjacency matrix for weight
			lowerCAmelCase__       :      str   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # dp[i][j] stores minimum distance from i to j




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	List[str]:
			lowerCAmelCase__       :      Optional[int]   					= w




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			for k in range(0		, self.n		):
					for i in range(0		, self.n		):
							for j in range(0		, self.n		):
									lowerCAmelCase__       :      Dict   					= min(self.dp[i][j]		, self.dp[i][k] + self.dp[k][j]		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
			return self.dp[u][v]


if __name__ == "__main__":
		lowerCamelCase						=		Graph(5)
		graph.add_edge(0, 2, 9)
		graph.add_edge(0, 4, 10)
		graph.add_edge(1, 3, 5)
		graph.add_edge(2, 3, 7)
		graph.add_edge(3, 0, 10)
		graph.add_edge(3, 1, 2)
		graph.add_edge(3, 2, 1)
		graph.add_edge(3, 4, 6)
		graph.add_edge(4, 1, 3)
		graph.add_edge(4, 2, 4)
		graph.add_edge(4, 3, 9)
		graph.floyd_warshall()
		graph.show_min(1, 4)
		graph.show_min(0, 3)




