




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 		__A   (      unittest.TestCase      ):

						@slow
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
										__magic_name__						:						Optional[Any]										=AutoTokenizer.from_pretrained("""google/mt5-small"""			)

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

										__magic_name__						:						List[str]										=shift_tokens_right(__snake_case     ,			model.config.pad_token_id     ,			model.config.decoder_start_token_id			)

										__magic_name__						:						Tuple										=model(__snake_case     ,			decoder_input_ids=__snake_case			).logits
										__magic_name__						:						Union[str, Any]										=optax.softmax_cross_entropy(__snake_case     ,			onehot(__snake_case     ,			logits.shape[-1]			)			).mean()

										__magic_name__						:						Union[str, Any]										=-(labels.shape[-1] * loss.item())

										__magic_name__						:						List[Any]										=-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)







'''simple docstring'''





from collections import defaultdict






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  int:
						lowercase :							Optional[Any]    		=1
						lowercase :							Union[str, Any]    		=True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(__magic_name__						)
						if ret % 2 == 0:
												cuts.append(__magic_name__						)
						return ret






def 				_lowerCAmelCase			(   )				->  int:
						dfs(1						)


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       10, 9
							UpperCamelCase_    						=       defaultdict(list)
							UpperCamelCase_    						=       {}
							UpperCamelCase_    						=       []
							UpperCamelCase_    						=       0
							UpperCamelCase_    						=       [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)





