

def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Union[str, Any]:
	# Return True if there is node that has not iterated.
	__lowerCamelCase    :    Optional[Any] 	=			[False] * len(lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			[]
	queue.append(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			True

	while queue:
		__lowerCamelCase    :    int 	=			queue.pop(0      )
		for ind in range(len(graph[u]      )      ):
			if visited[ind] is False and graph[u][ind] > 0:
				queue.append(lowerCamelCase__      )
				__lowerCamelCase    :    List[Any] 	=			True
				__lowerCamelCase    :    Dict 	=			u

	return visited[t]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	# This array is filled by BFS and to store path
	__lowerCamelCase    :    Tuple 	=			[-1] * (len(lowerCamelCase__      ))
	__lowerCamelCase    :    Tuple 	=			0
	while bfs(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      ):
		__lowerCamelCase    :    Tuple 	=			float('Inf'      )
		__lowerCamelCase    :    Tuple 	=			sink

		while s != source:
			# Find the minimum value in select path
			__lowerCamelCase    :    List[str] 	=			min(lowerCamelCase__     ,			graph[parent[s]][s]      )
			__lowerCamelCase    :    Union[str, Any] 	=			parent[s]

		max_flow += path_flow
		__lowerCamelCase    :    Union[str, Any] 	=			sink

		while v != source:
			__lowerCamelCase    :    List[Any] 	=			parent[v]
			graph[u][v] -= path_flow
			graph[v][u] += path_flow
			__lowerCamelCase    :    Optional[Any] 	=			parent[v]
	return max_flow


a		=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

a		,	a		=0, 5

print(ford_fulkerson(graph, source, sink))







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

								__lowerCamelCase    :    str 	=			tf.convert_to_tensor(
								    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

								__lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
								__lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
								self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
								# compare the actual values for a slice.
								__lowerCamelCase    :    Any 	=			tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]       ,dtype=tf.floataa       ,)
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




