


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roformer.modeling_flax_roformer import (
	    FlaxRoFormerForMaskedLM,
	    FlaxRoFormerForMultipleChoice,
	    FlaxRoFormerForQuestionAnswering,
	    FlaxRoFormerForSequenceClassification,
	    FlaxRoFormerForTokenClassification,
	    FlaxRoFormerModel,
	)





class       A    (   unittest.TestCase ):


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase=1_3      ,				lowerCAmelCase=7      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=9_9      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=5      ,				lowerCAmelCase=4      ,				lowerCAmelCase=3_7      ,				lowerCAmelCase="gelu"      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=5_1_2      ,				lowerCAmelCase=1_6      ,				lowerCAmelCase=2      ,				lowerCAmelCase=0.02      ,				lowerCAmelCase=4      ,				):
									__lowercase=			parent
									__lowercase=			batch_size
									__lowercase=			seq_length
									__lowercase=			is_training
									__lowercase=			use_attention_mask
									__lowercase=			use_token_type_ids
									__lowercase=			use_labels
									__lowercase=			vocab_size
									__lowercase=			hidden_size
									__lowercase=			num_hidden_layers
									__lowercase=			num_attention_heads
									__lowercase=			intermediate_size
									__lowercase=			hidden_act
									__lowercase=			hidden_dropout_prob
									__lowercase=			attention_probs_dropout_prob
									__lowercase=			max_position_embeddings
									__lowercase=			type_vocab_size
									__lowercase=			type_sequence_label_size
									__lowercase=			initializer_range
									__lowercase=			num_choices


							def 			_A       (self					):
									__lowercase=			ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size					)

									__lowercase=			None
									if self.use_attention_mask:
											__lowercase=			random_attention_mask([self.batch_size, self.seq_length]					)

									__lowercase=			None
									if self.use_token_type_ids:
											__lowercase=			ids_tensor([self.batch_size, self.seq_length]      ,				self.type_vocab_size					)

									__lowercase=			RoFormerConfig(
									    vocab_size=self.vocab_size      ,				hidden_size=self.hidden_size      ,				num_hidden_layers=self.num_hidden_layers      ,				num_attention_heads=self.num_attention_heads      ,				intermediate_size=self.intermediate_size      ,				hidden_act=self.hidden_act      ,				hidden_dropout_prob=self.hidden_dropout_prob      ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				type_vocab_size=self.type_vocab_size      ,				is_decoder=lowerCAmelCase      ,				initializer_range=self.initializer_range      ,				)

									return config, input_ids, token_type_ids, attention_mask







							def 			_A       (self					):
									__lowercase=			self.prepare_config_and_inputs()
									__lowercase,							__lowercase,							__lowercase,							__lowercase=			config_and_inputs
									__lowercase=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
									return config, inputs_dict





@require_flax
class       A    (   A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Dict       =True

							UpperCamelCase_   :							Optional[Any]       =(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)


							def 			_A       (self					):
									__lowercase=			FlaxRoFormerModelTester(self					)







							@slow
							def 			_A       (self					):
									for model_class_name in self.all_model_classes:
											__lowercase=			model_class_name.from_pretrained('junnyu/roformer_chinese_small'      ,				from_pt=lowerCAmelCase					)
											__lowercase=			model(np.ones((1, 1)					)					)
											self.assertIsNotNone(lowerCAmelCase					)





@require_flax
class       A    (   unittest.TestCase ):


							@slow
							def 			_A       (self					):
									__lowercase=			FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'					)
									__lowercase=			jnp.array([[0, 1, 2, 3, 4, 5]]					)
									__lowercase=			model(lowerCAmelCase					)[0]

									__lowercase=			5_0_0_0_0

									__lowercase=			(1, 6, vocab_size)
									self.assertEqual(output.shape      ,				lowerCAmelCase					)

									__lowercase=			jnp.array(
									    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]					)

									self.assertTrue(jnp.allclose(output[:, :3, :3]      ,				lowerCAmelCase      ,				atol=1E-4					)					)










def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			[False] * len(lowercase__	)
		__lowercase=			[]
		queue.append(lowercase__	)
		__lowercase=			True

		while queue:
				__lowercase=			queue.pop(0	)
				for ind in range(len(graph[u]	)	):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(lowercase__	)
								__lowercase=			True
								__lowercase=			u

		return visited[t]







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	int:

		'''simple docstring'''







		__lowercase=			[-1] * (len(lowercase__	))
		__lowercase=			0
		while bfs(lowercase__   , lowercase__   , lowercase__   , lowercase__	):
				__lowercase=			float('Inf'	)
				__lowercase=			sink

				while s != source:
						# Find the minimum value in select path
						__lowercase=			min(lowercase__   , graph[parent[s]][s]	)
						__lowercase=			parent[s]

				max_flow += path_flow
				__lowercase=			sink

				while v != source:
						__lowercase=			parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						__lowercase=			parent[v]
		return max_flow


lowerCAmelCase           =		[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

lowerCAmelCase       ,lowerCAmelCase           =		0, 5
print(ford_fulkerson(graph, source, sink))






