




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

from packaging import version


if TYPE_CHECKING:
						from ... import PreTrainedTokenizer, TensorType

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


UpperCamelCase__ :   str						=							logging.get_logger(__name__)

UpperCamelCase__ :   Any						=							{
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Dict						=     "bloom"
				__a     :       Any						=     ["past_key_values"]
				__a     :       Union[str, Any]						=     {
				    "num_hidden_layers": "n_layer",
				    "num_attention_heads": "n_head",
				}






				def __init__(			self      ,snake_case__=250880      ,snake_case__=64      ,snake_case__=2      ,snake_case__=8      ,snake_case__=1E-5      ,snake_case__=0.02      ,snake_case__=True      ,snake_case__=1      ,snake_case__=2      ,snake_case__=False      ,snake_case__=0.0      ,snake_case__=0.0      ,snake_case__=1      ,snake_case__=False      ,**snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      vocab_size
							# Backward compatibility with n_embed kwarg
							SCREAMING_SNAKE_CASE_   : Tuple					  =      kwargs.pop('n_embed'      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : int					  =      hidden_size if n_embed is None else n_embed
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      n_layer
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      n_head
							SCREAMING_SNAKE_CASE_   : List[str]					  =      layer_norm_epsilon
							SCREAMING_SNAKE_CASE_   : Tuple					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : List[str]					  =      use_cache
							SCREAMING_SNAKE_CASE_   : int					  =      pretraining_tp
							SCREAMING_SNAKE_CASE_   : int					  =      apply_residual_connection_post_layernorm
							SCREAMING_SNAKE_CASE_   : Tuple					  =      hidden_dropout
							SCREAMING_SNAKE_CASE_   : List[str]					  =      attention_dropout

							SCREAMING_SNAKE_CASE_   : str					  =      bos_token_id
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      eos_token_id
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      slow_but_exact

							super().__init__(bos_token_id=snake_case__      ,eos_token_id=snake_case__      ,**snake_case__ )







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[Any]						=     version.parse("1.12"							)






				def __init__(			self      ,snake_case__      ,snake_case__ = "default"      ,snake_case__ = None      ,snake_case__ = False      ,):
							super().__init__(snake_case__      ,task=snake_case__      ,patching_specs=snake_case__      ,use_past=snake_case__ )
							if not getattr(self._config      ,'pad_token_id'      ,snake_case__ ):
										# TODO: how to do that better?
										SCREAMING_SNAKE_CASE_   : str					  =      0






				@property
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
							if self.use_past:
										# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
										self.fill_with_past_key_values_(snake_case__      ,direction='inputs'      ,inverted_values_shape=snake_case__ )
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      {0: 'batch', 1: 'past_sequence + sequence'}
							else:
										SCREAMING_SNAKE_CASE_   : int					  =      {0: 'batch', 1: 'sequence'}

							return common_inputs






				@property
				def 		snake_case	(			self ):
							return self._config.n_layer






				@property
				def 		snake_case	(			self ):
							return self._config.n_head






				@property
				def 		snake_case	(			self ):
							return 1E-3






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = -1      ,snake_case__ = -1      ,snake_case__ = False      ,snake_case__ = None      ,):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      super(snake_case__      ,self ).generate_dummy_inputs(
							    snake_case__      ,batch_size=snake_case__      ,seq_length=snake_case__      ,is_pair=snake_case__      ,framework=snake_case__ )

							# We need to order the input in the way they appears in the forward()
							SCREAMING_SNAKE_CASE_   : Optional[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

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

							SCREAMING_SNAKE_CASE_   : Tuple					  =      common_inputs['attention_mask']
							if self.use_past:
										SCREAMING_SNAKE_CASE_   : Any					  =      ordered_inputs['attention_mask'].dtype
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.cat(
										    [ordered_inputs['attention_mask'], torch.ones(snake_case__      ,snake_case__      ,dtype=snake_case__ )]      ,dim=1 )

							return ordered_inputs






				@property
				def 		snake_case	(			self ):
							return 13











"""simple docstring"""





def   a    (     __UpperCAmelCase      :    List[Any]  )    ->							str:
						__magic_name__:	Optional[int]							=       [0] * len(__UpperCAmelCase  )
						__magic_name__:	str							=       []
						__magic_name__:	Any							=       []
						__magic_name__:	Union[str, Any]							=       0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(__UpperCAmelCase  )  ):
												if indegree[i] == 0:
																		queue.append(__UpperCAmelCase  )

						while queue:
												__magic_name__:	Optional[Any]							=       queue.pop(0  )
												cnt += 1
												topo.append(__UpperCAmelCase  )
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(__UpperCAmelCase  )

						if cnt != len(__UpperCAmelCase  ):
												print("""Cycle exists"""  )
						else:
												print(__UpperCAmelCase  )


# Adjacency List of Graph
__lowerCamelCase							=  {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}

topological_sort(graph)



