
"""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


_snake_case        =    logging.get_logger(__name__)

_snake_case        =    '''▁'''

_snake_case        =    {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

_snake_case        =    {
    '''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'''
        )
    },
}

_snake_case        =    {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

_snake_case        =    ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

_snake_case        =    {'''mustc''': MUSTC_LANGS}
class 							_SCREAMING_SNAKE_CASE	(	UpperCAmelCase				):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:				Optional[int]				=       VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_:				List[Any]				=       PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_:				List[Any]				=       MAX_MODEL_INPUT_SIZES
				SCREAMING_SNAKE_CASE_:				Dict				=       ["input_ids", "attention_mask"]

				SCREAMING_SNAKE_CASE_:				Optional[int]				=       []


				def __init__(       self						:      Dict					,  UpperCAmelCase_						:      str					,  UpperCAmelCase_						:      Dict					,  UpperCAmelCase_						:      List[str]="<s>"					,  UpperCAmelCase_						:      Optional[Any]="</s>"					,  UpperCAmelCase_						:      Optional[int]="<pad>"					,  UpperCAmelCase_						:      int="<unk>"					,  UpperCAmelCase_						:      Optional[int]=False					,  UpperCAmelCase_						:      int=False					,  UpperCAmelCase_						:      int=None					,  UpperCAmelCase_						:      str=None					,  UpperCAmelCase_						:      Union[str, Any] = None					,  **UpperCAmelCase_						:      Any					,  )		->	int:







											"""simple docstring"""




											_lowerCAmelCase							=      {} if sp_model_kwargs is None else sp_model_kwargs

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

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

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

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

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


				@property
				def 					__lowerCamelCase						(       self						:      Any	)		->	Tuple:







											"""simple docstring"""




											return len(self.encoder	)


				@property
				def 					__lowerCamelCase						(       self						:      Any	)		->	Tuple:







											"""simple docstring"""




											return self._tgt_lang


				@tgt_lang.setter
				def 					__lowerCamelCase						(       self						:      Any					,  UpperCAmelCase_						:      str	)		->	Any:







											"""simple docstring"""




											_lowerCAmelCase							=      new_tgt_lang
											self.set_tgt_lang_special_tokens(UpperCAmelCase_	)


				def 					__lowerCamelCase						(       self						:      List[str]					,  UpperCAmelCase_						:      int	)		->	int:







											"""simple docstring"""




											_lowerCAmelCase							=      self.lang_code_to_id[tgt_lang]
											_lowerCAmelCase							=      [lang_code_id]


				def 					__lowerCamelCase						(       self						:      Optional[int]					,  UpperCAmelCase_						:      Union[str, Any]	)		->	List[Any]:







											"""simple docstring"""




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


				def 					__lowerCamelCase						(       self						:      List[str]					,  UpperCAmelCase_						:      int	)		->	Union[str, Any]:







											"""simple docstring"""




											return self.encoder.get(UpperCAmelCase_					,  self.encoder[self.unk_token]	)


				def 					__lowerCamelCase						(       self						:      List[Any]					,  UpperCAmelCase_						:      List[str]	)		->	Union[str, Any]:







											"""simple docstring"""




											return self.decoder.get(UpperCAmelCase_					,  self.unk_token	)


				def 					__lowerCamelCase						(       self						:      Any					,  UpperCAmelCase_						:      Dict	)		->	Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase							=      []
											_lowerCAmelCase							=      ''
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									_lowerCAmelCase							=      self.sp_model.decode(UpperCAmelCase_	)
																									out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																									_lowerCAmelCase							=      []
																		else:
																									current_sub_tokens.append(UpperCAmelCase_	)
											_lowerCAmelCase							=      self.sp_model.decode(UpperCAmelCase_	)
											out_string += decoded.upper() if self.do_upper_case else decoded
											return out_string.strip()


				def 					__lowerCamelCase						(       self						:      Union[str, Any]					,  UpperCAmelCase_						:      Tuple					,  UpperCAmelCase_						:      Optional[int]=None	)		->	str:







											"""simple docstring"""




											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 					__lowerCamelCase						(       self						:      Any					,  UpperCAmelCase_						:      Optional[Any]					,  UpperCAmelCase_						:      List[Any] = None					,  UpperCAmelCase_						:      Any = False	)		->	List[str]:







											"""simple docstring"""




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

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


				def 					__lowerCamelCase						(       self						:      int	)		->	Dict:







											"""simple docstring"""




											_lowerCAmelCase							=      self.encoder.copy()
											vocab.update(self.added_tokens_encoder	)
											return vocab


				def __getstate__(       self						:      Optional[Any]	)		->	Dict:







											"""simple docstring"""




											_lowerCAmelCase							=      self.__dict__.copy()
											_lowerCAmelCase							=      None
											return state


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







											"""simple docstring"""




											_lowerCAmelCase							=      d

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

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







				def 					__lowerCamelCase						(       self						:      List[str]					,  UpperCAmelCase_						:      Optional[Any]					,  UpperCAmelCase_						:      Optional[int] = None	)		->	List[Any]:







											"""simple docstring"""




											_lowerCAmelCase							=      Path(UpperCAmelCase_	)
											assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
											_lowerCAmelCase							=      save_dir / (
											    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
											)
											_lowerCAmelCase							=      save_dir / (
											    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
											)

											save_json(self.encoder					,  UpperCAmelCase_	)

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

											return (str(UpperCAmelCase_	), str(UpperCAmelCase_	))






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Union[str, Any]  ,	SCREAMING_SNAKE_CASE:       str    ):






							"""simple docstring"""



							_lowerCAmelCase							=      sentencepiece.SentencePieceProcessor(**lowercase__    )
							spm.Load(str(lowercase__    )    )
							return spm






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       List[Any]    ):






							"""simple docstring"""



							with open(lowercase__  ,	'r'    ) as f:
														return json.load(lowercase__    )






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Dict  ,	SCREAMING_SNAKE_CASE:       int    ):






							"""simple docstring"""



							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 (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			def    A_       (    self    ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
			@property
			def    A_       (    self    ):
							torch.manual_seed(0    )
							_lowerCamelCase	:     Union[str, Any]       =       UNetaDModel(
							    sample_size=(32, 64)    , in_channels=1    , out_channels=1    , layers_per_block=2    , block_out_channels=(128, 128)    , down_block_types=('AttnDownBlock2D', 'DownBlock2D')    , up_block_types=('UpBlock2D', 'AttnUpBlock2D')    , )
							return model
			@property
			def    A_       (    self    ):
							torch.manual_seed(0    )
							_lowerCamelCase	:     Dict       =       UNetaDConditionModel(
							    sample_size=(64, 32)    , in_channels=1    , out_channels=1    , layers_per_block=2    , block_out_channels=(128, 128)    , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')    , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')    , cross_attention_dim=10    , )
							return model
			@property
			def    A_       (    self    ):
							torch.manual_seed(0    )
							_lowerCamelCase	:     List[str]       =       AutoencoderKL(
							    sample_size=(128, 64)    , in_channels=1    , out_channels=1    , latent_channels=1    , layers_per_block=2    , block_out_channels=(128, 128)    , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')    , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')    , )
							_lowerCamelCase	:     int       =       UNetaDModel(
							    sample_size=(64, 32)    , in_channels=1    , out_channels=1    , layers_per_block=2    , block_out_channels=(128, 128)    , down_block_types=('AttnDownBlock2D', 'DownBlock2D')    , up_block_types=('UpBlock2D', 'AttnUpBlock2D')    , )
							return vqvae, unet






			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       'cpu'  # ensure determinism for the device-dependent torch.Generator
							_lowerCamelCase	:     Optional[int]       =       Mel(
							    x_res=self.dummy_unet.config.sample_size[1]    , y_res=self.dummy_unet.config.sample_size[0]    , )

							_lowerCamelCase	:     List[str]       =       DDPMScheduler()
							_lowerCamelCase	:     List[Any]       =       AudioDiffusionPipeline(vqvae=lowercase    , unet=self.dummy_unet    , mel=lowercase    , scheduler=lowercase    )
							_lowerCamelCase	:     List[Any]       =       pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )

							_lowerCamelCase	:     Union[str, Any]       =       torch.Generator(device=lowercase    ).manual_seed(42    )
							_lowerCamelCase	:     Union[str, Any]       =       pipe(generator=lowercase    , steps=4    )
							_lowerCamelCase	:     Optional[Any]       =       output.audios[0]
							_lowerCamelCase	:     int       =       output.images[0]

							_lowerCamelCase	:     Dict       =       torch.Generator(device=lowercase    ).manual_seed(42    )
							_lowerCamelCase	:     Dict       =       pipe(generator=lowercase    , steps=4    , return_dict=lowercase    )
							_lowerCamelCase	:     List[str]       =       output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							_lowerCamelCase	:     Optional[Any]       =       np.frombuffer(image.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     Dict       =       np.frombuffer(image_from_tuple.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     List[str]       =       np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() == 0

							_lowerCamelCase	:     List[Any]       =       Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]    , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]    , )

							_lowerCamelCase	:     Dict       =       DDIMScheduler()
							_lowerCamelCase	:     Tuple       =       self.dummy_vqvae_and_unet
							_lowerCamelCase	:     List[str]       =       AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]    , unet=dummy_vqvae_and_unet[1]    , mel=lowercase    , scheduler=lowercase    )
							_lowerCamelCase	:     Tuple       =       pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )

							np.random.seed(0    )
							_lowerCamelCase	:     Any       =       np.random.uniform(-1    , 1    , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)    )
							_lowerCamelCase	:     Optional[int]       =       torch.Generator(device=lowercase    ).manual_seed(42    )
							_lowerCamelCase	:     Dict       =       pipe(raw_audio=lowercase    , generator=lowercase    , start_step=5    , steps=10    )
							_lowerCamelCase	:     str       =       output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							_lowerCamelCase	:     Dict       =       np.frombuffer(image.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     Union[str, Any]       =       np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0

							_lowerCamelCase	:     Dict       =       self.dummy_unet_condition
							_lowerCamelCase	:     Optional[int]       =       AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]    , unet=lowercase    , mel=lowercase    , scheduler=lowercase    )
							_lowerCamelCase	:     Dict       =       pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )

							np.random.seed(0    )
							_lowerCamelCase	:     Optional[int]       =       torch.rand((1, 1, 10)    )
							_lowerCamelCase	:     Optional[Any]       =       pipe(generator=lowercase    , encoding=lowercase    )
							_lowerCamelCase	:     Dict       =       output.images[0]
							_lowerCamelCase	:     Tuple       =       np.frombuffer(image.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     List[str]       =       np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0






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






			'''simple docstring'''



			def    A_       (    self    ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       torch_device

							_lowerCamelCase	:     Dict       =       DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'    )
							_lowerCamelCase	:     List[str]       =       pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )

							_lowerCamelCase	:     int       =       torch.Generator(device=lowercase    ).manual_seed(42    )
							_lowerCamelCase	:     Tuple       =       pipe(generator=lowercase    )
							_lowerCamelCase	:     Dict       =       output.audios[0]
							_lowerCamelCase	:     Dict       =       output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							_lowerCamelCase	:     Optional[int]       =       np.frombuffer(image.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     int       =       np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0