
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)




def  __magic_name__ (   __lowerCAmelCase	:						Union[tf.Tensor, np.ndarray]							)			->   List[int]:
				if isinstance(__lowerCAmelCase		,  np.ndarray							):
								return list(tensor.shape							)

				__lowerCamelCase    					=	tf.shape(__lowerCAmelCase							)

				if tensor.shape == tf.TensorShape(__lowerCAmelCase							):
								return dynamic

				__lowerCamelCase    					=	tensor.shape.as_list()

				return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCAmelCase							)]




def  __magic_name__ (   __lowerCAmelCase	:						tf.Tensor		,  __lowerCAmelCase	:						Optional[int] = None		,  __lowerCAmelCase	:						Optional[str] = None							)			->   tf.Tensor:
				return tf.nn.softmax(logits=logits + 1E-9		,  axis=__lowerCAmelCase		,  name=__lowerCAmelCase							)




def  __magic_name__ (   __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Union[str, Any]=1E-5		,  __lowerCAmelCase	:						Any=-1							)			->   List[Any]:
				# This is a very simplified functional layernorm, designed to duplicate
				# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
				# models in Transformers.

				if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCAmelCase		,  __lowerCAmelCase							):
								raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''							)

				# Get mean and variance on the axis to be normalized
				__lowerCamelCase					,    __lowerCamelCase    					=	tf.nn.moments(__lowerCAmelCase		,  axes=[axis]		,  keepdims=__lowerCAmelCase							)

				if axis != -1:
								# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
								# on every dimension except axis
								__lowerCamelCase    					=	[1] * inputs.shape.rank
								__lowerCamelCase    					=	shape_list(__lowerCAmelCase							)[axis]
								__lowerCamelCase    					=	tf.reshape(__lowerCAmelCase		,  __lowerCAmelCase							)
								__lowerCamelCase    					=	tf.reshape(__lowerCAmelCase		,  __lowerCAmelCase							)

				# Compute layer normalization using the batch_normalization
				# function.
				__lowerCamelCase    					=	tf.nn.batch_normalization(
				    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  offset=__lowerCAmelCase		,  scale=__lowerCAmelCase		,  variance_epsilon=__lowerCAmelCase		,  )
				return outputs




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						Tuple=0		,  __lowerCAmelCase	:						List[str]=-1							)			->   int:
				# Replicates the behavior of torch.flatten in TF

				# If end_dim or start_dim is negative, count them from the end
				if end_dim < 0:
								end_dim += input.shape.rank
				if start_dim < 0:
								start_dim += input.shape.rank

				if start_dim == end_dim:
								return input

				__lowerCamelCase    					=	tf.shape(__lowerCAmelCase							)
				__lowerCamelCase    					=	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]							)
				__lowerCamelCase    					=	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,  axis=0							)
				return tf.reshape(__lowerCAmelCase		,  __lowerCAmelCase							)




def  __magic_name__ (   __lowerCAmelCase	:						tf.Tensor							)			->   tf.Tensor:
				if not isinstance(__lowerCAmelCase		,  tf.Tensor							):
								__lowerCamelCase    					=	tf.convert_to_tensor(__lowerCAmelCase							)  # Catches stray NumPy inputs
				if encoder_attention_mask.shape.rank == 3:
								__lowerCamelCase    					=	encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.shape.rank == 2:
								__lowerCamelCase    					=	encoder_attention_mask[:, None, None, :]
				# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
				# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
				# /transformer/transformer_layers.py#L270
				# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
				# encoder_extended_attention_mask.transpose(-1, -2))
				__lowerCamelCase    					=	(
				    tf.cast(1		,  encoder_attention_mask.dtype							) - encoder_extended_attention_mask
				) * encoder_extended_attention_mask.dtype.min

				return encoder_extended_attention_mask




def  __magic_name__ (   __lowerCAmelCase	:						tf.Tensor		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						str = "input_ids"							)			->   None:
				tf.debugging.assert_less(
				    __lowerCAmelCase		,  tf.cast(__lowerCAmelCase		,  dtype=tensor.dtype							)		,  message=(
				        f'''The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCAmelCase							)}) must be smaller than the embedding '''
				        f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
				    )		,  )




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						Dict		,  __lowerCAmelCase	:						Optional[int]							)			->   Any:
				__lowerCamelCase    					=	6_4512
				# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
				# because in that case even chunking the array would not make the saving
				# possible.
				__lowerCamelCase    					=	[x for x in data if len(__lowerCAmelCase							) > HDF5_OBJECT_HEADER_LIMIT]

				# Expecting this to never be true.
				if bad_attributes:
								raise RuntimeError(
								    '''The following attributes cannot be saved to HDF5 file because '''
								    f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
								    f'''bytes: {bad_attributes}'''							)

				__lowerCamelCase    					=	np.asarray(__lowerCAmelCase							)

				__lowerCamelCase    					=	1
				__lowerCamelCase    					=	np.array_split(__lowerCAmelCase		,  __lowerCAmelCase							)

				# This will never loop forever thanks to the test above.
				while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data							):
								num_chunks += 1
								__lowerCamelCase    					=	np.array_split(__lowerCAmelCase		,  __lowerCAmelCase							)

				if num_chunks > 1:
								for chunk_id, chunk_data in enumerate(__lowerCAmelCase							):
												__lowerCamelCase    					=	chunk_data
				else:
								__lowerCamelCase    					=	data




def  __magic_name__ (   __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Union[str, Any]							)			->   str:
				if name in group.attrs:
								__lowerCamelCase    					=	[n.decode('''utf8'''							) if hasattr(__lowerCAmelCase		,  '''decode'''							) else n for n in group.attrs[name]]
				else:
								__lowerCamelCase    					=	[]
								__lowerCamelCase    					=	0
								while "%s%d" % (name, chunk_id) in group.attrs:
												data.extend(
												    [n.decode('''utf8'''							) if hasattr(__lowerCAmelCase		,  '''decode'''							) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]							)
												chunk_id += 1
				return data







def  __magic_name__ (   __lowerCAmelCase	:						str							)			->   List[str]:

				def _expand_single_ad_tensor(__lowerCAmelCase	:						Dict							):
								if isinstance(__lowerCAmelCase		,  tf.Tensor							) and t.shape.rank == 1:
												return tf.expand_dims(__lowerCAmelCase		,  axis=-1							)
								return t

				return tf.nest.map_structure(_expand_single_ad_tensor		,  __lowerCAmelCase							)





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

import datasets


SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    "\\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"

SCREAMING_SNAKE_CASE__   :			int												=    "\\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"

SCREAMING_SNAKE_CASE__   :			Optional[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    lowerCAmelCase__     (	datasets.Metric			):






		def  __A       (		self		:							Dict  )     ->							Tuple:
						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  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.CHAR_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.WORD_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.BETA							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			)     ->							Optional[Any]:
						__lowerCamelCase    					=	len(references[0]  )
						if any(len(SCREAMING_SNAKE_CASE__  ) != references_per_prediction for refs in references  ):
										raise ValueError('''Sacrebleu requires the same number of references for each prediction'''  )
						__lowerCamelCase    					=	[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__  )]

						__lowerCamelCase    					=	CHRF(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	sb_chrf.corpus_score(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

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



