

"""simple docstring"""


from sklearn.metrics import mean_squared_error

import datasets


a					:str    = "\\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"

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


a					:Union[str, Any]    = "\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      __a							(datasets.Metric):






     '''simple docstring'''

     def 					_a			(						self  )  ->      Dict:


           """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 					_a			(						self  )  ->      Tuple:


           """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 					_a			(						self	,    _a	,    _a	,    _a=None	,    _a="uniform_average"	,    _a=True  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	mean_squared_error(
               _a	,    _a	,    sample_weight=_a	,    multioutput=_a	,    squared=_a  )

           return {"mse": mse}







"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class      __a							(unittest.TestCase):






     '''simple docstring'''

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
           with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""  ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{
               """do_resize""": True,
               """size""": 20,
               """do_center_crop""": True,
               """crop_size""": 18,
               """do_normalize""": True,
               """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(self.tmpdirname	,    _a  )
           with open(self.image_processor_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
                 json.dump(_a	,    _a  )

     def 					_a			(						self	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           return BertTokenizer.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           return BertTokenizerFast.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    **_a  )  ->      Any:


           """simple docstring"""


           return EfficientNetImageProcessor.from_pretrained(self.tmpdirname	,    **_a  )

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


           """simple docstring"""


           shutil.rmtree(self.tmpdirname  )

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta  )]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[Image.fromarray(np.moveaxis(_a	,    0	,    -1  )  ) for x in image_inputs]
           return image_inputs

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_tokenizer()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_image_processor()

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )
           processor_slow.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      str      		=	AlignProcessor.from_pretrained(self.tmpdirname	,    use_fast=_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )
           processor_fast.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	AlignProcessor.from_pretrained(self.tmpdirname  )

           self.assertEqual(processor_slow.tokenizer.get_vocab()	,    tokenizer_slow.get_vocab()  )
           self.assertEqual(processor_fast.tokenizer.get_vocab()	,    tokenizer_fast.get_vocab()  )
           self.assertEqual(tokenizer_slow.get_vocab()	,    tokenizer_fast.get_vocab()  )
           self.assertIsInstance(processor_slow.tokenizer	,    _a  )
           self.assertIsInstance(processor_fast.tokenizer	,    _a  )

           self.assertEqual(processor_slow.image_processor.to_json_string()	,    image_processor.to_json_string()  )
           self.assertEqual(processor_fast.image_processor.to_json_string()	,    image_processor.to_json_string()  )
           self.assertIsInstance(processor_slow.image_processor	,    _a  )
           self.assertIsInstance(processor_fast.image_processor	,    _a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AlignProcessor(tokenizer=self.get_tokenizer()	,    image_processor=self.get_image_processor()  )
           processor.save_pretrained(self.tmpdirname  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_image_processor(do_normalize=_a	,    padding_value=1.0  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	AlignProcessor.from_pretrained(
               self.tmpdirname	,    bos_token="""(BOS)"""	,    eos_token="""(EOS)"""	,    do_normalize=_a	,    padding_value=1.0  )

           self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()  )
           self.assertIsInstance(processor.tokenizer	,    _a  )

           self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()  )
           self.assertIsInstance(processor.image_processor	,    _a  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image_processor(_a	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	processor(images=_a	,    return_tensors="""np"""  )

           for key in input_image_proc.keys():
                 self.assertAlmostEqual(input_image_proc[key].sum()	,    input_processor[key].sum()	,    delta=1E-2  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Any      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""lower newer"""

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	processor(text=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer(_a	,    padding="""max_length"""	,    max_length=64  )

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]  )

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      Any      		=	processor(text=_a	,    images=_a  )

           self.assertListEqual(list(inputs.keys()  )	,    ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]  )

           # test if it raises when no input is passed
           with pytest.raises(_a  ):
                 processor()

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	processor.batch_decode(_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.batch_decode(_a  )

           self.assertListEqual(_a	,    _a  )





     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	processor(text=_a	,    images=_a  )

           self.assertListEqual(list(inputs.keys()  )	,    processor.model_input_names  )




