
'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class 						lowercase__	:






   '''simple docstring'''







   _snake_case		   =							PegasusConfig
   _snake_case		   =							{}
   _snake_case		   =							'''gelu'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=1_3 ,   lowerCamelCase__=7 ,   lowerCamelCase__=True ,   lowerCamelCase__=False ,   lowerCamelCase__=9_9 ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=2 ,   lowerCamelCase__=4 ,   lowerCamelCase__=3_7 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=4_0 ,   lowerCamelCase__=2 ,   lowerCamelCase__=1 ,   lowerCamelCase__=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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length - 1] ,   self.vocab_size   )
      UpperCamelCase       =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   ) ,   1   )
      UpperCamelCase       =  tf.concat([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(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
      return config, inputs_dict







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  TFPegasusModel(config=lowerCamelCase__   ).get_decoder()
      UpperCamelCase       =  inputs_dict['''input_ids''']

      UpperCamelCase       =  input_ids[:1, :]
      UpperCamelCase       =  inputs_dict['''attention_mask'''][:1, :]
      UpperCamelCase       =  inputs_dict['''head_mask''']
      UpperCamelCase       =  1

      # first forward pass
      UpperCamelCase       =  model(lowerCamelCase__ ,   attention_mask=lowerCamelCase__ ,   head_mask=lowerCamelCase__ ,   use_cache=lowerCamelCase__   )

      UpperCamelCase     , UpperCamelCase       =  outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      UpperCamelCase       =  ids_tensor((self.batch_size, 3) ,   config.vocab_size   )
      UpperCamelCase       =  tf.cast(ids_tensor((self.batch_size, 3) ,   2   ) ,   tf.inta   )

      # append to next input_ids and
      UpperCamelCase       =  tf.concat([input_ids, next_tokens] ,   axis=-1   )
      UpperCamelCase       =  tf.concat([attention_mask, next_attn_mask] ,   axis=-1   )

      UpperCamelCase       =  model(lowerCamelCase__ ,   attention_mask=lowerCamelCase__   )[0]
      UpperCamelCase       =  model(lowerCamelCase__ ,   attention_mask=lowerCamelCase__ ,   past_key_values=lowerCamelCase__   )[0]

      self.parent.assertEqual(next_tokens.shape[1] ,   output_from_past.shape[1]   )

      # select random slice
      UpperCamelCase       =  int(ids_tensor((1,) ,   output_from_past.shape[-1]   )   )
      UpperCamelCase       =  output_from_no_past[:, -3:, random_slice_idx]
      UpperCamelCase       =  output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(lowerCamelCase__ ,   lowerCamelCase__ ,   rtol=1e-3   )




def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       List[str]=None,      _UpperCAmelCase					:       Dict=None,      _UpperCAmelCase					:       Optional[Any]=None,      _UpperCAmelCase					:       Any=None,      _UpperCAmelCase					:       str=None,      ):
   if attention_mask is None:
      UpperCamelCase       =  tf.cast(tf.math.not_equal(_UpperCAmelCase,      config.pad_token_id),      tf.inta)
   if decoder_attention_mask is None:
      UpperCamelCase       =  tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape,      dtype=tf.inta),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],      config.pad_token_id),      tf.inta),
          ],      axis=-1,      )
   if head_mask is None:
      UpperCamelCase       =  tf.ones((config.encoder_layers, config.encoder_attention_heads))
   if decoder_head_mask is None:
      UpperCamelCase       =  tf.ones((config.decoder_layers, config.decoder_attention_heads))
   if cross_attn_head_mask is None:
      UpperCamelCase       =  tf.ones((config.decoder_layers, config.decoder_attention_heads))
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


@require_tf
class 						lowercase__	(  snake_case_,					snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
   _snake_case		   =							(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
   _snake_case		   =							(
       {
           '''conversational''': TFPegasusForConditionalGeneration,
           '''feature-extraction''': TFPegasusModel,
           '''summarization''': TFPegasusForConditionalGeneration,
           '''text2text-generation''': TFPegasusForConditionalGeneration,
           '''translation''': TFPegasusForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   _snake_case		   =							True
   _snake_case		   =							False
   _snake_case		   =							False







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  TFPegasusModelTester(self   )
      UpperCamelCase       =  ConfigTester(self ,   config_class=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.config_tester.run_common_tests()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__   )




@require_sentencepiece
@require_tokenizers
@require_tf
class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							[
       ''' 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!" ''',
   ]
   _snake_case		   =							[
       '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
       ''' reduce the risk of wildfires.''',
       '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
   ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
   _snake_case		   =							'''google/pegasus-xsum'''







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return AutoTokenizer.from_pretrained(self.model_name   )







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
      return model







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.translate_src_text(**lowerCamelCase__   )
      assert self.expected_text == generated_words







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.tokenizer(self.src_text ,   **lowerCamelCase__ ,   padding=lowerCamelCase__ ,   return_tensors='''tf'''   )
      UpperCamelCase       =  self.model.generate(
          model_inputs.input_ids ,   attention_mask=model_inputs.attention_mask ,   num_beams=2 ,   use_cache=lowerCamelCase__ ,   )
      UpperCamelCase       =  self.tokenizer.batch_decode(generated_ids.numpy() ,   skip_special_tokens=lowerCamelCase__   )
      return generated_words







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self._assert_generated_batch_equal_expected()







'''simple docstring'''


import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_ :  Dict     =					logging.get_logger(__name__)

snake_case_ :  Union[str, Any]     =					{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							'''encodec'''







   def __init__(			self ,   lowerCamelCase__=[1.5, 3.0, 6.0, 12.0, 24.0] ,   lowerCamelCase__=2_4_0_0_0 ,   lowerCamelCase__=1 ,   lowerCamelCase__=False ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=1_2_8 ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=1 ,   lowerCamelCase__=[8, 5, 4, 2] ,   lowerCamelCase__="weight_norm" ,   lowerCamelCase__=7 ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=2 ,   lowerCamelCase__=True ,   lowerCamelCase__="reflect" ,   lowerCamelCase__=2 ,   lowerCamelCase__=2 ,   lowerCamelCase__=1.0 ,   lowerCamelCase__=1_0_2_4 ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  target_bandwidths
      UpperCamelCase       =  sampling_rate
      UpperCamelCase       =  audio_channels
      UpperCamelCase       =  normalize
      UpperCamelCase       =  chunk_length_s
      UpperCamelCase       =  overlap
      UpperCamelCase       =  hidden_size
      UpperCamelCase       =  num_filters
      UpperCamelCase       =  num_residual_layers
      UpperCamelCase       =  upsampling_ratios
      UpperCamelCase       =  norm_type
      UpperCamelCase       =  kernel_size
      UpperCamelCase       =  last_kernel_size
      UpperCamelCase       =  residual_kernel_size
      UpperCamelCase       =  dilation_growth_rate
      UpperCamelCase       =  use_causal_conv
      UpperCamelCase       =  pad_mode
      UpperCamelCase       =  compress
      UpperCamelCase       =  num_lstm_layers
      UpperCamelCase       =  trim_right_ratio
      UpperCamelCase       =  codebook_size
      UpperCamelCase       =  codebook_dim if codebook_dim is not None else hidden_size
      UpperCamelCase       =  use_conv_shortcut

      if self.norm_type not in ["weight_norm", "time_group_norm"]:
         raise ValueError(
             f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'   )

      super().__init__(**lowerCamelCase__   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if self.chunk_length_s is None:
         return None
      else:
         return int(self.chunk_length_s * self.sampling_rate   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if self.chunk_length_s is None or self.overlap is None:
         return None
      else:
         return max(1 ,   int((1.0 - self.overlap) * self.chunk_length   )   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  np.prod(self.upsampling_ratios   )
      return math.ceil(self.sampling_rate / hop_length   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)   )





