




'''simple docstring'''


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   """▁"""

UpperCAmelCase_					=   {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

UpperCAmelCase_					=   {
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

UpperCAmelCase_					=   {
    """facebook/s2t-small-librispeech-asr""": 10_24,
}

UpperCAmelCase_					=   ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

UpperCAmelCase_					=   {"""mustc""": MUSTC_LANGS}





class  __lowercase    (	a__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   MAX_MODEL_INPUT_SIZES
   _a    						=   ["""input_ids""", """attention_mask"""]

   _a    						=   []







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase="<s>"   ,				UpperCamelCase="</s>"   ,				UpperCamelCase="<pad>"   ,				UpperCamelCase="<unk>"   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     None:
     __a								=       {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=__a   ,				eos_token=__a   ,				unk_token=__a   ,				pad_token=__a   ,				do_upper_case=__a   ,				do_lower_case=__a   ,				tgt_lang=__a   ,				lang_codes=__a   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__a   ,				)
     __a								=       do_upper_case
     __a								=       do_lower_case

     __a								=       load_json(__a   )
     __a								=       {v: k for k, v in self.encoder.items()}
     __a								=       spm_file
     __a								=       load_spm(__a   ,				self.sp_model_kwargs   )

     if lang_codes is not None:
       __a								=       lang_codes
       __a								=       LANGUAGES[lang_codes]
       __a								=       [f"<lang:{lang}>" for lang in self.langs]
       __a								=       {lang: self.sp_model.PieceToId(f"<lang:{lang}>"   ) for lang in self.langs}

       __a								=       self.lang_tokens
       __a								=       tgt_lang if tgt_lang is not None else self.langs[0]

       self.set_tgt_lang_special_tokens(self._tgt_lang   )
     else:
       __a								=       {}







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return len(self.encoder   )







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return self._tgt_lang







   @tgt_lang.setter
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     None:
     __a								=       new_tgt_lang
     self.set_tgt_lang_special_tokens(__a   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     None:
     __a								=       self.lang_code_to_id[tgt_lang]
     __a								=       [lang_code_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     List[str]:
     return self.sp_model.encode(__a   ,				out_type=__a   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Any:
     return self.encoder.get(__a   ,				self.encoder[self.unk_token]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     str:
     return self.decoder.get(__a   ,				self.unk_token   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     str:
     __a								=       []
     __a								=       """"""
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         __a								=       self.sp_model.decode(__a   )
         out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
         __a								=       []
       else:
         current_sub_tokens.append(__a   )
     __a								=       self.sp_model.decode(__a   )
     out_string += decoded.upper() if self.do_upper_case else decoded
     return out_string.strip()







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[int]:
     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + [self.eos_token_id]
     # 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.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				UpperCamelCase = False   )					->     List[int]:

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

     __a								=       [1] * len(self.prefix_tokens   )
     __a								=       [1]
     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 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       self.encoder.copy()
     vocab.update(self.added_tokens_encoder   )
     return vocab







   def __getstate__(						self   )					->     Dict:
     __a								=       self.__dict__.copy()
     __a								=       None
     return state







   def __setstate__(						self   ,				UpperCamelCase   )					->     None:
     __a								=       d

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

     __a								=       load_spm(self.spm_file   ,				self.sp_model_kwargs   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     Tuple[str]:
     __a								=       Path(__a   )
     assert save_dir.is_dir(), f"{save_directory} should be a directory"
     __a								=       save_dir / (
         (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
     )
     __a								=       save_dir / (
         (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
     )

     save_json(self.encoder   ,				__a   )

     if os.path.abspath(self.spm_file   ) != os.path.abspath(__a   ) and os.path.isfile(self.spm_file   ):
       copyfile(self.spm_file   ,				__a   )
     elif not os.path.isfile(self.spm_file   ):
       with open(__a   ,				'wb'   ) as fi:
         __a								=       self.sp_model.serialized_model_proto()
         fi.write(__a   )

     return (str(__a   ), str(__a   ))


def     SCREAMING_SNAKE_CASE							(   a_						:				Any					,       a_						:				Optional[int]      ):
  __a								=       sentencepiece.SentencePieceProcessor(**__lowercase      )
  spm.Load(str(__lowercase      )      )
  return spm


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]      ):
  with open(__lowercase					,       'r'      ) as f:
    return json.load(__lowercase      )


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				List[str]      ):
  with open(__lowercase					,       'w'      ) as f:
    json.dump(__lowercase					,       __lowercase					,       indent=2      )









'''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 					_a (a__,		unittest.TestCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					DanceDiffusionPipeline
     lowerCAmelCase_  :     int          =					UNCONDITIONAL_AUDIO_GENERATION_PARAMS
     lowerCAmelCase_  :     Optional[int]          =					PipelineTesterMixin.required_optional_params - {
         """callback""",
         """latents""",
         """callback_steps""",
         """output_type""",
         """num_images_per_prompt""",
     }
     lowerCAmelCase_  :     Tuple          =					UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
     lowerCAmelCase_  :     Dict          =					False
     lowerCAmelCase_  :     Tuple          =					False







     def 			snake_case_      (				self							)      ->				List[Any]:
          torch.manual_seed(0							)
          snake_case			:      List[str]        =			UNetaDModel(
              block_out_channels=(32, 32, 64)							,extra_in_channels=16							,sample_size=512							,sample_rate=16_000							,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""")							,)
          snake_case			:      Dict        =			IPNDMScheduler()

          snake_case			:      int        =			{
              """unet""": unet,
              """scheduler""": scheduler,
          }
          return components







     def 			snake_case_      (				self							,__a							,__a=0							)      ->				Tuple:
          if str(__a							).startswith("""mps"""							):
               snake_case			:      Optional[Any]        =			torch.manual_seed(__a							)
          else:
               snake_case			:      Union[str, Any]        =			torch.Generator(device=__a							).manual_seed(__a							)
          snake_case			:      str        =			{
              """batch_size""": 1,
              """generator""": generator,
              """num_inference_steps""": 4,
          }
          return inputs







     def 			snake_case_      (				self							)      ->				Any:
          snake_case			:      Dict        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
          snake_case			:      str        =			self.get_dummy_components()
          snake_case			:      List[str]        =			DanceDiffusionPipeline(**__a							)
          snake_case			:      Dict        =			pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      Union[str, Any]        =			self.get_dummy_inputs(__a							)
          snake_case			:      str        =			pipe(**__a							)
          snake_case			:      Union[str, Any]        =			output.audios

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

          assert audio.shape == (1, 2, components["unet"].sample_size)
          snake_case			:      Optional[Any]        =			np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000]							)
          assert np.abs(audio_slice.flatten() - expected_slice							).max() < 1E-2







     @skip_mps
     def 			snake_case_      (				self							)      ->				List[str]:
          return super().test_save_load_local()







     @skip_mps
     def 			snake_case_      (				self							)      ->				Union[str, Any]:
          return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)







     @skip_mps
     def 			snake_case_      (				self							)      ->				Dict:
          return super().test_save_load_optional_components()







     @skip_mps
     def 			snake_case_      (				self							)      ->				Any:
          return super().test_attention_slicing_forward_pass()







     def 			snake_case_      (				self							)      ->				str:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







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





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				Optional[int]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      str        =			torch_device

          snake_case			:      List[str]        =			DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""							)
          snake_case			:      Any        =			pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      int        =			torch.manual_seed(0							)
          snake_case			:      int        =			pipe(generator=__a							,num_inference_steps=100							,audio_length_in_s=4.096							)
          snake_case			:      Dict        =			output.audios

          snake_case			:      Union[str, Any]        =			audio[0, -3:, -3:]

          assert audio.shape == (1, 2, pipe.unet.sample_size)
          snake_case			:      Tuple        =			np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020]							)

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







     def 			snake_case_      (				self							)      ->				str:
          snake_case			:      str        =			torch_device

          snake_case			:      List[Any]        =			DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""							,torch_dtype=torch.floataa							)
          snake_case			:      str        =			pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      str        =			torch.manual_seed(0							)
          snake_case			:      List[str]        =			pipe(generator=__a							,num_inference_steps=100							,audio_length_in_s=4.096							)
          snake_case			:      Optional[int]        =			output.audios

          snake_case			:      Any        =			audio[0, -3:, -3:]

          assert audio.shape == (1, 2, pipe.unet.sample_size)
          snake_case			:      Optional[int]        =			np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341]							)

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



