

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow






@require_torch
class      lowerCamelCase_      (	unittest.TestCase	):




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[Any]		    =			"""ylacombe/bark-small"""
		__A :			Any		    =			tempfile.mkdtemp()
		__A :			Optional[int]		    =			"""en_speaker_1"""
		__A :			List[Any]		    =			"""This is a test string"""
		__A :			int		    =			"""speaker_embeddings_path.json"""
		__A :			List[str]		    =			"""speaker_embeddings"""




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      **__A						:     Any							):
		return AutoTokenizer.from_pretrained(self.checkpoint					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		shutil.rmtree(self.tmpdirname							)




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			str		    =			self.get_tokenizer()

		__A :			Optional[Any]		    =			BarkProcessor(tokenizer=__A							)

		processor.save_pretrained(self.tmpdirname							)
		__A :			List[str]		    =			BarkProcessor.from_pretrained(self.tmpdirname							)

		self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer.get_vocab()							)




	@slow
	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			Optional[int]		    =			BarkProcessor.from_pretrained(
		    pretrained_processor_name_or_path=self.checkpoint					,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,      )
		processor.save_pretrained(
		    self.tmpdirname					,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,      speaker_embeddings_directory=self.speaker_embeddings_directory					,      )

		__A :			str		    =			self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""							)

		__A :			Optional[int]		    =			BarkProcessor.from_pretrained(
		    self.tmpdirname					,      self.speaker_embeddings_dict_path					,      bos_token="""(BOS)"""					,      eos_token="""(EOS)"""					,      )

		self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer_add_kwargs.get_vocab()							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			List[str]		    =			BarkProcessor.from_pretrained(
		    pretrained_processor_name_or_path=self.checkpoint					,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,      )

		__A :			int		    =			35
		__A :			Union[str, Any]		    =			2
		__A :			Optional[Any]		    =			8

		__A :			List[str]		    =			{
		    """semantic_prompt""": np.ones(__A							),
		    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)							),
		    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)							),
		}

		# test providing already loaded voice_preset
		__A :			Union[str, Any]		    =			processor(text=self.input_string					,      voice_preset=__A							)

		__A :			List[str]		    =			inputs["""history_prompt"""]
		for key in voice_preset:
			self.assertListEqual(voice_preset[key].tolist()					,      processed_voice_preset.get(__A					,      np.array([]							)							).tolist()							)

		# test loading voice preset from npz file
		__A :			List[str]		    =			os.path.join(self.tmpdirname					,      """file.npz"""							)
		np.savez(__A					,      **__A							)
		__A :			Optional[Any]		    =			processor(text=self.input_string					,      voice_preset=__A							)
		__A :			str		    =			inputs["""history_prompt"""]

		for key in voice_preset:
			self.assertListEqual(voice_preset[key].tolist()					,      processed_voice_preset.get(__A					,      np.array([]							)							).tolist()							)

		# test loading voice preset from the hub
		__A :			Dict		    =			processor(text=self.input_string					,      voice_preset=self.voice_preset							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			str		    =			self.get_tokenizer()

		__A :			str		    =			BarkProcessor(tokenizer=__A							)

		__A :			int		    =			processor(text=self.input_string							)

		__A :			int		    =			tokenizer(
		    self.input_string					,      padding="""max_length"""					,      max_length=256					,      add_special_tokens=__A					,      return_attention_mask=__A					,      return_token_type_ids=__A					,      )

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]					,      encoded_processor[key].squeeze().tolist()							)






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_albert import AlbertTokenizer
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		None

SCREAMING_SNAKE_CASE			:						str				  =		logging.get_logger(__name__)
SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE			:						Any				  =		{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		"""▁"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE     					=      AlbertTokenizer

  def __init__(		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				__SCREAMING_SNAKE_CASE			:   Tuple=True  ,				__SCREAMING_SNAKE_CASE			:   Dict=False  ,				__SCREAMING_SNAKE_CASE			:   Dict="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Dict="<unk>"  ,				__SCREAMING_SNAKE_CASE			:   str="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Any="<pad>"  ,				__SCREAMING_SNAKE_CASE			:   List[str]="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]="[MASK]"  ,				**__SCREAMING_SNAKE_CASE			:   Tuple  ,				):
   # Mask token behave like a normal word, i.e. include the space before it and
   # is included in the raw text, there should be a match in a non-normalized sentence.
   __a							=	(
       AddedToken(__SCREAMING_SNAKE_CASE  ,				lstrip=__SCREAMING_SNAKE_CASE  ,				rstrip=__SCREAMING_SNAKE_CASE  ,				normalized=__SCREAMING_SNAKE_CASE							)
       if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
       else mask_token
   )

   super().__init__(
       __SCREAMING_SNAKE_CASE  ,				tokenizer_file=__SCREAMING_SNAKE_CASE  ,				do_lower_case=__SCREAMING_SNAKE_CASE  ,				remove_space=__SCREAMING_SNAKE_CASE  ,				keep_accents=__SCREAMING_SNAKE_CASE  ,				bos_token=__SCREAMING_SNAKE_CASE  ,				eos_token=__SCREAMING_SNAKE_CASE  ,				unk_token=__SCREAMING_SNAKE_CASE  ,				sep_token=__SCREAMING_SNAKE_CASE  ,				pad_token=__SCREAMING_SNAKE_CASE  ,				cls_token=__SCREAMING_SNAKE_CASE  ,				mask_token=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	do_lower_case
   __a							=	remove_space
   __a							=	keep_accents
   __a							=	vocab_file
   __a							=	False if not self.vocab_file else True

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[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							) * [0] + len(token_ids_a + sep							) * [1]

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None							):
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
        "tokenizer."							)

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

   if os.path.abspath(self.vocab_file							) != os.path.abspath(__SCREAMING_SNAKE_CASE							):
    copyfile(self.vocab_file  ,				__SCREAMING_SNAKE_CASE							)

   return (out_vocab_file,)


