


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					,  _lowerCamelCase					,  _lowerCamelCase=99					,  _lowerCamelCase=13					,  _lowerCamelCase=16					,  _lowerCamelCase=7					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=False					,  _lowerCamelCase=True					,  _lowerCamelCase=2					,  _lowerCamelCase=32					,  _lowerCamelCase=4					,  _lowerCamelCase=4					,  _lowerCamelCase=30					,  _lowerCamelCase=0					,  _lowerCamelCase=1					,  _lowerCamelCase=2					,  _lowerCamelCase=None					,  ):
       a						:Union[str, Any] 							=       parent
       a						:Dict 							=       batch_size
       a						:Tuple 							=       decoder_seq_length
       # For common tests
       a						:int 							=       self.decoder_seq_length
       a						:Optional[int] 							=       is_training
       a						:Optional[Any] 							=       use_attention_mask
       a						:Tuple 							=       use_labels

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

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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size    )

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

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

       a						: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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  ):
       a						:int 							=       True
       a						:Any 							=       TrOCRDecoder(config=_lowerCamelCase    ).to(_lowerCamelCase    ).eval()
       a						:Tuple 							=       input_ids[:2]

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

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

       a						:Optional[Any] 							=       outputs['''past_key_values''']

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

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

       a						:List[Any] 							=       model(_lowerCamelCase    )['''last_hidden_state''']
       a						:List[Any] 							=       model(_lowerCamelCase					,  past_key_values=_lowerCamelCase    )['''last_hidden_state''']

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

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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       self.prepare_config_and_inputs()
       a							,				a							,				a							,				a						:Union[str, Any] 							=       config_and_inputs

       a						:Optional[Any] 							=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_torch
class 		_snake_case  (      _snake_case						,    _snake_case						,    _snake_case						,    unittest.TestCase							):
 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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       TrOCRStandaloneDecoderModelTester(self					,  is_training=_lowerCamelCase    )
       a						:Tuple 							=       ConfigTester(self					,  config_class=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self.config_tester.run_common_tests()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       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    ):
       pass






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  CpmAntTokenizer
  lowerCamelCase__   =  False







  def       __A   (							self       :				List[str]      )     ->		str:
        super().setUp()

        SCREAMING_SNAKE_CASE_      =     [
            "<d>",
            "</d>",
            "<s>",
            "</s>",
            "</_>",
            "<unk>",
            "<pad>",
            "</n>",
            "我",
            "是",
            "C",
            "P",
            "M",
            "A",
            "n",
            "t",
        ]
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







  @tooslow
  def       __A   (							self       :				int      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     ["今天", "天气", "真", "好", "！"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     [tokenizer.bos_token] + tokens

        SCREAMING_SNAKE_CASE_      =     [6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.decode(__magic_name__      )
        self.assertEqual(__magic_name__     ,   __magic_name__      )


