

"""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 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					List[Any]   		=	PegasusConfig
 __UpperCAmelCase	:					Optional[int]   		=	{}
 __UpperCAmelCase	:					Any   		=	"gelu"


 def __init__(			self							:			str,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Any=1_3,					UpperCAmelCase__							:			Optional[int]=7,					UpperCAmelCase__							:			Tuple=True,					UpperCAmelCase__							:			Any=False,					UpperCAmelCase__							:			Dict=9_9,					UpperCAmelCase__							:			List[str]=3_2,					UpperCAmelCase__							:			Tuple=2,					UpperCAmelCase__							:			Any=4,					UpperCAmelCase__							:			Union[str, Any]=3_7,					UpperCAmelCase__							:			str=0.1,					UpperCAmelCase__							:			Optional[Any]=0.1,					UpperCAmelCase__							:			List[str]=4_0,					UpperCAmelCase__							:			Union[str, Any]=2,					UpperCAmelCase__							:			List[str]=1,					UpperCAmelCase__							:			Tuple=0,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size

    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						eos_token_id
    __lowercase								=						pad_token_id
    __lowercase								=						bos_token_id


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

    __lowercase								=						ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)

    __lowercase								=						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,					)
    __lowercase								=						prepare_pegasus_inputs_dict(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)
    return config, inputs_dict



 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						TFPegasusModel(config=UpperCAmelCase__	).get_decoder()
    __lowercase								=						inputs_dict["input_ids"]

    __lowercase								=						input_ids[:1, :]
    __lowercase								=						inputs_dict["attention_mask"][:1, :]
    __lowercase								=						inputs_dict["head_mask"]
    __lowercase								=						1

    # first forward pass
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					head_mask=UpperCAmelCase__,					use_cache=UpperCAmelCase__	)

    __lowercase		,__lowercase								=						outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    __lowercase								=						ids_tensor((self.batch_size, 3),					config.vocab_size	)
    __lowercase								=						tf.cast(ids_tensor((self.batch_size, 3),					2	),					tf.inta	)

    # append to next input_ids and
    __lowercase								=						tf.concat([input_ids, next_tokens],					axis=-1	)
    __lowercase								=						tf.concat([attention_mask, next_attn_mask],					axis=-1	)

    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__	)[0]
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					past_key_values=UpperCAmelCase__	)[0]

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

    # select random slice
    __lowercase								=						int(ids_tensor((1,),					output_from_past.shape[-1]	)	)
    __lowercase								=						output_from_no_past[:, -3:, random_slice_idx]
    __lowercase								=						output_from_past[:, :, random_slice_idx]

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







def   _A  (  UpperCamelCase_     :       Any,			UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Any=None,			UpperCamelCase_     :       Union[str, Any]=None,			UpperCamelCase_     :       List[str]=None,			UpperCamelCase_     :       List[Any]=None,			UpperCamelCase_     :       Dict=None,			)	->						Tuple:
   '''simple docstring'''


   if attention_mask is None:
      __lowercase								=						tf.cast(tf.math.not_equal(UpperCamelCase_,			config.pad_token_id),			tf.inta)
   if decoder_attention_mask is None:
      __lowercase								=						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:
      __lowercase								=						tf.ones((config.encoder_layers, config.encoder_attention_heads))
   if decoder_head_mask is None:
      __lowercase								=						tf.ones((config.decoder_layers, config.decoder_attention_heads))
   if cross_attn_head_mask is None:
      __lowercase								=						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 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Dict   		=	(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
 __UpperCAmelCase	:					str   		=	(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
 __UpperCAmelCase	:					Optional[int]   		=	(
     {
         "conversational": TFPegasusForConditionalGeneration,
         "feature-extraction": TFPegasusModel,
         "summarization": TFPegasusForConditionalGeneration,
         "text2text-generation": TFPegasusForConditionalGeneration,
         "translation": TFPegasusForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 __UpperCAmelCase	:					List[Any]   		=	True
 __UpperCAmelCase	:					Union[str, Any]   		=	False
 __UpperCAmelCase	:					Dict   		=	False


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						TFPegasusModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any]	):
    self.config_tester.run_common_tests()



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__	)







@require_sentencepiece
@require_tokenizers
@require_tf
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	[
     " 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!\" ",
 ]
 __UpperCAmelCase	:					List[str]   		=	[
     "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
 __UpperCAmelCase	:					Dict   		=	"google/pegasus-xsum"


 @cached_property
 def 					_lowercase (			self							:			int	):
    return AutoTokenizer.from_pretrained(self.model_name	)


 @cached_property
 def 					_lowercase (			self							:			int	):
    __lowercase								=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
    return model


 def 					_lowercase (			self							:			str,					**UpperCAmelCase__							:			Tuple	):
    __lowercase								=						self.translate_src_text(**UpperCAmelCase__	)
    assert self.expected_text == generated_words


 def 					_lowercase (			self							:			Tuple,					**UpperCAmelCase__							:			Any	):
    __lowercase								=						self.tokenizer(self.src_text,					**UpperCAmelCase__,					padding=UpperCAmelCase__,					return_tensors="tf"	)
    __lowercase								=						self.model.generate(
        model_inputs.input_ids,					attention_mask=model_inputs.attention_mask,					num_beams=2,					use_cache=UpperCAmelCase__,					)
    __lowercase								=						self.tokenizer.batch_decode(generated_ids.numpy(),					skip_special_tokens=UpperCAmelCase__	)
    return generated_words



 @slow
 def 					_lowercase (			self							:			Dict	):
    self._assert_generated_batch_equal_expected()







"""simple docstring"""





from scipy.stats import spearmanr

import datasets


_a       =							'\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

_a       =							'\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

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




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION   )
class 					_lowerCAmelCase  (	datasets.Metric   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Dict	):
    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 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int]=False	):
    __lowercase								=						spearmanr(UpperCAmelCase__,					UpperCAmelCase__	)
    if return_pvalue:
       return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
    else:
       return {"spearmanr": results[0]}




