



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_      						=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      						=			{
    """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       snake_case_    (    __lowercase					):
							__lowerCAmelCase            =       '''trocr'''
							__lowerCAmelCase            =       ['''past_key_values''']
							__lowerCAmelCase            =       {
							    '''num_attention_heads''': '''decoder_attention_heads''',
							    '''hidden_size''': '''d_model''',
							    '''num_hidden_layers''': '''decoder_layers''',
							}






							def __init__( self      ,  a_=5_0_2_6_5      ,  a_=1_0_2_4      ,  a_=1_2      ,  a_=1_6      ,  a_=4_0_9_6      ,  a_="gelu"      ,  a_=5_1_2      ,  a_=0.1      ,  a_=0.0      ,  a_=0.0      ,  a_=2      ,  a_=0.02      ,  a_=0.0      ,  a_=True      ,  a_=False      ,  a_=True      ,  a_=True      ,  a_=1      ,  a_=0      ,  a_=2      ,  **a_      ,  ):
									a_  :   Optional[Any]			    =  vocab_size
									a_  :   Union[str, Any]			    =  d_model
									a_  :   List[Any]			    =  decoder_layers
									a_  :   Optional[Any]			    =  decoder_attention_heads
									a_  :   Optional[int]			    =  decoder_ffn_dim
									a_  :   Optional[int]			    =  activation_function
									a_  :   Dict			    =  max_position_embeddings
									a_  :   Optional[int]			    =  dropout
									a_  :   str			    =  attention_dropout
									a_  :   List[str]			    =  activation_dropout
									a_  :   str			    =  init_std
									a_  :   List[str]			    =  decoder_layerdrop
									a_  :   Any			    =  use_cache
									a_  :   int			    =  scale_embedding
									a_  :   List[Any]			    =  use_learned_position_embeddings
									a_  :   int			    =  layernorm_embedding

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






'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase					    =	"\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

__UpperCamelCase					    =	"\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

__UpperCamelCase					    =	"\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION		)
class  _A (					datasets.Metric		):

		def 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""simple docstring"""

					return datasets.MetricInfo(
					    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
					        {
					            """predictions""": datasets.Value("""int32"""			),
					            """references""": datasets.Value("""int32"""			),
					        }			)  ,      reference_urls=[
					        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
					    ]  ,      )

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Union[str, Any]=None			)					->    Optional[int]:


					"""simple docstring"""

					return {
					    "matthews_correlation": float(matthews_corrcoef(__magic_name__  ,      __magic_name__  ,      sample_weight=__magic_name__			)			),
					}


