





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				List[str]         =  BarthezTokenizer
   __A   :				str         =  BarthezTokenizerFast
   __A   :				Dict         =  True
   __A   :				Union[str, Any]         =  True

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          super().setUp()

          a__						:	Tuple =				BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez')
          tokenizer.save_pretrained(self.tmpdirname)
          tokenizer.save_pretrained(self.tmpdirname					,  legacy_format=lowercase)
          a__						:	str =				tokenizer

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	List[Any] =				'<pad>'
          a__						:	Optional[Any] =				1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)					,  lowercase)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)					,  lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Tuple =				list(self.get_tokenizer().get_vocab().keys())

          self.assertEqual(vocab_keys[0]					,  '<s>')
          self.assertEqual(vocab_keys[1]					,  '<pad>')
          self.assertEqual(vocab_keys[-1]					,  '<mask>')
          self.assertEqual(len(lowercase)					,  10_1122)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          self.assertEqual(self.get_tokenizer().vocab_size					,  10_1122)

   @require_torch
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[int] =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
          a__						:	int =				[0, 57, 3018, 7_0307, 91, 2]

          a__						:	List[str] =				self.tokenizer(
              lowercase					,  max_length=len(lowercase)					,  padding=lowercase					,  truncation=lowercase					,  return_tensors='pt')
          self.assertIsInstance(lowercase					,  lowercase)

          self.assertEqual((2, 6)					,  batch.input_ids.shape)
          self.assertEqual((2, 6)					,  batch.attention_mask.shape)
          a__						:	Dict =				batch.input_ids.tolist()[0]
          self.assertListEqual(lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          if not self.test_rust_tokenizer:
                 return

          a__						:	Optional[int] =				self.get_tokenizer()
          a__						:	Union[str, Any] =				self.get_rust_tokenizer()

          a__						:	Optional[Any] =				'I was born in 92000, and this is falsé.'

          a__						:	List[Any] =				tokenizer.tokenize(lowercase)
          a__						:	Dict =				rust_tokenizer.tokenize(lowercase)
          self.assertListEqual(lowercase					,  lowercase)

          a__						:	int =				tokenizer.encode(lowercase					,  add_special_tokens=lowercase)
          a__						:	List[str] =				rust_tokenizer.encode(lowercase					,  add_special_tokens=lowercase)
          self.assertListEqual(lowercase					,  lowercase)

          a__						:	Optional[Any] =				self.get_rust_tokenizer()
          a__						:	Optional[Any] =				tokenizer.encode(lowercase)
          a__						:	Union[str, Any] =				rust_tokenizer.encode(lowercase)
          self.assertListEqual(lowercase					,  lowercase)

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				{'input_ids': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # moussaKam/mbarthez is a french model. So we also use french texts.
          a__						:	Optional[Any] =				[
              'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
              'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
              'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
              'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
              'telles que la traduction et la synthèse de texte.',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=lowercase					,  model_name='moussaKam/mbarthez'					,  revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'					,  sequences=lowercase					,  )









from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowercase      :       Any									=  logging.get_logger(__name__)  # pylint: disable=invalid-name





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

          super().__init__()
          self.register_modules(unet=lowercase					,  scheduler=lowercase)

   @torch.no_grad()
   def __call__(					self					,  lowercase = 1					,  lowercase = 100					,  lowercase = None					,  lowercase = None					,  lowercase = True					,  )			->						Union[AudioPipelineOutput, Tuple]:







          '''simple docstring'''

          if audio_length_in_s is None:
                 a__						:	Optional[int] =				self.unet.config.sample_size / self.unet.config.sample_rate

          a__						:	int =				audio_length_in_s * self.unet.config.sample_rate

          a__						:	Union[str, Any] =				2 ** len(self.unet.up_blocks)
          if sample_size < 3 * down_scale_factor:
                 raise ValueError(
                     F'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
                     F' {3 * down_scale_factor / self.unet.config.sample_rate}.')

          a__						:	str =				int(lowercase)
          if sample_size % down_scale_factor != 0:
                 a__						:	List[str] =				(
                     (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                 ) * down_scale_factor
                 logger.info(
                     F'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
                     F' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
                     ' process.')
          a__						:	List[Any] =				int(lowercase)

          a__						:	int =				next(iter(self.unet.parameters())).dtype
          a__						:	Tuple =				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(lowercase					,  lowercase) and len(lowercase) != batch_size:
                 raise ValueError(
                     F'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
                     F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

          a__						:	Optional[Any] =				randn_tensor(lowercase					,  generator=lowercase					,  device=self.device					,  dtype=lowercase)

          # set step values
          self.scheduler.set_timesteps(lowercase					,  device=audio.device)
          a__						:	Union[str, Any] =				self.scheduler.timesteps.to(lowercase)

          for t in self.progress_bar(self.scheduler.timesteps):
                 # 1. predict noise model_output
                 a__						:	Dict =				self.unet(lowercase					,  lowercase).sample

                 # 2. compute previous image: x_t -> t_t-1
                 a__						:	Any =				self.scheduler.step(lowercase					,  lowercase					,  lowercase).prev_sample

          a__						:	str =				audio.clamp(-1					,  1).float().cpu().numpy()

          a__						:	List[Any] =				audio[:, :, :original_sample_size]

          if not return_dict:
                 return (audio,)

          return AudioPipelineOutput(audios=lowercase)


