






'''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 UpperCAmelCase_       :




      """simple docstring"""




      def __init__(			self      :		Optional[int]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		Optional[int]=99  ,   UpperCAmelCase      :		Optional[Any]=13  ,   UpperCAmelCase      :		str=16  ,   UpperCAmelCase      :		Union[str, Any]=7  ,   UpperCAmelCase      :		str=True  ,   UpperCAmelCase      :		Union[str, Any]=True  ,   UpperCAmelCase      :		Union[str, Any]=True  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Dict=True  ,   UpperCAmelCase      :		Any=2  ,   UpperCAmelCase      :		Optional[int]=32  ,   UpperCAmelCase      :		List[str]=4  ,   UpperCAmelCase      :		Optional[Any]=4  ,   UpperCAmelCase      :		Tuple=30  ,   UpperCAmelCase      :		Union[str, Any]=0  ,   UpperCAmelCase      :		Any=1  ,   UpperCAmelCase      :		int=2  ,   UpperCAmelCase      :		Optional[Any]=None  ,   )	->	Dict:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=parent
       lowercase       : str						=batch_size
       lowercase       : Optional[Any]						=decoder_seq_length
       # For common tests
       lowercase       : List[Any]						=self.decoder_seq_length
       lowercase       : int						=is_training
       lowercase       : int						=use_attention_mask
       lowercase       : Optional[Any]						=use_labels

       lowercase       : List[Any]						=vocab_size
       lowercase       : Optional[Any]						=d_model
       lowercase       : int						=d_model
       lowercase       : Optional[int]						=decoder_layers
       lowercase       : str						=decoder_layers
       lowercase       : str						=decoder_ffn_dim
       lowercase       : Dict						=decoder_attention_heads
       lowercase       : List[Any]						=decoder_attention_heads
       lowercase       : str						=eos_token_id
       lowercase       : List[str]						=bos_token_id
       lowercase       : str						=pad_token_id
       lowercase       : Optional[Any]						=decoder_start_token_id
       lowercase       : str						=use_cache
       lowercase       : Tuple						=max_position_embeddings

       lowercase       : Optional[Any]						=None
       lowercase       : Optional[Any]						=decoder_seq_length
       lowercase       : Optional[Any]						=2
       lowercase       : Optional[int]						=1






      def 					A__   (			self      :		Dict		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=ids_tensor([self.batch_size, self.decoder_seq_length]  ,   self.vocab_size		)

       lowercase       : List[str]						=None
       if self.use_attention_mask:
        lowercase       : Tuple						=ids_tensor([self.batch_size, self.decoder_seq_length]  ,   vocab_size=2		)

       lowercase       : str						=None
       if self.use_labels:
        lowercase       : Union[str, Any]						=ids_tensor([self.batch_size, self.decoder_seq_length]  ,   self.vocab_size		)

       lowercase       : Dict						=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      :		Dict  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Optional[int]  ,   )	->	int:


       '''simple docstring'''

       lowercase       : Optional[int]						=True
       lowercase       : Optional[Any]						=TrOCRDecoder(config=UpperCAmelCase		).to(UpperCAmelCase		).eval()
       lowercase       : str						=input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       lowercase       : Dict						=model(UpperCAmelCase  ,   use_cache=UpperCAmelCase		)
       lowercase       : List[str]						=model(UpperCAmelCase		)
       lowercase       : Any						=model(UpperCAmelCase  ,   use_cache=UpperCAmelCase		)

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

       lowercase       : Any						=outputs['''past_key_values''']

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

       # append to next input_ids and
       lowercase       : List[str]						=torch.cat([input_ids, next_tokens]  ,   dim=-1		)

       lowercase       : Union[str, Any]						=model(UpperCAmelCase		)['''last_hidden_state''']
       lowercase       : str						=model(UpperCAmelCase  ,   past_key_values=UpperCAmelCase		)['''last_hidden_state''']

       # select random slice
       lowercase       : Union[str, Any]						=ids_tensor((1,)  ,   output_from_past.shape[-1]		).item()
       lowercase       : Dict						=output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       lowercase       : Dict						=output_from_past[:, 0, random_slice_idx].detach()

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






      def 					A__   (			self      :		List[str]		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Dict						=self.prepare_config_and_inputs()
       lowercase	,		lowercase	,		lowercase	,		lowercase       : Union[str, Any]						=config_and_inputs

       lowercase       : Tuple						={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict





@require_torch
class UpperCAmelCase_       (     __A					,							__A					,							__A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      UpperCamelCase_             =      (TrOCRForCausalLM,) if is_torch_available() else ()
      UpperCamelCase_             =      {'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
      UpperCamelCase_             =      True
      UpperCamelCase_             =      False






      def 					A__   (			self      :		Any		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=TrOCRStandaloneDecoderModelTester(self  ,   is_training=UpperCAmelCase		)
       lowercase       : List[str]						=ConfigTester(self  ,   config_class=UpperCAmelCase		)






      def 					A__   (			self      :		int		)	->	Optional[int]:


       '''simple docstring'''

       pass






      def 					A__   (			self      :		int		)	->	Tuple:


       '''simple docstring'''

       pass






      def 					A__   (			self      :		Optional[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       pass






      def 					A__   (			self      :		str		)	->	Tuple:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      def 					A__   (			self      :		List[str]		)	->	Any:


       '''simple docstring'''

       lowercase       : str						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*UpperCAmelCase		)






      def 					A__   (			self      :		str		)	->	int:


       '''simple docstring'''

       return






      @unittest.skip('''The model doesn\'t support left padding'''		)  # and it's not used enough to be worth fixing :)
      def 					A__   (			self      :		List[Any]		)	->	int:


       '''simple docstring'''

       pass











'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np


def 				lowercase_					(     __A			:							dict       ,   __A			:							str       ,   __A			:							set       ,   __A			:							set       ,   __A			:							dict       ,   __A			:							dict       ,   __A			:							PriorityQueue       ,   __A			:							dict       ,   __A			:							float | int       ,   )							->   float | int:



 """simple docstring"""




 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  lowercase       : str						=cst_fwd.get(__A       ,   np.inf      )
  lowercase       : str						=cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)      )
   lowercase       : int						=new_cost_f
   lowercase       : Optional[int]						=v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    lowercase       : List[Any]						=cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance


def 				lowercase_					(     __A			:							str       ,   __A			:							str       ,   __A			:							dict       ,   __A			:							dict      )							->   int:



 """simple docstring"""




 lowercase       : Optional[Any]						=-1

 lowercase       : Tuple						=set()
 lowercase       : str						=set()
 lowercase       : List[str]						={source: 0}
 lowercase       : List[str]						={destination: 0}
 lowercase       : Tuple						={source: None}
 lowercase       : List[Any]						={destination: None}
 lowercase       : PriorityQueue[Any]						=PriorityQueue()
 lowercase       : PriorityQueue[Any]						=PriorityQueue()

 lowercase       : 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():
  lowercase	,		lowercase       : int						=queue_forward.get()
  visited_forward.add(__A      )

  lowercase	,		lowercase       : int						=queue_backward.get()
  visited_backward.add(__A      )

  lowercase       : Dict						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  lowercase       : List[Any]						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

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

 if shortest_distance != np.inf:
  lowercase       : Union[str, Any]						=shortest_distance
 return shortest_path_distance


SCREAMING_SNAKE_CASE							=						{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
SCREAMING_SNAKE_CASE							=						{
    '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()



