

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase				:   Optional[int]					   =							logging.get_logger(__name__)

_lowerCamelCase				:   List[str]					   =							{
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class 					__UpperCAmelCase     (       __A						):
   UpperCamelCase        =     "gpt_neo"
   UpperCamelCase        =     ["past_key_values"]
   UpperCamelCase        =     {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



   def __init__(							self							:     Optional[int],		__A							:     Dict=5_0_2_5_7,		__A							:     Any=2_0_4_8,		__A							:     Optional[Any]=2_0_4_8,		__A							:     Optional[Any]=2_4,		__A							:     Optional[Any]=[[["global", "local"], 1_2]],		__A							:     Optional[Any]=1_6,		__A							:     List[Any]=None,		__A							:     Union[str, Any]=2_5_6,		__A							:     Optional[int]="gelu_new",		__A							:     Optional[int]=0.0,		__A							:     Tuple=0.0,		__A							:     List[str]=0.0,		__A							:     Any=0.1,		__A							:     List[Any]=1E-5,		__A							:     Dict=0.0_2,		__A							:     Union[str, Any]=True,		__A							:     Dict=5_0_2_5_6,		__A							:     int=5_0_2_5_6,		**__A							:     Optional[int],		):
     UpperCAmelCase       :							Dict  = vocab_size
     UpperCAmelCase       :							Any  = max_position_embeddings
     UpperCAmelCase       :							Optional[Any]  = hidden_size
     UpperCAmelCase       :							Tuple  = num_layers
     UpperCAmelCase       :							Dict  = num_heads
     UpperCAmelCase       :							Tuple  = intermediate_size
     UpperCAmelCase       :							Union[str, Any]  = window_size
     UpperCAmelCase       :							List[str]  = activation_function
     UpperCAmelCase       :							Tuple  = resid_dropout
     UpperCAmelCase       :							List[str]  = embed_dropout
     UpperCAmelCase       :							Union[str, Any]  = attention_dropout
     UpperCAmelCase       :							Union[str, Any]  = classifier_dropout
     UpperCAmelCase       :							Tuple  = layer_norm_epsilon
     UpperCAmelCase       :							Dict  = initializer_range
     UpperCAmelCase       :							Union[str, Any]  = use_cache

     UpperCAmelCase       :							Optional[int]  = bos_token_id
     UpperCAmelCase       :							Optional[Any]  = eos_token_id

     UpperCAmelCase       :							Tuple  = attention_types
     UpperCAmelCase       :							Union[str, Any]  = self.expand_attention_types_params(_UpperCamelCase						)

     if len(self.attention_layers						) != self.num_layers:
       raise ValueError(
           '''Configuration for convolutional module is incorrect. '''
           '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
           F'''but is `len(config.attention_layers) = {len(self.attention_layers						)}`, '''
           F'''`config.num_layers = {self.num_layers}`. '''
           '''`config.attention_layers` is prepared using `config.attention_types`. '''
           '''Please verify the value of `config.attention_types` argument.'''						)

     super().__init__(bos_token_id=_UpperCamelCase,		eos_token_id=_UpperCamelCase,		**_UpperCamelCase						)





   @staticmethod
   def        __magic_name__    (							__A							:     Optional[int]						):
     UpperCAmelCase       :							List[str]  = []
     for item in attention_types:
       for _ in range(item[1]						):
         attentions.extend(item[0]						)
     return attentions






def 			a__     (		UpperCAmelCase							:			Optional[int]				, UpperCAmelCase							:			str				, UpperCAmelCase							:			Optional[Any]				, UpperCAmelCase							:			List[str]			)  ->						Dict:
  import torch

  UpperCAmelCase       :							Any  = input.size()
  UpperCAmelCase       :							Optional[int]  = len(SCREAMING_SNAKE_CASE__			)
  UpperCAmelCase       :							Optional[int]  = shape[dimension]

  UpperCAmelCase       :							str  = torch.arange(0				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__			)
  UpperCAmelCase       :							Any  = torch.div(sizedim - size				, SCREAMING_SNAKE_CASE__				, rounding_mode='''floor'''			) + 1
  UpperCAmelCase       :							List[Any]  = torch.arange(SCREAMING_SNAKE_CASE__			) + low_indices[:min_length][:, None]

  UpperCAmelCase       :							str  = [slice(SCREAMING_SNAKE_CASE__			)] * rank
  UpperCAmelCase       :							List[Any]  = indices
  UpperCAmelCase       :							List[str]  = input[s]

  UpperCAmelCase       :							Union[str, Any]  = list(range(0				, rank + 1			)			)
  perm.append(perm.pop(dimension + 1			)			)

  return sliced.permute(SCREAMING_SNAKE_CASE__			)

def 			a__     (		UpperCAmelCase							:			Dict				, UpperCAmelCase							:			Optional[Any]			)  ->						Dict:
  import torch

  UpperCAmelCase       :							List[Any]  = torch.arange(1				, SCREAMING_SNAKE_CASE__			)
  UpperCAmelCase       :							Union[str, Any]  = torch.remainder(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__			)
  UpperCAmelCase       :							List[Any]  = remainders == 0
  UpperCAmelCase       :							Optional[Any]  = candidates[divisor_indices]
  UpperCAmelCase       :							int  = torch.max(SCREAMING_SNAKE_CASE__			)
  return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, rounding_mode='''floor'''			)






class 					__UpperCAmelCase     (       __A						):



   @property
   def        __magic_name__    (							self							:     List[Any]						):
     UpperCAmelCase       :							str  = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}						)
     if self.use_past:
       self.fill_with_past_key_values_(_UpperCamelCase,		direction='''inputs'''						)
       UpperCAmelCase       :							str  = {0: '''batch''', 1: '''past_sequence + sequence'''}
     else:
       UpperCAmelCase       :							Any  = {0: '''batch''', 1: '''sequence'''}

     return common_inputs



   @property
   def        __magic_name__    (							self							:     str						):
     return self._config.num_heads



   def        __magic_name__    (							self							:     Optional[int],		__A							:     PreTrainedTokenizer,		__A							:     int = -1,		__A							:     int = -1,		__A							:     bool = False,		__A							:     Optional[TensorType] = None,		):
     UpperCAmelCase       :							Tuple  = super(_UpperCamelCase,		self						).generate_dummy_inputs(
         _UpperCamelCase,		batch_size=_UpperCamelCase,		seq_length=_UpperCamelCase,		is_pair=_UpperCamelCase,		framework=_UpperCamelCase						)

     # We need to order the input in the way they appears in the forward()
     UpperCAmelCase       :							str  = OrderedDict({'''input_ids''': common_inputs['''input_ids''']}						)

     # Need to add the past_keys
     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
       else:
         import torch

         UpperCAmelCase ,						UpperCAmelCase       :							Optional[Any]  = common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         UpperCAmelCase       :							Any  = seqlen + 2
         UpperCAmelCase       :							Dict  = (
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         UpperCAmelCase       :							Any  = [
             (torch.zeros(_UpperCamelCase						), torch.zeros(_UpperCamelCase						)) for _ in range(self.num_layers						)
         ]

     UpperCAmelCase       :							List[str]  = common_inputs['''attention_mask''']
     if self.use_past:
       UpperCAmelCase       :							List[str]  = ordered_inputs['''attention_mask'''].dtype
       UpperCAmelCase       :							str  = torch.cat(
           [ordered_inputs['''attention_mask'''], torch.ones(_UpperCamelCase,		_UpperCamelCase,		dtype=_UpperCamelCase						)],		dim=1						)

     return ordered_inputs





   @property
   def        __magic_name__    (							self							:     int						):
     return 1_3








from sklearn.metrics import mean_squared_error

import datasets


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

lowerCAmelCase_       	=	'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


lowerCAmelCase_       	=	'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''





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







			'''simple docstring'''

			def 				snake_case__(     self :				Optional[int]							)      ->List[Any]:
									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 :				List[Any]							)      ->Optional[int]:
									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 :				int							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Any=None							,   _UpperCamelCase :				Optional[int]="uniform_average"							,   _UpperCamelCase :				Tuple=True							)      ->Tuple:
									snake_case_   =	mean_squared_error(
									    _UpperCamelCase							,   _UpperCamelCase							,   sample_weight=_UpperCamelCase							,   multioutput=_UpperCamelCase							,   squared=_UpperCamelCase							)

									return {"mse": mse}