'''simple docstring'''




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     a__      (   unittest.TestCase ):






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''ylacombe/bark-small'''
         __lowerCamelCase		=  tempfile.mkdtemp()
         __lowerCamelCase		=  '''en_speaker_1'''
         __lowerCamelCase		=  '''This is a test string'''
         __lowerCamelCase		=  '''speaker_embeddings_path.json'''
         __lowerCamelCase		=  '''speaker_embeddings'''






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]  ,				**a		:		Dict    ):





         """simple docstring"""



         return AutoTokenizer.from_pretrained(self.checkpoint  ,				**a    )






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





         """simple docstring"""



         shutil.rmtree(self.tmpdirname    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  self.get_tokenizer()

         __lowerCamelCase		=  BarkProcessor(tokenizer=a    )

         processor.save_pretrained(self.tmpdirname    )
         __lowerCamelCase		=  BarkProcessor.from_pretrained(self.tmpdirname    )

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






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  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  ,				)

         __lowerCamelCase		=  self.get_tokenizer(bos_token='''(BOS)'''  ,				eos_token='''(EOS)'''    )

         __lowerCamelCase		=  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    SCREAMING_SNAKE_CASE__   ( self		:		List[str]    ):





         """simple docstring"""



         __lowerCamelCase		=  BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint  ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,				)

         __lowerCamelCase		=  35
         __lowerCamelCase		=  2
         __lowerCamelCase		=  8

         __lowerCamelCase		=  {
             '''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
         __lowerCamelCase		=  processor(text=self.input_string  ,				voice_preset=a    )

         __lowerCamelCase		=  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
         __lowerCamelCase		=  os.path.join(self.tmpdirname  ,				'''file.npz'''    )
         np.savez(a  ,				**a    )
         __lowerCamelCase		=  processor(text=self.input_string  ,				voice_preset=a    )
         __lowerCamelCase		=  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
         __lowerCamelCase		=  processor(text=self.input_string  ,				voice_preset=self.voice_preset    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         __lowerCamelCase		=  self.get_tokenizer()

         __lowerCamelCase		=  BarkProcessor(tokenizer=a    )

         __lowerCamelCase		=  processor(text=self.input_string    )

         __lowerCamelCase		=  tokenizer(
             self.input_string  ,				padding='''max_length'''  ,				max_length=2_56  ,				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_fnet import FNetTokenizer
else:
  _snake_case										=       None

_snake_case										=       logging.get_logger(__name__)
_snake_case										=       {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_snake_case										=       {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

_snake_case										=       {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_snake_case										=       "▁"







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						VOCAB_FILES_NAMES
     _a							 =						PRETRAINED_VOCAB_FILES_MAP
     _a							 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a							 =						["input_ids", "token_type_ids"]
     _a							 =						FNetTokenizer

     def __init__(  self		,     _a=None		,     _a=None		,     _a=False		,     _a=True		,     _a=True		,     _a="<unk>"		,     _a="[SEP]"		,     _a="<pad>"		,     _a="[CLS]"		,     _a="[MASK]"		,     **_a		,     )						->							Optional[int]:
           # 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							:     int			=						(
               AddedToken(_a		,     lstrip=_a		,     rstrip=_a		,     normalized=_a				)
               if isinstance(_a		,     _a				)
               else mask_token
           )

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

           _A							:     Optional[int]			=						do_lower_case
           _A							:     List[Any]			=						remove_space
           _A							:     str			=						keep_accents
           _A							:     int			=						vocab_file
           _A							:     int			=						False if not self.vocab_file else True

     def 					a__	(  self		,     _a		,     _a = None				)						->							List[int]:
           _A							:     str			=						[self.sep_token_id]
           _A							:     Dict			=						[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 					a__	(  self		,     _a		,     _a = None				)						->							List[int]:
           _A							:     Any			=						[self.sep_token_id]
           _A							:     Any			=						[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 					a__	(  self		,     _a		,     _a = None				)						->							Tuple[str]:
           if not os.path.isdir(_a				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           _A							:     List[str]			=						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				):
                 copyfile(self.vocab_file		,     _a				)

           return (out_vocab_file,)

