





import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		Dict			 =	AudioLDMPipeline
   _UpperCamelCase   :		str			 =	TEXT_TO_AUDIO_PARAMS
   _UpperCamelCase   :		int			 =	TEXT_TO_AUDIO_BATCH_PARAMS
   _UpperCamelCase   :		Dict			 =	frozenset(
       [
           'num_inference_steps',
           'num_waveforms_per_prompt',
           'generator',
           'latents',
           'output_type',
           'return_dict',
           'callback',
           'callback_steps',
       ]				)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       str:





     """simple docstring"""







     torch.manual_seed(0	)
     lowercase__           =  UNetaDConditionModel(
         block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=4      ,					out_channels=4      ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,					cross_attention_dim=(32, 64)      ,					class_embed_type='simple_projection'      ,					projection_class_embeddings_input_dim=32      ,					class_embeddings_concat=a      ,					)
     lowercase__           =  DDIMScheduler(
         beta_start=0.00085      ,					beta_end=0.012      ,					beta_schedule='scaled_linear'      ,					clip_sample=a      ,					set_alpha_to_one=a      ,					)
     torch.manual_seed(0	)
     lowercase__           =  AutoencoderKL(
         block_out_channels=[32, 64]      ,					in_channels=1      ,					out_channels=1      ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,					latent_channels=4      ,					)
     torch.manual_seed(0	)
     lowercase__           =  ClapTextConfig(
         bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-0_5      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1_000      ,					projection_dim=32      ,					)
     lowercase__           =  ClapTextModelWithProjection(a	)
     lowercase__           =  RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'      ,					model_max_length=77	)

     lowercase__           =  SpeechTaHifiGanConfig(
         model_in_dim=8      ,					sampling_rate=16_000      ,					upsample_initial_channel=16      ,					upsample_rates=[2, 2]      ,					upsample_kernel_sizes=[4, 4]      ,					resblock_kernel_sizes=[3, 7]      ,					resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]      ,					normalize_before=a      ,					)

     lowercase__           =  SpeechTaHifiGan(a	)

     lowercase__           =  {
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'vocoder': vocoder,
     }
     return components


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    Optional[Any]      ,					a      :    Dict=0	)->       str:





     """simple docstring"""







     if str(a	).startswith('mps'	):
       lowercase__           =  torch.manual_seed(a	)
     else:
       lowercase__           =  torch.Generator(device=a	).manual_seed(a	)
     lowercase__           =  {
         'prompt': 'A hammer hitting a wooden surface',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 6.0,
     }
     return inputs


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





     """simple docstring"""







     lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator

     lowercase__           =  self.get_dummy_components()
     lowercase__           =  AudioLDMPipeline(**a	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  audioldm_pipe(**a	)
     lowercase__           =  output.audios[0]

     assert audio.ndim == 1
     assert len(a	) == 256

     lowercase__           =  audio[:10]
     lowercase__           =  np.array(
         [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       str:





     """simple docstring"""







     lowercase__           =  self.get_dummy_components()
     lowercase__           =  AudioLDMPipeline(**a	)
     lowercase__           =  audioldm_pipe.to(a	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  3 * [inputs['prompt']]

     # forward
     lowercase__           =  audioldm_pipe(**a	)
     lowercase__           =  output.audios[0]

     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  3 * [inputs.pop('prompt'	)]

     lowercase__           =  audioldm_pipe.tokenizer(
         a      ,					padding='max_length'      ,					max_length=audioldm_pipe.tokenizer.model_max_length      ,					truncation=a      ,					return_tensors='pt'      ,					)
     lowercase__           =  text_inputs['input_ids'].to(a	)

     lowercase__           =  audioldm_pipe.text_encoder(
         a      ,					)
     lowercase__           =  prompt_embeds.text_embeds
     # additional L_2 normalization over each hidden-state
     lowercase__           =  F.normalize(a      ,					dim=-1	)

     lowercase__           =  prompt_embeds

     # forward
     lowercase__           =  audioldm_pipe(**a	)
     lowercase__           =  output.audios[0]

     assert np.abs(audio_a - audio_a	).max() < 1E-2


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  self.get_dummy_components()
     lowercase__           =  AudioLDMPipeline(**a	)
     lowercase__           =  audioldm_pipe.to(a	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  3 * ['this is a negative prompt']
     lowercase__           =  negative_prompt
     lowercase__           =  3 * [inputs['prompt']]

     # forward
     lowercase__           =  audioldm_pipe(**a	)
     lowercase__           =  output.audios[0]

     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  3 * [inputs.pop('prompt'	)]

     lowercase__           =  []
     for p in [prompt, negative_prompt]:
       lowercase__           =  audioldm_pipe.tokenizer(
           a      ,					padding='max_length'      ,					max_length=audioldm_pipe.tokenizer.model_max_length      ,					truncation=a      ,					return_tensors='pt'      ,					)
       lowercase__           =  text_inputs['input_ids'].to(a	)

       lowercase__           =  audioldm_pipe.text_encoder(
           a      ,					)
       lowercase__           =  text_embeds.text_embeds
       # additional L_2 normalization over each hidden-state
       lowercase__           =  F.normalize(a      ,					dim=-1	)

       embeds.append(a	)

     lowercase__      ,   lowercase__           =  embeds

     # forward
     lowercase__           =  audioldm_pipe(**a	)
     lowercase__           =  output.audios[0]

     assert np.abs(audio_a - audio_a	).max() < 1E-2


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       int:





     """simple docstring"""







     lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator
     lowercase__           =  self.get_dummy_components()
     lowercase__           =  PNDMScheduler(skip_prk_steps=a	)
     lowercase__           =  AudioLDMPipeline(**a	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  'egg cracking'
     lowercase__           =  audioldm_pipe(**a      ,					negative_prompt=a	)
     lowercase__           =  output.audios[0]

     assert audio.ndim == 1
     assert len(a	) == 256

     lowercase__           =  audio[:10]
     lowercase__           =  np.array(
         [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Any:





     """simple docstring"""







     lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator
     lowercase__           =  self.get_dummy_components()
     lowercase__           =  PNDMScheduler(skip_prk_steps=a	)
     lowercase__           =  AudioLDMPipeline(**a	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  'A hammer hitting a wooden surface'

     # test num_waveforms_per_prompt=1 (default)
     lowercase__           =  audioldm_pipe(a      ,					num_inference_steps=2	).audios

     assert audios.shape == (1, 256)

     # test num_waveforms_per_prompt=1 (default) for batch of prompts
     lowercase__           =  2
     lowercase__           =  audioldm_pipe([prompt] * batch_size      ,					num_inference_steps=2	).audios

     assert audios.shape == (batch_size, 256)

     # test num_waveforms_per_prompt for single prompt
     lowercase__           =  2
     lowercase__           =  audioldm_pipe(a      ,					num_inference_steps=2      ,					num_waveforms_per_prompt=a	).audios

     assert audios.shape == (num_waveforms_per_prompt, 256)

     # test num_waveforms_per_prompt for batch of prompts
     lowercase__           =  2
     lowercase__           =  audioldm_pipe(
         [prompt] * batch_size      ,					num_inference_steps=2      ,					num_waveforms_per_prompt=a	).audios

     assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       List[str]:





     """simple docstring"""







     lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator
     lowercase__           =  self.get_dummy_components()
     lowercase__           =  AudioLDMPipeline(**a	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)
     lowercase__           =  audioldm_pipe.vocoder.config.sampling_rate

     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  audioldm_pipe(audio_length_in_s=0.016      ,					**a	)
     lowercase__           =  output.audios[0]

     assert audio.ndim == 1
     assert len(a	) / vocoder_sampling_rate == 0.016

     lowercase__           =  audioldm_pipe(audio_length_in_s=0.032      ,					**a	)
     lowercase__           =  output.audios[0]

     assert audio.ndim == 1
     assert len(a	) / vocoder_sampling_rate == 0.032


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  self.get_dummy_components()
     lowercase__           =  AudioLDMPipeline(**a	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  ['hey']

     lowercase__           =  audioldm_pipe(a      ,					num_inference_steps=1	)
     lowercase__           =  output.audios.shape
     assert audio_shape == (1, 256)

     lowercase__           =  audioldm_pipe.vocoder.config
     config.model_in_dim *= 2
     lowercase__           =  SpeechTaHifiGan(a	).to(a	)
     lowercase__           =  audioldm_pipe(a      ,					num_inference_steps=1	)
     lowercase__           =  output.audios.shape
     # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
     assert audio_shape == (1, 256)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       List[str]:





     """simple docstring"""







     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a	)


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





     """simple docstring"""







     self._test_inference_batch_single_identical(test_mean_pixel_difference=a	)


   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()      ,					reason='XFormers attention is only available with CUDA and `xformers` installed'      ,					)
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Any:





     """simple docstring"""







     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a	)






@slow
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       int:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    List[str]      ,					a      :    Dict="cpu"      ,					a      :    int=torch.floataa      ,					a      :    int=0	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  torch.Generator(device=a	).manual_seed(a	)
     lowercase__           =  np.random.RandomState(a	).standard_normal((1, 8, 128, 16)	)
     lowercase__           =  torch.from_numpy(a	).to(device=a      ,					dtype=a	)
     lowercase__           =  {
         'prompt': 'A hammer hitting a wooden surface',
         'latents': latents,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 2.5,
     }
     return inputs


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       int:





     """simple docstring"""







     lowercase__           =  AudioLDMPipeline.from_pretrained('cvssp/audioldm'	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_inputs(a	)
     lowercase__           =  25
     lowercase__           =  audioldm_pipe(**a	).audios[0]

     assert audio.ndim == 1
     assert len(a	) == 81_920

     lowercase__           =  audio[77_230:77_240]
     lowercase__           =  np.array(
         [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]	)
     lowercase__           =  np.abs(expected_slice - audio_slice	).max()
     assert max_diff < 1E-2


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





     """simple docstring"""







     lowercase__           =  AudioLDMPipeline.from_pretrained('cvssp/audioldm'	)
     lowercase__           =  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config	)
     lowercase__           =  audioldm_pipe.to(a	)
     audioldm_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_inputs(a	)
     lowercase__           =  audioldm_pipe(**a	).audios[0]

     assert audio.ndim == 1
     assert len(a	) == 81_920

     lowercase__           =  audio[27_780:27_790]
     lowercase__           =  np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]	)
     lowercase__           =  np.abs(expected_slice - audio_slice	).max()
     assert max_diff < 3E-2









import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


lowercase_          =		logging.get_logger(__name__)
lowercase_          =		{"""vocab_file""": """spiece.model"""}

lowercase_          =		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowercase_          =		{
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		List[str]			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		List[Any]			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Union[str, Any]			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Any			 =	['input_ids', 'attention_mask']


   def __init__(   self      :    Optional[Any]      ,					a      :    Tuple      ,					a      :    Optional[int]=False      ,					a      :    str=False      ,					a      :    str=False      ,					a      :    Tuple=None      ,					a      :    Any=None      ,					a      :    Union[str, Any]=None      ,					a      :    Union[str, Any]=None      ,					a      :    Optional[Dict[str, Any]] = None      ,					**a      :    Optional[int]      ,					)->       None:





     """simple docstring"""







     lowercase__           =  {} if sp_model_kwargs is None else sp_model_kwargs

     lowercase__           =  kwargs.get('name_or_path'	)
     if name_or_path is None:
       logger.warning(
           'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
           ' you are testing the model, this can safely be ignored'	)
       lowercase__           =  'None'

     # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
     lowercase__           =  '<|endoftext|>' if eos_token is None else eos_token
     lowercase__           =  '<unk>' if unk_token is None else unk_token
     if "gpt-sw3-7b" in name_or_path:
       lowercase__           =  unk_token if pad_token is None else pad_token
       lowercase__           =  eos_token if bos_token is None else bos_token
     else:
       lowercase__           =  '<pad>' if pad_token is None else pad_token
       lowercase__           =  '<s>' if bos_token is None else bos_token

     super().__init__(
         do_lower_case=a      ,					remove_space=a      ,					keep_accents=a      ,					bos_token=a      ,					eos_token=a      ,					unk_token=a      ,					pad_token=a      ,					sp_model_kwargs=self.sp_model_kwargs      ,					**a      ,					)

     lowercase__           =  do_lower_case
     lowercase__           =  remove_space
     lowercase__           =  keep_accents
     lowercase__           =  vocab_file

     lowercase__           =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(a	)

     # Used for whitespace normalization in input texts
     # fmt : off
     lowercase__           =  {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
     # fmt : on

     # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
     lowercase__           =  re.compile(
         f"""[{"".join(map(a      ,					list(range(0      ,					9	)	) + list(range(11      ,					32	)	) + list(range(127      ,					160	)	) + [160, 173, 8_203]	)	)}]"""	)


   def __getstate__(   self      :    Any	)->       str:





     """simple docstring"""







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


   def __setstate__(   self      :    int      ,					a      :    Optional[Any]	)->       int:





     """simple docstring"""







     lowercase__           =  d

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

     lowercase__           =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(self.vocab_file	)


   @property
   # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       int:





     """simple docstring"""







     return len(self.sp_model	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    str	)->       str:





     """simple docstring"""







     lowercase__           =  self.non_printing_characters_re.sub(''      ,					a	)

     # Normalize whitespaces
     lowercase__           =  ''.join([char if char not in self.whitespaces else ' ' for char in text]	)

     # NFC Unicode normalization
     lowercase__           =  unicodedata.normalize('NFC'      ,					a	)
     return text


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					**a      :    Tuple	)->       List[str]:





     """simple docstring"""







     lowercase__           =  self.preprocess_text(a	)
     return self.sp_model.encode(a      ,					out_type=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str	)->       int:





     """simple docstring"""







     return self.sp_model.PieceToId(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       str:





     """simple docstring"""







     return self.sp_model.IdToPiece(a	)


   @staticmethod
   def 	SCREAMING_SNAKE_CASE_							(   a      :    str	)->       str:





     """simple docstring"""







     return out_string


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    List[str]	)->       str:





     """simple docstring"""







     lowercase__           =  []
     lowercase__           =  ''
     lowercase__           =  False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
         if not prev_is_special:
           out_string += " "

         out_string += self.sp_model.decode(a	) + token
         lowercase__           =  True
         lowercase__           =  []
       else:
         current_sub_tokens.append(a	)
         lowercase__           =  False
     out_string += self.sp_model.decode(a	)

     return out_string


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, int]:





     """simple docstring"""







     lowercase__           =  {self.convert_ids_to_tokens(a	): i for i in range(self.vocab_size	)}
     vocab.update(self.added_tokens_encoder	)
     return vocab


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







     if not os.path.isdir(a	):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
     lowercase__           =  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:
         lowercase__           =  self.sp_model.serialized_model_proto()
         fi.write(a	)

     return (out_vocab_file,)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Union[str, List[str]]      ,					a      :    Union[str, bool] = False	)->       Union[List[int], List[List[int]], "torch.Tensor"]:





     """simple docstring"""







     if isinstance(a      ,					a	):
       lowercase__           =  self.preprocess_text(a	)
       lowercase__           =  self.sp_model.encode(a	)
     else:
       lowercase__           =  [self.preprocess_text(a	) for t in text]
       lowercase__           =  self.sp_model.encode(a	)

     if return_tensors is True or return_tensors == "pt":
       lowercase__           =  torch.tensor(a	)

     return token_ids


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





     """simple docstring"""







     return self.sp_model.decode(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    "Conversation"	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
     lowercase__           =  (
         f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(a	) + f"""{self.bos_token}Bot:"""
     )
     return self.encode(text=a	)


