"""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
     _a   :	List[str] 					=     'platform'
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class 		__A							:
     _UpperCamelCase							:    List[str]	      =	PegasusConfig
     _UpperCamelCase							:    Dict	      =	{}
     _UpperCamelCase							:    Optional[Any]	      =	"""gelu"""






     def __init__(	self       ,  a__       ,  a__=13       ,  a__=7       ,  a__=True       ,  a__=False       ,  a__=99       ,  a__=32       ,  a__=5       ,  a__=4       ,  a__=37       ,  a__=0.1       ,  a__=0.1       ,  a__=20       ,  a__=2       ,  a__=1       ,  a__=0       ,  ):
       _lowerCAmelCase		: Dict						    =  parent
       _lowerCAmelCase		: Optional[Any]						    =  batch_size
       _lowerCAmelCase		: Optional[int]						    =  seq_length
       _lowerCAmelCase		: List[Any]						    =  is_training
       _lowerCAmelCase		: int						    =  use_labels
       _lowerCAmelCase		: Optional[int]						    =  vocab_size
       _lowerCAmelCase		: Optional[int]						    =  hidden_size
       _lowerCAmelCase		: Any						    =  num_hidden_layers
       _lowerCAmelCase		: Optional[int]						    =  num_attention_heads
       _lowerCAmelCase		: Any						    =  intermediate_size

       _lowerCAmelCase		: Optional[int]						    =  hidden_dropout_prob
       _lowerCAmelCase		: Any						    =  attention_probs_dropout_prob
       _lowerCAmelCase		: List[str]						    =  max_position_embeddings
       _lowerCAmelCase		: Any						    =  eos_token_id
       _lowerCAmelCase		: str						    =  pad_token_id
       _lowerCAmelCase		: Union[str, Any]						    =  bos_token_id






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

       _lowerCAmelCase		: Tuple						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)

       _lowerCAmelCase		: List[str]						    =  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		: Tuple						    =  prepare_pegasus_inputs_dict(UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_						)
       return config, inputs_dict






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Any						    =  20
       _lowerCAmelCase		: int						    =  model_class_name(UpperCAmelCase_						)

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

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

       _lowerCAmelCase		: List[str]						    =  model.init_cache(decoder_input_ids.shape[0]       ,  UpperCAmelCase_       ,  UpperCAmelCase_						)
       _lowerCAmelCase		: Union[str, Any]						    =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,  dtype="""i4"""						)

       _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		: Any						    =  model.decode(
           decoder_input_ids[:, :-1]       ,  UpperCAmelCase_       ,  decoder_attention_mask=UpperCAmelCase_       ,  past_key_values=UpperCAmelCase_       ,  decoder_position_ids=UpperCAmelCase_       ,  )

       _lowerCAmelCase		: Optional[Any]						    =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,  dtype="""i4"""						)
       _lowerCAmelCase		: Tuple						    =  model.decode(
           decoder_input_ids[:, -1:]       ,  UpperCAmelCase_       ,  decoder_attention_mask=UpperCAmelCase_       ,  past_key_values=outputs_cache.past_key_values       ,  decoder_position_ids=UpperCAmelCase_       ,  )

       _lowerCAmelCase		: str						    =  model.decode(UpperCAmelCase_       ,  UpperCAmelCase_						)

       _lowerCAmelCase		: 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						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Union[str, Any]						    =  20
       _lowerCAmelCase		: List[str]						    =  model_class_name(UpperCAmelCase_						)

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

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

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

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

       _lowerCAmelCase		: Optional[int]						    =  model.decode(
           decoder_input_ids[:, :-1]       ,  UpperCAmelCase_       ,  decoder_attention_mask=UpperCAmelCase_       ,  past_key_values=UpperCAmelCase_       ,  decoder_position_ids=UpperCAmelCase_       ,  )
       _lowerCAmelCase		: str						    =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,  dtype="""i4"""						)
       _lowerCAmelCase		: int						    =  model.decode(
           decoder_input_ids[:, -1:]       ,  UpperCAmelCase_       ,  past_key_values=outputs_cache.past_key_values       ,  decoder_attention_mask=UpperCAmelCase_       ,  decoder_position_ids=UpperCAmelCase_       ,  )

       _lowerCAmelCase		: Any						    =  model.decode(UpperCAmelCase_       ,  UpperCAmelCase_       ,  decoder_attention_mask=UpperCAmelCase_						)

       _lowerCAmelCase		: 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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: int    ,_lowerCamelCase		: Union[str, Any]=None    ,_lowerCamelCase		: int=None    ,) ->   Dict:
  if attention_mask is None:
    _lowerCAmelCase		: List[Any]						    =  np.not_equal(_SCREAMING_SNAKE_CASE    ,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 		__A							(				snake_case__					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[int]	      =	(
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     _UpperCamelCase							:    Union[str, Any]	      =	(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     _UpperCamelCase							:    List[str]	      =	True
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    Optional[int]	      =	False
     _UpperCamelCase							:    List[str]	      =	False






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  FlaxPegasusModelTester(self						)
       _lowerCAmelCase		: List[str]						    =  ConfigTester(self       ,  config_class=UpperCAmelCase_						)






     def 					__A						(	self						):
       self.config_tester.run_common_tests()






     def 					__A						(	self						):
       _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(UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: 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(UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: 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__						):
           _lowerCAmelCase		: Union[str, Any]						    =  self._prepare_for_class(UpperCAmelCase_       ,  UpperCAmelCase_						)
           _lowerCAmelCase		: int						    =  model_class(UpperCAmelCase_						)

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

           with self.subTest("""JIT Enabled"""						):
             _lowerCAmelCase		: Optional[int]						    =  encode_jitted(**UpperCAmelCase_						).to_tuple()

           with self.subTest("""JIT Disabled"""						):
             with jax.disable_jit():
               _lowerCAmelCase		: List[str]						    =  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						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  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[str]						    =  model_class(UpperCAmelCase_						)
           _lowerCAmelCase		: Union[str, Any]						    =  model.encode(inputs_dict["""input_ids"""]       ,  inputs_dict["""attention_mask"""]						)

           _lowerCAmelCase		: List[str]						    =  {
               """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__       ,  a__       ,  a__						):
             return model.decode(
                 decoder_input_ids=UpperCAmelCase_       ,  decoder_attention_mask=UpperCAmelCase_       ,  encoder_outputs=UpperCAmelCase_       ,  )

           with self.subTest("""JIT Enabled"""						):
             _lowerCAmelCase		: Dict						    =  decode_jitted(**UpperCAmelCase_						).to_tuple()

           with self.subTest("""JIT Disabled"""						):
             with jax.disable_jit():
               _lowerCAmelCase		: List[str]						    =  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						):



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






     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""						)
       _lowerCAmelCase		: Union[str, Any]						    =  PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""						)

       _lowerCAmelCase		: int						    =  [
           """ 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		: List[str]						    =  [
           """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[str]						    =  tokenizer(UpperCAmelCase_       ,  return_tensors="""np"""       ,  truncation=UpperCAmelCase_       ,  max_length=512       ,  padding=UpperCAmelCase_						)
       _lowerCAmelCase		: Union[str, Any]						    =  model.generate(**UpperCAmelCase_       ,  num_beams=2						).sequences
       _lowerCAmelCase		: List[Any]						    =  tokenizer.batch_decode(UpperCAmelCase_       ,  skip_special_tokens=UpperCAmelCase_						)
       assert tgt_text == decoded





"""simple docstring"""




import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class 		__A							(				unittest.TestCase	):
     _UpperCamelCase							:    str	      =	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     _UpperCamelCase							:    Any	      =	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  AudioClassificationPipeline(model=a__       ,  feature_extractor=a__						)

       # test with a raw waveform
       _lowerCAmelCase		: Optional[int]						    =  np.zeros((34000,)						)
       _lowerCAmelCase		: Optional[Any]						    =  np.zeros((14000,)						)
       return audio_classifier, [audioa, audio]






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  examples
       _lowerCAmelCase		: List[Any]						    =  audio_classifier(a__						)
       # by default a model is initialized with num_labels=2
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )
       _lowerCAmelCase		: Tuple						    =  audio_classifier(a__       ,  top_k=1						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )

       self.run_torchaudio(a__						)






     @require_torchaudio
     def 					__A						(	self       ,  a__						):
       import datasets

       # test with a local file
       _lowerCAmelCase		: int						    =  datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""       ,  """clean"""       ,  split="""validation"""						)
       _lowerCAmelCase		: List[Any]						    =  dataset[0]["""audio"""]["""array"""]
       _lowerCAmelCase		: str						    =  audio_classifier(a__						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )






     @require_torch
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  """anton-l/wav2vec2-random-tiny-classifier"""

       _lowerCAmelCase		: Optional[Any]						    =  pipeline("""audio-classification"""       ,  model=a__						)

       _lowerCAmelCase		: Any						    =  np.ones((8000,)						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)

       _lowerCAmelCase		: List[str]						    =  [
           {"""score""": 0.0_8_4_2, """label""": """no"""},
           {"""score""": 0.0_8_3_8, """label""": """up"""},
           {"""score""": 0.0_8_3_7, """label""": """go"""},
           {"""score""": 0.0_8_3_4, """label""": """right"""},
       ]
       _lowerCAmelCase		: str						    =  [
           {"""score""": 0.0_8_4_5, """label""": """stop"""},
           {"""score""": 0.0_8_4_4, """label""": """on"""},
           {"""score""": 0.0_8_4_1, """label""": """right"""},
           {"""score""": 0.0_8_3_4, """label""": """left"""},
       ]
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

       _lowerCAmelCase		: int						    =  {"""array""": np.ones((8000,)						), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
       _lowerCAmelCase		: int						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)






     @require_torch
     @slow
     def 					__A						(	self						):
       import datasets

       _lowerCAmelCase		: Optional[Any]						    =  """superb/wav2vec2-base-superb-ks"""

       _lowerCAmelCase		: List[str]						    =  pipeline("""audio-classification"""       ,  model=a__						)
       _lowerCAmelCase		: str						    =  datasets.load_dataset("""anton-l/superb_dummy"""       ,  """ks"""       ,  split="""test"""						)

       _lowerCAmelCase		: Optional[Any]						    =  np.array(dataset[3]["""speech"""]       ,  dtype=np.floataa						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertEqual(
           nested_simplify(a__       ,  decimals=3						)       ,  [
               {"""score""": 0.9_8_1, """label""": """go"""},
               {"""score""": 0.0_0_7, """label""": """up"""},
               {"""score""": 0.0_0_6, """label""": """_unknown_"""},
               {"""score""": 0.0_0_1, """label""": """down"""},
           ]       ,  )






     @require_tf
     @unittest.skip("""Audio classification is not implemented for TF"""						)
     def 					__A						(	self						):
       pass




