




"""simple docstring"""

import unittest

from transformers import BertGenerationConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import BertGenerationDecoder, BertGenerationEncoder

class        lowercase      :







				def __init__(							self   :					Optional[Any]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					str=13      ,     _lowerCamelCase   :					str=7      ,     _lowerCamelCase   :					List[str]=True      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					Any=99      ,     _lowerCamelCase   :					Union[str, Any]=32      ,     _lowerCamelCase   :					List[str]=5      ,     _lowerCamelCase   :					Optional[Any]=4      ,     _lowerCamelCase   :					Any=37      ,     _lowerCamelCase   :					str="gelu"      ,     _lowerCamelCase   :					str=0.1      ,     _lowerCamelCase   :					Union[str, Any]=0.1      ,     _lowerCamelCase   :					List[Any]=50      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					str=None      ,     ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		parent
					A_  :				str	   =		batch_size
					A_  :				str	   =		seq_length
					A_  :				List[Any]	   =		is_training
					A_  :				List[Any]	   =		use_input_mask
					A_  :				Optional[Any]	   =		vocab_size
					A_  :				List[Any]	   =		hidden_size
					A_  :				str	   =		num_hidden_layers
					A_  :				List[str]	   =		num_attention_heads
					A_  :				Tuple	   =		intermediate_size
					A_  :				Dict	   =		hidden_act
					A_  :				List[str]	   =		hidden_dropout_prob
					A_  :				List[Any]	   =		attention_probs_dropout_prob
					A_  :				Any	   =		max_position_embeddings
					A_  :				Dict	   =		initializer_range
					A_  :				Any	   =		use_labels
					A_  :				Any	   =		scope







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				Dict	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

					A_  :				Optional[int]	   =		None
					if self.use_input_mask:
						A_  :				Dict	   =		random_attention_mask([self.batch_size, self.seq_length]       )

					if self.use_labels:
						A_  :				Optional[Any]	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

					A_  :				List[str]	   =		self.get_config()

					return config, input_ids, input_mask, token_labels







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					return BertGenerationConfig(
					    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      ,     is_decoder=_lowerCamelCase      ,     initializer_range=self.initializer_range      ,     )







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					(
					    (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , 
					)  :				List[Any]	   =		self.prepare_config_and_inputs()

					A_  :				Dict	   =		True
					A_  :				List[Any]	   =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
					A_  :				Dict	   =		ids_tensor([self.batch_size, self.seq_length]      ,     vocab_size=2       )

					return (
					    config,
					    input_ids,
					    input_mask,
					    token_labels,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					List[Any]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					List[str]      ,     **_lowerCamelCase   :					Dict      ,     ):



					"""simple docstring"""

					A_  :				str	   =		BertGenerationEncoder(config=_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				Dict	   =		model(_lowerCamelCase      ,     attention_mask=_lowerCamelCase       )
					A_  :				int	   =		model(_lowerCamelCase       )
					self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)       )







				def 					a_  (							self   :					Dict      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					List[str]      ,     **_lowerCamelCase   :					Any      ,     ):



					"""simple docstring"""

					A_  :				List[str]	   =		True
					A_  :				Optional[Any]	   =		BertGenerationEncoder(config=_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				int	   =		model(
					    _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     encoder_hidden_states=_lowerCamelCase      ,     encoder_attention_mask=_lowerCamelCase      ,     )
					A_  :				List[str]	   =		model(
					    _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     encoder_hidden_states=_lowerCamelCase      ,     )
					self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)       )







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					str      ,     **_lowerCamelCase   :					Tuple      ,     ):



					"""simple docstring"""

					A_  :				Dict	   =		True
					A_  :				Optional[Any]	   =		True
					A_  :				Optional[Any]	   =		BertGenerationDecoder(config=_lowerCamelCase       ).to(_lowerCamelCase       ).eval()

					# first forward pass
					A_  :				int	   =		model(
					    _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     encoder_hidden_states=_lowerCamelCase      ,     encoder_attention_mask=_lowerCamelCase      ,     use_cache=_lowerCamelCase      ,     )
					A_  :				Tuple	   =		outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					A_  :				int	   =		ids_tensor((self.batch_size, 3)      ,     config.vocab_size       )
					A_  :				Union[str, Any]	   =		ids_tensor((self.batch_size, 3)      ,     vocab_size=2       )

					# append to next input_ids and
					A_  :				Any	   =		torch.cat([input_ids, next_tokens]      ,     dim=-1       )
					A_  :				Optional[Any]	   =		torch.cat([input_mask, next_mask]      ,     dim=-1       )

					A_  :				Optional[Any]	   =		model(
					    _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     encoder_hidden_states=_lowerCamelCase      ,     encoder_attention_mask=_lowerCamelCase      ,     output_hidden_states=_lowerCamelCase      ,     )['''hidden_states'''][0]
					A_  :				Optional[int]	   =		model(
					    _lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     encoder_hidden_states=_lowerCamelCase      ,     encoder_attention_mask=_lowerCamelCase      ,     past_key_values=_lowerCamelCase      ,     output_hidden_states=_lowerCamelCase      ,     )['''hidden_states'''][0]

					# select random slice
					A_  :				Union[str, Any]	   =		ids_tensor((1,)      ,     output_from_past.shape[-1]       ).item()
					A_  :				int	   =		output_from_no_past[:, -3:, random_slice_idx].detach()
					A_  :				List[str]	   =		output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(_lowerCamelCase      ,     _lowerCamelCase      ,     atol=1E-3       )       )







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					Optional[Any]      ,     *_lowerCamelCase   :					Any      ,     ):



					"""simple docstring"""

					A_  :				int	   =		BertGenerationDecoder(_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				Dict	   =		model(_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     labels=_lowerCamelCase       )
					self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.vocab_size)       )







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_   , A_   , A_   , A_  :				Optional[Any]	   =		self.prepare_config_and_inputs()
					A_  :				int	   =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict

@require_torch
class        lowercase      (    __UpperCAmelCase     ,	__UpperCAmelCase     ,	__UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    Dict         =   (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
				__lowerCAmelCase    :    Optional[int]         =   (BertGenerationDecoder,) if is_torch_available() else ()
				__lowerCAmelCase    :    str         =   (
				    {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
				    if is_torch_available()
				    else {}
				)







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		BertGenerationEncoderTester(self       )
					A_  :				Tuple	   =		ConfigTester(self      ,     config_class=_lowerCamelCase      ,     hidden_size=37       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					self.config_tester.run_common_tests()







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				Dict	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_lowerCamelCase       )







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_   , A_   , A_   , A_  :				Tuple	   =		self.model_tester.prepare_config_and_inputs()
					A_  :				List[str]	   =		'''bert'''
					self.model_tester.create_and_check_model(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase       )







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowerCamelCase       )







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					(
					    (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , 
					)  :				Optional[int]	   =		self.model_tester.prepare_config_and_inputs_for_decoder()

					A_  :				Optional[Any]	   =		None

					self.model_tester.create_and_check_model_as_decoder(
					    _lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     )







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				str	   =		self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_for_causal_lm(*_lowerCamelCase       )







				@slow
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				str	   =		BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
					self.assertIsNotNone(_lowerCamelCase       )

@require_torch
class        lowercase      (    unittest.TestCase):







				@slow
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Dict	   =		BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
					A_  :				List[Any]	   =		torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]]       )
					with torch.no_grad():
						A_  :				Tuple	   =		model(_lowerCamelCase       )[0]
					A_  :				Any	   =		torch.Size([1, 8, 10_24]       )
					self.assertEqual(output.shape      ,     _lowerCamelCase       )
					A_  :				List[Any]	   =		torch.tensor(
					    [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]       )
					self.assertTrue(torch.allclose(output[:, :3, :3]      ,     _lowerCamelCase      ,     atol=1E-4       )       )

@require_torch
class        lowercase      (    unittest.TestCase):







				@slow
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
					A_  :				List[str]	   =		torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]]       )
					with torch.no_grad():
						A_  :				List[Any]	   =		model(_lowerCamelCase       )[0]
					A_  :				Optional[int]	   =		torch.Size([1, 8, 5_03_58]       )
					self.assertEqual(output.shape      ,     _lowerCamelCase       )
					A_  :				List[Any]	   =		torch.tensor(
					    [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]       )
					self.assertTrue(torch.allclose(output[:, :3, :3]      ,     _lowerCamelCase      ,     atol=1E-4       )       )











"""simple docstring"""

from queue import PriorityQueue
from typing import Any

import numpy as np

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      ):




	"""simple docstring"""






	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A_  :				Tuple	   =		cst_fwd.get(_UpperCAmelCase  ,      np.inf	)
		A_  :				Any	   =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt)	)
			A_  :				Tuple	   =		new_cost_f
			A_  :				Dict	   =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A_  :				Optional[Any]	   =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		-1

	A_  :				Any	   =		set()
	A_  :				Tuple	   =		set()
	A_  :				Optional[int]	   =		{source: 0}
	A_  :				Dict	   =		{destination: 0}
	A_  :				Any	   =		{source: None}
	A_  :				Optional[Any]	   =		{destination: None}
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()

	A_  :				Union[str, Any]	   =		np.inf

	queue_forward.put((0, source)	)
	queue_backward.put((0, destination)	)

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A_   , A_  :				Union[str, Any]	   =		queue_forward.get()
		visited_forward.add(_UpperCAmelCase	)

		A_   , A_  :				Optional[int]	   =		queue_backward.get()
		visited_backward.add(_UpperCAmelCase	)

		A_  :				Any	   =		pass_and_relaxation(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      )

		A_  :				Dict	   =		pass_and_relaxation(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      )

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A_  :				Optional[Any]	   =		shortest_distance
	return shortest_path_distance


_lowerCamelCase  :			Optional[Any]        =       {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
_lowerCamelCase  :			Any        =       {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()





