

"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		"""▁"""

_lowerCAmelCase						=		{"""vocab_file""": """sentencepiece.bpe.model"""}

_lowerCAmelCase						=		{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

_lowerCAmelCase						=		{
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
_lowerCAmelCase						=		["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		["input_ids", "attention_mask"]

  _UpperCAmelCase          =		[]
  _UpperCAmelCase          =		[]

  def __init__(		self    ,_A    ,_A="<s>"    ,_A="</s>"    ,_A="</s>"    ,_A="<s>"    ,_A="<unk>"    ,_A="<pad>"    ,_A="<mask>"    ,_A=None    ,_A=None    ,_A=None    ,_A = None    ,_A=None    ,_A=False    ,**_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    AddedToken(_A    ,lstrip=_A    ,rstrip=_A      ) if isinstance(_A    ,_A      ) else mask_token

      _lowerCAmelCase       :   Optional[int]  =    {} if sp_model_kwargs is None else sp_model_kwargs
      _lowerCAmelCase       :   int  =    legacy_behaviour

      super().__init__(
          bos_token=_A    ,eos_token=_A    ,unk_token=_A    ,sep_token=_A    ,cls_token=_A    ,pad_token=_A    ,mask_token=_A    ,tokenizer_file=_A    ,src_lang=_A    ,tgt_lang=_A    ,additional_special_tokens=_A    ,sp_model_kwargs=self.sp_model_kwargs    ,legacy_behaviour=_A    ,**_A    ,)

      _lowerCAmelCase       :   Union[str, Any]  =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(str(_A      )      )
      _lowerCAmelCase       :   Dict  =    vocab_file
      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
      # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
      # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

      # Mimic fairseq token-to-id alignment for the first 4 token
      _lowerCAmelCase       :   Union[str, Any]  =    {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      _lowerCAmelCase       :   Union[str, Any]  =    1

      _lowerCAmelCase       :   Any  =    len(self.sp_model      )
      _lowerCAmelCase       :   Union[str, Any]  =    {
          code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_A      )
      }
      _lowerCAmelCase       :   Union[str, Any]  =    {v: k for k, v in self.lang_code_to_id.items()}
      _lowerCAmelCase       :   Optional[Any]  =    len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

      self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
      _lowerCAmelCase       :   List[Any]  =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
      _lowerCAmelCase       :   List[str]  =    list(self.lang_code_to_id.keys()      )

      if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          self._additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in self._additional_special_tokens]      )

      _lowerCAmelCase       :   List[Any]  =    src_lang if src_lang is not None else 'eng_Latn'
      _lowerCAmelCase       :   Tuple  =    self.lang_code_to_id[self._src_lang]
      _lowerCAmelCase       :   Tuple  =    tgt_lang
      self.set_src_lang_special_tokens(self._src_lang      )

  def __getstate__(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.__dict__.copy()
      _lowerCAmelCase       :   Dict  =    None
      _lowerCAmelCase       :   List[str]  =    self.sp_model.serialized_model_proto()
      return state

  def __setstate__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    d

      # for backward compatibility
      if not hasattr(self    ,'sp_model_kwargs'      ):
          _lowerCAmelCase       :   Tuple  =    {}

      _lowerCAmelCase       :   str  =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self._src_lang

  @src_lang.setter
  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    new_src_lang
      self.set_src_lang_special_tokens(self._src_lang      )

  def __lowerCamelCase		(		self    ,_A    ,_A = None    ,_A = False      ):
      '''simple docstring'''

      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=_A    ,token_ids_a=_A    ,already_has_special_tokens=_A      )

      _lowerCAmelCase       :   Any  =    [1] * len(self.prefix_tokens      )
      _lowerCAmelCase       :   Any  =    [1] * len(self.suffix_tokens      )
      if token_ids_a is None:
          return prefix_ones + ([0] * len(_A      )) + suffix_ones
      return prefix_ones + ([0] * len(_A      )) + ([0] * len(_A      )) + suffix_ones

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    [self.sep_token_id]
      _lowerCAmelCase       :   List[str]  =    [self.cls_token_id]

      if token_ids_a is None:
          return len(cls + token_ids_a + sep      ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,**_A      ):
      '''simple docstring'''

      if src_lang is None or tgt_lang is None:
          raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'      )
      _lowerCAmelCase       :   Any  =    src_lang
      _lowerCAmelCase       :   Union[str, Any]  =    self(_A    ,add_special_tokens=_A    ,return_tensors=_A    ,**_A      )
      _lowerCAmelCase       :   str  =    self.convert_tokens_to_ids(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    tgt_lang_id
      return inputs

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    {self.convert_ids_to_tokens(_A      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return self.sp_model.encode(_A    ,out_type=_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
      _lowerCAmelCase       :   int  =    self.sp_model.PieceToId(_A      )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    ''.join(_A      ).replace(_A    ,' '      ).strip()
      return out_string

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      if not os.path.isdir(_A      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      _lowerCAmelCase       :   Any  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

      if os.path.abspath(self.vocab_file      ) != os.path.abspath(_A      ) and os.path.isfile(self.vocab_file      ):
          copyfile(self.vocab_file    ,_A      )
      elif not os.path.isfile(self.vocab_file      ):
          with open(_A    ,'wb'      ) as fi:
              _lowerCAmelCase       :   List[str]  =    self.sp_model.serialized_model_proto()
              fi.write(_A      )

      return (out_vocab_file,)

  def __lowerCamelCase		(		self    ,_A    ,_A = "eng_Latn"    ,_A = None    ,_A = "fra_Latn"    ,**_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    src_lang
      _lowerCAmelCase       :   int  =    tgt_lang
      return super().prepare_seqaseq_batch(_A    ,_A    ,**_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.set_src_lang_special_tokens(self.src_lang      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.set_tgt_lang_special_tokens(self.tgt_lang      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.lang_code_to_id[src_lang]
      if self.legacy_behaviour:
          _lowerCAmelCase       :   int  =    []
          _lowerCAmelCase       :   List[str]  =    [self.eos_token_id, self.cur_lang_code]
      else:
          _lowerCAmelCase       :   Any  =    [self.cur_lang_code]
          _lowerCAmelCase       :   Optional[Any]  =    [self.eos_token_id]







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.lang_code_to_id[lang]
      if self.legacy_behaviour:
          _lowerCAmelCase       :   Union[str, Any]  =    []
          _lowerCAmelCase       :   List[Any]  =    [self.eos_token_id, self.cur_lang_code]
      else:
          _lowerCAmelCase       :   List[str]  =    [self.cur_lang_code]
          _lowerCAmelCase       :   int  =    [self.eos_token_id]








"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		DanceDiffusionPipeline
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "callback",
      "latents",
      "callback_steps",
      "output_type",
      "num_images_per_prompt",
  }
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )
      _lowerCAmelCase       :   List[Any]  =    UNetaDModel(
          block_out_channels=(32, 32, 64)    ,extra_in_channels=16    ,sample_size=512    ,sample_rate=1_6000    ,in_channels=2    ,out_channels=2    ,flip_sin_to_cos=_A    ,use_timestep_embedding=_A    ,time_embedding_type='fourier'    ,mid_block_type='UNetMidBlock1D'    ,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')    ,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')    ,)
      _lowerCAmelCase       :   int  =    IPNDMScheduler()

      _lowerCAmelCase       :   Union[str, Any]  =    {
          'unet': unet,
          'scheduler': scheduler,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   str  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   int  =    {
          'batch_size': 1,
          'generator': generator,
          'num_inference_steps': 4,
      }
      return inputs

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   Optional[Any]  =    DanceDiffusionPipeline(**_A      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   List[str]  =    pipe(**_A      )
      _lowerCAmelCase       :   List[Any]  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, components["unet"].sample_size)
      _lowerCAmelCase       :   Optional[Any]  =    np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]      )
      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_save_load_local()

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_save_load_optional_components()

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_attention_slicing_forward_pass()







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







@slow
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    torch_device

      _lowerCAmelCase       :   int  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[int]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   str  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   str  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   Union[str, Any]  =    np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]      )

      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    torch_device

      _lowerCAmelCase       :   Tuple  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'    ,torch_dtype=torch.floataa      )
      _lowerCAmelCase       :   Optional[int]  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   Union[str, Any]  =    output.audios

      _lowerCAmelCase       :   int  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   List[str]  =    np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]      )

      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2





