




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch
@is_pipeline_test
class       a__   ( unittest.TestCase				):
						_a							:   Dict =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						_a							:   Optional[int] =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TextaTextGenerationPipeline(model=_A		,      tokenizer=_A      )
											return generator, ["Something to write", "Something else"]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	generator("Something there"      )
											self.assertEqual(_A		,      [{"generated_text": ANY(_A      )}]      )
											# These are encoder decoder, they don't just append to incoming string
											self.assertFalse(outputs[0]["generated_text"].startswith("Something there"      )      )

											__lowerCAmelCase           =	generator(["This is great !", "Something else"]		,      num_return_sequences=2		,      do_sample=_A      )
											self.assertEqual(
											    _A		,      [
											        [{"generated_text": ANY(_A      )}, {"generated_text": ANY(_A      )}],
											        [{"generated_text": ANY(_A      )}, {"generated_text": ANY(_A      )}],
											    ]		,      )

											__lowerCAmelCase           =	generator(
											    ["This is great !", "Something else"]		,      num_return_sequences=2		,      batch_size=2		,      do_sample=_A      )
											self.assertEqual(
											    _A		,      [
											        [{"generated_text": ANY(_A      )}, {"generated_text": ANY(_A      )}],
											        [{"generated_text": ANY(_A      )}, {"generated_text": ANY(_A      )}],
											    ]		,      )

											with self.assertRaises(_A      ):
																generator(4      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	pipeline("text2text-generation"		,      model="patrickvonplaten/t5-tiny-random"		,      framework="pt"      )
											# do_sample=False necessary for reproducibility
											__lowerCAmelCase           =	generator("Something there"		,      do_sample=_A      )
											self.assertEqual(_A		,      [{"generated_text": ""}]      )

											__lowerCAmelCase           =	3
											__lowerCAmelCase           =	generator(
											    "Something there"		,      num_return_sequences=_A		,      num_beams=_A		,      )
											__lowerCAmelCase           =	[
											    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
											    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
											    {"generated_text": ""},
											]
											self.assertEqual(_A		,      _A      )

											__lowerCAmelCase           =	generator("This is a test"		,      do_sample=_A		,      num_return_sequences=2		,      return_tensors=_A      )
											self.assertEqual(
											    _A		,      [
											        {"generated_token_ids": ANY(torch.Tensor      )},
											        {"generated_token_ids": ANY(torch.Tensor      )},
											    ]		,      )
											__lowerCAmelCase           =	generator.model.config.eos_token_id
											__lowerCAmelCase           =	"<pad>"
											__lowerCAmelCase           =	generator(
											    ["This is a test", "This is a second test"]		,      do_sample=_A		,      num_return_sequences=2		,      batch_size=2		,      return_tensors=_A		,      )
											self.assertEqual(
											    _A		,      [
											        [
											            {"generated_token_ids": ANY(torch.Tensor      )},
											            {"generated_token_ids": ANY(torch.Tensor      )},
											        ],
											        [
											            {"generated_token_ids": ANY(torch.Tensor      )},
											            {"generated_token_ids": ANY(torch.Tensor      )},
											        ],
											    ]		,      )






						@require_tf
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	pipeline("text2text-generation"		,      model="patrickvonplaten/t5-tiny-random"		,      framework="tf"      )
											# do_sample=False necessary for reproducibility
											__lowerCAmelCase           =	generator("Something there"		,      do_sample=_A      )
											self.assertEqual(_A		,      [{"generated_text": ""}]      )










from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__         =      """\
@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}
}
"""

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


UpperCamelCase__         =      """
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       a__   ( datasets.Metric				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											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      ):




											"""simple docstring"""



											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		,      _A		,      _A		,      _A=None		,      _A="uniform_average"		,      _A=True      ):




											"""simple docstring"""



											__lowerCAmelCase           =	mean_squared_error(
											    _A		,      _A		,      sample_weight=_A		,      multioutput=_A		,      squared=_A      )

											return {"mse": mse}




