from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


_UpperCAmelCase          =						logging.get_logger(__name__)

# General docstring
_UpperCAmelCase          =						'RegNetConfig'

# Base docstring
_UpperCAmelCase          =						'facebook/regnet-y-040'
_UpperCAmelCase          =						[1, 1088, 7, 7]

# Image classification docstring
_UpperCAmelCase          =						'facebook/regnet-y-040'
_UpperCAmelCase          =						'tabby, tabby cat'

_UpperCAmelCase          =						[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase = 3						,      lowercase = 1						,      lowercase = 1						,      lowercase = "relu"						,      **lowercase						,      ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )
						# The padding and conv has been verified in
						# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
						A_						:							Optional[int]      = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2   )
						A_						:							Tuple      = tf.keras.layers.ConvaD(
						    filters=_UpperCAmelCase						,      kernel_size=_UpperCAmelCase						,      strides=_UpperCAmelCase						,      padding='VALID'						,      groups=_UpperCAmelCase						,      use_bias=_UpperCAmelCase						,      name='convolution'						,      )
						A_						:							str      = tf.keras.layers.BatchNormalization(epsilon=1E-5						,      momentum=0.9						,      name='normalization'   )
						A_						:							int      = ACTaFN[activation] if activation is not None else tf.identity







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							Union[str, Any]      = self.convolution(self.padding(_UpperCAmelCase   )   )
						A_						:							Tuple      = self.normalization(_UpperCAmelCase   )
						A_						:							Optional[int]      = self.activation(_UpperCAmelCase   )
						return hidden_state





class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )
						A_						:							List[str]      = config.num_channels
						A_						:							Optional[Any]      = TFRegNetConvLayer(
						    out_channels=config.embedding_size						,      kernel_size=3						,      stride=2						,      activation=config.hidden_act						,      name='embedder'						,      )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							Tuple      = shape_list(_UpperCAmelCase   )[1]
						if tf.executing_eagerly() and num_channels != self.num_channels:
								raise ValueError(
								    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'   )

						# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
						# So change the input format from `NCHW` to `NHWC`.
						# shape = (batch_size, in_height, in_width, in_channels=num_channels)
						A_						:							Optional[Any]      = tf.transpose(_UpperCAmelCase						,      perm=(0, 2, 3, 1)   )
						A_						:							Optional[Any]      = self.embedder(_UpperCAmelCase   )
						return hidden_state





class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase = 2						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )
						A_						:							Optional[int]      = tf.keras.layers.ConvaD(
						    filters=_UpperCAmelCase						,      kernel_size=1						,      strides=_UpperCAmelCase						,      use_bias=_UpperCAmelCase						,      name='convolution'   )
						A_						:							Tuple      = tf.keras.layers.BatchNormalization(epsilon=1E-5						,      momentum=0.9						,      name='normalization'   )







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = False   ):



						"""simple docstring"""





						return self.normalization(self.convolution(_UpperCAmelCase   )						,      training=_UpperCAmelCase   )





class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )
						A_						:							Optional[Any]      = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCAmelCase						,      name='pooler'   )
						A_						:							Union[str, Any]      = [
						    tf.keras.layers.ConvaD(filters=_UpperCAmelCase						,      kernel_size=1						,      activation='relu'						,      name='attention.0'   ),
						    tf.keras.layers.ConvaD(filters=_UpperCAmelCase						,      kernel_size=1						,      activation='sigmoid'						,      name='attention.2'   ),
						]







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							Dict      = self.pooler(_UpperCAmelCase   )
						for layer_module in self.attention:
								A_						:							Union[str, Any]      = layer_module(_UpperCAmelCase   )
						A_						:							Any      = hidden_state * pooled
						return hidden_state





class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase = 1						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )
						A_						:							int      = in_channels != out_channels or stride != 1
						A_						:							List[Any]      = max(1						,      out_channels // config.groups_width   )
						A_						:							Union[str, Any]      = (
						    TFRegNetShortCut(_UpperCAmelCase						,      stride=_UpperCAmelCase						,      name='shortcut'   )
						    if should_apply_shortcut
						    else tf.keras.layers.Activation('linear'						,      name='shortcut'   )
						)
						# `self.layers` instead of `self.layer` because that is a reserved argument.
						A_						:							int      = [
						    TFRegNetConvLayer(_UpperCAmelCase						,      kernel_size=1						,      activation=config.hidden_act						,      name='layer.0'   ),
						    TFRegNetConvLayer(
						        _UpperCAmelCase						,      stride=_UpperCAmelCase						,      groups=_UpperCAmelCase						,      activation=config.hidden_act						,      name='layer.1'   ),
						    TFRegNetConvLayer(_UpperCAmelCase						,      kernel_size=1						,      activation=_UpperCAmelCase						,      name='layer.2'   ),
						]
						A_						:							List[Any]      = ACTaFN[config.hidden_act]







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							Union[str, Any]      = hidden_state
						for layer_module in self.layers:
								A_						:							Union[str, Any]      = layer_module(_UpperCAmelCase   )
						A_						:							List[str]      = self.shortcut(_UpperCAmelCase   )
						hidden_state += residual
						A_						:							List[Any]      = self.activation(_UpperCAmelCase   )
						return hidden_state





class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase = 1						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )
						A_						:							Union[str, Any]      = in_channels != out_channels or stride != 1
						A_						:							Dict      = max(1						,      out_channels // config.groups_width   )
						A_						:							Tuple      = (
						    TFRegNetShortCut(_UpperCAmelCase						,      stride=_UpperCAmelCase						,      name='shortcut'   )
						    if should_apply_shortcut
						    else tf.keras.layers.Activation('linear'						,      name='shortcut'   )
						)
						A_						:							Dict      = [
						    TFRegNetConvLayer(_UpperCAmelCase						,      kernel_size=1						,      activation=config.hidden_act						,      name='layer.0'   ),
						    TFRegNetConvLayer(
						        _UpperCAmelCase						,      stride=_UpperCAmelCase						,      groups=_UpperCAmelCase						,      activation=config.hidden_act						,      name='layer.1'   ),
						    TFRegNetSELayer(_UpperCAmelCase						,      reduced_channels=int(round(in_channels / 4   )   )						,      name='layer.2'   ),
						    TFRegNetConvLayer(_UpperCAmelCase						,      kernel_size=1						,      activation=_UpperCAmelCase						,      name='layer.3'   ),
						]
						A_						:							str      = ACTaFN[config.hidden_act]







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							Any      = hidden_state
						for layer_module in self.layers:
								A_						:							Tuple      = layer_module(_UpperCAmelCase   )
						A_						:							Optional[Any]      = self.shortcut(_UpperCAmelCase   )
						hidden_state += residual
						A_						:							Dict      = self.activation(_UpperCAmelCase   )
						return hidden_state





class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase = 2						,      lowercase = 2						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )

						A_						:							List[Any]      = TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
						A_						:							int      = [
						    # downsampling is done in the first layer with stride of 2
						    layer(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      stride=_UpperCAmelCase						,      name='layers.0'   ),
						    *[layer(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      name=F'''layers.{i+1}'''   ) for i in range(depth - 1   )],
						]







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						for layer_module in self.layers:
								A_						:							List[str]      = layer_module(_UpperCAmelCase   )
						return hidden_state





class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )
						A_						:							Optional[Any]      = []
						# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
						self.stages.append(
						    TFRegNetStage(
						        _UpperCAmelCase						,      config.embedding_size						,      config.hidden_sizes[0]						,      stride=2 if config.downsample_in_first_stage else 1						,      depth=config.depths[0]						,      name='stages.0'						,      )   )
						A_						:							str      = zip(config.hidden_sizes						,      config.hidden_sizes[1:]   )
						for i, ((in_channels, out_channels), depth) in enumerate(zip(_UpperCAmelCase						,      config.depths[1:]   )   ):
								self.stages.append(TFRegNetStage(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      depth=_UpperCAmelCase						,      name=F'''stages.{i+1}'''   )   )







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = False						,      lowercase = True   ):



						"""simple docstring"""





						A_						:							Union[str, Any]      = () if output_hidden_states else None

						for stage_module in self.stages:
								if output_hidden_states:
										A_						:							Optional[Any]      = hidden_states + (hidden_state,)

								A_						:							str      = stage_module(_UpperCAmelCase   )

						if output_hidden_states:
								A_						:							List[Any]      = hidden_states + (hidden_state,)

						if not return_dict:
								return tuple(v for v in [hidden_state, hidden_states] if v is not None   )

						return TFBaseModelOutputWithNoAttention(last_hidden_state=_UpperCAmelCase						,      hidden_states=_UpperCAmelCase   )





@keras_serializable
class UpperCAmelCase		(   tf.keras.layers.Layer    ):


				'''simple docstring'''






				lowerCamelCase_        =   RegNetConfig
				def __init__(		self						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(**_UpperCAmelCase   )
						A_						:							Dict      = config
						A_						:							Optional[int]      = TFRegNetEmbeddings(_UpperCAmelCase						,      name='embedder'   )
						A_						:							Any      = TFRegNetEncoder(_UpperCAmelCase						,      name='encoder'   )
						A_						:							Any      = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCAmelCase						,      name='pooler'   )







				@unpack_inputs
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None						,      lowercase = None						,      lowercase = False						,      ):



						"""simple docstring"""





						A_						:							Dict      = (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						A_						:							List[Any]      = return_dict if return_dict is not None else self.config.use_return_dict

						A_						:							Any      = self.embedder(_UpperCAmelCase						,      training=_UpperCAmelCase   )

						A_						:							List[Any]      = self.encoder(
						    _UpperCAmelCase						,      output_hidden_states=_UpperCAmelCase						,      return_dict=_UpperCAmelCase						,      training=_UpperCAmelCase   )

						A_						:							Dict      = encoder_outputs[0]
						A_						:							Union[str, Any]      = self.pooler(_UpperCAmelCase   )

						# Change to NCHW output format have uniformity in the modules
						A_						:							int      = tf.transpose(_UpperCAmelCase						,      perm=(0, 3, 1, 2)   )
						A_						:							Optional[Any]      = tf.transpose(_UpperCAmelCase						,      perm=(0, 3, 1, 2)   )

						# Change the other hidden state outputs to NCHW as well
						if output_hidden_states:
								A_						:							Union[str, Any]      = tuple([tf.transpose(_UpperCAmelCase						,      perm=(0, 3, 1, 2)   ) for h in encoder_outputs[1]]   )

						if not return_dict:
								return (last_hidden_state, pooled_output) + encoder_outputs[1:]

						return TFBaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=_UpperCAmelCase						,      pooler_output=_UpperCAmelCase						,      hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states						,      )





class UpperCAmelCase		(   UpperCAmelCase__    ):


				'''simple docstring'''






				lowerCamelCase_        =   RegNetConfig
				lowerCamelCase_        =   '''regnet'''
				lowerCamelCase_        =   '''pixel_values'''
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)						,      dtype=tf.floataa   )}

_UpperCAmelCase          =						r'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

_UpperCAmelCase          =						r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.'''   ,			UpperCAmelCase__   ,			)
class UpperCAmelCase		(   UpperCAmelCase__    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      *lowercase						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(_UpperCAmelCase						,      *_UpperCAmelCase						,      **_UpperCAmelCase   )
						A_						:							List[Any]      = TFRegNetMainLayer(_UpperCAmelCase						,      name='regnet'   )







				@unpack_inputs
				@add_start_docstrings_to_model_forward(_UpperCAmelCase   )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC						,      output_type=_UpperCAmelCase						,      config_class=_CONFIG_FOR_DOC						,      modality='vision'						,      expected_output=_EXPECTED_OUTPUT_SHAPE						,      )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None						,      lowercase = None						,      lowercase=False						,      ):



						"""simple docstring"""





						A_						:							Any      = (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						A_						:							List[str]      = return_dict if return_dict is not None else self.config.use_return_dict

						A_						:							Union[str, Any]      = self.regnet(
						    pixel_values=_UpperCAmelCase						,      output_hidden_states=_UpperCAmelCase						,      return_dict=_UpperCAmelCase						,      training=_UpperCAmelCase						,      )
						if not return_dict:
								return (outputs[0],) + outputs[1:]

						return TFBaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=outputs.last_hidden_state						,      pooler_output=outputs.pooler_output						,      hidden_states=outputs.hidden_states						,      )





@add_start_docstrings(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''   ,			UpperCAmelCase__   ,			)
class UpperCAmelCase		(   UpperCAmelCase__   ,			UpperCAmelCase__    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      *lowercase						,      **lowercase   ):



						"""simple docstring"""





						super().__init__(_UpperCAmelCase						,      *_UpperCAmelCase						,      **_UpperCAmelCase   )
						A_						:							List[Any]      = config.num_labels
						A_						:							Union[str, Any]      = TFRegNetMainLayer(_UpperCAmelCase						,      name='regnet'   )
						# classification head
						A_						:							Optional[int]      = [
						    tf.keras.layers.Flatten(),
						    tf.keras.layers.Dense(config.num_labels						,      name='classifier.1'   ) if config.num_labels > 0 else tf.identity,
						]







				@unpack_inputs
				@add_start_docstrings_to_model_forward(_UpperCAmelCase   )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT						,      output_type=_UpperCAmelCase						,      config_class=_CONFIG_FOR_DOC						,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,      )
				def     lowerCAmelCase_  (		self						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase=False						,      ):



						"""simple docstring"""





						A_						:							Any      = (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						A_						:							List[str]      = return_dict if return_dict is not None else self.config.use_return_dict

						A_						:							List[str]      = self.regnet(
						    _UpperCAmelCase						,      output_hidden_states=_UpperCAmelCase						,      return_dict=_UpperCAmelCase						,      training=_UpperCAmelCase   )

						A_						:							Optional[Any]      = outputs.pooler_output if return_dict else outputs[1]

						A_						:							Tuple      = self.classifier[0](_UpperCAmelCase   )
						A_						:							List[Any]      = self.classifier[1](_UpperCAmelCase   )

						A_						:							Dict      = None if labels is None else self.hf_compute_loss(labels=_UpperCAmelCase						,      logits=_UpperCAmelCase   )

						if not return_dict:
								A_						:							List[Any]      = (logits,) + outputs[2:]
								return ((loss,) + output) if loss is not None else output

						return TFSequenceClassifierOutput(loss=_UpperCAmelCase						,      logits=_UpperCAmelCase						,      hidden_states=outputs.hidden_states   )










import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


A			:						Dict        =  '\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

A			:						List[str]        =  '\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

A			:						Any        =  '\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION				)
class 				A       (			datasets.Metric				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  Union[str, Any]:




							"""simple docstring"""


							if version.parse(scb.__version__						) < version.parse("""1.4.12"""						):
									raise ImportWarning(
									    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
									    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""						)
							return datasets.MetricInfo(
							    description=_DESCRIPTION ,   citation=_CITATION ,   homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf""" ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string""" ,   id="""sequence"""						),
							            """references""": datasets.Sequence(datasets.Value("""string""" ,   id="""sequence"""						) ,   id="""references"""						),
							        }						) ,   codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""] ,   reference_urls=[
							        """https://github.com/m-popovic/chrF""",
							    ] ,   )


					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int = CHRF.CHAR_ORDER ,   _UpperCAmelCase       :		int = CHRF.WORD_ORDER ,   _UpperCAmelCase       :		int = CHRF.BETA ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						len(references[0]						)
							if any(len(_UpperCAmelCase						) != references_per_prediction for refs in references						):
									raise ValueError("""Sacrebleu requires the same number of references for each prediction"""						)
							lowercase__	     =						[[refs[i] for refs in references] for i in range(_UpperCAmelCase						)]

							lowercase__	     =						CHRF(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							lowercase__	     =						sb_chrf.corpus_score(_UpperCAmelCase ,   _UpperCAmelCase						)

							return {
							    "score": output.score,
							    "char_order": output.char_order,
							    "word_order": output.word_order,
							    "beta": output.beta,
							}

