
"""simple docstring"""




import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A     :			Any  						=			get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							XLMProphetNetTokenizer
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							True
   def      __magic_name__     (     self							):
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowercase       :							Union[str, Any]									=				XLMProphetNetTokenizer(__a      ,       keep_accents=__a							)
          tokenizer.save_pretrained(self.tmpdirname							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				"[PAD]"
          lowercase       :							Optional[int]									=				0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a							)      ,       __a							)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a							)      ,       __a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				list(self.get_tokenizer().get_vocab().keys()							)

          self.assertEqual(vocab_keys[0]      ,       "[PAD]"							)
          self.assertEqual(vocab_keys[1]      ,       "[CLS]"							)
          self.assertEqual(vocab_keys[-1]      ,       "j"							)
          self.assertEqual(len(__a							)      ,       1_012							)
   def      __magic_name__     (     self							):
          self.assertEqual(self.get_tokenizer().vocab_size      ,       1_012							)
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				XLMProphetNetTokenizer(__a      ,       keep_accents=__a							)

          lowercase       :							Optional[int]									=				tokenizer.tokenize("This is a test"							)
          self.assertListEqual(__a      ,       ["▁This", "▁is", "▁a", "▁t", "est"]							)

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(__a							)      ,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,       )

          lowercase       :							Optional[int]									=				tokenizer.tokenize("I was born in 92000, and this is falsé."							)
          self.assertListEqual(
              __a      ,       [
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "9",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "é",
                  ".",
              ]      ,       )
          lowercase       :							List[str]									=				tokenizer.convert_tokens_to_ids(__a							)
          self.assertListEqual(
              __a      ,       [
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
              ]      ,       )

          lowercase       :							Optional[int]									=				tokenizer.convert_ids_to_tokens(__a							)
          self.assertListEqual(
              __a      ,       [
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "[UNK]",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "[UNK]",
                  ".",
              ]      ,       )
   @cached_property
   def      __magic_name__     (     self							):
          return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"							)
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				"Hello World!"
          lowercase       :							List[str]									=				[35_389, 6_672, 49, 2]
          self.assertListEqual(__a      ,       self.big_tokenizer.encode(__a							)							)

   @slow
   def      __magic_name__     (     self							):
          # fmt: off
          lowercase       :							Any									=				{"input_ids": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0]], "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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=__a      ,       model_name="microsoft/xprophetnet-large-wiki100-cased"      ,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"      ,       )



'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     A ( UpperCAmelCase ):





							def __init__(   self					:				Dict  ,		__a					:				List[Any]  ,		__a					:				Optional[Any]						)				->						Dict:
								super().__init__()
								self.register_modules(unet=__a  ,		scheduler=__a						)






							@torch.no_grad()
							def __call__(   self					:				Tuple  ,		__a					:				int = 1  ,		__a					:				int = 1_0_0  ,		__a					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,		__a					:				Optional[float] = None  ,		__a					:				bool = True  ,		)				->						Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
									__UpperCAmelCase      					=      self.unet.config.sample_size / self.unet.config.sample_rate

								__UpperCAmelCase      					=      audio_length_in_s * self.unet.config.sample_rate

								__UpperCAmelCase      					=      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}."""						)

								__UpperCAmelCase      					=      int(__a						)
								if sample_size % down_scale_factor != 0:
									__UpperCAmelCase      					=      (
									    (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.'''						)
								__UpperCAmelCase      					=      int(__a						)

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

								__UpperCAmelCase      					=      randn_tensor(__a  ,		generator=__a  ,		device=self.device  ,		dtype=__a						)

								# set step values
								self.scheduler.set_timesteps(__a  ,		device=audio.device						)
								__UpperCAmelCase      					=      self.scheduler.timesteps.to(__a						)

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

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

								__UpperCAmelCase      					=      audio.clamp(-1  ,		1						).float().cpu().numpy()

								__UpperCAmelCase      					=      audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=__a						)
