



"""simple docstring"""





from __future__ import annotations
def 	__lowerCAmelCase						(		__UpperCamelCase      :			list[float]					, __UpperCamelCase      :			Any    ):


						'''simple docstring'''






						print(F'Vertex\tShortest Distance from vertex {src}'    )
						for i, d in enumerate(__UpperCamelCase    ):
												print(F'{i}\t\t{d}'    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			list[dict[str, int]]					, __UpperCamelCase      :			list[float]					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						for j in range(__UpperCamelCase    ):
												snake_case_			,		snake_case_			,		snake_case_   : Any						=			(graph[j][k] for k in ["""src""", """dst""", """weight"""])
												if distance[u] != float("""inf"""    ) and distance[u] + w < distance[v]:
																		return True
						return False
def 	__lowerCAmelCase						(		__UpperCamelCase      :			list[dict[str, int]]					, __UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : str						=			[float("""inf"""    )] * vertex_count
						snake_case_   : List[str]						=			0.0

						for _ in range(vertex_count - 1    ):
												for j in range(__UpperCamelCase    ):
																		snake_case_			,		snake_case_			,		snake_case_   : Tuple						=			(graph[j][k] for k in ["""src""", """dst""", """weight"""])

																		if distance[u] != float("""inf"""    ) and distance[u] + w < distance[v]:
																								snake_case_   : Optional[Any]						=			distance[u] + w

						snake_case_   : Tuple						=			check_negative_cycle(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						if negative_cycle_exists:
												raise Exception("""Negative cycle found"""    )

						return distance


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					__lowerCAmelCase       :					List[str]      					=					int(input('''Enter number of vertices: ''').strip())
					__lowerCAmelCase       :					List[Any]      					=					int(input('''Enter number of edges: ''').strip())

					__lowerCAmelCase       :					list[dict[str, int]]      					=					[{} for _ in range(E)]

					for i in range(E):
										print('''Edge ''', i + 1)
										__lowerCAmelCase					, __lowerCAmelCase					, __lowerCAmelCase       :					Dict      					=					(
										    int(x)
										    for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
										)
										__lowerCAmelCase       :					List[str]      					=					{'''src''': src, '''dst''': dest, '''weight''': weight}

					__lowerCAmelCase       :					List[Any]      					=					int(input('''\nEnter shortest path source:''').strip())
					__lowerCAmelCase       :					Union[str, Any]      					=					bellman_ford(graph, V, E, source)
					print_distance(shortest_distance, 0)






"""simple docstring"""





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
					import optax
					from flax.training.common_utils import onehot

					from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
					from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""   )
								snake_case_   : List[Any]						=			AutoTokenizer.from_pretrained("""google/mt5-small"""   )

								snake_case_   : Dict						=			tokenizer("""Hello there"""							,    return_tensors="""np"""   ).input_ids
								snake_case_   : List[str]						=			tokenizer("""Hi I am"""							,    return_tensors="""np"""   ).input_ids

								snake_case_   : Optional[Any]						=			shift_tokens_right(_lowercase							,    model.config.pad_token_id							,    model.config.decoder_start_token_id   )

								snake_case_   : Tuple						=			model(_lowercase							,    decoder_input_ids=_lowercase   ).logits
								snake_case_   : Tuple						=			optax.softmax_cross_entropy(_lowercase							,    onehot(_lowercase							,    logits.shape[-1]   )   ).mean()

								snake_case_   : List[str]						=			-(labels.shape[-1] * loss.item())

								snake_case_   : Optional[int]						=			-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )

