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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				decoder_seq_length
     # For common tests
     SCREAMING_SNAKE_CASE__      							=				self.decoder_seq_length
     SCREAMING_SNAKE_CASE__      							=				is_training
     SCREAMING_SNAKE_CASE__      							=				use_attention_mask
     SCREAMING_SNAKE_CASE__      							=				use_labels

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

     SCREAMING_SNAKE_CASE__      							=				None
     SCREAMING_SNAKE_CASE__      							=				decoder_seq_length
     SCREAMING_SNAKE_CASE__      							=				2
     SCREAMING_SNAKE_CASE__      							=				1




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				ids_tensor([self.batch_size, self.decoder_seq_length]	,     self.vocab_size				)

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

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

     SCREAMING_SNAKE_CASE__      							=				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      :    Optional[Any]	,     _lowercase      :    Dict	,     _lowercase      :    List[str]	,     _lowercase      :    Optional[int]	,     _lowercase      :    Any	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				True
     SCREAMING_SNAKE_CASE__      							=				TrOCRDecoder(config=_lowercase				).to(_lowercase				).eval()
     SCREAMING_SNAKE_CASE__      							=				input_ids[:2]

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

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

     SCREAMING_SNAKE_CASE__      							=				outputs["""past_key_values"""]

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

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

     SCREAMING_SNAKE_CASE__      							=				model(_lowercase				)["""last_hidden_state"""]
     SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     past_key_values=_lowercase				)["""last_hidden_state"""]

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

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




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				config_and_inputs

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







@require_torch
class __snake_case    (						lowerCamelCase_       ,		lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
   lowerCAmelCase_														=							(TrOCRForCausalLM,) if is_torch_available() else ()
   lowerCAmelCase_														=							{"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							False




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TrOCRStandaloneDecoderModelTester(self	,     is_training=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				ConfigTester(self	,     config_class=_lowercase				)




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     pass




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     pass




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     pass




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past(*_lowercase				)




   def      __a						(   self      :    Dict				):



     """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[str]				):



     """simple docstring"""






     pass

from __future__ import annotations

__lowerCamelCase      :			Tuple							       =		list[list[int]]

# assigning initial values to the grid
__lowerCamelCase      :			Matrix							       =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
__lowerCamelCase      :			Matrix							       =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int     )							-> bool:







  """simple docstring"""





  for i in range(9     ):
    if grid[row][i] == n or grid[i][column] == n:
      return False

  for i in range(3     ):
    for j in range(3     ):
      if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
        return False

  return True



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> tuple[int, int] | None:







  """simple docstring"""





  for i in range(9     ):
    for j in range(9     ):
      if grid[i][j] == 0:
        return i, j
  return None



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> Matrix | None:







  """simple docstring"""





  if location := find_empty_location(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				location
  else:
    # If the location is ``None``, then the grid is solved.
    return grid

  for digit in range(1			,					10     ):
    if is_safe(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     ):
      SCREAMING_SNAKE_CASE__      							=				digit

      if sudoku(__UpperCamelCase     ) is not None:
        return grid

      SCREAMING_SNAKE_CASE__      							=				0

  return None



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> None:







  """simple docstring"""





  for row in grid:
    for cell in row:
      print(__UpperCamelCase			,					end=""" """     )
    print()


if __name__ == "__main__":
   # make a copy of grid so that you can compare with the unmodified grid
   for example_grid in (initial_grid, no_solution):
      print('''\nExample grid:\n''' + '''=''' * 20)
      print_solution(example_grid)
      print('''\nExample grid solution:''')
      __lowerCamelCase      :			str							       =		sudoku(example_grid)
      if solution is not None:
         print_solution(solution)
      else:
         print('''Cannot find a solution.''')
