






'''simple docstring'''
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 							_UpperCAmelCase    (		snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Optional[int]									=     AudioLDMPipeline
    __lowerCamelCase: Tuple									=     TEXT_TO_AUDIO_PARAMS
    __lowerCamelCase: str									=     TEXT_TO_AUDIO_BATCH_PARAMS
    __lowerCamelCase: str									=     frozenset(
        [
            'num_inference_steps',
            'num_waveforms_per_prompt',
            'generator',
            'latents',
            'output_type',
            'return_dict',
            'callback',
            'callback_steps',
        ]      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       torch.manual_seed(0      )
       lowercase_						:     Optional[Any]							     =  UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=4	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=(3_2, 6_4)	,  class_embed_type="simple_projection"	,  projection_class_embeddings_input_dim=3_2	,  class_embeddings_concat=a	,  )
       lowercase_						:     str							     =  DDIMScheduler(
           beta_start=0.0_0085	,  beta_end=0.012	,  beta_schedule="scaled_linear"	,  clip_sample=a	,  set_alpha_to_one=a	,  )
       torch.manual_seed(0      )
       lowercase_						:     int							     =  AutoencoderKL(
           block_out_channels=[3_2, 6_4]	,  in_channels=1	,  out_channels=1	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
       torch.manual_seed(0      )
       lowercase_						:     Any							     =  ClapTextConfig(
           bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  projection_dim=3_2	,  )
       lowercase_						:     List[str]							     =  ClapTextModelWithProjection(a      )
       lowercase_						:     Tuple							     =  RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"	,  model_max_length=7_7      )

       lowercase_						:     Dict							     =  SpeechTaHifiGanConfig(
           model_in_dim=8	,  sampling_rate=1_6_0_0_0	,  upsample_initial_channel=1_6	,  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_						:     str							     =  SpeechTaHifiGan(a      )

       lowercase_						:     Any							     =  {
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "vocoder": vocoder,
       }
       return components




    def 					lowerCAmelCase__					(      self	:							Dict	,  a	:							Union[str, Any]	,  a	:							str=0      ):






       '''simple docstring'''






       if str(a      ).startswith("mps"      ):
          lowercase_						:     Optional[int]							     =  torch.manual_seed(a      )
       else:
          lowercase_						:     Union[str, Any]							     =  torch.Generator(device=a      ).manual_seed(a      )
       lowercase_						:     Union[str, Any]							     =  {
           "prompt": "A hammer hitting a wooden surface",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
       }
       return inputs




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  "cpu"  # ensure determinism for the device-dependent torch.Generator

       lowercase_						:     List[str]							     =  self.get_dummy_components()
       lowercase_						:     Tuple							     =  AudioLDMPipeline(**a      )
       lowercase_						:     Dict							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     str							     =  self.get_dummy_inputs(a      )
       lowercase_						:     int							     =  audioldm_pipe(**a      )
       lowercase_						:     Union[str, Any]							     =  output.audios[0]

       assert audio.ndim == 1
       assert len(a      ) == 2_5_6

       lowercase_						:     Optional[int]							     =  audio[:1_0]
       lowercase_						:     Union[str, Any]							     =  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 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  self.get_dummy_components()
       lowercase_						:     Optional[Any]							     =  AudioLDMPipeline(**a      )
       lowercase_						:     Any							     =  audioldm_pipe.to(a      )
       lowercase_						:     Optional[Any]							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Optional[Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     List[Any]							     =  3 * [inputs["prompt"]]

       # forward
       lowercase_						:     Optional[Any]							     =  audioldm_pipe(**a      )
       lowercase_						:     List[Any]							     =  output.audios[0]

       lowercase_						:     int							     =  self.get_dummy_inputs(a      )
       lowercase_						:     int							     =  3 * [inputs.pop("prompt"      )]

       lowercase_						:     int							     =  audioldm_pipe.tokenizer(
           a	,  padding="max_length"	,  max_length=audioldm_pipe.tokenizer.model_max_length	,  truncation=a	,  return_tensors="pt"	,  )
       lowercase_						:     str							     =  text_inputs["input_ids"].to(a      )

       lowercase_						:     Dict							     =  audioldm_pipe.text_encoder(
           a	,  )
       lowercase_						:     Any							     =  prompt_embeds.text_embeds
       # additional L_2 normalization over each hidden-state
       lowercase_						:     Optional[Any]							     =  F.normalize(a	,  dim=-1      )

       lowercase_						:     List[str]							     =  prompt_embeds

       # forward
       lowercase_						:     Tuple							     =  audioldm_pipe(**a      )
       lowercase_						:     List[Any]							     =  output.audios[0]

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




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  self.get_dummy_components()
       lowercase_						:     Optional[int]							     =  AudioLDMPipeline(**a      )
       lowercase_						:     List[Any]							     =  audioldm_pipe.to(a      )
       lowercase_						:     Optional[int]							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Dict							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Union[str, Any]							     =  3 * ["this is a negative prompt"]
       lowercase_						:     Union[str, Any]							     =  negative_prompt
       lowercase_						:     str							     =  3 * [inputs["prompt"]]

       # forward
       lowercase_						:     Optional[Any]							     =  audioldm_pipe(**a      )
       lowercase_						:     List[str]							     =  output.audios[0]

       lowercase_						:     int							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Optional[Any]							     =  3 * [inputs.pop("prompt"      )]

       lowercase_						:     Any							     =  []
       for p in [prompt, negative_prompt]:
          lowercase_						:     Dict							     =  audioldm_pipe.tokenizer(
              a	,  padding="max_length"	,  max_length=audioldm_pipe.tokenizer.model_max_length	,  truncation=a	,  return_tensors="pt"	,  )
          lowercase_						:     Tuple							     =  text_inputs["input_ids"].to(a      )

          lowercase_						:     Any							     =  audioldm_pipe.text_encoder(
              a	,  )
          lowercase_						:     List[str]							     =  text_embeds.text_embeds
          # additional L_2 normalization over each hidden-state
          lowercase_						:     Union[str, Any]							     =  F.normalize(a	,  dim=-1      )

          embeds.append(a      )

       lowercase_     ,		lowercase_						:     Any							     =  embeds

       # forward
       lowercase_						:     int							     =  audioldm_pipe(**a      )
       lowercase_						:     Tuple							     =  output.audios[0]

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




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  "cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase_						:     Any							     =  self.get_dummy_components()
       lowercase_						:     int							     =  PNDMScheduler(skip_prk_steps=a      )
       lowercase_						:     str							     =  AudioLDMPipeline(**a      )
       lowercase_						:     Optional[Any]							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Union[str, Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Tuple							     =  "egg cracking"
       lowercase_						:     str							     =  audioldm_pipe(**a	,  negative_prompt=a      )
       lowercase_						:     Any							     =  output.audios[0]

       assert audio.ndim == 1
       assert len(a      ) == 2_5_6

       lowercase_						:     str							     =  audio[:1_0]
       lowercase_						:     List[Any]							     =  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 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  "cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase_						:     int							     =  self.get_dummy_components()
       lowercase_						:     Optional[Any]							     =  PNDMScheduler(skip_prk_steps=a      )
       lowercase_						:     List[str]							     =  AudioLDMPipeline(**a      )
       lowercase_						:     Dict							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Union[str, Any]							     =  "A hammer hitting a wooden surface"

       # test num_waveforms_per_prompt=1 (default)
       lowercase_						:     Optional[int]							     =  audioldm_pipe(a	,  num_inference_steps=2      ).audios

       assert audios.shape == (1, 2_5_6)

       # test num_waveforms_per_prompt=1 (default) for batch of prompts
       lowercase_						:     int							     =  2
       lowercase_						:     List[str]							     =  audioldm_pipe([prompt] * batch_size	,  num_inference_steps=2      ).audios

       assert audios.shape == (batch_size, 2_5_6)

       # test num_waveforms_per_prompt for single prompt
       lowercase_						:     Dict							     =  2
       lowercase_						:     Optional[int]							     =  audioldm_pipe(a	,  num_inference_steps=2	,  num_waveforms_per_prompt=a      ).audios

       assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

       # test num_waveforms_per_prompt for batch of prompts
       lowercase_						:     List[Any]							     =  2
       lowercase_						:     Optional[int]							     =  audioldm_pipe(
           [prompt] * batch_size	,  num_inference_steps=2	,  num_waveforms_per_prompt=a      ).audios

       assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  "cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase_						:     List[Any]							     =  self.get_dummy_components()
       lowercase_						:     int							     =  AudioLDMPipeline(**a      )
       lowercase_						:     Tuple							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )
       lowercase_						:     Any							     =  audioldm_pipe.vocoder.config.sampling_rate

       lowercase_						:     List[str]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     List[str]							     =  audioldm_pipe(audio_length_in_s=0.016	,  **a      )
       lowercase_						:     List[Any]							     =  output.audios[0]

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

       lowercase_						:     Optional[Any]							     =  audioldm_pipe(audio_length_in_s=0.032	,  **a      )
       lowercase_						:     Any							     =  output.audios[0]

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




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  self.get_dummy_components()
       lowercase_						:     int							     =  AudioLDMPipeline(**a      )
       lowercase_						:     Optional[Any]							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     str							     =  ["hey"]

       lowercase_						:     str							     =  audioldm_pipe(a	,  num_inference_steps=1      )
       lowercase_						:     str							     =  output.audios.shape
       assert audio_shape == (1, 2_5_6)

       lowercase_						:     Any							     =  audioldm_pipe.vocoder.config
       config.model_in_dim *= 2
       lowercase_						:     int							     =  SpeechTaHifiGan(a      ).to(a      )
       lowercase_						:     Optional[int]							     =  audioldm_pipe(a	,  num_inference_steps=1      )
       lowercase_						:     Union[str, Any]							     =  output.audios.shape
       # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
       assert audio_shape == (1, 2_5_6)




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''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 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a      )


@slow
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Optional[Any]	,  a	:							List[str]	,  a	:							Union[str, Any]="cpu"	,  a	:							List[Any]=torch.floataa	,  a	:							Optional[int]=0      ):






       '''simple docstring'''






       lowercase_						:     int							     =  torch.Generator(device=a      ).manual_seed(a      )
       lowercase_						:     int							     =  np.random.RandomState(a      ).standard_normal((1, 8, 1_2_8, 1_6)      )
       lowercase_						:     Dict							     =  torch.from_numpy(a      ).to(device=a	,  dtype=a      )
       lowercase_						:     List[Any]							     =  {
           "prompt": "A hammer hitting a wooden surface",
           "latents": latents,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 2.5,
       }
       return inputs




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  AudioLDMPipeline.from_pretrained("cvssp/audioldm"      )
       lowercase_						:     int							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Union[str, Any]							     =  self.get_inputs(a      )
       lowercase_						:     Optional[Any]							     =  2_5
       lowercase_						:     int							     =  audioldm_pipe(**a      ).audios[0]

       assert audio.ndim == 1
       assert len(a      ) == 8_1_9_2_0

       lowercase_						:     Tuple							     =  audio[7_7_2_3_0:7_7_2_4_0]
       lowercase_						:     Dict							     =  np.array(
           [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]      )
       lowercase_						:     List[str]							     =  np.abs(expected_slice - audio_slice      ).max()
       assert max_diff < 1e-2





    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  AudioLDMPipeline.from_pretrained("cvssp/audioldm"      )
       lowercase_						:     Union[str, Any]							     =  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config      )
       lowercase_						:     int							     =  audioldm_pipe.to(a      )
       audioldm_pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Tuple							     =  self.get_inputs(a      )
       lowercase_						:     Optional[Any]							     =  audioldm_pipe(**a      ).audios[0]

       assert audio.ndim == 1
       assert len(a      ) == 8_1_9_2_0

       lowercase_						:     Any							     =  audio[2_7_7_8_0:2_7_7_9_0]
       lowercase_						:     List[str]							     =  np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]      )
       lowercase_						:     Optional[int]							     =  np.abs(expected_slice - audio_slice      ).max()
       assert max_diff < 3e-2












'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


UpperCamelCase__			=      logging.get_logger(__name__)


UpperCamelCase__			=      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__			=      {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

UpperCamelCase__			=      {
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}




@lru_cache()
def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     List[Any]							     =  (
       list(range(ord("!"				)    ,		ord("~"				) + 1				)				) + list(range(ord("¡"				)    ,		ord("¬"				) + 1				)				) + list(range(ord("®"				)    ,		ord("ÿ"				) + 1				)				)
   )
   lowercase_						:     str							     =  bs[:]
   lowercase_						:     Dict							     =  0
   for b in range(2**8				):
      if b not in bs:
         bs.append(_UpperCamelCase				)
         cs.append(2**8 + n				)
         n += 1
   lowercase_						:     Optional[int]							     =  [chr(_UpperCamelCase				) for n in cs]
   return dict(zip(_UpperCamelCase    ,		_UpperCamelCase				)				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  set()
   lowercase_						:     Union[str, Any]							     =  word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      lowercase_						:     Dict							     =  char
   return pairs





class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Dict									=     VOCAB_FILES_NAMES
    __lowerCamelCase: int									=     PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase: Dict									=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase: str									=     ['input_ids', 'attention_mask']




    def __init__(      self	:							Tuple	,  a	:							Tuple	,  a	:							Tuple	,  a	:							int="replace"	,  a	:							Optional[int]="<s>"	,  a	:							Tuple="</s>"	,  a	:							Tuple="</s>"	,  a	:							Tuple="<s>"	,  a	:							Optional[Any]="<unk>"	,  a	:							Dict="<pad>"	,  a	:							List[str]="<mask>"	,  a	:							Tuple=False	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else bos_token
       lowercase_						:     str							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else eos_token
       lowercase_						:     str							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else sep_token
       lowercase_						:     Any							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else cls_token
       lowercase_						:     Dict							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else unk_token
       lowercase_						:     List[Any]							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       lowercase_						:     Union[str, Any]							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else mask_token

       super().__init__(
           errors=a	,  bos_token=a	,  eos_token=a	,  unk_token=a	,  sep_token=a	,  cls_token=a	,  pad_token=a	,  mask_token=a	,  add_prefix_space=a	,  **a	,  )

       with open(a	,  encoding="utf-8"      ) as vocab_handle:
          lowercase_						:     Any							     =  json.load(a      )
       lowercase_						:     Union[str, Any]							     =  {v: k for k, v in self.encoder.items()}
       lowercase_						:     Dict							     =  errors  # how to handle errors in decoding
       lowercase_						:     Any							     =  bytes_to_unicode()
       lowercase_						:     List[Any]							     =  {v: k for k, v in self.byte_encoder.items()}
       with open(a	,  encoding="utf-8"      ) as merges_handle:
          lowercase_						:     Optional[Any]							     =  merges_handle.read().split("\n"      )[1:-1]
       lowercase_						:     Any							     =  [tuple(merge.split()      ) for merge in bpe_merges]
       lowercase_						:     List[str]							     =  dict(zip(a	,  range(len(a      )      )      )      )
       lowercase_						:     Optional[Any]							     =  {}
       lowercase_						:     Union[str, Any]							     =  add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       lowercase_						:     str							     =  re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"      )




    @property
    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       return len(self.encoder      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       return dict(self.encoder	,  **self.added_tokens_encoder      )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							int      ):






       '''simple docstring'''






       if token in self.cache:
          return self.cache[token]
       lowercase_						:     Optional[Any]							     =  tuple(a      )
       lowercase_						:     Tuple							     =  get_pairs(a      )

       if not pairs:
          return token

       while True:
          lowercase_						:     Any							     =  min(a	,  key=lambda a      : self.bpe_ranks.get(a	,  float("inf"      )      )      )
          if bigram not in self.bpe_ranks:
             break
          lowercase_     ,		lowercase_						:     Dict							     =  bigram
          lowercase_						:     List[Any]							     =  []
          lowercase_						:     Optional[Any]							     =  0
          while i < len(a      ):
             try:
                lowercase_						:     Union[str, Any]							     =  word.index(a	,  a      )
             except ValueError:
                new_word.extend(word[i:]      )
                break
             else:
                new_word.extend(word[i:j]      )
                lowercase_						:     Any							     =  j

             if word[i] == first and i < len(a      ) - 1 and word[i + 1] == second:
                new_word.append(first + second      )
                i += 2
             else:
                new_word.append(word[i]      )
                i += 1
          lowercase_						:     Any							     =  tuple(a      )
          lowercase_						:     List[str]							     =  new_word
          if len(a      ) == 1:
             break
          else:
             lowercase_						:     Union[str, Any]							     =  get_pairs(a      )
       lowercase_						:     List[str]							     =  " ".join(a      )
       lowercase_						:     Optional[int]							     =  word
       return word




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							str      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  []
       for token in re.findall(self.pat	,  a      ):
          lowercase_						:     Tuple							     =  "".join(
              self.byte_encoder[b] for b in token.encode("utf-8"      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a      ).split(" "      )      )
       return bpe_tokens




    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							Dict      ):






       '''simple docstring'''






       return self.encoder.get(a	,  self.encoder.get(self.unk_token      )      )




    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							str      ):






       '''simple docstring'''






       return self.decoder.get(a      )




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  "".join(a      )
       lowercase_						:     str							     =  bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"	,  errors=self.errors      )
       return text




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							str	,  a	:							Optional[str] = None      ):






       '''simple docstring'''






       if not os.path.isdir(a      ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
       lowercase_						:     Any							     =  os.path.join(
           a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
       lowercase_						:     Optional[int]							     =  os.path.join(
           a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

       with open(a	,  "w"	,  encoding="utf-8"      ) as f:
          f.write(json.dumps(self.encoder	,  indent=2	,  sort_keys=a	,  ensure_ascii=a      ) + "\n"      )

       lowercase_						:     Dict							     =  0
       with open(a	,  "w"	,  encoding="utf-8"      ) as writer:
          writer.write("#version: 0.2\n"      )
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,  key=lambda a      : kv[1]      ):
             if index != token_index:
                logger.warning(
                    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                    " Please check that the tokenizer is not corrupted!"      )
                lowercase_						:     Optional[Any]							     =  token_index
             writer.write(" ".join(a      ) + "\n"      )
             index += 1

       return vocab_file, merge_file




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							List[int]	,  a	:							Optional[List[int]] = None      ):






       '''simple docstring'''






       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase_						:     List[str]							     =  [self.cls_token_id]
       lowercase_						:     List[Any]							     =  [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							List[int]	,  a	:							Optional[List[int]] = None	,  a	:							bool = 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      )

       if token_ids_a is None:
          return [1] + ([0] * len(a      )) + [1]
       return [1] + ([0] * len(a      )) + [1, 1] + ([0] * len(a      )) + [1]




    def 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							List[int]	,  a	:							Optional[List[int]] = None      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  [self.sep_token_id]
       lowercase_						:     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	:							str	,  a	:							Any	,  a	:							int=False	,  **a	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  kwargs.pop("add_prefix_space"	,  self.add_prefix_space      )
       if (is_split_into_words or add_prefix_space) and (len(a      ) > 0 and not text[0].isspace()):
          lowercase_						:     str							     =  " " + text
       return (text, kwargs)




