





'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE     :Tuple       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :List[str]       =				{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""trocr"""
 _lowerCamelCase  : List[str]           =							["""past_key_values"""]
 _lowerCamelCase  : int           =							{
     """num_attention_heads""": """decoder_attention_heads""",
     """hidden_size""": """d_model""",
     """num_hidden_layers""": """decoder_layers""",
 }




 def __init__(					self       :    Union[str, Any]	,			snake_case_       :    Dict=5_0_2_6_5	,			snake_case_       :    Dict=1_0_2_4	,			snake_case_       :    Optional[Any]=1_2	,			snake_case_       :    int=1_6	,			snake_case_       :    Tuple=4_0_9_6	,			snake_case_       :    List[Any]="gelu"	,			snake_case_       :    Dict=5_1_2	,			snake_case_       :    str=0.1	,			snake_case_       :    Optional[Any]=0.0	,			snake_case_       :    Optional[int]=0.0	,			snake_case_       :    List[str]=2	,			snake_case_       :    int=0.0_2	,			snake_case_       :    int=0.0	,			snake_case_       :    Union[str, Any]=True	,			snake_case_       :    str=False	,			snake_case_       :    Tuple=True	,			snake_case_       :    List[str]=True	,			snake_case_       :    Optional[int]=1	,			snake_case_       :    int=0	,			snake_case_       :    Dict=2	,			**snake_case_       :    List[Any]	,			):
      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   decoder_layers
      _UpperCAmelCase     							=   decoder_attention_heads
      _UpperCAmelCase     							=   decoder_ffn_dim
      _UpperCAmelCase     							=   activation_function
      _UpperCAmelCase     							=   max_position_embeddings
      _UpperCAmelCase     							=   dropout
      _UpperCAmelCase     							=   attention_dropout
      _UpperCAmelCase     							=   activation_dropout
      _UpperCAmelCase     							=   init_std
      _UpperCAmelCase     							=   decoder_layerdrop
      _UpperCAmelCase     							=   use_cache
      _UpperCAmelCase     							=   scale_embedding
      _UpperCAmelCase     							=   use_learned_position_embeddings
      _UpperCAmelCase     							=   layernorm_embedding

      super().__init__(
          pad_token_id=snake_case_	,			bos_token_id=snake_case_	,			eos_token_id=snake_case_	,			decoder_start_token_id=snake_case_	,			**snake_case_	,			)











'''simple docstring'''




import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import fa_score, matthews_corrcoef


__SCREAMING_SNAKE_CASE     :List[str]       =				(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Tuple       )		->       int:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     return (preds == labels).mean()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      str       )		->       Optional[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   simple_accuracy(__lowercase			,     __lowercase       )
     _UpperCAmelCase     							=   fa_score(y_true=__lowercase			,     y_pred=__lowercase       )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      List[str]       )		->       List[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   pearsonr(__lowercase			,     __lowercase       )[0]
     _UpperCAmelCase     							=   spearmanr(__lowercase			,     __lowercase       )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      str			,     __lowercase							:      str       )		->       Tuple:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     assert len(__lowercase       ) == len(__lowercase       ), f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'
     if task_name == "cola":
          return {"mcc": matthews_corrcoef(__lowercase			,     __lowercase       )}
     elif task_name == "sst-2":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mrpc":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "sts-b":
          return pearson_and_spearman(__lowercase			,     __lowercase       )
     elif task_name == "qqp":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "mnli":
          return {"mnli/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mnli-mm":
          return {"mnli-mm/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "qnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "rte":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "wnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "hans":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )






def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Dict			,     __lowercase							:      str       )		->       Union[str, Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     if len(__lowercase       ) != len(__lowercase       ):
          raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'       )
     if task_name == "xnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )




