

from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase				:    Optional[int]        =		"""\
@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				:    List[str]        =		"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


lowerCAmelCase				:    str        =		"""
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     __lowercase							(					datasets.Metric      ):




			"""simple docstring"""







			def 			_SCREAMING_SNAKE_CASE     (					self    :	List[str]):
									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 			_SCREAMING_SNAKE_CASE     (					self    :	List[str]):




									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 			_SCREAMING_SNAKE_CASE     (					self    :	List[str]  ,		lowerCAmelCase__    :	Any  ,		lowerCAmelCase__    :	Union[str, Any]  ,		lowerCAmelCase__    :	List[str]=None  ,		lowerCAmelCase__    :	Dict="uniform_average"  ,		lowerCAmelCase__    :	str=True):
									SCREAMING_SNAKE_CASE_:					Optional[int]    						=			mean_squared_error(
									    UpperCamelCase__  ,		UpperCamelCase__  ,		sample_weight=UpperCamelCase__  ,		multioutput=UpperCamelCase__  ,		squared=UpperCamelCase__)

									return {"mse": mse}










"""simple docstring"""



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_keras_nlp_available():
  from transformers.models.gpta import TFGPTaTokenizer


__snake_case       =       ["""gpt2"""]
__snake_case       =       """gpt2"""

if is_tf_available():







  class _lowerCAmelCase							( tf.Module      ):







     def __init__(      self		,      UpperCamelCase__		)      ->						Optional[Any]:

         '''simple docstring'''




         super().__init__()
         snake_case       : Dict    =       tokenizer
         snake_case       : List[str]    =       AutoConfig.from_pretrained(UpperCamelCase__		)
         snake_case       : List[str]    =       TFGPTaLMHeadModel.from_config(UpperCamelCase__		)







     @tf.function(input_signature=(tf.TensorSpec((None,)		,      tf.string		,      name="text"		),)		)
     def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Optional[int]:

         '''simple docstring'''




         snake_case       : List[Any]    =       self.tokenizer(UpperCamelCase__		)
         snake_case       : Tuple    =       tokenized["input_ids"].to_tensor()

         snake_case       : str    =       tf.cast(input_ids_dense > 0		,      tf.intaa		)
         # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

         snake_case       : int    =       self.model(input_ids=UpperCamelCase__		,      attention_mask=UpperCamelCase__		)["logits"]

         return outputs







@require_tf
@require_keras_nlp
class _lowerCAmelCase							( unittest.TestCase      ):







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       super().setUp()

       snake_case       : Optional[int]    =       [GPTaTokenizer.from_pretrained(UpperCamelCase__		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       snake_case       : str    =       [TFGPTaTokenizer.from_pretrained(UpperCamelCase__		) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers		) == len(self.tf_tokenizers		)

       snake_case       : Optional[int]    =       [
           "This is a straightforward English test sentence.",
           "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
           "Now we're going to add some Chinese: 一 二 三 一二三",
           "And some much more rare Chinese: 齉 堃 齉堃",
           "Je vais aussi écrire en français pour tester les accents",
           "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
       ]
       snake_case       : str    =       list(zip(self.test_sentences		,      self.test_sentences[::-1]		)		)







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       for tokenizer, tf_tokenizer in zip(self.tokenizers		,      self.tf_tokenizers		):
           for test_inputs in self.test_sentences:
               snake_case       : Any    =       tokenizer([test_inputs]		,      return_tensors="tf"		)
               snake_case       : str    =       tf_tokenizer([test_inputs]		)

               for key in python_outputs.keys():
                   # convert them to numpy to avoid messing with ragged tensors
                   snake_case       : Any    =       python_outputs[key].numpy()
                   snake_case       : Any    =       tf_outputs[key].numpy()

                   self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
                   self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase__		,      tf.intaa		) == tf_outputs_values		)		)







   @slow
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
           snake_case       : str    =       tf.function(UpperCamelCase__		)
           for test_inputs in self.test_sentences:
               snake_case       : str    =       tf.constant(UpperCamelCase__		)
               snake_case       : Optional[int]    =       compiled_tokenizer(UpperCamelCase__		)
               snake_case       : Dict    =       tf_tokenizer(UpperCamelCase__		)

               for key in eager_outputs.keys():
                   self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







   @slow
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
           snake_case       : Tuple    =       ModelToSave(tokenizer=UpperCamelCase__		)
           snake_case       : Tuple    =       tf.convert_to_tensor([self.test_sentences[0]]		)
           snake_case       : Union[str, Any]    =       model.serving(UpperCamelCase__		)  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
               snake_case       : int    =       Path(UpperCamelCase__		) / "saved.model"
               tf.saved_model.save(UpperCamelCase__		,      UpperCamelCase__		,      signatures={"serving_default": model.serving}		)
               snake_case       : Optional[Any]    =       tf.saved_model.load(UpperCamelCase__		)
           snake_case       : Union[str, Any]    =       loaded_model.signatures["serving_default"](UpperCamelCase__		)["output_0"]
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertTrue(tf.reduce_all(out == loaded_output		)		)







   @slow
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
           snake_case       : Any    =       tf.convert_to_tensor([self.test_sentences[0]]		)
           snake_case       : List[str]    =       tf_tokenizer(UpperCamelCase__		)  # Build model with some sample inputs

           snake_case       : Dict    =       tf_tokenizer.get_config()
           snake_case       : Union[str, Any]    =       TFGPTaTokenizer.from_config(UpperCamelCase__		)
           snake_case       : Optional[Any]    =       model_from_config(UpperCamelCase__		)

           for key in from_config_output.keys():
               self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)







   @slow
   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           snake_case       : List[str]    =       12_3123

           for max_length in [3, 5, 1024]:
               snake_case       : str    =       tf.convert_to_tensor([self.test_sentences[0]]		)
               snake_case       : List[Any]    =       tf_tokenizer(UpperCamelCase__		,      max_length=UpperCamelCase__		)

               snake_case       : str    =       out["input_ids"].numpy().shape[1]

               assert out_length == max_length





