


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 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple=9_9  ,							SCREAMING_SNAKE_CASE__       : Tuple=1_3  ,							SCREAMING_SNAKE_CASE__       : str=1_6  ,							SCREAMING_SNAKE_CASE__       : List[str]=7  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=True  ,							SCREAMING_SNAKE_CASE__       : List[Any]=True  ,							SCREAMING_SNAKE_CASE__       : str=True  ,							SCREAMING_SNAKE_CASE__       : Dict=False  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : List[Any]=2  ,							SCREAMING_SNAKE_CASE__       : List[Any]=3_2  ,							SCREAMING_SNAKE_CASE__       : Tuple=4  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=4  ,							SCREAMING_SNAKE_CASE__       : int=3_0  ,							SCREAMING_SNAKE_CASE__       : Tuple=0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=1  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=2  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=None  ,							)	->    str:
             a_      :	Optional[int]							=						parent
             a_      :	Union[str, Any]							=						batch_size
             a_      :	int							=						decoder_seq_length
             # For common tests
             a_      :	Tuple							=						self.decoder_seq_length
             a_      :	str							=						is_training
             a_      :	List[Any]							=						use_attention_mask
             a_      :	List[Any]							=						use_labels

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

             a_      :	Union[str, Any]							=						None
             a_      :	List[Any]							=						decoder_seq_length
             a_      :	int							=						2
             a_      :	Dict							=						1
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Dict:
             a_      :	str							=						ids_tensor([self.batch_size, self.decoder_seq_length]  ,							self.vocab_size  )

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

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

             a_      :	Optional[int]							=						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  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							)	->    int:
             a_      :	Union[str, Any]							=						True
             a_      :	str							=						TrOCRDecoder(config=SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  ).eval()
             a_      :	Tuple							=						input_ids[:2]

             input_ids[input_ids == 0] += 1
             # first forward pass
             a_      :	str							=						model(SCREAMING_SNAKE_CASE__  ,							use_cache=SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						model(SCREAMING_SNAKE_CASE__  )
             a_      :	Union[str, Any]							=						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  )

             a_      :	Any							=						outputs['past_key_values']

             # create hypothetical next token and extent to next_input_ids
             a_      :	Optional[Any]							=						ids_tensor((2, 1)  ,							config.vocab_size - 1  ) + 1

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

             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  )['last_hidden_state']
             a_      :	Optional[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  )['last_hidden_state']

             # select random slice
             a_      :	Optional[int]							=						ids_tensor((1,)  ,							output_from_past.shape[-1]  ).item()
             a_      :	List[str]							=						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
             a_      :	Dict							=						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  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             a_      :	Any							=						self.prepare_config_and_inputs()
             a_      :	List[str]							=						config_and_inputs

             a_      :	Optional[int]							=						{'input_ids': input_ids, 'attention_mask': attention_mask}
             return config, inputs_dict
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Dict       =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      snake_case__       :  Optional[int]       =		(TrOCRForCausalLM,) if is_torch_available() else ()
      snake_case__       :  Union[str, Any]       =		{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
      snake_case__       :  str       =		True
      snake_case__       :  List[Any]       =		False
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Optional[int]:
             a_      :	int							=						TrOCRStandaloneDecoderModelTester(self  ,							is_training=SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						ConfigTester(self  ,							config_class=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Dict:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    List[str]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    str:
             self.config_tester.run_common_tests()
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             a_      :	str							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Dict:
             return

      @unittest.skip('The model doesn\'t support left padding'  )  # and it's not used enough to be worth fixing :)
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             pass






def  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	list:

       """simple docstring"""

       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__A       )       )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
   __import__('doctest').testmod()


