
"""simple docstring"""


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowercase							      = logging.get_logger(__name__)

_lowercase							      = {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class  lowerCAmelCase_ (					snake_case_			):



						'''simple docstring'''


						_lowerCamelCase:			Optional[int]					=			'''umt5'''
						_lowerCamelCase:			List[str]					=			['''past_key_values''']


						def __init__(       self   :					Optional[Any]     ,A_   :					int=25_0112     ,A_   :					List[str]=512     ,A_   :					List[str]=64     ,A_   :					int=1024     ,A_   :					List[Any]=8     ,A_   :					Union[str, Any]=None     ,A_   :					Any=6     ,A_   :					Union[str, Any]=32     ,A_   :					int=128     ,A_   :					int=0.1     ,A_   :					str=1e-6     ,A_   :					int=1.0     ,A_   :					int="gated-gelu"     ,A_   :					Optional[int]=True     ,A_   :					Any=True     ,A_   :					Optional[Any]="T5Tokenizer"     ,A_   :					str=True     ,A_   :					Any=0     ,A_   :					Tuple=1     ,A_   :					Dict=0     ,**A_   :					Optional[Any]     ,)   ->      List[Any]:
							super().__init__(
							    is_encoder_decoder=A_     ,tokenizer_class=A_     ,tie_word_embeddings=A_     ,pad_token_id=A_     ,eos_token_id=A_     ,decoder_start_token_id=A_     ,**A_     ,)
							A        =      vocab_size
							A        =      d_model
							A        =      d_kv
							A        =      d_ff
							A        =      num_layers
							A        =      (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							A        =      num_heads
							A        =      relative_attention_num_buckets
							A        =      relative_attention_max_distance
							A        =      dropout_rate
							A        =      layer_norm_epsilon
							A        =      initializer_factor
							A        =      feed_forward_proj
							A        =      use_cache

							A        =      self.feed_forward_proj.split('-'					)
							A        =      act_info[-1]
							A        =      act_info[0] == 'gated'

							if len(A_					) > 1 and act_info[0] != "gated" or len(A_					) > 2:
								raise ValueError(
								    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
								    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
								    '\'gated-gelu\' or \'relu\''					)

							if feed_forward_proj == "gated-gelu":
								A        =      'gelu_new'


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Optional[Any]:
							return self.d_model


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      str:
							return self.num_heads




						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Any:
							return self.num_layers







class  lowerCAmelCase_ (					snake_case_			):



						'''simple docstring'''


						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Mapping[str, Mapping[int, str]]:
							A        =      {
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
								A        =      'past_encoder_sequence + sequence'
								A        =      {0: 'batch'}
								A        =      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
								A        =      {0: 'batch', 1: 'decoder_sequence'}
								A        =      {0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
								self.fill_with_past_key_values_(A_     ,direction='inputs'					)

							return common_inputs


						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      int:
							return 13




						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      float:
							return 5e-4

from collections import deque





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )
				lowerCamelCase_						=			deque()
				lowerCamelCase_						=			[False for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			[-1 for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			index_of[:]

				def strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			index  # the number when this node is seen
								lowerCamelCase_						=			index  # lowest rank node reachable from here
								index += 1
								stack.append(lowerCamelCase__    )
								lowerCamelCase_						=			True

								for w in g[v]:
												if index_of[w] == -1:
																lowerCamelCase_						=			strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												lowerCamelCase_						=			[]
												lowerCamelCase_						=			stack.pop()
												lowerCamelCase_						=			False
												component.append(lowerCamelCase__    )
												while w != v:
																lowerCamelCase_						=			stack.pop()
																lowerCamelCase_						=			False
																component.append(lowerCamelCase__    )
												components.append(lowerCamelCase__    )
								return index

				lowerCamelCase_						=			[]
				for v in range(lowerCamelCase__    ):
								if index_of[v] == -1:
												strong_connect(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return components





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[[] for _ in range(lowerCamelCase__    )]
				for u, v in edges:
								g[u].append(lowerCamelCase__    )
				return g


if __name__ == "__main__":
			# Test
			__A				=7
			__A				=[0, 0, 1, 2, 3, 3, 4, 4, 6]
			__A				=[1, 3, 2, 0, 1, 4, 5, 6, 5]
			__A				=[(u, v) for u, v in zip(source, target)]
			__A				=create_graph(n_vertices, edges)

			assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


