import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


lowerCamelCase_    :		Any							=	1E-4

if is_torch_available():
		import torch

		from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
		from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder


@require_torch
class 						a__							:



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase=1_6				,						UpperCAmelCase=1_3				,						UpperCAmelCase=7				,						UpperCAmelCase=1_4				,						UpperCAmelCase=1_0				,						UpperCAmelCase=1_9				,						UpperCAmelCase=5				,						UpperCAmelCase=4				,						UpperCAmelCase=True				,						UpperCAmelCase=1_6				,						UpperCAmelCase=2				,						UpperCAmelCase=4				,						UpperCAmelCase=4				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.1				,						UpperCAmelCase=[1, 2, 3, 4, 5]				,						UpperCAmelCase=2_5				,						UpperCAmelCase=5				,						)			->   Any:
								__a		 =       d_model
								__a		 =       parent
								__a		 =       batch_size
								__a		 =       prediction_length
								__a		 =       context_length
								__a		 =       cardinality
								__a		 =       num_time_features
								__a		 =       lags_sequence
								__a		 =       embedding_dimension
								__a		 =       is_training
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       num_attention_heads
								__a		 =       intermediate_size
								__a		 =       hidden_act
								__a		 =       hidden_dropout_prob
								__a		 =       attention_probs_dropout_prob

								__a		 =       context_length
								__a		 =       prediction_length + label_length
								__a		 =       label_length

								__a		 =       moving_average
								__a		 =       autocorrelation_factor



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								return AutoformerConfig(
								    d_model=self.d_model				,						encoder_layers=self.num_hidden_layers				,						decoder_layers=self.num_hidden_layers				,						encoder_attention_heads=self.num_attention_heads				,						decoder_attention_heads=self.num_attention_heads				,						encoder_ffn_dim=self.intermediate_size				,						decoder_ffn_dim=self.intermediate_size				,						dropout=self.hidden_dropout_prob				,						attention_dropout=self.attention_probs_dropout_prob				,						prediction_length=self.prediction_length				,						context_length=self.context_length				,						label_length=self.label_length				,						lags_sequence=self.lags_sequence				,						num_time_features=self.num_time_features				,						num_static_categorical_features=1				,						cardinality=[self.cardinality]				,						embedding_dimension=[self.embedding_dimension]				,						moving_average=self.moving_average				,						)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[int]:
								__a		 =       config.context_length + max(config.lags_sequence   )

								__a		 =       ids_tensor([self.batch_size, 1]				,						config.cardinality[0]   )
								__a		 =       floats_tensor([self.batch_size, _past_length, config.num_time_features]   )
								__a		 =       floats_tensor([self.batch_size, _past_length]   )
								__a		 =       floats_tensor([self.batch_size, _past_length]   ) > 0.5

								# decoder inputs
								__a		 =       floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]   )
								__a		 =       floats_tensor([self.batch_size, config.prediction_length]   )

								__a		 =       {
								    'past_values': past_values,
								    'static_categorical_features': static_categorical_features,
								    'past_time_features': past_time_features,
								    'past_observed_mask': past_observed_mask,
								    'future_time_features': future_time_features,
								    'future_values': future_values,
								}
								return inputs_dict



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       self.get_config()
								__a		 =       self.prepare_autoformer_inputs_dict(_a   )
								return config, inputs_dict



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a ,				__a		 =       self.prepare_config_and_inputs()
								return config, inputs_dict







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Union[str, Any]:
								__a		 =       AutoformerModel(config=_a   ).to(_a   ).eval()
								__a		 =       model(**_a   )

								__a		 =       outputs.encoder_last_hidden_state
								__a		 =       outputs.last_hidden_state

								with tempfile.TemporaryDirectory() as tmpdirname:
													__a		 =       model.get_encoder()
													encoder.save_pretrained(_a   )
													__a		 =       AutoformerEncoder.from_pretrained(_a   ).to(_a   )

								__a ,				__a ,				__a ,				__a ,				__a		 =       model.create_network_inputs(**_a   )
								__a ,				__a		 =       model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]   )

								__a		 =       torch.cat(
								    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])				,						dim=-1				,						)
								__a		 =       encoder(inputs_embeds=_a   )[0]
								self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3   )

								__a		 =       (
								    torch.mean(transformer_inputs[:, : config.context_length, ...]				,						dim=1   )
								    .unsqueeze(1   )
								    .repeat(1				,						config.prediction_length				,						1   )
								)
								__a		 =       torch.zeros(
								    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]				,						device=enc_input.device				,						)

								__a		 =       torch.cat(
								    (
								        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)				,						dim=1   ),
								        feature[:, config.context_length - config.label_length :, ...],
								    )				,						dim=-1				,						)
								__a		 =       torch.cat(
								    (
								        torch.cat((trend_input[:, -config.label_length :, ...], mean)				,						dim=1   ),
								        feature[:, config.context_length - config.label_length :, ...],
								    )				,						dim=-1				,						)

								with tempfile.TemporaryDirectory() as tmpdirname:
													__a		 =       model.get_decoder()
													decoder.save_pretrained(_a   )
													__a		 =       AutoformerDecoder.from_pretrained(_a   ).to(_a   )

								__a		 =       decoder(
								    trend=_a				,						inputs_embeds=_a				,						encoder_hidden_states=_a				,						)[0]

								self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3   )






@require_torch
class 						a__							(       __lowercase				,						__lowercase				,						unittest.TestCase		):
			A__    :       Tuple        =      (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
			A__    :       Any        =      (AutoformerForPrediction,) if is_torch_available() else ()
			A__    :       Any        =      {'feature-extraction': AutoformerModel} if is_torch_available() else {}
			A__    :       Any        =      False
			A__    :       List[str]        =      False
			A__    :       Dict        =      False
			A__    :       int        =      False
			A__    :       Union[str, Any]        =      False
			A__    :       Optional[int]        =      False



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       AutoformerModelTester(self   )
								__a		 =       ConfigTester(self				,						config_class=_a				,						has_text_modality=_a   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								self.config_tester.run_common_tests()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a ,				__a		 =       self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
													__a		 =       model_class(_a   )

													with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_pretrained(_a   )
																		__a ,				__a		 =       model_class.from_pretrained(_a				,						output_loading_info=_a   )
													self.assertEqual(info['missing_keys']				,						[]   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_encoder_decoder_model_standalone(*_a   )



			@unittest.skip(reason='Model has no tokens embeddings'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								pass



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       inspect.signature(getattr(_a				,						'forward'   )   )
								# The main input is the name of the argument after `self`
								__a		 =       list(model_signature.parameters.keys()   )[1]
								self.assertEqual(AutoformerModel.main_input_name				,						_a   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a ,				__a		 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													__a		 =       model_class(_a   )
													__a		 =       inspect.signature(model.forward   )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__a		 =       [*signature.parameters.keys()]

													__a		 =       [
													    'past_values',
													    'past_time_features',
													    'past_observed_mask',
													    'static_categorical_features',
													    'static_real_features',
													    'future_values',
													    'future_time_features',
													]

													if model.__class__.__name__ in ["AutoformerForPrediction"]:
																		expected_arg_names.append('future_observed_mask'   )

													expected_arg_names.extend(
													    [
													        'decoder_attention_mask',
													        'head_mask',
													        'decoder_head_mask',
													        'cross_attn_head_mask',
													        'encoder_outputs',
													        'past_key_values',
													        'output_hidden_states',
													        'output_attentions',
													        'use_cache',
													        'return_dict',
													    ]   )

													self.assertListEqual(arg_names[: len(_a   )]				,						_a   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a ,				__a		 =       self.model_tester.prepare_config_and_inputs_for_common()
								__a		 =       True

								__a		 =       getattr(self.model_tester				,						'seq_length'				,						_a   )
								__a		 =       getattr(self.model_tester				,						'decoder_seq_length'				,						_a   )
								__a		 =       getattr(self.model_tester				,						'encoder_seq_length'				,						_a   )
								__a		 =       getattr(self.model_tester				,						'd_model'				,						_a   )
								__a		 =       getattr(self.model_tester				,						'num_attention_heads'				,						_a   )
								__a		 =       d_model // num_attention_heads

								for model_class in self.all_model_classes:
													__a		 =       True
													__a		 =       False
													__a		 =       True
													__a		 =       model_class(_a   )
													model.to(_a   )
													model.eval()
													with torch.no_grad():
																		__a		 =       model(**self._prepare_for_class(_a				,						_a   )   )
													__a		 =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(_a   )				,						self.model_tester.num_hidden_layers   )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													__a		 =       True
													__a		 =       model_class(_a   )
													model.to(_a   )
													model.eval()
													with torch.no_grad():
																		__a		 =       model(**self._prepare_for_class(_a				,						_a   )   )
													__a		 =       outputs.encoder_attentions
													self.assertEqual(len(_a   )				,						self.model_tester.num_hidden_layers   )

													self.assertListEqual(
													    list(attentions[0].shape[-3:]   )				,						[self.model_tester.num_attention_heads, encoder_seq_length, dim]				,						)
													__a		 =       len(_a   )

													__a		 =       7

													if "last_hidden_state" in outputs:
																		correct_outlen += 1

													if "trend" in outputs:
																		correct_outlen += 1

													if "past_key_values" in outputs:
																		correct_outlen += 1  # past_key_values have been returned

													if "loss" in outputs:
																		correct_outlen += 1

													if "params" in outputs:
																		correct_outlen += 1

													self.assertEqual(_a				,						_a   )

													# decoder attentions
													__a		 =       outputs.decoder_attentions
													self.assertIsInstance(_a				,						(list, tuple)   )
													self.assertEqual(len(_a   )				,						self.model_tester.num_hidden_layers   )
													self.assertListEqual(
													    list(decoder_attentions[0].shape[-3:]   )				,						[self.model_tester.num_attention_heads, decoder_seq_length, dim]				,						)

													# cross attentions
													__a		 =       outputs.cross_attentions
													self.assertIsInstance(_a				,						(list, tuple)   )
													self.assertEqual(len(_a   )				,						self.model_tester.num_hidden_layers   )
													self.assertListEqual(
													    list(cross_attentions[0].shape[-3:]   )				,						[self.model_tester.num_attention_heads, decoder_seq_length, dim]				,						)

								# Check attention is always last and order is fine
								__a		 =       True
								__a		 =       True
								__a		 =       model_class(_a   )
								model.to(_a   )
								model.eval()
								with torch.no_grad():
													__a		 =       model(**self._prepare_for_class(_a				,						_a   )   )

								self.assertEqual(out_len + 2				,						len(_a   )   )

								__a		 =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

								self.assertEqual(len(_a   )				,						self.model_tester.num_hidden_layers   )
								self.assertListEqual(
								    list(self_attentions[0].shape[-3:]   )				,						[self.model_tester.num_attention_heads, encoder_seq_length, dim]				,						)







			@is_flaky()
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								super().test_retain_grad_hidden_states_attentions()






def 							lowerCAmelCase(     __lowerCamelCase="train-batch.pt"						):
					__a		 =       hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'				,    filename=__a				,    repo_type='dataset'						)
					__a		 =       torch.load(__a				,    map_location=__a						)
					return batch



@require_torch
@slow
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(_a   )
								__a		 =       prepare_batch()

								with torch.no_grad():
													__a		 =       model(
													    past_values=batch['past_values']				,						past_time_features=batch['past_time_features']				,						past_observed_mask=batch['past_observed_mask']				,						static_categorical_features=batch['static_categorical_features']				,						future_values=batch['future_values']				,						future_time_features=batch['future_time_features']				,						)[0]

								__a		 =       torch.Size(
								    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)   )
								self.assertEqual(output.shape				,						_a   )

								__a		 =       torch.tensor(
								    [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]				,						device=_a   )
								self.assertTrue(torch.allclose(output[0, :3, :3]				,						_a				,						atol=_a   )   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(_a   )
								__a		 =       prepare_batch('val-batch.pt'   )
								with torch.no_grad():
													__a		 =       model(
													    past_values=batch['past_values']				,						past_time_features=batch['past_time_features']				,						past_observed_mask=batch['past_observed_mask']				,						static_categorical_features=batch['static_categorical_features']				,						).encoder_last_hidden_state
								__a		 =       torch.Size((6_4, model.config.context_length, model.config.d_model)   )
								self.assertEqual(output.shape				,						_a   )

								__a		 =       torch.tensor(
								    [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]				,						device=_a   )
								self.assertTrue(torch.allclose(output[0, :3, :3]				,						_a				,						atol=_a   )   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'   ).to(_a   )
								__a		 =       prepare_batch('val-batch.pt'   )
								with torch.no_grad():
													__a		 =       model.generate(
													    static_categorical_features=batch['static_categorical_features']				,						past_time_features=batch['past_time_features']				,						past_values=batch['past_values']				,						future_time_features=batch['future_time_features']				,						past_observed_mask=batch['past_observed_mask']				,						)
								__a		 =       torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)   )
								self.assertEqual(outputs.sequences.shape				,						_a   )

								__a		 =       torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6]				,						device=_a   )
								__a		 =       outputs.sequences.mean(dim=1   )
								self.assertTrue(torch.allclose(mean_prediction[0, -3:]				,						_a				,						rtol=1e-1   )   )





def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# Return True if there is node that has not iterated.
					__a		 =       [False] * len(__lowerCamelCase						)
					__a		 =       []
					queue.append(__lowerCamelCase						)
					__a		 =       True

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

					return visited[t]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# This array is filled by BFS and to store path
					__a		 =       [-1] * (len(__lowerCamelCase						))
					__a		 =       0
					while bfs(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
										__a		 =       float('Inf'						)
										__a		 =       sink

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

										max_flow += path_flow
										__a		 =       sink

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


lowerCamelCase_    :		str							=	[
    [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],
]

lowerCamelCase_						,  lowerCamelCase_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))




