"""simple docstring"""



import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, 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.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM






@require_torch
class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=99  ,						lowerCamelCase=13  ,						lowerCamelCase=16  ,						lowerCamelCase=7  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=False  ,						lowerCamelCase=True  ,						lowerCamelCase=2  ,						lowerCamelCase=32  ,						lowerCamelCase=4  ,						lowerCamelCase=4  ,						lowerCamelCase=30  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						lowerCamelCase=2  ,						lowerCamelCase=None  ,						):
        __a     				=  parent
        __a     				=  batch_size
        __a     				=  decoder_seq_length
        # For common tests
        __a     				=  self.decoder_seq_length
        __a     				=  is_training
        __a     				=  use_attention_mask
        __a     				=  use_labels

        __a     				=  vocab_size
        __a     				=  d_model
        __a     				=  d_model
        __a     				=  decoder_layers
        __a     				=  decoder_layers
        __a     				=  decoder_ffn_dim
        __a     				=  decoder_attention_heads
        __a     				=  decoder_attention_heads
        __a     				=  eos_token_id
        __a     				=  bos_token_id
        __a     				=  pad_token_id
        __a     				=  decoder_start_token_id
        __a     				=  use_cache
        __a     				=  max_position_embeddings

        __a     				=  None
        __a     				=  decoder_seq_length
        __a     				=  2
        __a     				=  1


 def 		a__ (				self		):
        __a     				=  ids_tensor([self.batch_size, self.decoder_seq_length]  ,						self.vocab_size		)

        __a     				=  None
        if self.use_attention_mask:
               __a     				=  ids_tensor([self.batch_size, self.decoder_seq_length]  ,						vocab_size=2		)

        __a     				=  None
        if self.use_labels:
               __a     				=  ids_tensor([self.batch_size, self.decoder_seq_length]  ,						self.vocab_size		)

        __a     				=  TrOCRConfig(
            vocab_size=self.vocab_size  ,						d_model=self.d_model  ,						decoder_layers=self.decoder_layers  ,						decoder_ffn_dim=self.decoder_ffn_dim  ,						decoder_attention_heads=self.decoder_attention_heads  ,						eos_token_id=self.eos_token_id  ,						bos_token_id=self.bos_token_id  ,						use_cache=self.use_cache  ,						pad_token_id=self.pad_token_id  ,						decoder_start_token_id=self.decoder_start_token_id  ,						max_position_embeddings=self.max_position_embeddings  ,						)

        return (config, input_ids, attention_mask, lm_labels)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						):
        __a     				=  True
        __a     				=  TrOCRDecoder(config=lowerCamelCase		).to(lowerCamelCase		).eval()
        __a     				=  input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        __a     				=  model(lowerCamelCase  ,						use_cache=lowerCamelCase		)
        __a     				=  model(lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						use_cache=lowerCamelCase		)

        self.parent.assertTrue(len(lowerCamelCase		) == len(lowerCamelCase		)		)
        self.parent.assertTrue(len(lowerCamelCase		) == len(lowerCamelCase		) + 1		)

        __a     				=  outputs["past_key_values"]

        # create hypothetical next token and extent to next_input_ids
        __a     				=  ids_tensor((2, 1)  ,						config.vocab_size - 1		) + 1

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

        __a     				=  model(lowerCamelCase		)["last_hidden_state"]
        __a     				=  model(lowerCamelCase  ,						past_key_values=lowerCamelCase		)["last_hidden_state"]

        # select random slice
        __a     				=  ids_tensor((1,)  ,						output_from_past.shape[-1]		).item()
        __a     				=  output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        __a     				=  output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-3		)






 def 		a__ (				self		):
        __a     				=  self.prepare_config_and_inputs()
        __a				, __a				, __a				, __a     				=  config_and_inputs

        __a     				=  {"input_ids": input_ids, "attention_mask": attention_mask}
        return config, inputs_dict






@require_torch
class 				snake_case__     (   snake_case_,		snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			List[str]           =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 _snake_case   :			Optional[int]           =		(TrOCRForCausalLM,) if is_torch_available() else ()
 _snake_case   :			str           =		{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
 _snake_case   :			Optional[Any]           =		True
 _snake_case   :			Any           =		False


 def 		a__ (				self		):
        __a     				=  TrOCRStandaloneDecoderModelTester(self  ,						is_training=lowerCamelCase		)
        __a     				=  ConfigTester(self  ,						config_class=lowerCamelCase		)


 def 		a__ (				self		):
        pass


 def 		a__ (				self		):
        pass


 def 		a__ (				self		):
        pass


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


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase		)


 def 		a__ (				self		):
        return






 @unittest.skip("The model doesn't support left padding"		)  # and it's not used enough to be worth fixing :)
 def 		a__ (				self		):
        pass
"""simple docstring"""



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



def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  [[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_:
                            __a     				=  max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,  dp[i - 1][w_]							)
                     else:
                            __a     				=  dp[i - 1][w_]

       return dp[n][w_], dp



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

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

       __a				, __a     				=  knapsack(a ,  a ,  a ,  a							)
       __a     				=  set()
       _construct_solution(a ,  a ,  a ,  a ,  a							)

       return optimal_val, example_optional_set



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a							):
       # 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(a ,  a ,  i - 1 ,  a ,  a							)
              else:
                     optimal_set.add(a							)
                     _construct_solution(a ,  a ,  i - 1 ,  j - wt[i - 1] ,  a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Tuple			=	[3, 2, 4, 4]
   SCREAMING_SNAKE_CASE__:List[str]			=	[4, 3, 2, 3]
   SCREAMING_SNAKE_CASE__:List[str]			=	4
   SCREAMING_SNAKE_CASE__:List[str]			=	6
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[int]			=	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
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[Any]			=	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)
