
"""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        A_      :



							"""simple docstring"""






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





										"""simple docstring"""

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

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

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





							def  UpperCAmelCase__				(    self    :Dict			):





										"""simple docstring"""

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

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

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

										lowerCamelCase__      :	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  UpperCAmelCase__				(    self    :Dict	,				lowerCamelCase_    :Dict	,				lowerCamelCase_    :Dict	,				lowerCamelCase_    :Dict	,				lowerCamelCase_    :Optional[int]	,				):





										"""simple docstring"""

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

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

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

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

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

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

										lowerCamelCase__      :	Union[str, Any]						=model(lowerCamelCase_			)['''last_hidden_state''']
										lowerCamelCase__      :	str						=model(lowerCamelCase_	,				past_key_values=lowerCamelCase_			)['''last_hidden_state''']

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

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





							def  UpperCAmelCase__				(    self    :List[str]			):





										"""simple docstring"""

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

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






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



							"""simple docstring"""






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





							def  UpperCAmelCase__				(    self    :Any			):





										"""simple docstring"""

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





							def  UpperCAmelCase__				(    self    :int			):





										"""simple docstring"""

										pass





							def  UpperCAmelCase__				(    self    :int			):





										"""simple docstring"""

										pass





							def  UpperCAmelCase__				(    self    :Optional[Any]			):





										"""simple docstring"""

										pass





							def  UpperCAmelCase__				(    self    :str			):





										"""simple docstring"""

										self.config_tester.run_common_tests()





							def  UpperCAmelCase__				(    self    :List[str]			):





										"""simple docstring"""

										lowerCamelCase__      :	str						=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase_			)





							def  UpperCAmelCase__				(    self    :str			):





										"""simple docstring"""

										return





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





										"""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()



