






"""simple docstring"""






import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
       from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase_     :				int         =				256




class 							lowerCAmelCase__       (    A_				):






      '''simple docstring'''

      __UpperCamelCase							=						['''melgan''']
      def __init__(		self  :						str		,			lowercase_  :						Tuple		,			lowercase_  :						int		,			lowercase_  :						Dict		,			lowercase_  :						Any		,			lowercase_  :						Dict		,			):




          '''simple docstring'''


          super().__init__()

          # From MELGAN
          SCREAMING_SNAKE_CASE_   :						Any							=	math.log(1e-5)  # Matches MelGAN training.
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	4.0  # Largest value for most examples
          SCREAMING_SNAKE_CASE_   :						Dict							=	128

          self.register_modules(
              notes_encoder=lowercase_		,			continuous_encoder=lowercase_		,			decoder=lowercase_		,			scheduler=lowercase_		,			melgan=lowercase_		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			lowercase_  :						List[str]		,			lowercase_  :						Optional[Any]=(-1.0, 1.0)		,			lowercase_  :						Optional[int]=False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	output_range
          if clip:
              SCREAMING_SNAKE_CASE_   :						Dict							=	torch.clip(lowercase_		,			self.min_value		,			self.max_value)
          # Scale to [0, 1].
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(features - self.min_value) / (self.max_value - self.min_value)
          # Scale to [min_out, max_out].
          return zero_one * (max_out - min_out) + min_out
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						List[Any]		,			lowercase_  :						Optional[int]=(-1.0, 1.0)		,			lowercase_  :						Union[str, Any]=False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	input_range
          SCREAMING_SNAKE_CASE_   :						int							=	torch.clip(lowercase_		,			lowercase_		,			lowercase_) if clip else outputs
          # Scale to [0, 1].
          SCREAMING_SNAKE_CASE_   :						Tuple							=	(outputs - min_out) / (max_out - min_out)
          # Scale to [self.min_value, self.max_value].
          return zero_one * (self.max_value - self.min_value) + self.min_value
      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						Tuple		,			lowercase_  :						List[Any]		,			lowercase_  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	input_tokens > 0
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.notes_encoder(
              encoder_input_tokens=lowercase_		,			encoder_inputs_mask=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.continuous_encoder(
              encoder_inputs=lowercase_		,			encoder_inputs_mask=lowercase_)

          return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						Optional[Any]		,			lowercase_  :						Any		,			lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	noise_time
          if not torch.is_tensor(lowercase_):
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.tensor([timesteps]		,			dtype=torch.long		,			device=input_tokens.device)
          elif torch.is_tensor(lowercase_) and len(timesteps.shape) == 0:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	timesteps[None].to(input_tokens.device)

          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	timesteps * torch.ones(input_tokens.shape[0]		,			dtype=timesteps.dtype		,			device=timesteps.device)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.decoder(
              encodings_and_masks=lowercase_		,			decoder_input_tokens=lowercase_		,			decoder_noise_time=lowercase_)
          return logits



      @torch.no_grad()
      def __call__(		self  :						Tuple		,			lowercase_  :						str		,			lowercase_  :						Dict = None		,			lowercase_  :						List[str] = 100		,			lowercase_  :						List[Any] = True		,			lowercase_  :						int = "numpy"		,			lowercase_  :						Dict = None		,			lowercase_  :						List[str] = 1		,			):




          '''simple docstring'''


          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(lowercase_		,			lowercase_) or callback_steps <= 0)
          ):
              raise ValueError(
                  F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                  F' {type(lowercase_)}.')

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]		,			dtype=np.floataa)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.zeros([1, 0, self.n_dims]		,			np.floataa)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.ones((1, TARGET_FEATURE_LENGTH)		,			dtype=lowercase_		,			device=self.device)

          for i, encoder_input_tokens in enumerate(lowercase_):
              if i == 0:
                  SCREAMING_SNAKE_CASE_   :						Any							=	torch.from_numpy(pred_mel[:1].copy()).to(
                      device=self.device		,			dtype=self.decoder.dtype)
                  # The first chunk has no previous context.
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.zeros((1, TARGET_FEATURE_LENGTH)		,			dtype=lowercase_		,			device=self.device)
              else:
                  # The full song pipeline does not feed in a context feature, so the mask
                  # will be all 0s after the feature converter. Because we know we're
                  # feeding in a full context chunk from the previous prediction, set it
                  # to all 1s.
                  SCREAMING_SNAKE_CASE_   :						int							=	ones

              SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.scale_features(
                  lowercase_		,			output_range=[-1.0, 1.0]		,			clip=lowercase_)

              SCREAMING_SNAKE_CASE_   :						int							=	self.encode(
                  input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device)		,			continuous_inputs=lowercase_		,			continuous_mask=lowercase_		,			)

              # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
              SCREAMING_SNAKE_CASE_   :						int							=	randn_tensor(
                  shape=encoder_continuous_inputs.shape		,			generator=lowercase_		,			device=self.device		,			dtype=self.decoder.dtype		,			)

              # set step values
              self.scheduler.set_timesteps(lowercase_)

              # Denoising diffusion loop
              for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.decode(
                      encodings_and_masks=lowercase_		,			input_tokens=lowercase_		,			noise_time=t / self.scheduler.config.num_train_timesteps		,			)

                  # Compute previous output: x_t -> x_t-1
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	self.scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			generator=lowercase_).prev_sample

              SCREAMING_SNAKE_CASE_   :						Any							=	self.scale_to_features(lowercase_		,			input_range=[-1.0, 1.0])
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	mel[:1]
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	mel.cpu().float().numpy()

              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.concatenate([full_pred_mel, pred_mel[:1]]		,			axis=1)

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                  callback(lowercase_		,			lowercase_)

              logger.info('''Generated segment'''		,			lowercase_)

          if output_type == "numpy" and not is_onnx_available():
              raise ValueError(
                  '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''')
          elif output_type == "numpy" and self.melgan is None:
              raise ValueError(
                  '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''')

          if output_type == "numpy":
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.melgan(input_features=full_pred_mel.astype(np.floataa))
          else:
              SCREAMING_SNAKE_CASE_   :						Any							=	full_pred_mel

          if not return_dict:
              return (output,)

          return AudioPipelineOutput(audios=lowercase_)










"""simple docstring"""






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  UpperCAmelCase_     :				Tuple         =				"""pt"""
elif is_tf_available():
  UpperCAmelCase_     :				Optional[Any]         =				"""tf"""
else:
  UpperCAmelCase_     :				str         =				"""jax"""




class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						ByTaTokenizer
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          super().setUp()
          SCREAMING_SNAKE_CASE_   :						Dict							=	ByTaTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)
      @cached_property
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          return ByTaTokenizer.from_pretrained('''google/byt5-small''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			**lowercase_  :						Tuple):




          '''simple docstring'''


          return self.tokenizer_class.from_pretrained(self.tmpdirname		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[Any]=False		,			lowercase_  :						Tuple=20		,			lowercase_  :						List[str]=5):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
          for i in range(len(lowercase_)):
              try:
                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.decode([i]		,			clean_up_tokenization_spaces=lowercase_)
              except UnicodeDecodeError:
                  pass
              toks.append((i, tok))

          SCREAMING_SNAKE_CASE_   :						Dict							=	list(filter(lambda lowercase_: re.match(r'''^[ a-zA-Z]+$'''		,			t[1])		,			lowercase_))
          SCREAMING_SNAKE_CASE_   :						Any							=	list(filter(lambda lowercase_: [t[0]] == tokenizer.encode(t[1]		,			add_special_tokens=lowercase_)		,			lowercase_))
          if max_length is not None and len(lowercase_) > max_length:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	toks[:max_length]
          if min_length is not None and len(lowercase_) < min_length and len(lowercase_) > 0:
              while len(lowercase_) < min_length:
                  SCREAMING_SNAKE_CASE_   :						int							=	toks + toks
        # toks_str = [t[1] for t in toks]
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[t[0] for t in toks]

          # Ensure consistency
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer.decode(lowercase_		,			clean_up_tokenization_spaces=lowercase_)
          if " " not in output_txt and len(lowercase_) > 1:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	(
                  tokenizer.decode([toks_ids[0]]		,			clean_up_tokenization_spaces=lowercase_)
                  + ''' '''
                  + tokenizer.decode(toks_ids[1:]		,			clean_up_tokenization_spaces=lowercase_)
              )
          if with_prefix_space:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	''' ''' + output_txt
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
          return output_txt, output_ids
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''])
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(['''hi''', '''I went to the gym''', ''''''])
          self.assertListEqual(batch_with_eos_added['''input_ids''']		,			batch_without_eos_added['''input_ids'''])
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Any							=	'''Unicode €.'''
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)

          # decoding
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''Unicode €.</s>''')

          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer('''e è é ê ë''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)
          # decoding
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''e è é ê ë</s>''')

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))		,			'''e è é ê ë</s>''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Dict							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
          # fmt: on
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          self.assertIsInstance(lowercase_		,			lowercase_)

          if FRAMEWORK != "jax":
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	list(batch.input_ids.numpy()[0])
          else:
              SCREAMING_SNAKE_CASE_   :						int							=	list(batch.input_ids.tolist()[0])

          self.assertListEqual(lowercase_		,			lowercase_)

          self.assertEqual((2, 37)		,			batch.input_ids.shape)
          self.assertEqual((2, 37)		,			batch.attention_mask.shape)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn('''input_ids'''		,			lowercase_)
          self.assertIn('''attention_mask'''		,			lowercase_)
          self.assertNotIn('''decoder_input_ids'''		,			lowercase_)
          self.assertNotIn('''decoder_attention_mask'''		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						List[str]							=	[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer(
              text_target=lowercase_		,			max_length=32		,			padding='''max_length'''		,			truncation=lowercase_		,			return_tensors=lowercase_)
          self.assertEqual(32		,			targets['''input_ids'''].shape[1])
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	['''A long paragraph for summarization. </s>''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''Summary of the text. </s>''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
          SCREAMING_SNAKE_CASE_   :						Tuple							=	[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
          # fmt: on

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(lowercase_		,			text_target=lowercase_)

          self.assertEqual(lowercase_		,			batch['''input_ids'''][0])
          self.assertEqual(lowercase_		,			batch['''labels'''][0])
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  self.assertNotEqual(tokenizer.model_max_length		,			42)

        # Now let's start the test
          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	''' He is very happy, UNwant\u00E9d,running'''
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)

                  shutil.rmtree(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	self.get_tokenizers(model_max_length=42)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Any							=	''' He is very happy, UNwant\u00E9d,running'''
                  tokenizer.add_tokens(['''bim''', '''bambam'''])
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.additional_special_tokens
                  additional_special_tokens.append('''new_additional_special_token''')
                  tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)
                  self.assertIn('''new_additional_special_token'''		,			after_tokenizer.additional_special_tokens)
                  self.assertEqual(after_tokenizer.model_max_length		,			42)

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.__class__.from_pretrained(lowercase_		,			model_max_length=43)
                  self.assertEqual(tokenizer.model_max_length		,			43)

                  shutil.rmtree(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(lowercase_)

                  with open(os.path.join(lowercase_		,			'''special_tokens_map.json''')		,			encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_   :						List[Any]							=	json.load(lowercase_)

                  with open(os.path.join(lowercase_		,			'''tokenizer_config.json''')		,			encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_   :						Tuple							=	json.load(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	[F'<extra_id_{i}>' for i in range(125)]

                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]
                  SCREAMING_SNAKE_CASE_   :						int							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]

                  with open(os.path.join(lowercase_		,			'''special_tokens_map.json''')		,			'''w'''		,			encoding='''utf-8''') as outfile:
                      json.dump(lowercase_		,			lowercase_)
                  with open(os.path.join(lowercase_		,			'''tokenizer_config.json''')		,			'''w'''		,			encoding='''utf-8''') as outfile:
                      json.dump(lowercase_		,			lowercase_)

                  # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                  # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                  # "special_tokens_map.json" files
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer_class.from_pretrained(
                      lowercase_		,			)
                  self.assertIn(
                      '''an_additional_special_token'''		,			tokenizer_without_change_in_init.additional_special_tokens)
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  self.assertEqual(
                      ['''an_additional_special_token''']		,			tokenizer_without_change_in_init.convert_ids_to_tokens(
                          tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']))		,			)

                  # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''		,			lstrip=lowercase_)]
                  SCREAMING_SNAKE_CASE_   :						int							=	tokenizer_class.from_pretrained(
                      lowercase_		,			additional_special_tokens=lowercase_		,			)

                  self.assertIn('''a_new_additional_special_token'''		,			tokenizer.additional_special_tokens)
                  self.assertEqual(
                      ['''a_new_additional_special_token''']		,			tokenizer.convert_ids_to_tokens(
                          tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']))		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer_class.from_pretrained(lowercase_)

                  self.assertTrue(tokenizer.decode([255]) == '''''')
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.get_tokenizers(fast=lowercase_		,			do_lower_case=lowercase_)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						Any							=	['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.convert_tokens_to_string(lowercase_)

                  self.assertIsInstance(lowercase_		,			lowercase_)



      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						int							=	[
                      '''bos_token''',
                      '''eos_token''',
                      '''unk_token''',
                      '''sep_token''',
                      '''pad_token''',
                      '''cls_token''',
                      '''mask_token''',
                  ]

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	0
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.convert_ids_to_tokens(
                      lowercase_		,			skip_special_tokens=lowercase_)

                  for attr in attributes_list:
                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[])

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[token_id_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[token_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[token_id_to_test_setters])


