





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch

@require_torch
@require_optimum
@slow
class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     'hf-internal-testing/tiny-random-t5'
			__lowercase											=     AutoTokenizer.from_pretrained(__lowerCamelCase   )
			__lowercase											=     AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase   )

			__lowercase											=     tokenizer('This is me'					,      return_tensors='pt'   )

			__lowercase											=     model.to_bettertransformer()

			self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()   )   )

			__lowercase											=     model.generate(**__lowerCamelCase   )

			__lowercase											=     model.reverse_bettertransformer()

			self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()   )   )

			with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(__lowerCamelCase   )

					__lowercase											=     AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase   )

					self.assertFalse(
					    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()   )   )

					__lowercase											=     model_reloaded.generate(**__lowerCamelCase   )
					self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase   )   )






	def    UpperCAmelCase			(						self							:     str   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     'hf-internal-testing/tiny-random-t5'
			__lowercase											=     AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase   )

			__lowercase											=     model.to_bettertransformer()

			with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(__lowerCamelCase   ):
							model.save_pretrained(__lowerCamelCase   )

					__lowercase											=     model.reverse_bettertransformer()
					model.save_pretrained(__lowerCamelCase   )











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    int:
		# Return True if there is node that has not iterated.
		__lowercase											=     [False] * len(snake_case							)
		__lowercase											=     []
		queue.append(snake_case							)
		__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(snake_case							)
								__lowercase											=     True
								__lowercase											=     u

		return visited[t]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		# This array is filled by BFS and to store path
		__lowercase											=     [-1] * (len(snake_case							))
		__lowercase											=     0
		while bfs(snake_case						,   snake_case						,   snake_case						,   snake_case							):
				__lowercase											=     float('Inf'							)
				__lowercase											=     sink

				while s != source:
						# Find the minimum value in select path
						__lowercase											=     min(snake_case						,   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


SCREAMING_SNAKE_CASE_   :      Any			=   [
    [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],
]

SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   0, 5


print(ford_fulkerson(graph, source, sink))




