





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  UpperCAmelCase_					=					'platform'
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class        lowercase__			:

   '''simple docstring'''


   a		:				Any  						=						PegasusConfig
   a		:				int  						=						{}
   a		:				Dict  						=						"gelu"




   def __init__(       self,						__magic_name__,						__magic_name__=13,						__magic_name__=7,						__magic_name__=True,						__magic_name__=False,						__magic_name__=99,						__magic_name__=32,						__magic_name__=5,						__magic_name__=4,						__magic_name__=37,						__magic_name__=0.1,						__magic_name__=0.1,						__magic_name__=20,						__magic_name__=2,						__magic_name__=1,						__magic_name__=0,						)					-> str:






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

       UpperCamelCase__				:							Tuple			  = hidden_dropout_prob
       UpperCamelCase__				:							Optional[Any]			  = attention_probs_dropout_prob
       UpperCamelCase__				:							List[Any]			  = max_position_embeddings
       UpperCamelCase__				:							List[str]			  = eos_token_id
       UpperCamelCase__				:							Optional[int]			  = pad_token_id
       UpperCamelCase__				:							List[Any]			  = bos_token_id




   def  UpperCamelCase__    (       self				)					-> Optional[int]:






       """simple docstring"""
       UpperCamelCase__				:							str			  = ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size				).clip(3,						self.vocab_size				)
       UpperCamelCase__				:							Tuple			  = np.expand_dims(np.array([self.eos_token_id] * self.batch_size				),						1				)
       UpperCamelCase__				:							Any			  = np.concatenate([input_ids, eos_tensor],						axis=1				)

       UpperCamelCase__				:							List[str]			  = ids_tensor([self.batch_size, self.seq_length],						self.vocab_size				)

       UpperCamelCase__				:							Union[str, Any]			  = 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,						)
       UpperCamelCase__				:							Union[str, Any]			  = prepare_pegasus_inputs_dict(snake_case_,						snake_case_,						snake_case_				)
       return config, inputs_dict




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__,						__magic_name__				)					-> Dict:






       """simple docstring"""
       UpperCamelCase__				:							List[Any]			  = 20
       UpperCamelCase__				:							int			  = model_class_name(snake_case_				)

       UpperCamelCase__				:							List[Any]			  = model.encode(inputs_dict['''input_ids''']				)

       UpperCamelCase__  ,UpperCamelCase__				:							Tuple			  = (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCamelCase__				:							Dict			  = model.init_cache(decoder_input_ids.shape[0],						snake_case_,						snake_case_				)
       UpperCamelCase__				:							Union[str, Any]			  = jnp.ones((decoder_input_ids.shape[0], max_decoder_length),						dtype='''i4'''				)

       UpperCamelCase__				:							int			  = jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :],						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),						)
       UpperCamelCase__				:							Union[str, Any]			  = model.decode(
           decoder_input_ids[:, :-1],						snake_case_,						decoder_attention_mask=snake_case_,						past_key_values=snake_case_,						decoder_position_ids=snake_case_,						)

       UpperCamelCase__				:							Dict			  = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],						dtype='''i4'''				)
       UpperCamelCase__				:							Any			  = model.decode(
           decoder_input_ids[:, -1:],						snake_case_,						decoder_attention_mask=snake_case_,						past_key_values=outputs_cache.past_key_values,						decoder_position_ids=snake_case_,						)

       UpperCamelCase__				:							Any			  = model.decode(snake_case_,						snake_case_				)

       UpperCamelCase__				:							Union[str, Any]			  = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
       self.parent.assertTrue(diff < 1E-3,						msg=f"Max diff is {diff}"				)




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__,						__magic_name__				)					-> List[str]:






       """simple docstring"""
       UpperCamelCase__				:							Optional[int]			  = 20
       UpperCamelCase__				:							Optional[int]			  = model_class_name(snake_case_				)

       UpperCamelCase__				:							Optional[int]			  = model.encode(inputs_dict['''input_ids''']				)

       UpperCamelCase__  ,UpperCamelCase__				:							Optional[int]			  = (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCamelCase__				:							Optional[Any]			  = jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
           ],						axis=-1,						)

       UpperCamelCase__				:							List[str]			  = model.init_cache(decoder_input_ids.shape[0],						snake_case_,						snake_case_				)
       UpperCamelCase__				:							str			  = jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :],						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),						)

       UpperCamelCase__				:							str			  = model.decode(
           decoder_input_ids[:, :-1],						snake_case_,						decoder_attention_mask=snake_case_,						past_key_values=snake_case_,						decoder_position_ids=snake_case_,						)
       UpperCamelCase__				:							Optional[int]			  = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],						dtype='''i4'''				)
       UpperCamelCase__				:							Tuple			  = model.decode(
           decoder_input_ids[:, -1:],						snake_case_,						past_key_values=outputs_cache.past_key_values,						decoder_attention_mask=snake_case_,						decoder_position_ids=snake_case_,						)

       UpperCamelCase__				:							Dict			  = model.decode(snake_case_,						snake_case_,						decoder_attention_mask=snake_case_				)

       UpperCamelCase__				:							Optional[int]			  = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
       self.parent.assertTrue(diff < 1E-3,						msg=f"Max diff is {diff}"				)




def 						lowerCAmelCase_       (    __UpperCAmelCase:     Dict						,  __UpperCAmelCase:     str						,  __UpperCAmelCase:     Tuple						,  __UpperCAmelCase:     Dict=None						,  __UpperCAmelCase:     int=None						,  ) ->      str:
    if attention_mask is None:
        UpperCamelCase__				:							Any			  = np.not_equal(_lowerCAmelCase						,  config.pad_token_id ).astype(np.inta )
    if decoder_attention_mask is None:
        UpperCamelCase__				:							Dict			  = np.concatenate(
            [
                np.ones(decoder_input_ids[:, :1].shape						,  dtype=np.inta ),
                np.not_equal(decoder_input_ids[:, 1:]						,  config.pad_token_id ).astype(np.inta ),
            ]						,  axis=-1						,  )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }






@require_flax
class        lowercase__			(      A__     ,							unittest.TestCase							):

   '''simple docstring'''


   a		:				int  						=						(
       (
           FlaxPegasusForConditionalGeneration,
           FlaxPegasusModel,
       )
       if is_flax_available()
       else ()
   )
   a		:				List[str]  						=						(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
   a		:				Dict  						=						True
   a		:				str  						=						False
   a		:				List[str]  						=						False
   a		:				List[Any]  						=						False




   def  UpperCamelCase__    (       self				)					-> List[str]:






       """simple docstring"""
       UpperCamelCase__				:							Optional[Any]			  = FlaxPegasusModelTester(self				)
       UpperCamelCase__				:							Union[str, Any]			  = ConfigTester(self,						config_class=snake_case_				)




   def  UpperCamelCase__    (       self				)					-> Dict:






       """simple docstring"""
       self.config_tester.run_common_tests()




   def  UpperCamelCase__    (       self				)					-> List[Any]:






       """simple docstring"""
       UpperCamelCase__  ,UpperCamelCase__				:							Any			  = self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case_,						snake_case_,						snake_case_				)




   def  UpperCamelCase__    (       self				)					-> str:






       """simple docstring"""
       UpperCamelCase__  ,UpperCamelCase__				:							int			  = self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(snake_case_,						snake_case_,						snake_case_				)




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






       """simple docstring"""
       UpperCamelCase__  ,UpperCamelCase__				:							int			  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__				):
               UpperCamelCase__				:							str			  = self._prepare_for_class(snake_case_,						snake_case_				)
               UpperCamelCase__				:							Optional[Any]			  = model_class(snake_case_				)

               @jax.jit
               def encode_jitted(__magic_name__,						__magic_name__=None,						**__magic_name__				):
                   return model.encode(input_ids=snake_case_,						attention_mask=snake_case_				)

               with self.subTest('''JIT Enabled'''				):
                   UpperCamelCase__				:							Optional[Any]			  = encode_jitted(**snake_case_				).to_tuple()

               with self.subTest('''JIT Disabled'''				):
                   with jax.disable_jit():
                       UpperCamelCase__				:							List[str]			  = encode_jitted(**snake_case_				).to_tuple()

               self.assertEqual(len(snake_case_				),						len(snake_case_				)				)
               for jitted_output, output in zip(snake_case_,						snake_case_				):
                   self.assertEqual(jitted_output.shape,						output.shape				)




   def  UpperCamelCase__    (       self				)					-> Optional[int]:






       """simple docstring"""
       UpperCamelCase__  ,UpperCamelCase__				:							Any			  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__				):
               UpperCamelCase__				:							Dict			  = model_class(snake_case_				)
               UpperCamelCase__				:							List[str]			  = model.encode(inputs_dict['''input_ids'''],						inputs_dict['''attention_mask''']				)

               UpperCamelCase__				:							Dict			  = {
                   '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                   '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                   '''encoder_outputs''': encoder_outputs,
               }

               @jax.jit
               def decode_jitted(__magic_name__,						__magic_name__,						__magic_name__				):
                   return model.decode(
                       decoder_input_ids=snake_case_,						decoder_attention_mask=snake_case_,						encoder_outputs=snake_case_,						)

               with self.subTest('''JIT Enabled'''				):
                   UpperCamelCase__				:							Optional[Any]			  = decode_jitted(**snake_case_				).to_tuple()

               with self.subTest('''JIT Disabled'''				):
                   with jax.disable_jit():
                       UpperCamelCase__				:							Optional[Any]			  = decode_jitted(**snake_case_				).to_tuple()

               self.assertEqual(len(snake_case_				),						len(snake_case_				)				)
               for jitted_output, output in zip(snake_case_,						snake_case_				):
                   self.assertEqual(jitted_output.shape,						output.shape				)




   @slow
   def  UpperCamelCase__    (       self				)					-> Tuple:






       """simple docstring"""
       for model_class_name in self.all_model_classes:
           UpperCamelCase__				:							Dict			  = model_class_name.from_pretrained('''google/pegasus-large''',						from_pt=snake_case_				)
           UpperCamelCase__				:							List[str]			  = np.ones((1, 1)				)
           UpperCamelCase__				:							Optional[int]			  = model(snake_case_				)
           self.assertIsNotNone(snake_case_				)




   @slow
   def  UpperCamelCase__    (       self				)					-> Any:






       """simple docstring"""
       UpperCamelCase__				:							Dict			  = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''				)
       UpperCamelCase__				:							str			  = PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''				)

       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__				:							Dict			  = [
           '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
           '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
       ]

       UpperCamelCase__				:							Optional[int]			  = tokenizer(snake_case_,						return_tensors='''np''',						truncation=snake_case_,						max_length=512,						padding=snake_case_				)
       UpperCamelCase__				:							List[str]			  = model.generate(**snake_case_,						num_beams=2				).sequences
       UpperCamelCase__				:							List[Any]			  = tokenizer.batch_decode(snake_case_,						skip_special_tokens=snake_case_				)
       assert tgt_text == decoded



"""simple docstring"""
from scipy.stats import pearsonr

import datasets


SCREAMING_SNAKE_CASE_					     =	'''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


SCREAMING_SNAKE_CASE_					     =	'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


SCREAMING_SNAKE_CASE_					     =	'''
@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, Ilhan 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, Antonio 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       lowerCAmelCase_		(				datasets.Metric ):



							'''simple docstring'''





							def    A__						( self				)       ->  Union[str, Any]:
													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.pearsonr.html"""]    ,				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_=False				)       ->  Dict:
													if return_pvalue:
																			__lowerCAmelCase     =  pearsonr(snake_case_    ,				snake_case_				)
																			return {"pearsonr": results[0], "p-value": results[1]}
													else:
																			return {"pearsonr": float(pearsonr(snake_case_    ,				snake_case_				)[0]				)}
