
'''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
 UpperCAmelCase_       :   Optional[int]   =       'platform'
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		PegasusConfig
    lowerCAmelCase_				    =		{}
    lowerCAmelCase_				    =		'gelu'



    def __init__(	self      :       List[Any],__A      :       Optional[Any],__A      :       Tuple=1_3,__A      :       Any=7,__A      :       List[str]=True,__A      :       str=False,__A      :       int=9_9,__A      :       Any=3_2,__A      :       List[str]=5,__A      :       int=4,__A      :       int=3_7,__A      :       Dict=0.1,__A      :       str=0.1,__A      :       Union[str, Any]=2_0,__A      :       int=2,__A      :       str=1,__A      :       List[Any]=0,):
          _lowerCamelCase				:   str								=   parent
          _lowerCamelCase				:   Any								=   batch_size
          _lowerCamelCase				:   Tuple								=   seq_length
          _lowerCamelCase				:   Any								=   is_training
          _lowerCamelCase				:   int								=   use_labels
          _lowerCamelCase				:   Union[str, Any]								=   vocab_size
          _lowerCamelCase				:   int								=   hidden_size
          _lowerCamelCase				:   Dict								=   num_hidden_layers
          _lowerCamelCase				:   Optional[int]								=   num_attention_heads
          _lowerCamelCase				:   List[str]								=   intermediate_size

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



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   List[str]								=   ids_tensor([self.batch_size, self.seq_length - 1],self.vocab_size     ).clip(3,self.vocab_size     )
          _lowerCamelCase				:   Optional[int]								=   np.expand_dims(np.array([self.eos_token_id] * self.batch_size     ),1     )
          _lowerCamelCase				:   Dict								=   np.concatenate([input_ids, eos_tensor],axis=1     )

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

          _lowerCamelCase				:   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,)
          _lowerCamelCase				:   int								=   prepare_pegasus_inputs_dict(__A,__A,__A     )
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       List[str],__A      :       List[Any],__A      :       str     ):
          _lowerCamelCase				:   Union[str, Any]								=   2_0
          _lowerCamelCase				:   Tuple								=   model_class_name(__A     )

          _lowerCamelCase				:   Optional[int]								=   model.encode(inputs_dict["input_ids"]     )

          _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   (
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          _lowerCamelCase				:   Union[str, Any]								=   model.init_cache(decoder_input_ids.shape[0],__A,__A     )
          _lowerCamelCase				:   int								=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length),dtype="i4"     )

          _lowerCamelCase				:   Optional[Any]								=   jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)
          _lowerCamelCase				:   int								=   model.decode(
              decoder_input_ids[:, :-1],__A,decoder_attention_mask=__A,past_key_values=__A,decoder_position_ids=__A,)

          _lowerCamelCase				:   Tuple								=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4"     )
          _lowerCamelCase				:   List[str]								=   model.decode(
              decoder_input_ids[:, -1:],__A,decoder_attention_mask=__A,past_key_values=outputs_cache.past_key_values,decoder_position_ids=__A,)

          _lowerCamelCase				:   str								=   model.decode(__A,__A     )

          _lowerCamelCase				:   List[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   lowerCamelCase_  (	self      :       int,__A      :       str,__A      :       Dict,__A      :       Optional[Any]     ):
          _lowerCamelCase				:   List[str]								=   2_0
          _lowerCamelCase				:   Tuple								=   model_class_name(__A     )

          _lowerCamelCase				:   Any								=   model.encode(inputs_dict["input_ids"]     )

          _lowerCamelCase						,		_lowerCamelCase				:   str								=   (
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

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

          _lowerCamelCase				:   str								=   model.init_cache(decoder_input_ids.shape[0],__A,__A     )
          _lowerCamelCase				:   int								=   jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)

          _lowerCamelCase				:   List[str]								=   model.decode(
              decoder_input_ids[:, :-1],__A,decoder_attention_mask=__A,past_key_values=__A,decoder_position_ids=__A,)
          _lowerCamelCase				:   Dict								=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4"     )
          _lowerCamelCase				:   str								=   model.decode(
              decoder_input_ids[:, -1:],__A,past_key_values=outputs_cache.past_key_values,decoder_attention_mask=__A,decoder_position_ids=__A,)

          _lowerCamelCase				:   List[Any]								=   model.decode(__A,__A,decoder_attention_mask=__A     )

          _lowerCamelCase				:   str								=   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_       (	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Optional[int]=None				,	_lowerCAmelCase   :    Tuple=None				,	):



      """simple docstring"""




      if attention_mask is None:
            _lowerCamelCase				:   Dict								=   np.not_equal(_lowerCAmelCase				,	config.pad_token_id							).astype(np.inta							)
      if decoder_attention_mask is None:
            _lowerCamelCase				:   Union[str, Any]								=   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  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    lowerCAmelCase_				    =		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    lowerCAmelCase_				    =		True
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   int								=   FlaxPegasusModelTester(self     )
          _lowerCamelCase				:   Optional[Any]								=   ConfigTester(self,config_class=__A     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          self.config_tester.run_common_tests()



    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Dict								=   self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward(__A,__A,__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase						,		_lowerCamelCase				:   List[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_with_attn_mask(__A,__A,__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__     ):
                      _lowerCamelCase				:   str								=   self._prepare_for_class(__A,__A     )
                      _lowerCamelCase				:   List[str]								=   model_class(__A     )

                      @jax.jit
                      def encode_jitted(__A      :       Union[str, Any],__A      :       int=None,**__A      :       List[Any]     ):
                            return model.encode(input_ids=__A,attention_mask=__A     )

                      with self.subTest("JIT Enabled"     ):
                            _lowerCamelCase				:   Dict								=   encode_jitted(**__A     ).to_tuple()

                      with self.subTest("JIT Disabled"     ):
                            with jax.disable_jit():
                                  _lowerCamelCase				:   Any								=   encode_jitted(**__A     ).to_tuple()

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



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   str								=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__     ):
                      _lowerCamelCase				:   List[Any]								=   model_class(__A     )
                      _lowerCamelCase				:   int								=   model.encode(inputs_dict["input_ids"],inputs_dict["attention_mask"]     )

                      _lowerCamelCase				:   Optional[Any]								=   {
                          "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(__A      :       Tuple,__A      :       List[str],__A      :       Tuple     ):
                            return model.decode(
                                decoder_input_ids=__A,decoder_attention_mask=__A,encoder_outputs=__A,)

                      with self.subTest("JIT Enabled"     ):
                            _lowerCamelCase				:   Optional[int]								=   decode_jitted(**__A     ).to_tuple()

                      with self.subTest("JIT Disabled"     ):
                            with jax.disable_jit():
                                  _lowerCamelCase				:   Optional[Any]								=   decode_jitted(**__A     ).to_tuple()

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



    @slow
    def   lowerCamelCase_  (	self      :       List[str]     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   Optional[int]								=   model_class_name.from_pretrained("google/pegasus-large",from_pt=__A     )
                _lowerCamelCase				:   Any								=   np.ones((1, 1)     )
                _lowerCamelCase				:   List[str]								=   model(__A     )
                self.assertIsNotNone(__A     )



    @slow
    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   List[str]								=   FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"     )
          _lowerCamelCase				:   Dict								=   PegasusTokenizer.from_pretrained("google/pegasus-xsum"     )

          _lowerCamelCase				:   List[str]								=   [
              " 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!\" ",
          ]

          _lowerCamelCase				:   Optional[int]								=   [
              "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.",
          ]

          _lowerCamelCase				:   List[Any]								=   tokenizer(__A,return_tensors="np",truncation=__A,max_length=5_1_2,padding=__A     )
          _lowerCamelCase				:   Tuple								=   model.generate(**__A,num_beams=2     ).sequences
          _lowerCamelCase				:   Dict								=   tokenizer.batch_decode(__A,skip_special_tokens=__A     )
          assert tgt_text == decoded

'''simple docstring'''






import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Union[str, Any],__A      :       Optional[int]=1_3,__A      :       Optional[Any]=7,__A      :       int=True,__A      :       Any=True,__A      :       Dict=True,__A      :       List[Any]=True,__A      :       Optional[Any]=9_9,__A      :       List[str]=3_2,__A      :       int=5,__A      :       str=4,__A      :       int=3_7,__A      :       Optional[Any]="gelu",__A      :       Union[str, Any]=0.1,__A      :       str=0.1,__A      :       Any=5_1_2,__A      :       Any=1_6,__A      :       Optional[int]=2,__A      :       int=0.02,__A      :       Optional[int]=4,):
          _lowerCamelCase				:   Union[str, Any]								=   parent
          _lowerCamelCase				:   Tuple								=   batch_size
          _lowerCamelCase				:   List[str]								=   seq_length
          _lowerCamelCase				:   Optional[int]								=   is_training
          _lowerCamelCase				:   str								=   use_attention_mask
          _lowerCamelCase				:   str								=   use_token_type_ids
          _lowerCamelCase				:   List[str]								=   use_labels
          _lowerCamelCase				:   Tuple								=   vocab_size
          _lowerCamelCase				:   Dict								=   hidden_size
          _lowerCamelCase				:   str								=   num_hidden_layers
          _lowerCamelCase				:   List[str]								=   num_attention_heads
          _lowerCamelCase				:   Optional[Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[int]								=   hidden_act
          _lowerCamelCase				:   Any								=   hidden_dropout_prob
          _lowerCamelCase				:   List[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   Tuple								=   max_position_embeddings
          _lowerCamelCase				:   str								=   type_vocab_size
          _lowerCamelCase				:   str								=   type_sequence_label_size
          _lowerCamelCase				:   int								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[Any]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Union[str, Any]								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Tuple								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   Any								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   int								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   int								=   AlbertConfig(
              vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   str								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   config_and_inputs
          _lowerCamelCase				:   Any								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   FlaxAlbertModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   str								=   model_class_name.from_pretrained("albert-base-v2"     )
                _lowerCamelCase				:   Any								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )


@require_flax
class  UpperCAmelCase__						(       unittest.TestCase ):



    @slow
    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Optional[int]								=   FlaxAlbertModel.from_pretrained("albert-base-v2"     )
          _lowerCamelCase				:   int								=   np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
          _lowerCamelCase				:   Union[str, Any]								=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
          _lowerCamelCase				:   Optional[Any]								=   model(__A,attention_mask=__A     )[0]
          _lowerCamelCase				:   Dict								=   (1, 1_1, 7_6_8)
          self.assertEqual(output.shape,__A     )
          _lowerCamelCase				:   Tuple								=   np.array(
              [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]     )

          self.assertTrue(jnp.allclose(output[:, 1:4, 1:4],__A,atol=1e-4     )     )