






"""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_   : Dict   			= """platform"""
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 							lowerCAmelCase__				:






      '''simple docstring'''

      __UpperCamelCase	 =  PegasusConfig
      __UpperCamelCase	 =  {}
      __UpperCamelCase	 =  "gelu"
      def __init__(						self     :  Tuple						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  List[Any]=13						,	lowercase_     :  str=7						,	lowercase_     :  int=True						,	lowercase_     :  Optional[int]=False						,	lowercase_     :  List[Any]=99						,	lowercase_     :  List[str]=32						,	lowercase_     :  Optional[Any]=5						,	lowercase_     :  Optional[Any]=4						,	lowercase_     :  Optional[int]=37						,	lowercase_     :  List[str]=0.1						,	lowercase_     :  Optional[Any]=0.1						,	lowercase_     :  str=20						,	lowercase_     :  int=2						,	lowercase_     :  str=1						,	lowercase_     :  Optional[Any]=0						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		parent
          SCREAMING_SNAKE_CASE_      :  str    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  int    						=		seq_length
          SCREAMING_SNAKE_CASE_      :  Dict    						=		is_training
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  str    						=		vocab_size
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  Dict    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		intermediate_size

          SCREAMING_SNAKE_CASE_      :  Any    						=		hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		eos_token_id
          SCREAMING_SNAKE_CASE_      :  Any    						=		pad_token_id
          SCREAMING_SNAKE_CASE_      :  Dict    						=		bos_token_id
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


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

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

          SCREAMING_SNAKE_CASE_      :  Optional[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						,	)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		prepare_pegasus_inputs_dict(lowercase_						,	lowercase_						,	lowercase_)
          return config, inputs_dict
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  List[str]						,	lowercase_     :  List[str]						,	lowercase_     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		20
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model_class_name(lowercase_)

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		model.encode(inputs_dict['''input_ids'''])

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(
              inputs_dict['''decoder_input_ids'''],
              inputs_dict['''decoder_attention_mask'''],
          )

          SCREAMING_SNAKE_CASE_      :  Dict    						=		model.init_cache(decoder_input_ids.shape[0]						,	lowercase_						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,	dtype='''i4''')

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

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

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model.decode(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		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     :  Optional[int]						,	lowercase_     :  List[Any]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		20
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model_class_name(lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model.encode(inputs_dict['''input_ids'''])

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(
              inputs_dict['''decoder_input_ids'''],
              inputs_dict['''decoder_attention_mask'''],
          )

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

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

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

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model.decode(lowercase_						,	lowercase_						,	decoder_attention_mask=lowercase_)

          SCREAMING_SNAKE_CASE_      :  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 					_A			(__a    ,				__a    ,				__a    ,				__a=None    ,				__a=None    ,				) ->       str:
    """simple docstring"""






    if attention_mask is None:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		np.not_equal(__a    ,				config.pad_token_id							).astype(np.inta							)
    if decoder_attention_mask is None:
        SCREAMING_SNAKE_CASE_      :  int    						=		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 							lowerCAmelCase__				(			UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (
          (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
          )
          if is_flax_available()
          else ()
      )
      __UpperCamelCase	 =  (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
      __UpperCamelCase	 =  True
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		FlaxPegasusModelTester(self)
          SCREAMING_SNAKE_CASE_      :  Any    						=		ConfigTester(self						,	config_class=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  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(lowercase_						,	lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		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     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                  SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_)
                  SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)

                  @jax.jit
                  def encode_jitted(lowercase_     :  str						,	lowercase_     :  Optional[int]=None						,	**lowercase_     :  Tuple):
                      return model.encode(input_ids=lowercase_						,	attention_mask=lowercase_)

                  with self.subTest('''JIT Enabled'''):
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		encode_jitted(**lowercase_).to_tuple()

                  with self.subTest('''JIT Disabled'''):
                      with jax.disable_jit():
                          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model_class(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  Any    						=		model.encode(inputs_dict['''input_ids''']						,	inputs_dict['''attention_mask'''])

                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{
                      '''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_     :  Optional[int]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Union[str, Any]):
                      return model.decode(
                          decoder_input_ids=lowercase_						,	decoder_attention_mask=lowercase_						,	encoder_outputs=lowercase_						,	)

                  with self.subTest('''JIT Enabled'''):
                      SCREAMING_SNAKE_CASE_      :  List[str]    						=		decode_jitted(**lowercase_).to_tuple()

                  with self.subTest('''JIT Disabled'''):
                      with jax.disable_jit():
                          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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     :  Optional[Any]):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model_class_name.from_pretrained('''google/pegasus-large'''						,	from_pt=lowercase_)
              SCREAMING_SNAKE_CASE_      :  str    						=		np.ones((1, 1))
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model(lowercase_)
              self.assertIsNotNone(lowercase_)



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')

          SCREAMING_SNAKE_CASE_      :  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!" ''',
          ]

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[
              '''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.''',
          ]

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









'''simple docstring'''

import math
import time

from transformers import Trainer, 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    UpperCAmelCase	(       UpperCamelCase__				):
 def __init__(				self      :Optional[Any]						,       *lowercase_      :int						,       lowercase_      :Any=None						,       lowercase_      :List[str]=None						,       **lowercase_      :Any							)->							Any:
     super().__init__(*lowercase_						,       **lowercase_							)
     A__				=			eval_examples
     A__				=			post_process_function
 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :str=None						,       lowercase_      :Optional[int]=None						,       lowercase_      :Optional[int]=None						,       lowercase_      :str = "eval"							)->							Union[str, Any]:
     A__				=			self.eval_dataset if eval_dataset is None else eval_dataset
     A__				=			self.get_eval_dataloader(lowercase_							)
     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__				=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     A__				=			time.time()
     try:
         A__				=			eval_loop(
             lowercase_						,       description="Evaluation"						,       prediction_loss_only=True if compute_metrics is None else None						,       ignore_keys=lowercase_						,       metric_key_prefix=lowercase_						,       )
     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(
             lowercase_						,       lowercase_						,       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(lowercase_						,       lowercase_						,       output.predictions							)
         A__				=			self.compute_metrics(lowercase_							)

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()							):
             if not key.startswith(F"{metric_key_prefix}_"							):
                 A__				=			metrics.pop(lowercase_							)
         metrics.update(output.metrics							)
     else:
         A__				=			output.metrics

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

     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						,       lowercase_							)
     return metrics



 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :List[Any]						,       lowercase_      :str						,       lowercase_      :Any=None						,       lowercase_      :str = "test"							)->							List[Any]:
     A__				=			self.get_test_dataloader(lowercase_							)

     # Temporarily disable metric computation, we will do it in the loop here.
     A__				=			self.compute_metrics
     A__				=			None
     A__				=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     A__				=			time.time()
     try:
         A__				=			eval_loop(
             lowercase_						,       description="Prediction"						,       prediction_loss_only=True if compute_metrics is None else None						,       ignore_keys=lowercase_						,       metric_key_prefix=lowercase_						,       )
     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(
             lowercase_						,       lowercase_						,       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(lowercase_						,       lowercase_						,       output.predictions						,       "predict"							)
     A__				=			self.compute_metrics(lowercase_							)

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



