



'''simple docstring'''






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OpenAIGPTConfig,
						    OpenAIGPTDoubleHeadsModel,
						    OpenAIGPTForSequenceClassification,
						    OpenAIGPTLMHeadModel,
						    OpenAIGPTModel,
						)

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  Optional[int]	,    __snake_case    :  List[Any]	,    __snake_case    :  Dict=13	,    __snake_case    :  List[Any]=7	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  Any=True	,    __snake_case    :  Optional[int]=True	,    __snake_case    :  Union[str, Any]=99	,    __snake_case    :  Union[str, Any]=32	,    __snake_case    :  List[str]=5	,    __snake_case    :  List[str]=4	,    __snake_case    :  Dict=37	,    __snake_case    :  List[Any]="gelu"	,    __snake_case    :  int=0.1	,    __snake_case    :  Optional[int]=0.1	,    __snake_case    :  Tuple=512	,    __snake_case    :  Union[str, Any]=16	,    __snake_case    :  Optional[Any]=2	,    __snake_case    :  Optional[Any]=0.02	,    __snake_case    :  Optional[int]=3	,    __snake_case    :  str=4	,    __snake_case    :  Optional[Any]=None	,    )    ->	Dict:
								UpperCAmelCase				: Optional[Any]											=	parent
								UpperCAmelCase				: str											=	batch_size
								UpperCAmelCase				: int											=	seq_length
								UpperCAmelCase				: Union[str, Any]											=	is_training
								UpperCAmelCase				: Optional[Any]											=	use_token_type_ids
								UpperCAmelCase				: int											=	use_labels
								UpperCAmelCase				: Dict											=	vocab_size
								UpperCAmelCase				: List[Any]											=	hidden_size
								UpperCAmelCase				: Tuple											=	num_hidden_layers
								UpperCAmelCase				: Optional[int]											=	num_attention_heads
								UpperCAmelCase				: int											=	intermediate_size
								UpperCAmelCase				: Tuple											=	hidden_act
								UpperCAmelCase				: int											=	hidden_dropout_prob
								UpperCAmelCase				: Dict											=	attention_probs_dropout_prob
								UpperCAmelCase				: Any											=	max_position_embeddings
								UpperCAmelCase				: Optional[Any]											=	type_vocab_size
								UpperCAmelCase				: Tuple											=	type_sequence_label_size
								UpperCAmelCase				: int											=	initializer_range
								UpperCAmelCase				: Optional[Any]											=	num_labels
								UpperCAmelCase				: str											=	num_choices
								UpperCAmelCase				: Optional[Any]											=	scope
								UpperCAmelCase				: List[Any]											=	self.vocab_size - 1







	def   A			(		self    :  Any	)    ->	Optional[Any]:
								UpperCAmelCase				: List[Any]											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

								UpperCAmelCase				: List[Any]											=	None
								if self.use_token_type_ids:
															UpperCAmelCase				: Optional[int]											=	ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size	)

								UpperCAmelCase				: int											=	None
								UpperCAmelCase				: str											=	None
								UpperCAmelCase				: Union[str, Any]											=	None
								if self.use_labels:
															UpperCAmelCase				: Optional[int]											=	ids_tensor([self.batch_size]	,    self.type_sequence_label_size	)
															UpperCAmelCase				: str											=	ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels	)
															UpperCAmelCase				: Any											=	ids_tensor([self.batch_size]	,    self.num_choices	)

								UpperCAmelCase				: List[Any]											=	OpenAIGPTConfig(
								    vocab_size=self.vocab_size	,    n_embd=self.hidden_size	,    n_layer=self.num_hidden_layers	,    n_head=self.num_attention_heads	,    n_positions=self.max_position_embeddings	,    pad_token_id=self.pad_token_id	,    )

								UpperCAmelCase				: Tuple											=	ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,    2	)

								return (
								    config,
								    input_ids,
								    head_mask,
								    token_type_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)







	def   A			(		self    :  Optional[int]	,    __snake_case    :  List[str]	,    __snake_case    :  str	,    __snake_case    :  int	,    __snake_case    :  int	,    *__snake_case    :  Union[str, Any]	)    ->	Tuple:
								UpperCAmelCase				: Optional[Any]											=	OpenAIGPTModel(config=_a	)
								model.to(_a	)
								model.eval()

								UpperCAmelCase				: Optional[int]											=	model(_a	,    token_type_ids=_a	,    head_mask=_a	)
								UpperCAmelCase				: str											=	model(_a	,    token_type_ids=_a	)
								UpperCAmelCase				: Dict											=	model(_a	)

								self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)	)







	def   A			(		self    :  Dict	,    __snake_case    :  Optional[int]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Dict	,    __snake_case    :  List[str]	,    *__snake_case    :  str	)    ->	Dict:
								UpperCAmelCase				: str											=	OpenAIGPTLMHeadModel(_a	)
								model.to(_a	)
								model.eval()

								UpperCAmelCase				: Any											=	model(_a	,    token_type_ids=_a	,    labels=_a	)
								self.parent.assertEqual(result.loss.shape	,    ()	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)	)







	def   A			(		self    :  Any	,    __snake_case    :  Dict	,    __snake_case    :  List[Any]	,    __snake_case    :  Dict	,    __snake_case    :  Union[str, Any]	,    *__snake_case    :  str	)    ->	Any:
								UpperCAmelCase				: Any											=	OpenAIGPTDoubleHeadsModel(_a	)
								model.to(_a	)
								model.eval()

								UpperCAmelCase				: Optional[int]											=	model(_a	,    token_type_ids=_a	,    labels=_a	)
								self.parent.assertEqual(result.loss.shape	,    ()	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)	)







	def   A			(		self    :  List[str]	,    __snake_case    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  Dict	,    __snake_case    :  Tuple	,    *__snake_case    :  Dict	)    ->	Tuple:
								UpperCAmelCase				: List[str]											=	self.num_labels
								UpperCAmelCase				: int											=	OpenAIGPTForSequenceClassification(_a	)
								model.to(_a	)
								model.eval()

								UpperCAmelCase				: Dict											=	ids_tensor([self.batch_size]	,    self.type_sequence_label_size	)
								UpperCAmelCase				: Optional[Any]											=	model(_a	,    token_type_ids=_a	,    labels=_a	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)	)







	def   A			(		self    :  Tuple	)    ->	Dict:
								UpperCAmelCase				: Union[str, Any]											=	self.prepare_config_and_inputs()
								(
								    UpperCAmelCase					
								)				: str											=	config_and_inputs
								UpperCAmelCase				: int											=	{
								    """input_ids""": input_ids,
								    """token_type_ids""": token_type_ids,
								    """head_mask""": head_mask,
								}

								return config, inputs_dict

@require_torch
class        SCREAMING_SNAKE_CASE(							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= (
	    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase__    				= (
	    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
	lowerCamelCase__    				= (
	    {
	        """feature-extraction""": OpenAIGPTModel,
	        """text-classification""": OpenAIGPTForSequenceClassification,
	        """text-generation""": OpenAIGPTLMHeadModel,
	        """zero-shot""": OpenAIGPTForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)







	def   A			(		self    :  Tuple	,    __snake_case    :  Optional[int]	,    __snake_case    :  str	,    __snake_case    :  List[str]	,    __snake_case    :  List[str]	,    __snake_case    :  Any	)    ->	Tuple:




								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
															# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
															# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
															# tiny config could not be created.
															return True

								return False







	def   A			(		self    :  Optional[int]	,    __snake_case    :  str	,    __snake_case    :  Dict	,    __snake_case    :  Optional[int]=False	)    ->	Optional[int]:
								UpperCAmelCase				: Any											=	super()._prepare_for_class(_a	,    _a	,    return_labels=_a	)

								if return_labels:
															if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																						UpperCAmelCase				: Union[str, Any]											=	torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)	,    dtype=torch.long	,    device=_a	,    )
																						UpperCAmelCase				: Any											=	inputs_dict["""labels"""]
																						UpperCAmelCase				: Any											=	inputs_dict["""labels"""]
																						UpperCAmelCase				: Tuple											=	torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices)	,    dtype=torch.long	,    device=_a	,    )
																						UpperCAmelCase				: int											=	torch.zeros(
																						    self.model_tester.batch_size	,    dtype=torch.long	,    device=_a	)
								return inputs_dict







	def   A			(		self    :  Union[str, Any]	)    ->	Dict:
								UpperCAmelCase				: Tuple											=	OpenAIGPTModelTester(self	)
								UpperCAmelCase				: Optional[int]											=	ConfigTester(self	,    config_class=_a	,    n_embd=37	)







	def   A			(		self    :  Any	)    ->	str:
								self.config_tester.run_common_tests()







	def   A			(		self    :  Optional[Any]	)    ->	Dict:
								UpperCAmelCase				: int											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_openai_gpt_model(*_a	)







	def   A			(		self    :  Tuple	)    ->	Union[str, Any]:
								UpperCAmelCase				: Dict											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*_a	)







	def   A			(		self    :  List[Any]	)    ->	Any:
								UpperCAmelCase				: str											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_double_lm_head_model(*_a	)







	def   A			(		self    :  Union[str, Any]	)    ->	str:
								UpperCAmelCase				: List[Any]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_a	)







	@slow
	def   A			(		self    :  List[Any]	)    ->	List[str]:




								for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase				: Union[str, Any]											=	OpenAIGPTModel.from_pretrained(_a	)
															self.assertIsNotNone(_a	)

@require_torch
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	@slow
	def   A			(		self    :  List[str]	)    ->	str:
								UpperCAmelCase				: Dict											=	OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''	)
								model.to(_a	)
								UpperCAmelCase				: Dict											=	torch.tensor([[481, 4735, 544]]	,    dtype=torch.long	,    device=_a	)  # the president is
								UpperCAmelCase				: Dict											=	[
								    481,
								    4735,
								    544,
								    246,
								    963,
								    870,
								    762,
								    239,
								    244,
								    40477,
								    244,
								    249,
								    719,
								    881,
								    487,
								    544,
								    240,
								    244,
								    603,
								    481,
								]  # the president is a very good man. " \n " i\'m sure he is, " said the

								UpperCAmelCase				: int											=	model.generate(_a	,    do_sample=_a	)
								self.assertListEqual(output_ids[0].tolist()	,    _a	)










'''simple docstring'''






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

							while queue:
														UpperCAmelCase				: Optional[int]											=	queue.pop(0						)
														for ind in range(len(graph[u]						)						):
																					if visited[ind] is False and graph[u][ind] > 0:
																												queue.append(_lowerCAmelCase						)
																												UpperCAmelCase				: Union[str, Any]											=	True
																												UpperCAmelCase				: Optional[int]											=	u

							return visited[t]


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[str]						)   ->     Any:
							# This array is filled by BFS and to store path
							UpperCAmelCase				: Dict											=	[-1] * (len(_lowerCAmelCase						))
							UpperCAmelCase				: Any											=	0
							while bfs(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						):
														UpperCAmelCase				: List[str]											=	float('''Inf'''						)
														UpperCAmelCase				: str											=	sink

														while s != source:
																					# Find the minimum value in select path
																					UpperCAmelCase				: int											=	min(_lowerCAmelCase     ,   graph[parent[s]][s]						)
																					UpperCAmelCase				: Tuple											=	parent[s]

														max_flow += path_flow
														UpperCAmelCase				: Optional[Any]											=	sink

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


UpperCamelCase__:     Tuple										=    [
    [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],
]

UpperCamelCase__			,      UpperCamelCase__:     int										=    0, 5




print(ford_fulkerson(graph, source, sink))





