





'''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  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[Any]								=       "trocr"
 __lowerCamelCase    :				Optional[int]								=       ["past_key_values"]
 __lowerCamelCase    :				List[str]								=       {
     "num_attention_heads": "decoder_attention_heads",
     "hidden_size": "d_model",
     "num_hidden_layers": "decoder_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=50265      ,  _lowerCAmelCase=1024      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=4096      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=2      ,  **_lowerCAmelCase      ,  )     ->				Any:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   d_model
     _lowerCAmelCase						      =   decoder_layers
     _lowerCAmelCase						      =   decoder_attention_heads
     _lowerCAmelCase						      =   decoder_ffn_dim
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   dropout
     _lowerCAmelCase						      =   attention_dropout
     _lowerCAmelCase						      =   activation_dropout
     _lowerCAmelCase						      =   init_std
     _lowerCAmelCase						      =   decoder_layerdrop
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   scale_embedding
     _lowerCAmelCase						      =   use_learned_position_embeddings
     _lowerCAmelCase						      =   layernorm_embedding

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









'''simple docstring'''


from sklearn.metrics import mean_squared_error

import datasets


_SCREAMING_SNAKE_CASE									=				"\\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"

_SCREAMING_SNAKE_CASE									=				"\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"

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




 def 				_snake_case (						self		)     ->				Dict:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(self._get_feature_types()		)      ,  reference_urls=[
             "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
         ]      ,  )




 def 				_snake_case (						self		)     ->				Tuple:
     if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"		)		),
             "references": datasets.Sequence(datasets.Value("float"		)		),
         }
     else:
         return {
             "predictions": datasets.Value("float"		),
             "references": datasets.Value("float"		),
         }





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="uniform_average"      ,  _lowerCAmelCase=True		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   mean_squared_error(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  sample_weight=_lowerCAmelCase      ,  multioutput=_lowerCAmelCase      ,  squared=_lowerCAmelCase		)

     return {"mse": mse}


