


'''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  ,					a  ,					a=13  ,					a=7  ,					a=True  ,					a=True  ,					a=99  ,					a=32  ,					a=5  ,					a=4  ,					a=37  ,					a="gelu"  ,					a=0.1  ,					a=0.1  ,					a=50  ,					a=0.02  ,					a=True  ,					a=None  ,					):
          UpperCamelCase__										=				parent
          UpperCamelCase__										=				batch_size
          UpperCamelCase__										=				seq_length
          UpperCamelCase__										=				is_training
          UpperCamelCase__										=				use_input_mask
          UpperCamelCase__										=				vocab_size
          UpperCamelCase__										=				hidden_size
          UpperCamelCase__										=				num_hidden_layers
          UpperCamelCase__										=				num_attention_heads
          UpperCamelCase__										=				intermediate_size
          UpperCamelCase__										=				hidden_act
          UpperCamelCase__										=				hidden_dropout_prob
          UpperCamelCase__										=				attention_probs_dropout_prob
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				initializer_range
          UpperCamelCase__										=				use_labels
          UpperCamelCase__										=				scope


    def    __a							(     self    ):
          UpperCamelCase__										=				ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size    )

          UpperCamelCase__										=				None
          if self.use_input_mask:
                UpperCamelCase__										=				random_attention_mask([self.batch_size, self.seq_length]    )

          if self.use_labels:
                UpperCamelCase__										=				ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size    )

          UpperCamelCase__										=				self.get_config()

          return config, input_ids, input_mask, token_labels


    def    __a							(     self    ):
          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=a  ,					initializer_range=self.initializer_range  ,					)


    def    __a							(     self    ):
          (
              (
              UpperCamelCase__					
          )					,	(
              UpperCamelCase__					
          )					,	(
              UpperCamelCase__					
          )					,	(
              UpperCamelCase__					
          )					,						
          )										=				self.prepare_config_and_inputs()

          UpperCamelCase__										=				True
          UpperCamelCase__										=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
          UpperCamelCase__										=				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  ,					a  ,					a  ,					a  ,					a  ,					**a  ,					):
          UpperCamelCase__										=				BertGenerationEncoder(config=a    )
          model.to(a    )
          model.eval()
          UpperCamelCase__										=				model(a  ,					attention_mask=a    )
          UpperCamelCase__										=				model(a    )
          self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					**a  ,					):
          UpperCamelCase__										=				True
          UpperCamelCase__										=				BertGenerationEncoder(config=a    )
          model.to(a    )
          model.eval()
          UpperCamelCase__										=				model(
              a  ,					attention_mask=a  ,					encoder_hidden_states=a  ,					encoder_attention_mask=a  ,					)
          UpperCamelCase__										=				model(
              a  ,					attention_mask=a  ,					encoder_hidden_states=a  ,					)
          self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					**a  ,					):
          UpperCamelCase__										=				True
          UpperCamelCase__										=				True
          UpperCamelCase__										=				BertGenerationDecoder(config=a    ).to(a    ).eval()

          # first forward pass
          UpperCamelCase__										=				model(
              a  ,					attention_mask=a  ,					encoder_hidden_states=a  ,					encoder_attention_mask=a  ,					use_cache=a  ,					)
          UpperCamelCase__										=				outputs.past_key_values

          # create hypothetical multiple next token and extent to next_input_ids
          UpperCamelCase__										=				ids_tensor((self.batch_size, 3)  ,					config.vocab_size    )
          UpperCamelCase__										=				ids_tensor((self.batch_size, 3)  ,					vocab_size=2    )

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

          UpperCamelCase__										=				model(
              a  ,					attention_mask=a  ,					encoder_hidden_states=a  ,					encoder_attention_mask=a  ,					output_hidden_states=a  ,					)["hidden_states"][0]
          UpperCamelCase__										=				model(
              a  ,					attention_mask=a  ,					encoder_hidden_states=a  ,					encoder_attention_mask=a  ,					past_key_values=a  ,					output_hidden_states=a  ,					)["hidden_states"][0]

          # select random slice
          UpperCamelCase__										=				ids_tensor((1,)  ,					output_from_past.shape[-1]    ).item()
          UpperCamelCase__										=				output_from_no_past[:, -3:, random_slice_idx].detach()
          UpperCamelCase__										=				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(a  ,					a  ,					atol=1e-3    )    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					*a  ,					):
          UpperCamelCase__										=				BertGenerationDecoder(a    )
          model.to(a    )
          model.eval()
          UpperCamelCase__										=				model(a  ,					attention_mask=a  ,					labels=a    )
          self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)    )


    def    __a							(     self    ):
          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				self.prepare_config_and_inputs()
          UpperCamelCase__										=				{"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict

@require_torch
class 					lowercase_     (    __lowerCamelCase  ,						__lowerCamelCase  ,						__lowerCamelCase  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
    __UpperCAmelCase   			=	(BertGenerationDecoder,) if is_torch_available() else ()
    __UpperCAmelCase   			=	(
        {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
        if is_torch_available()
        else {}
    )


    def    __a							(     self    ):
          UpperCamelCase__										=				BertGenerationEncoderTester(self    )
          UpperCamelCase__										=				ConfigTester(self  ,					config_class=a  ,					hidden_size=37    )


    def    __a							(     self    ):
          self.config_tester.run_common_tests()


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*a    )


    def    __a							(     self    ):
          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          UpperCamelCase__										=				"bert"
          self.model_tester.create_and_check_model(a  ,					a  ,					a  ,					a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*a    )


    def    __a							(     self    ):
          # This regression test was failing with PyTorch < 1.3
          (
              (
              UpperCamelCase__					
          )					,	(
              UpperCamelCase__					
          )					,	(
              UpperCamelCase__					
          )					,	(
              UpperCamelCase__					
          )					,	(
              UpperCamelCase__					
          )					,	(
              UpperCamelCase__					
          )					,						
          )										=				self.model_tester.prepare_config_and_inputs_for_decoder()

          UpperCamelCase__										=				None

          self.model_tester.create_and_check_model_as_decoder(
              a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					)


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_for_causal_lm(*a    )


    @slow
    def    __a							(     self    ):
          UpperCamelCase__										=				BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"    )
          self.assertIsNotNone(a    )

@require_torch
class 					lowercase_     (    unittest.TestCase   ):


    @slow
    def    __a							(     self    ):
          UpperCamelCase__										=				BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"    )
          UpperCamelCase__										=				torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]]    )
          with torch.no_grad():
                UpperCamelCase__										=				model(a    )[0]
          UpperCamelCase__										=				torch.Size([1, 8, 10_24]    )
          self.assertEqual(output.shape  ,					a    )
          UpperCamelCase__										=				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]  ,					a  ,					atol=1e-4    )    )

@require_torch
class 					lowercase_     (    unittest.TestCase   ):


    @slow
    def    __a							(     self    ):
          UpperCamelCase__										=				BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"    )
          UpperCamelCase__										=				torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]]    )
          with torch.no_grad():
                UpperCamelCase__										=				model(a    )[0]
          UpperCamelCase__										=				torch.Size([1, 8, 5_03_58]    )
          self.assertEqual(output.shape  ,					a    )
          UpperCamelCase__										=				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]  ,					a  ,					atol=1e-4    )    )







def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			lowercase       =    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    max(
			    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) ,							mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							j - wt[i - 1]							) + val[i - 1] ,							)
		lowercase       =    val
	return f[i][j]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [[0] * (w + 1) for _ in range(n + 1							)]

	for i in range(1 ,							n + 1							):
		for w_ in range(1 ,							w + 1							):
			if wt[i - 1] <= w_:
				lowercase       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,							dp[i - 1][w_]							)
			else:
				lowercase       =    dp[i - 1][w_]

	return dp[n][w_], dp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if not (isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							) and isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							)):
		raise ValueError(
		    'Both the weights and values vectors must be either lists or tuples'							)

	lowercase       =    len(__SCREAMING_SNAKE_CASE							)
	if num_items != len(__SCREAMING_SNAKE_CASE							):
		lowercase       =    (
		    'The number of weights must be the same as the number of values.\n'
		    F'''But got {num_items} weights and {len(__SCREAMING_SNAKE_CASE							)} values'''
		)
		raise ValueError(__SCREAMING_SNAKE_CASE							)
	for i in range(__SCREAMING_SNAKE_CASE							):
		if not isinstance(wt[i] ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    (
			    'All weights must be integers but got weight of '
			    F'''type {type(wt[i]							)} at index {i}'''
			)
			raise TypeError(__SCREAMING_SNAKE_CASE							)

	lowercase  ,					lowercase       =    knapsack(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    set()
	_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	return optimal_val, example_optional_set






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			optimal_set.add(__SCREAMING_SNAKE_CASE							)
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							j - wt[i - 1] ,							__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = [3, 2, 4, 4]
	UpperCAmelCase	      = [4, 3, 2, 3]
	UpperCAmelCase	      = 4
	UpperCAmelCase	      = 6
	UpperCAmelCase	      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
	UpperCAmelCase      , UpperCAmelCase	      = knapsack(w, wt, val, n)
	print(optimal_solution)
	print(mf_knapsack(n, wt, val, w))  # switched the n and w

	# testing the dynamic programming problem with example
	# the optimal subset for the above example are items 3 and 4
	UpperCAmelCase      , UpperCAmelCase	      = knapsack_with_example_solution(w, wt, val)
	assert optimal_solution == 8
	assert optimal_subset == {3, 4}
	print('''optimal_value = ''', optimal_solution)
	print('''An optimal subset corresponding to the optimal value''', optimal_subset)

