



"""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       SCREAMING_SNAKE_CASE__       :
    lowercase__          =      PegasusConfig
    lowercase__          =      {}
    lowercase__          =      "gelu"

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







      """simple docstring"""







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

      lowercase_	    =       hidden_dropout_prob
      lowercase_	    =       attention_probs_dropout_prob
      lowercase_	    =       max_position_embeddings
      lowercase_	    =       eos_token_id
      lowercase_	    =       pad_token_id
      lowercase_	    =       bos_token_id

    def     _UpperCAmelCase		( self	: str):







      """simple docstring"""







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

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

      lowercase_	    =       self.config_cls(
          vocab_size=self.vocab_size       ,     d_model=self.hidden_size       ,     encoder_layers=self.num_hidden_layers       ,     decoder_layers=self.num_hidden_layers       ,     encoder_attention_heads=self.num_attention_heads       ,     decoder_attention_heads=self.num_attention_heads       ,     encoder_ffn_dim=self.intermediate_size       ,     decoder_ffn_dim=self.intermediate_size       ,     dropout=self.hidden_dropout_prob       ,     attention_dropout=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     eos_token_ids=[2]       ,     bos_token_id=self.bos_token_id       ,     pad_token_id=self.pad_token_id       ,     decoder_start_token_id=self.pad_token_id       ,     **self.config_updates       ,     )
      lowercase_	    =       prepare_pegasus_inputs_dict(__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase)
      return config, inputs_dict

    def     _UpperCAmelCase		( self	: Optional[int]       ,     lowerCAmelCase_	: List[str]       ,     lowerCAmelCase_	: Union[str, Any]       ,     lowerCAmelCase_	: str):







      """simple docstring"""







      lowercase_	    =       2_0
      lowercase_	    =       model_class_name(__lowerCAmelCase)

      lowercase_	    =       model.encode(inputs_dict["""input_ids"""])

      lowercase_    ,			lowercase_	    =       (
          inputs_dict["""decoder_input_ids"""],
          inputs_dict["""decoder_attention_mask"""],
      )

      lowercase_	    =       model.init_cache(decoder_input_ids.shape[0]       ,     __lowerCAmelCase       ,     __lowerCAmelCase)
      lowercase_	    =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,     dtype="""i4""")

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

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

      lowercase_	    =       model.decode(__lowerCAmelCase       ,     __lowerCAmelCase)

      lowercase_	    =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
      self.parent.assertTrue(diff < 1E-3       ,     msg=F'''Max diff is {diff}''')




    def     _UpperCAmelCase		( self	: Optional[int]       ,     lowerCAmelCase_	: Tuple       ,     lowerCAmelCase_	: Any       ,     lowerCAmelCase_	: int):







      """simple docstring"""







      lowercase_	    =       2_0
      lowercase_	    =       model_class_name(__lowerCAmelCase)

      lowercase_	    =       model.encode(inputs_dict["""input_ids"""])

      lowercase_    ,			lowercase_	    =       (
          inputs_dict["""decoder_input_ids"""],
          inputs_dict["""decoder_attention_mask"""],
      )

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

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

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

      lowercase_	    =       model.decode(__lowerCAmelCase       ,     __lowerCAmelCase       ,     decoder_attention_mask=__lowerCAmelCase)

      lowercase_	    =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
      self.parent.assertTrue(diff < 1E-3       ,     msg=F'''Max diff is {diff}''')







def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase=None       ,     __lowerCAmelCase=None       ,     )       ->  Any:







  '''simple docstring'''


  if attention_mask is None:
    lowercase_	    =       np.not_equal(__snake_case       ,     config.pad_token_id							).astype(np.inta							)
  if decoder_attention_mask is None:
    lowercase_	    =       np.concatenate(
        [
            np.ones(decoder_input_ids[:, :1].shape       ,     dtype=np.inta							),
            np.not_equal(decoder_input_ids[:, 1:]       ,     config.pad_token_id							).astype(np.inta							),
        ]       ,     axis=-1       ,     )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
  }


@require_flax
class       SCREAMING_SNAKE_CASE__       (							__UpperCAmelCase					,     unittest.TestCase      ):
    lowercase__          =      (
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    lowercase__          =      (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    lowercase__          =      True
    lowercase__          =      False
    lowercase__          =      False
    lowercase__          =      False

    def     _UpperCAmelCase		( self	: List[str]):







      """simple docstring"""







      lowercase_	    =       FlaxPegasusModelTester(self)
      lowercase_	    =       ConfigTester(self       ,     config_class=__lowerCAmelCase)

    def     _UpperCAmelCase		( self	: int):







      """simple docstring"""







      self.config_tester.run_common_tests()

    def     _UpperCAmelCase		( self	: Dict):







      """simple docstring"""







      lowercase_    ,			lowercase_	    =       self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase)

    def     _UpperCAmelCase		( self	: List[str]):







      """simple docstring"""







      lowercase_    ,			lowercase_	    =       self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase)

    def     _UpperCAmelCase		( self	: Any):







      """simple docstring"""







      lowercase_    ,			lowercase_	    =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__):
          lowercase_	    =       self._prepare_for_class(__lowerCAmelCase       ,     __lowerCAmelCase)
          lowercase_	    =       model_class(__lowerCAmelCase)

          @jax.jit
          def encode_jitted(lowerCAmelCase_	: int       ,     lowerCAmelCase_	: Any=None       ,     **lowerCAmelCase_	: str):
            return model.encode(input_ids=__lowerCAmelCase       ,     attention_mask=__lowerCAmelCase)

          with self.subTest("""JIT Enabled"""):
            lowercase_	    =       encode_jitted(**__lowerCAmelCase).to_tuple()

          with self.subTest("""JIT Disabled"""):
            with jax.disable_jit():
              lowercase_	    =       encode_jitted(**__lowerCAmelCase).to_tuple()

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

    def     _UpperCAmelCase		( self	: int):







      """simple docstring"""







      lowercase_    ,			lowercase_	    =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__):
          lowercase_	    =       model_class(__lowerCAmelCase)
          lowercase_	    =       model.encode(inputs_dict["""input_ids"""]       ,     inputs_dict["""attention_mask"""])

          lowercase_	    =       {
              """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
              """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
              """encoder_outputs""": encoder_outputs,
          }

          @jax.jit
          def decode_jitted(lowerCAmelCase_	: Any       ,     lowerCAmelCase_	: int       ,     lowerCAmelCase_	: Optional[int]):
            return model.decode(
                decoder_input_ids=__lowerCAmelCase       ,     decoder_attention_mask=__lowerCAmelCase       ,     encoder_outputs=__lowerCAmelCase       ,     )

          with self.subTest("""JIT Enabled"""):
            lowercase_	    =       decode_jitted(**__lowerCAmelCase).to_tuple()

          with self.subTest("""JIT Disabled"""):
            with jax.disable_jit():
              lowercase_	    =       decode_jitted(**__lowerCAmelCase).to_tuple()

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

    @slow
    def     _UpperCAmelCase		( self	: str):







      """simple docstring"""







      for model_class_name in self.all_model_classes:
        lowercase_	    =       model_class_name.from_pretrained("""google/pegasus-large"""       ,     from_pt=__lowerCAmelCase)
        lowercase_	    =       np.ones((1, 1))
        lowercase_	    =       model(__lowerCAmelCase)
        self.assertIsNotNone(__lowerCAmelCase)




    @slow
    def     _UpperCAmelCase		( self	: List[str]):







      """simple docstring"""







      lowercase_	    =       FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
      lowercase_	    =       PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")

      lowercase_	    =       [
          """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
          """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
      ]

      lowercase_	    =       [
          """California\'s largest electricity provider has turned off power to hundreds of thousands of customers.""",
          """Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.""",
      ]

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








import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""Speech2TextFeatureExtractor"""
			lowerCAmelCase_							     =						"""Speech2TextTokenizer"""





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		self.feature_extractor
				lowerCamelCase__								=		False





			def __call__(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				if self._in_target_context_manager:
					return self.current_processor(*__lowerCAmelCase       ,  **__lowerCAmelCase							)

				if "raw_speech" in kwargs:
					warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
					lowerCamelCase__								=		kwargs.pop('''raw_speech'''							)
				else:
					lowerCamelCase__								=		kwargs.pop('''audio'''       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		kwargs.pop('''sampling_rate'''       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		kwargs.pop('''text'''       ,  __lowerCAmelCase							)
				if len(__lowerCAmelCase							) > 0:
					lowerCamelCase__								=		args[0]
					lowerCamelCase__								=		args[1:]

				if audio is None and text is None:
					raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

				if audio is not None:
					lowerCamelCase__								=		self.feature_extractor(__lowerCAmelCase       ,  *__lowerCAmelCase       ,  sampling_rate=__lowerCAmelCase       ,  **__lowerCAmelCase							)
				if text is not None:
					lowerCamelCase__								=		self.tokenizer(__lowerCAmelCase       ,  **__lowerCAmelCase							)

				if text is None:
					return inputs
				elif audio is None:
					return encodings
				else:
					lowerCamelCase__								=		encodings['''input_ids''']
					return inputs





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.batch_decode(*__lowerCAmelCase       ,  **__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.decode(*__lowerCAmelCase       ,  **__lowerCAmelCase							)





			@contextmanager
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				warnings.warn(
				    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
				    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
				    '''your audio inputs, or in a separate call.'''							)
				lowerCamelCase__								=		True
				lowerCamelCase__								=		self.tokenizer
				yield
				lowerCamelCase__								=		self.feature_extractor
				lowerCamelCase__								=		False



