



'''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	:       str      	=	"platform"
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  PegasusConfig
    _SCREAMING_SNAKE_CASE	  =  {}
    _SCREAMING_SNAKE_CASE	  =  """gelu"""






    def __init__(  self      :					Tuple  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any]=1_3  ,						UpperCamelCase__      :					List[Any]=7  ,						UpperCamelCase__      :					Optional[Any]=True  ,						UpperCamelCase__      :					Any=False  ,						UpperCamelCase__      :					str=9_9  ,						UpperCamelCase__      :					Union[str, Any]=3_2  ,						UpperCamelCase__      :					List[str]=5  ,						UpperCamelCase__      :					Optional[int]=4  ,						UpperCamelCase__      :					List[Any]=3_7  ,						UpperCamelCase__      :					Optional[int]=0.1  ,						UpperCamelCase__      :					Any=0.1  ,						UpperCamelCase__      :					Union[str, Any]=2_0  ,						UpperCamelCase__      :					Dict=2  ,						UpperCamelCase__      :					Union[str, Any]=1  ,						UpperCamelCase__      :					int=0  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  seq_length
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  hidden_size
       UpperCamelCase					=  num_hidden_layers
       UpperCamelCase					=  num_attention_heads
       UpperCamelCase					=  intermediate_size

       UpperCamelCase					=  hidden_dropout_prob
       UpperCamelCase					=  attention_probs_dropout_prob
       UpperCamelCase					=  max_position_embeddings
       UpperCamelCase					=  eos_token_id
       UpperCamelCase					=  pad_token_id
       UpperCamelCase					=  bos_token_id






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length - 1]  ,						self.vocab_size ).clip(3  ,						self.vocab_size )
       UpperCamelCase					=  np.expand_dims(np.array([self.eos_token_id] * self.batch_size )  ,						1 )
       UpperCamelCase					=  np.concatenate([input_ids, eos_tensor]  ,						axis=1 )

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

       UpperCamelCase					=  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					=  prepare_pegasus_inputs_dict(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       return config, inputs_dict






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  2_0
       UpperCamelCase					=  model_class_name(UpperCamelCase__ )

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

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

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

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

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

       UpperCamelCase					=  model.decode(UpperCamelCase__  ,						UpperCamelCase__ )

       UpperCamelCase					=  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       (  self      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  2_0
       UpperCamelCase					=  model_class_name(UpperCamelCase__ )

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

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

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

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

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

       UpperCamelCase					=  model.decode(UpperCamelCase__  ,						UpperCamelCase__  ,						decoder_attention_mask=UpperCamelCase__ )

       UpperCamelCase					=  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				(   A__    ,						A__    ,						A__    ,						A__=None    ,						A__=None    ,						)       ->	Optional[int]:






   """simple docstring"""
   if attention_mask is None:
      UpperCamelCase					=  np.not_equal(A__    ,						config.pad_token_id    ).astype(np.inta    )
   if decoder_attention_mask is None:
      UpperCamelCase					=  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  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    _SCREAMING_SNAKE_CASE	  =  True
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  FlaxPegasusModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__ )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       self.config_tester.run_common_tests()






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  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(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             UpperCamelCase					=  self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ )
             UpperCamelCase					=  model_class(UpperCamelCase__ )

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

             with self.subTest('JIT Enabled' ):
                UpperCamelCase					=  encode_jitted(**UpperCamelCase__ ).to_tuple()

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

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






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase					=  {
                 '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(UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Any ):
                return model.decode(
                    decoder_input_ids=UpperCamelCase__  ,						decoder_attention_mask=UpperCamelCase__  ,						encoder_outputs=UpperCamelCase__  ,						)

             with self.subTest('JIT Enabled' ):
                UpperCamelCase					=  decode_jitted(**UpperCamelCase__ ).to_tuple()

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

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






    @slow
    def  A       (  self      :					Any ):


       """simple docstring"""







       for model_class_name in self.all_model_classes:
          UpperCamelCase					=  model_class_name.from_pretrained('google/pegasus-large'  ,						from_pt=UpperCamelCase__ )
          UpperCamelCase					=  np.ones((1, 1) )
          UpperCamelCase					=  model(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )






    @slow
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum' )
       UpperCamelCase					=  PegasusTokenizer.from_pretrained('google/pegasus-xsum' )

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






import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm
 import torch_xla.debug.metrics as met

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     List[Any]     ,		*__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				eval_examples
           __a						       =				post_process_function



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[Dataset] = None     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[str]] = None     ,		__SCREAMING_SNAKE_CASE							:     str = "eval"     ,		**__SCREAMING_SNAKE_CASE							:     Any     ,		):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()
           __a						       =				(
               gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
           )
           __a						       =				(
               gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
           )
           __a						       =				gen_kwargs

           __a						       =				self.eval_dataset if eval_dataset is None else eval_dataset
           __a						       =				self.get_eval_dataloader(__SCREAMING_SNAKE_CASE)
           __a						       =				self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				time.time()
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Evaluation'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics
           __a						       =				self.args.eval_batch_size * self.args.world_size
           if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
               start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
           output.metrics.update(
               speed_metrics(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		num_samples=output.num_samples     ,		num_steps=math.ceil(output.num_samples / total_batch_size)     ,		))

           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
               # Only the main node write the results by default
               __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()):
                   if not key.startswith(F'{metric_key_prefix}_'):
                       __a						       =				metrics.pop(__SCREAMING_SNAKE_CASE)

               metrics.update(output.metrics)
           else:
               __a						       =				output.metrics

           if self.args.should_log:
               # Only the main node log the results by default
               self.log(__SCREAMING_SNAKE_CASE)

           if self.args.tpu_metrics_debug or self.args.debug:
               # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
               xm.master_print(met.metrics_report())

           __a						       =				self.callback_handler.on_evaluate(self.args     ,		self.state     ,		self.control     ,		__SCREAMING_SNAKE_CASE)
           return metrics





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     str = "test"     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()

           __a						       =				self.get_test_dataloader(__SCREAMING_SNAKE_CASE)

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				time.time()
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Prediction'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics

           __a						       =				self.args.eval_batch_size * self.args.world_size
           if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
               start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
           output.metrics.update(
               speed_metrics(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		num_samples=output.num_samples     ,		num_steps=math.ceil(output.num_samples / total_batch_size)     ,		))
           if self.post_process_function is None or self.compute_metrics is None:
               return output

           __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		'''predict''')
           __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()):
               if not key.startswith(F'{metric_key_prefix}_'):
                   __a						       =				metrics.pop(__SCREAMING_SNAKE_CASE)
           metrics.update(output.metrics)
           return PredictionOutput(predictions=predictions.predictions     ,		label_ids=predictions.label_ids     ,		metrics=__SCREAMING_SNAKE_CASE)





