

'''simple docstring'''






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
 lowerCAmelCase__											=	'''platform'''
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			PegasusConfig
  SCREAMING_SNAKE_CASE    :  str							=			{}
  SCREAMING_SNAKE_CASE    :  Tuple							=			'gelu'






  def __init__(      self   :     Any					,lowercase__   :     Tuple					,lowercase__   :     Dict=1_3					,lowercase__   :     Optional[int]=7					,lowercase__   :     Tuple=True					,lowercase__   :     Optional[int]=False					,lowercase__   :     Union[str, Any]=9_9					,lowercase__   :     str=3_2					,lowercase__   :     Tuple=5					,lowercase__   :     List[str]=4					,lowercase__   :     Dict=3_7					,lowercase__   :     Optional[int]=0.1					,lowercase__   :     int=0.1					,lowercase__   :     Union[str, Any]=2_0					,lowercase__   :     Optional[Any]=2					,lowercase__   :     int=1					,lowercase__   :     Optional[Any]=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 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = ids_tensor([self.batch_size, self.seq_length - 1]					,self.vocab_size			).clip(3					,self.vocab_size			)
   __lowercase							  = np.expand_dims(np.array([self.eos_token_id] * self.batch_size			)					,1			)
   __lowercase							  = np.concatenate([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(lowercase__					,lowercase__					,lowercase__			)
   return config, inputs_dict






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     str					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]			):
   __lowercase							  = 2_0
   __lowercase							  = model_class_name(lowercase__			)

   __lowercase							  = model.encode(inputs_dict['''input_ids''']			)

   __lowercase  ,	__lowercase							  = (
       inputs_dict['''decoder_input_ids'''],
       inputs_dict['''decoder_attention_mask'''],
   )

   __lowercase							  = model.init_cache(decoder_input_ids.shape[0]					,lowercase__					,lowercase__			)
   __lowercase							  = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,dtype='''i4'''			)

   __lowercase							  = jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]					,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,)
   __lowercase							  = model.decode(
       decoder_input_ids[:, :-1]					,lowercase__					,decoder_attention_mask=lowercase__					,past_key_values=lowercase__					,decoder_position_ids=lowercase__					,)

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

   __lowercase							  = model.decode(lowercase__					,lowercase__			)

   __lowercase							  = 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 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     List[str]					,lowercase__   :     Union[str, Any]					,lowercase__   :     str			):
   __lowercase							  = 2_0
   __lowercase							  = model_class_name(lowercase__			)

   __lowercase							  = model.encode(inputs_dict['''input_ids''']			)

   __lowercase  ,	__lowercase							  = (
       inputs_dict['''decoder_input_ids'''],
       inputs_dict['''decoder_attention_mask'''],
   )

   __lowercase							  = jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
       ]					,axis=-1					,)

   __lowercase							  = model.init_cache(decoder_input_ids.shape[0]					,lowercase__					,lowercase__			)
   __lowercase							  = jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]					,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,)

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

   __lowercase							  = model.decode(lowercase__					,lowercase__					,decoder_attention_mask=lowercase__			)

   __lowercase							  = 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 		_A     (    A__     ,				A__     ,				A__     ,				A__=None     ,				A__=None     ,				):






 """simple docstring"""



 if attention_mask is None:
  __lowercase							  = np.not_equal(A__     ,				config.pad_token_id      ).astype(np.inta      )
 if decoder_attention_mask is None:
  __lowercase							  = 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_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			(
      (
          FlaxPegasusForConditionalGeneration,
          FlaxPegasusModel,
      )
      if is_flax_available()
      else ()
  )
  SCREAMING_SNAKE_CASE    :  Tuple							=			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE    :  str							=			True
  SCREAMING_SNAKE_CASE    :  Dict							=			False
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			False
  SCREAMING_SNAKE_CASE    :  Any							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = FlaxPegasusModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   self.config_tester.run_common_tests()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
    self.model_tester.check_use_cache_forward(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase  ,	__lowercase							  = 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(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__			):
     __lowercase							  = self._prepare_for_class(lowercase__					,lowercase__			)
     __lowercase							  = model_class(lowercase__			)

     @jax.jit
     def encode_jitted(lowercase__   :     List[Any]					,lowercase__   :     Dict=None					,**lowercase__   :     Optional[int]			):
      return model.encode(input_ids=lowercase__					,attention_mask=lowercase__			)

     with self.subTest('''JIT Enabled'''			):
      __lowercase							  = encode_jitted(**lowercase__			).to_tuple()

     with self.subTest('''JIT Disabled'''			):
      with jax.disable_jit():
       __lowercase							  = encode_jitted(**lowercase__			).to_tuple()

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__			):
     __lowercase							  = model_class(lowercase__			)
     __lowercase							  = model.encode(inputs_dict['''input_ids''']					,inputs_dict['''attention_mask''']			)

     __lowercase							  = {
         '''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(lowercase__   :     List[Any]					,lowercase__   :     Tuple					,lowercase__   :     str			):
      return model.decode(
          decoder_input_ids=lowercase__					,decoder_attention_mask=lowercase__					,encoder_outputs=lowercase__					,)

     with self.subTest('''JIT Enabled'''			):
      __lowercase							  = decode_jitted(**lowercase__			).to_tuple()

     with self.subTest('''JIT Disabled'''			):
      with jax.disable_jit():
       __lowercase							  = decode_jitted(**lowercase__			).to_tuple()

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






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   for model_class_name in self.all_model_classes:
    __lowercase							  = model_class_name.from_pretrained('''google/pegasus-large'''					,from_pt=lowercase__			)
    __lowercase							  = np.ones((1, 1)			)
    __lowercase							  = model(lowercase__			)
    self.assertIsNotNone(lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''			)
   __lowercase							  = PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''			)

   __lowercase							  = [
       ''' 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!" ''',
   ]

   __lowercase							  = [
       '''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.''',
   ]

   __lowercase							  = tokenizer(lowercase__					,return_tensors='''np'''					,truncation=lowercase__					,max_length=5_1_2					,padding=lowercase__			)
   __lowercase							  = model.generate(**lowercase__					,num_beams=2			).sequences
   __lowercase							  = tokenizer.batch_decode(lowercase__					,skip_special_tokens=lowercase__			)
   assert tgt_text == decoded




'''simple docstring'''






from __future__ import annotations

from decimal import Decimal

from numpy import array


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = Decimal

 # Check if the provided matrix has 2 rows and 2 columns
 # since this implementation only works for 2x2 matrices
 if len(A__      ) == 2 and len(matrix[0]      ) == 2 and len(matrix[1]      ) == 2:
  # Calculate the determinant of the matrix
  __lowercase							  = float(
      d(matrix[0][0]      ) * d(matrix[1][1]      ) - d(matrix[1][0]      ) * d(matrix[0][1]      )      )
  if determinant == 0:
   raise ValueError('''This matrix has no inverse.'''      )

  # Creates a copy of the matrix with swapped positions of the elements
  __lowercase							  = [[0.0, 0.0], [0.0, 0.0]]
  __lowercase  ,	__lowercase							  = matrix[1][1], matrix[0][0]
  __lowercase  ,	__lowercase							  = -matrix[1][0], -matrix[0][1]

  # Calculate the inverse of the matrix
  return [
      [(float(d(A__      )      ) / determinant) or 0.0 for n in row] for row in swapped_matrix
  ]
 elif (
     len(A__      ) == 3
     and len(matrix[0]      ) == 3
     and len(matrix[1]      ) == 3
     and len(matrix[2]      ) == 3
 ):
  # Calculate the determinant of the matrix using Sarrus rule
  __lowercase							  = float(
      (
          (d(matrix[0][0]      ) * d(matrix[1][1]      ) * d(matrix[2][2]      ))
          + (d(matrix[0][1]      ) * d(matrix[1][2]      ) * d(matrix[2][0]      ))
          + (d(matrix[0][2]      ) * d(matrix[1][0]      ) * d(matrix[2][1]      ))
      )
      - (
          (d(matrix[0][2]      ) * d(matrix[1][1]      ) * d(matrix[2][0]      ))
          + (d(matrix[0][1]      ) * d(matrix[1][0]      ) * d(matrix[2][2]      ))
          + (d(matrix[0][0]      ) * d(matrix[1][2]      ) * d(matrix[2][1]      ))
      )      )
  if determinant == 0:
   raise ValueError('''This matrix has no inverse.'''      )

  # Creating cofactor matrix
  __lowercase							  = [
      [d(0.0      ), d(0.0      ), d(0.0      )],
      [d(0.0      ), d(0.0      ), d(0.0      )],
      [d(0.0      ), d(0.0      ), d(0.0      )],
  ]
  __lowercase							  = (d(matrix[1][1]      ) * d(matrix[2][2]      )) - (
      d(matrix[1][2]      ) * d(matrix[2][1]      )
  )
  __lowercase							  = -(
      (d(matrix[1][0]      ) * d(matrix[2][2]      )) - (d(matrix[1][2]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[1][0]      ) * d(matrix[2][1]      )) - (
      d(matrix[1][1]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][1]      ) * d(matrix[2][2]      )) - (d(matrix[0][2]      ) * d(matrix[2][1]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[2][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[2][1]      )) - (d(matrix[0][1]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[0][1]      ) * d(matrix[1][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[1][1]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[1][2]      )) - (d(matrix[0][2]      ) * d(matrix[1][0]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[1][1]      )) - (
      d(matrix[0][1]      ) * d(matrix[1][0]      )
  )

  # Transpose the cofactor matrix (Adjoint matrix)
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    __lowercase							  = cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    inverse_matrix[i][j] /= d(A__      )

        # Calculate the inverse of the matrix
  return [[float(d(A__      )      ) or 0.0 for n in row] for row in inverse_matrix]
 raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''      )

