






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  :







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


      '''simple docstring'''






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

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

      UpperCamelCase__			:   Optional[Any]       				=  None
      UpperCamelCase__			:   int       				=  decoder_seq_length
      UpperCamelCase__			:   Any       				=  2
      UpperCamelCase__			:   Dict       				=  1







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  ids_tensor([self.batch_size, self.decoder_seq_length]							,   self.vocab_size						)

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

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

      UpperCamelCase__			:   List[Any]       				=  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 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  True
      UpperCamelCase__			:   Dict       				=  TrOCRDecoder(config=SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						).eval()
      UpperCamelCase__			:   Optional[Any]       				=  input_ids[:2]

      input_ids[input_ids == 0] += 1
      # first forward pass
      UpperCamelCase__			:   Dict       				=  model(SCREAMING_SNAKE_CASE							,   use_cache=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   int       				=  model(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  model(SCREAMING_SNAKE_CASE							,   use_cache=SCREAMING_SNAKE_CASE						)

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

      UpperCamelCase__			:   Union[str, Any]       				=  outputs["past_key_values"]

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

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

      UpperCamelCase__			:   Tuple       				=  model(SCREAMING_SNAKE_CASE						)["last_hidden_state"]
      UpperCamelCase__			:   Optional[Any]       				=  model(SCREAMING_SNAKE_CASE							,   past_key_values=SCREAMING_SNAKE_CASE						)["last_hidden_state"]

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

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







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.prepare_config_and_inputs()
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Any       				=  config_and_inputs

      UpperCamelCase__			:   Dict       				=  {"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict







@require_torch
class 			__a  (    A__       ,   A__       ,   A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       str        = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 _lowerCAmelCase       :       Union[str, Any]        = (TrOCRForCausalLM,) if is_torch_available() else ()
 _lowerCAmelCase       :       Union[str, Any]        = {'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
 _lowerCAmelCase       :       str        = True
 _lowerCAmelCase       :       Optional[int]        = False







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  TrOCRStandaloneDecoderModelTester(self							,   is_training=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  ConfigTester(self							,   config_class=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      self.config_tester.run_common_tests()







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past(*SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		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 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      pass







lowerCamelCase	:							List[Any]				=[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Optional[int]       				=  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
     UpperCamelCase__			:   Any       				=  0
     UpperCamelCase__			:   Any       				=  0
     while place < len(__lowerCAmelCase			):
          if (place + 1 < len(__lowerCAmelCase			)) and (vals[roman[place]] < vals[roman[place + 1]]):
               total += vals[roman[place + 1]] - vals[roman[place]]
               place += 2
          else:
               total += vals[roman[place]]
               place += 1
     return total







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Optional[int]       				=  []
     for arabic, roman in ROMAN:
          ((UpperCamelCase__)				,     (UpperCamelCase__))			:   Optional[int]       				=  divmod(__lowerCAmelCase					,    __lowerCAmelCase			)
          result.append(roman * factor			)
          if number == 0:
               break
     return "".join(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()