
'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel







@require_tf
class    A_	:
							"""simple docstring"""




							a__   		=	PegasusConfig
							a__   		=	{}
							a__   		=	'''gelu'''

							def __init__(		self			:Tuple       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:List[str]=13       ,							lowerCAmelCase__			:List[Any]=7       ,							lowerCAmelCase__			:List[Any]=True       ,							lowerCAmelCase__			:Dict=False       ,							lowerCAmelCase__			:Optional[Any]=99       ,							lowerCAmelCase__			:List[Any]=32       ,							lowerCAmelCase__			:int=2       ,							lowerCAmelCase__			:Optional[Any]=4       ,							lowerCAmelCase__			:str=37       ,							lowerCAmelCase__			:Tuple=0.1       ,							lowerCAmelCase__			:List[str]=0.1       ,							lowerCAmelCase__			:Optional[Any]=40       ,							lowerCAmelCase__			:Dict=2       ,							lowerCAmelCase__			:List[str]=1       ,							lowerCAmelCase__			:Union[str, Any]=0       ,							)				->  int:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       parent
										snake_case_				:					Any   =       batch_size
										snake_case_				:					Optional[int]   =       seq_length
										snake_case_				:					Tuple   =       is_training
										snake_case_				:					Tuple   =       use_labels
										snake_case_				:					Optional[int]   =       vocab_size
										snake_case_				:					str   =       hidden_size
										snake_case_				:					List[Any]   =       num_hidden_layers
										snake_case_				:					Dict   =       num_attention_heads
										snake_case_				:					int   =       intermediate_size

										snake_case_				:					int   =       hidden_dropout_prob
										snake_case_				:					int   =       attention_probs_dropout_prob
										snake_case_				:					str   =       max_position_embeddings
										snake_case_				:					List[Any]   =       eos_token_id
										snake_case_				:					str   =       pad_token_id
										snake_case_				:					List[Any]   =       bos_token_id

							def 							_A	(		self			:List[Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					Tuple   =       ids_tensor([self.batch_size, self.seq_length - 1]       ,							self.vocab_size				)
										snake_case_				:					str   =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)       ,							1				)
										snake_case_				:					Tuple   =       tf.concat([input_ids, eos_tensor]       ,							axis=1				)

										snake_case_				:					Optional[int]   =       ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size				)

										snake_case_				:					Dict   =       self.config_cls(
										    vocab_size=self.vocab_size       ,							d_model=self.hidden_size       ,							encoder_layers=self.num_hidden_layers       ,							decoder_layers=self.num_hidden_layers       ,							encoder_attention_heads=self.num_attention_heads       ,							decoder_attention_heads=self.num_attention_heads       ,							encoder_ffn_dim=self.intermediate_size       ,							decoder_ffn_dim=self.intermediate_size       ,							dropout=self.hidden_dropout_prob       ,							attention_dropout=self.attention_probs_dropout_prob       ,							max_position_embeddings=self.max_position_embeddings       ,							eos_token_ids=[2]       ,							bos_token_id=self.bos_token_id       ,							pad_token_id=self.pad_token_id       ,							decoder_start_token_id=self.pad_token_id       ,							**self.config_updates       ,							)
										snake_case_				:					Any   =       prepare_pegasus_inputs_dict(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)
										return config, inputs_dict






							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Dict				)				->  str:


										'''simple docstring'''




										snake_case_				:					Tuple   =       TFPegasusModel(config=lowerCAmelCase__				).get_decoder()
										snake_case_				:					Optional[int]   =       inputs_dict["input_ids"]

										snake_case_				:					Tuple   =       input_ids[:1, :]
										snake_case_				:					Dict   =       inputs_dict["attention_mask"][:1, :]
										snake_case_				:					List[Any]   =       inputs_dict["head_mask"]
										snake_case_				:					Optional[Any]   =       1

										# first forward pass
										snake_case_				:					Tuple   =       model(lowerCAmelCase__       ,							attention_mask=lowerCAmelCase__       ,							head_mask=lowerCAmelCase__       ,							use_cache=lowerCAmelCase__				)

										snake_case_				:					Optional[Any]   =       outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										snake_case_				:					List[Any]   =       ids_tensor((self.batch_size, 3)       ,							config.vocab_size				)
										snake_case_				:					Dict   =       tf.cast(ids_tensor((self.batch_size, 3)       ,							2				)       ,							tf.inta				)

										# append to next input_ids and
										snake_case_				:					Dict   =       tf.concat([input_ids, next_tokens]       ,							axis=-1				)
										snake_case_				:					List[str]   =       tf.concat([attention_mask, next_attn_mask]       ,							axis=-1				)

										snake_case_				:					str   =       model(lowerCAmelCase__       ,							attention_mask=lowerCAmelCase__				)[0]
										snake_case_				:					str   =       model(lowerCAmelCase__       ,							attention_mask=lowerCAmelCase__       ,							past_key_values=lowerCAmelCase__				)[0]

										self.parent.assertEqual(next_tokens.shape[1]       ,							output_from_past.shape[1]				)

										# select random slice
										snake_case_				:					Union[str, Any]   =       int(ids_tensor((1,)       ,							output_from_past.shape[-1]				)				)
										snake_case_				:					Optional[int]   =       output_from_no_past[:, -3:, random_slice_idx]
										snake_case_				:					Tuple   =       output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(lowerCAmelCase__       ,							lowerCAmelCase__       ,							rtol=1E-3				)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__=None     ,__magic_name__=None     ,__magic_name__=None     ,__magic_name__=None     ,__magic_name__=None     ,)->    Optional[Any]:



			"""simple docstring"""



			if attention_mask is None:
						snake_case_				:					Tuple   =       tf.cast(tf.math.not_equal(__magic_name__     ,config.pad_token_id						)     ,tf.inta						)
			if decoder_attention_mask is None:
						snake_case_				:					Union[str, Any]   =       tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape     ,dtype=tf.inta						),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,config.pad_token_id						)     ,tf.inta						),
						    ]     ,axis=-1     ,)
			if head_mask is None:
						snake_case_				:					Optional[int]   =       tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
			if decoder_head_mask is None:
						snake_case_				:					int   =       tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
			if cross_attn_head_mask is None:
						snake_case_				:					Any   =       tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}







@require_tf
class    A_	(a_						,					a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							a__   		=	(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							a__   		=	(
							    {
							        '''conversational''': TFPegasusForConditionalGeneration,
							        '''feature-extraction''': TFPegasusModel,
							        '''summarization''': TFPegasusForConditionalGeneration,
							        '''text2text-generation''': TFPegasusForConditionalGeneration,
							        '''translation''': TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							a__   		=	True
							a__   		=	False
							a__   		=	False

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


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       TFPegasusModelTester(self				)
										snake_case_				:					int   =       ConfigTester(self       ,							config_class=lowerCAmelCase__				)

							def 							_A	(		self			:Tuple				)				->  Union[str, Any]:


										'''simple docstring'''




										self.config_tester.run_common_tests()






							def 							_A	(		self			:str				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__				)







@require_sentencepiece
@require_tokenizers
@require_tf
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							a__   		=	[
							    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
							    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
							]
							a__   		=	[
							    '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
							    ''' reduce the risk of wildfires.''',
							    '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
							]  # differs slightly from pytorch, likely due to numerical differences in linear layers
							a__   		=	'''google/pegasus-xsum'''

							@cached_property
							def 							_A	(		self			:Optional[int]				)				->  Tuple:


										'''simple docstring'''




										return AutoTokenizer.from_pretrained(self.model_name				)

							@cached_property
							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					List[str]   =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
										return model

							def 							_A	(		self			:Union[str, Any]       ,							**lowerCAmelCase__			:str				)				->  int:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       self.translate_src_text(**lowerCAmelCase__				)
										assert self.expected_text == generated_words

							def 							_A	(		self			:str       ,							**lowerCAmelCase__			:Dict				)				->  str:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.tokenizer(self.src_text       ,							**lowerCAmelCase__       ,							padding=lowerCAmelCase__       ,							return_tensors="tf"				)
										snake_case_				:					List[Any]   =       self.model.generate(
										    model_inputs.input_ids       ,							attention_mask=model_inputs.attention_mask       ,							num_beams=2       ,							use_cache=lowerCAmelCase__       ,							)
										snake_case_				:					Union[str, Any]   =       self.tokenizer.batch_decode(generated_ids.numpy()       ,							skip_special_tokens=lowerCAmelCase__				)
										return generated_words






							@slow
							def 							_A	(		self			:Optional[int]				)				->  Any:


										'''simple docstring'''




										self._assert_generated_batch_equal_expected()












'''simple docstring'''




from scipy.stats import spearmanr

import datasets


__lowerCamelCase   :					str   							=					'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

__lowerCamelCase   :					int   							=					'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

__lowerCamelCase   :					List[str]   							=					R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''







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




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


										'''simple docstring'''




										return datasets.MetricInfo(
										    description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
										        {
										            "predictions": datasets.Value("float"				),
										            "references": datasets.Value("float"				),
										        }				)       ,							reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"]       ,							)






							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Optional[Any]=False				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       spearmanr(lowerCAmelCase__       ,							lowerCAmelCase__				)
										if return_pvalue:
													return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
										else:
													return {"spearmanr": results[0]}




