'''simple docstring'''




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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


UpperCamelCase__ :			int												=				logging.get_logger(__name__)

UpperCamelCase__ :			Optional[int]												=				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[Any]    					=	'''perceiver'''

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				str=2_5_6							,  lowerCAmelCase__  :				List[Any]=1_2_8_0							,  lowerCAmelCase__  :				Union[str, Any]=7_6_8							,  lowerCAmelCase__  :				str=1							,  lowerCAmelCase__  :				Tuple=2_6							,  lowerCAmelCase__  :				str=8							,  lowerCAmelCase__  :				Dict=8							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Union[str, Any]=None							,  lowerCAmelCase__  :				Any="kv"							,  lowerCAmelCase__  :				Any=1							,  lowerCAmelCase__  :				List[Any]=1							,  lowerCAmelCase__  :				List[Any]="gelu"							,  lowerCAmelCase__  :				Any=0.1							,  lowerCAmelCase__  :				int=0.02							,  lowerCAmelCase__  :				List[Any]=1E-12							,  lowerCAmelCase__  :				Tuple=True							,  lowerCAmelCase__  :				Dict=2_6_2							,  lowerCAmelCase__  :				Any=2_0_4_8							,  lowerCAmelCase__  :				Dict=5_6							,  lowerCAmelCase__  :				Tuple=[3_6_8, 4_9_6]							,  lowerCAmelCase__  :				Optional[Any]=1_6							,  lowerCAmelCase__  :				List[str]=1_9_2_0							,  lowerCAmelCase__  :				Tuple=1_6							,  lowerCAmelCase__  :				str=[1, 1_6, 2_2_4, 2_2_4]							,  **lowerCAmelCase__  :				List[Any]							,  ):





					"""simple docstring"""






					super().__init__(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_latents
					__SCREAMING_SNAKE_CASE :	Any					 =	d_latents
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	d_model
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	num_blocks
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	num_self_attends_per_block
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	num_self_attention_heads
					__SCREAMING_SNAKE_CASE :	Dict					 =	num_cross_attention_heads
					__SCREAMING_SNAKE_CASE :	str					 =	qk_channels
					__SCREAMING_SNAKE_CASE :	int					 =	v_channels
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	cross_attention_shape_for_attention
					__SCREAMING_SNAKE_CASE :	str					 =	self_attention_widening_factor
					__SCREAMING_SNAKE_CASE :	List[str]					 =	cross_attention_widening_factor
					__SCREAMING_SNAKE_CASE :	List[str]					 =	hidden_act
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE :	Any					 =	initializer_range
					__SCREAMING_SNAKE_CASE :	int					 =	layer_norm_eps
					__SCREAMING_SNAKE_CASE :	List[str]					 =	use_query_residual
					# masked language modeling attributes
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	vocab_size
					__SCREAMING_SNAKE_CASE :	int					 =	max_position_embeddings
					# image classification attributes
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	image_size
					# flow attributes
					__SCREAMING_SNAKE_CASE :	int					 =	train_size
					# multimodal autoencoding attributes
					__SCREAMING_SNAKE_CASE :	Tuple					 =	num_frames
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	audio_samples_per_frame
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	samples_per_patch
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	output_shape






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			@property
			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					if self.task == "multiple-choice":
							__SCREAMING_SNAKE_CASE :	Tuple					 =	{0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	{0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""inputs""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]		)

			@property
			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					return 1E-4



			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,  lowerCAmelCase__  :				int = -1							,  lowerCAmelCase__  :				int = -1							,  lowerCAmelCase__  :				int = -1							,  lowerCAmelCase__  :				bool = False							,  lowerCAmelCase__  :				Optional[TensorType] = None							,  lowerCAmelCase__  :				int = 3							,  lowerCAmelCase__  :				int = 4_0							,  lowerCAmelCase__  :				int = 4_0							,  ):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							__SCREAMING_SNAKE_CASE :	Dict					 =	compute_effective_axis_dimension(
							    lowerCAmelCase__							,  fixed_dimension=OnnxConfig.default_fixed_batch							,  num_token_to_add=0		)
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							__SCREAMING_SNAKE_CASE :	int					 =	preprocessor.num_special_tokens_to_add(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	compute_effective_axis_dimension(
							    lowerCAmelCase__							,  fixed_dimension=OnnxConfig.default_fixed_sequence							,  num_token_to_add=lowerCAmelCase__		)
							# Generate dummy inputs according to compute batch and sequence
							__SCREAMING_SNAKE_CASE :	List[str]					 =	[""" """.join(["""a"""]		) * seq_length] * batch_size
							__SCREAMING_SNAKE_CASE :	int					 =	dict(preprocessor(lowerCAmelCase__							,  return_tensors=lowerCAmelCase__		)		)
							__SCREAMING_SNAKE_CASE :	str					 =	inputs.pop("""input_ids"""		)
							return inputs
					elif isinstance(lowerCAmelCase__							,  lowerCAmelCase__		) and preprocessor.model_input_names[0] == "pixel_values":
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							__SCREAMING_SNAKE_CASE :	List[str]					 =	compute_effective_axis_dimension(lowerCAmelCase__							,  fixed_dimension=OnnxConfig.default_fixed_batch		)
							__SCREAMING_SNAKE_CASE :	Tuple					 =	self._generate_dummy_images(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Dict					 =	dict(preprocessor(images=lowerCAmelCase__							,  return_tensors=lowerCAmelCase__		)		)
							__SCREAMING_SNAKE_CASE :	str					 =	inputs.pop("""pixel_values"""		)
							return inputs
					else:
							raise ValueError(
							    """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""		)




'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__ :			Optional[Any]												=				'''\
@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__ :			List[str]												=				'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


UpperCamelCase__ :			Dict												=				'''
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 		_UpperCamelCase						(							datasets.Metric					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				str		):





					"""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 UpperCamelCase__      (							self  :				Optional[Any]		):





					"""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 UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Optional[int]="uniform_average"							,  lowerCAmelCase__  :				int=True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	mean_squared_error(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  sample_weight=lowerCAmelCase__							,  multioutput=lowerCAmelCase__							,  squared=lowerCAmelCase__		)

					return {"mse": mse}