import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					logging.get_logger(__name__)

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'AutoTokenizer'
  lowercase_			=     ['tokenizer']

  lowercase_			=     {
      'semantic_prompt': 1,
      'coarse_prompt': 2,
      'fine_prompt': 2,
  }




  def __init__(      self     :	Union[str, Any]     , a_     :	List[Any]     , a_     :	Dict=None				)->       Any:




       """simple docstring"""
       super().__init__(a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		speaker_embeddings




  @classmethod
  def 	__lowercase(      cls     :	List[str]     , a_     :	Union[str, Any]     , a_     :	Any="speaker_embeddings_path.json"     , **a_     :	int				)->       List[str]:




       """simple docstring"""

       if speaker_embeddings_dict_path is not None:
            SCREAMING_SNAKE_CASE__				:				Dict			=		get_file_from_repo(
                a_     , a_     , subfolder=kwargs.pop('subfolder'     , a_				)     , cache_dir=kwargs.pop('cache_dir'     , a_				)     , force_download=kwargs.pop('force_download'     , a_				)     , proxies=kwargs.pop('proxies'     , a_				)     , resume_download=kwargs.pop('resume_download'     , a_				)     , local_files_only=kwargs.pop('local_files_only'     , a_				)     , use_auth_token=kwargs.pop('use_auth_token'     , a_				)     , revision=kwargs.pop('revision'     , a_				)     , )
            if speaker_embeddings_path is None:
                 logger.warning(
                     F'''`{os.path.join(a_     , a_				)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''				)
                 SCREAMING_SNAKE_CASE__				:				int			=		None
            else:
                 with open(a_				) as speaker_embeddings_json:
                      SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		json.load(a_				)
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		None

       SCREAMING_SNAKE_CASE__				:				Tuple			=		AutoTokenizer.from_pretrained(a_     , **a_				)

       return cls(tokenizer=a_     , speaker_embeddings=a_				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Tuple     , a_     :	Dict="speaker_embeddings_path.json"     , a_     :	int="speaker_embeddings"     , a_     :	bool = False     , **a_     :	Tuple     , )->       Optional[Any]:




       """simple docstring"""
       if self.speaker_embeddings is not None:
            os.makedirs(os.path.join(a_     , a_     , 'v2'				)     , exist_ok=a_				)

            SCREAMING_SNAKE_CASE__				:				List[str]			=		{}

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		save_directory

            for prompt_key in self.speaker_embeddings:
                 if prompt_key != "repo_or_path":
                      SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self._load_voice_preset(a_				)

                      SCREAMING_SNAKE_CASE__				:				List[Any]			=		{}
                      for key in self.speaker_embeddings[prompt_key]:
                           np.save(
                               os.path.join(
                                   embeddings_dict['repo_or_path']     , a_     , F'''{prompt_key}_{key}'''				)     , voice_preset[key]     , allow_pickle=a_     , )
                           SCREAMING_SNAKE_CASE__				:				int			=		os.path.join(a_     , F'''{prompt_key}_{key}.npy'''				)

                      SCREAMING_SNAKE_CASE__				:				str			=		tmp_dict

            with open(os.path.join(a_     , a_				)     , 'w'				) as fp:
                 json.dump(a_     , a_				)

       super().save_pretrained(a_     , a_     , **a_				)




  def 	__lowercase(      self     :	List[Any]     , a_     :	str = None     , **a_     :	Optional[int]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.speaker_embeddings[voice_preset]

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{}
       for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
            if key not in voice_preset_paths:
                 raise ValueError(
                     F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''				)

            SCREAMING_SNAKE_CASE__				:				Tuple			=		get_file_from_repo(
                self.speaker_embeddings.get('repo_or_path'     , '/'				)     , voice_preset_paths[key]     , subfolder=kwargs.pop('subfolder'     , a_				)     , cache_dir=kwargs.pop('cache_dir'     , a_				)     , force_download=kwargs.pop('force_download'     , a_				)     , proxies=kwargs.pop('proxies'     , a_				)     , resume_download=kwargs.pop('resume_download'     , a_				)     , local_files_only=kwargs.pop('local_files_only'     , a_				)     , use_auth_token=kwargs.pop('use_auth_token'     , a_				)     , revision=kwargs.pop('revision'     , a_				)     , )
            if path is None:
                 raise ValueError(
                     F'''`{os.path.join(self.speaker_embeddings.get('repo_or_path'     , '/'				)     , voice_preset_paths[key]				)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''				)

            SCREAMING_SNAKE_CASE__				:				Tuple			=		np.load(a_				)

       return voice_preset_dict




  def 	__lowercase(      self     :	List[Any]     , a_     :	Optional[dict] = None				)->       List[str]:




       """simple docstring"""
       for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
            if key not in voice_preset:
                 raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.'''				)

            if not isinstance(voice_preset[key]     , np.ndarray				):
                 raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]				)}D ndarray.'''				)

            if len(voice_preset[key].shape				) != self.preset_shape[key]:
                 raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]				)}D ndarray.'''				)




  def __call__(      self     :	Optional[Any]     , a_     :	Dict=None     , a_     :	Optional[Any]=None     , a_     :	Tuple="pt"     , a_     :	Union[str, Any]=256     , a_     :	str=False     , a_     :	List[Any]=True     , a_     :	int=False     , **a_     :	List[str]     , )->       Optional[Any]:




       """simple docstring"""
       if voice_preset is not None and not isinstance(a_     , a_				):
            if (
                isinstance(a_     , a_				)
                and self.speaker_embeddings is not None
                and voice_preset in self.speaker_embeddings
            ):
                 SCREAMING_SNAKE_CASE__				:				int			=		self._load_voice_preset(a_				)

            else:
                 if isinstance(a_     , a_				) and not voice_preset.endswith('.npz'				):
                      SCREAMING_SNAKE_CASE__				:				int			=		voice_preset + '.npz'

                 SCREAMING_SNAKE_CASE__				:				Tuple			=		np.load(a_				)

       if voice_preset is not None:
            self._validate_voice_preset_dict(a_     , **a_				)
            SCREAMING_SNAKE_CASE__				:				str			=		BatchFeature(data=a_     , tensor_type=a_				)

       SCREAMING_SNAKE_CASE__				:				Any			=		self.tokenizer(
           a_     , return_tensors=a_     , padding='max_length'     , max_length=a_     , return_attention_mask=a_     , return_token_type_ids=a_     , add_special_tokens=a_     , **a_     , )

       if voice_preset is not None:
            SCREAMING_SNAKE_CASE__				:				Dict			=		voice_preset

       return encoded_text




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__		:					List[Any]						=					get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PegasusTokenizer
  lowercase_			=     PegasusTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




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




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		PegasusTokenizer(a_				)
       tokenizer.save_pretrained(self.tmpdirname				)




  @cached_property
  def 	__lowercase(      self     :	Optional[Any]				)->       Optional[int]:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained('google/pegasus-large'				)




  def 	__lowercase(      self     :	Any     , **a_     :	Optional[Any]				)->       PegasusTokenizer:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **a_				)




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




       """simple docstring"""
       return ("This is a test", "This is a test")




  def 	__lowercase(      self     :	Optional[int]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		'</s>'
       SCREAMING_SNAKE_CASE__				:				Any			=		1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_				)     , a_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_				)     , a_				)




  def 	__lowercase(      self     :	Dict				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]     , '<pad>'				)
       self.assertEqual(vocab_keys[1]     , '</s>'				)
       self.assertEqual(vocab_keys[-1]     , 'v'				)
       self.assertEqual(len(a_				)     , 1103				)




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




       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size     , 1103				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
           ' </s> <pad> <pad> <pad>'
       )
       SCREAMING_SNAKE_CASE__				:				List[str]			=		rust_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		py_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       SCREAMING_SNAKE_CASE__				:				Any			=		'<mask_1> To ensure a <mask_2> flow of bank resolutions.'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer([raw_input_str]     , return_tensors=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_6103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1024
       SCREAMING_SNAKE_CASE__				:				int			=		'To ensure a smooth flow of bank resolutions.'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer([raw_input_str]     , return_tensors=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




  @require_torch
  def 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		['This is going to be way too long.' * 150, 'short example']
       SCREAMING_SNAKE_CASE__				:				int			=		['not super long but more than 5 tokens', 'tiny']
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer(a_     , padding=a_     , truncation=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._large_tokenizer(
           text_target=a_     , max_length=5     , padding=a_     , truncation=a_     , return_tensors='pt'				)

       assert batch.input_ids.shape == (2, 1024)
       assert batch.attention_mask.shape == (2, 1024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(a_				) == 2  # input_ids, attention_mask.




  @slow
  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       # fmt: off
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=a_     , model_name='google/bigbird-pegasus-large-arxiv'     , revision='ba85d0851d708441f91440d509690f1ab6353415'     , )




@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PegasusTokenizer
  lowercase_			=     PegasusTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




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




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		PegasusTokenizer(a_     , offset=0     , mask_token_sent=a_     , mask_token='[MASK]'				)
       tokenizer.save_pretrained(self.tmpdirname				)




  @cached_property
  def 	__lowercase(      self     :	Optional[Any]				)->       List[str]:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'				)




  def 	__lowercase(      self     :	List[str]     , **a_     :	Optional[Any]				)->       PegasusTokenizer:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Tuple				)->       str:




       """simple docstring"""
       return ("This is a test", "This is a test")




  def 	__lowercase(      self     :	str				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
           ' <pad> <pad> <pad>'
       )
       SCREAMING_SNAKE_CASE__				:				str			=		rust_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       SCREAMING_SNAKE_CASE__				:				str			=		py_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




  @require_torch
  def 	__lowercase(      self     :	List[str]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		['This is going to be way too long.' * 1000, 'short example']
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		['not super long but more than 5 tokens', 'tiny']
       SCREAMING_SNAKE_CASE__				:				str			=		self._large_tokenizer(a_     , padding=a_     , truncation=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer(
           text_target=a_     , max_length=5     , padding=a_     , truncation=a_     , return_tensors='pt'				)

       assert batch.input_ids.shape == (2, 4096)
       assert batch.attention_mask.shape == (2, 4096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(a_				) == 2  # input_ids, attention_mask.




  def 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		(
           'This is an example string that is used to test the original TF implementation against the HF'
           ' implementation'
       )

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self._large_tokenizer(a_				).input_ids

       self.assertListEqual(
           a_     , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]     , )



