
'''simple docstring'''






import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     __snake_case		(   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	RobertaTokenizer
					_lowerCAmelCase			     =	RobertaTokenizerFast
					_lowerCAmelCase			     =	True
					_lowerCAmelCase			     =	{'''cls_token''': '''<s>'''}
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									lowerCamelCase					:			Union[str, Any]									= [
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    '\u0120',
									    '\u0120l',
									    '\u0120n',
									    '\u0120lo',
									    '\u0120low',
									    'er',
									    '\u0120lowest',
									    '\u0120newer',
									    '\u0120wider',
									    '<unk>',
									]
									lowerCamelCase					:			Tuple									= dict(zip(A,    range(len(A      )      )      )      )
									lowerCamelCase					:			Dict									= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
									lowerCamelCase					:			str									= {'unk_token': '<unk>'}

									lowerCamelCase					:			Optional[Any]									= os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES['vocab_file']      )
									lowerCamelCase					:			Optional[Any]									= os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES['merges_file']      )
									with open(self.vocab_file,    'w',    encoding='utf-8'      ) as fp:
													fp.write(json.dumps(A      ) + '\n'      )
									with open(self.merges_file,    'w',    encoding='utf-8'      ) as fp:
													fp.write('\n'.join(A      )      )
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									kwargs.update(self.special_tokens_map      )
									return self.tokenizer_class.from_pretrained(self.tmpdirname,    **A      )
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									kwargs.update(self.special_tokens_map      )
									return RobertaTokenizerFast.from_pretrained(self.tmpdirname,    **A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= 'lower newer'
									lowerCamelCase					:			int									= 'lower newer'
									return input_text, output_text
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.tokenizer_class(self.vocab_file,    self.merges_file,    **self.special_tokens_map      )
									lowerCamelCase					:			str									= 'lower newer'
									lowerCamelCase					:			Optional[int]									= ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
									lowerCamelCase					:			Dict									= tokenizer.tokenize(A      )  # , add_prefix_space=True)
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Tuple									= tokens + [tokenizer.unk_token]
									lowerCamelCase					:			Dict									= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(A      ),    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self.get_tokenizer()

									self.assertListEqual(tokenizer.encode('Hello world!',    add_special_tokens=A      ),    [0, 3_1414, 232, 328, 2]      )
									self.assertListEqual(
									    tokenizer.encode('Hello world! cécé herlolip 418',    add_special_tokens=A      ),    [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2],    )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.tokenizer_class.from_pretrained('roberta-base'      )

									lowerCamelCase					:			str									= tokenizer.encode('sequence builders',    add_special_tokens=A      )
									lowerCamelCase					:			Dict									= tokenizer.encode('multi-sequence build',    add_special_tokens=A      )

									lowerCamelCase					:			Union[str, Any]									= tokenizer.encode(
									    'sequence builders',    add_special_tokens=A,    add_prefix_space=A      )
									lowerCamelCase					:			Any									= tokenizer.encode(
									    'sequence builders',    'multi-sequence build',    add_special_tokens=A,    add_prefix_space=A      )

									lowerCamelCase					:			Union[str, Any]									= tokenizer.build_inputs_with_special_tokens(A      )
									lowerCamelCase					:			Tuple									= tokenizer.build_inputs_with_special_tokens(A,    A      )

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= self.get_tokenizer()

									lowerCamelCase					:			Optional[int]									= 'Encode this sequence.'
									lowerCamelCase					:			List[str]									= tokenizer.byte_encoder[' '.encode('utf-8'      )[0]]

									# Testing encoder arguments
									lowerCamelCase					:			List[str]									= tokenizer.encode(A,    add_special_tokens=A,    add_prefix_space=A      )
									lowerCamelCase					:			List[str]									= tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
									self.assertNotEqual(A,    A      )

									lowerCamelCase					:			Optional[int]									= tokenizer.encode(A,    add_special_tokens=A,    add_prefix_space=A      )
									lowerCamelCase					:			Optional[Any]									= tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
									self.assertEqual(A,    A      )

									tokenizer.add_special_tokens({'bos_token': '<s>'}      )
									lowerCamelCase					:			str									= tokenizer.encode(A,    add_special_tokens=A      )
									lowerCamelCase					:			Tuple									= tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
									self.assertNotEqual(A,    A      )

									# Testing spaces after special tokens
									lowerCamelCase					:			int									= '<mask>'
									tokenizer.add_special_tokens(
									    {'mask_token': AddedToken(A,    lstrip=A,    rstrip=A      )}      )  # mask token has a left space
									lowerCamelCase					:			List[str]									= tokenizer.convert_tokens_to_ids(A      )

									lowerCamelCase					:			Union[str, Any]									= 'Encode <mask> sequence'
									lowerCamelCase					:			Optional[Any]									= 'Encode <mask>sequence'

									lowerCamelCase					:			int									= tokenizer.encode(A      )
									lowerCamelCase					:			List[str]									= encoded.index(A      )
									lowerCamelCase					:			Union[str, Any]									= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
									self.assertEqual(A,    A      )

									lowerCamelCase					:			int									= tokenizer.encode(A      )
									lowerCamelCase					:			Dict									= encoded.index(A      )
									lowerCamelCase					:			Optional[int]									= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
									self.assertNotEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																	lowerCamelCase					:			Any									= self.rust_tokenizer_class.from_pretrained(A,    **A      )
																	lowerCamelCase					:			str									= self.tokenizer_class.from_pretrained(A,    **A      )
																	lowerCamelCase					:			str									= 'A, <mask> AllenNLP sentence.'
																	lowerCamelCase					:			Union[str, Any]									= tokenizer_r.encode_plus(A,    add_special_tokens=A,    return_token_type_ids=A      )
																	lowerCamelCase					:			Dict									= tokenizer_p.encode_plus(A,    add_special_tokens=A,    return_token_type_ids=A      )

																	# token_type_ids should put 0 everywhere
																	self.assertEqual(sum(tokens_r['token_type_ids']      ),    sum(tokens_p['token_type_ids']      )      )

																	# attention_mask should put 1 everywhere, so sum over length should be 1
																	self.assertEqual(
																	    sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      ),    sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      ),    )

																	lowerCamelCase					:			str									= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																	lowerCamelCase					:			Dict									= tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

																	# Rust correctly handles the space before the mask while python doesnt
																	self.assertSequenceEqual(tokens_p['input_ids'],    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
																	self.assertSequenceEqual(tokens_r['input_ids'],    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )

																	self.assertSequenceEqual(
																	    A,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
																	self.assertSequenceEqual(
																	    A,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for trim_offsets, add_prefix_space in itertools.product([True, False],    repeat=2      ):
													lowerCamelCase					:			Optional[Any]									= self.rust_tokenizer_class.from_pretrained(
													    self.tmpdirname,    use_fast=A,    add_prefix_space=A,    trim_offsets=A      )

													lowerCamelCase					:			Any									= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
													lowerCamelCase					:			Dict									= json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

													self.assertEqual(pre_tokenizer_state['add_prefix_space'],    A      )

													self.assertEqual(post_processor_state['add_prefix_space'],    A      )
													self.assertEqual(post_processor_state['trim_offsets'],    A      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																	lowerCamelCase					:			Any									= 'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
																	lowerCamelCase					:			Any									= F'''{text_of_1_token} {text_of_1_token}'''

																	lowerCamelCase					:			List[str]									= self.rust_tokenizer_class.from_pretrained(
																	    A,    use_fast=A,    add_prefix_space=A,    trim_offsets=A      )
																	lowerCamelCase					:			Tuple									= tokenizer_r(A,    return_offsets_mapping=A,    add_special_tokens=A      )
																	self.assertEqual(encoding.offset_mapping[0],    (0, len(A      ))      )
																	self.assertEqual(
																	    encoding.offset_mapping[1],    (len(A      ) + 1, len(A      ) + 1 + len(A      )),    )

																	lowerCamelCase					:			Optional[int]									= self.rust_tokenizer_class.from_pretrained(
																	    A,    use_fast=A,    add_prefix_space=A,    trim_offsets=A      )
																	lowerCamelCase					:			int									= tokenizer_r(A,    return_offsets_mapping=A,    add_special_tokens=A      )
																	self.assertEqual(encoding.offset_mapping[0],    (0, len(A      ))      )
																	self.assertEqual(
																	    encoding.offset_mapping[1],    (len(A      ) + 1, len(A      ) + 1 + len(A      )),    )

																	lowerCamelCase					:			str									= self.rust_tokenizer_class.from_pretrained(
																	    A,    use_fast=A,    add_prefix_space=A,    trim_offsets=A      )
																	lowerCamelCase					:			Tuple									= tokenizer_r(A,    return_offsets_mapping=A,    add_special_tokens=A      )
																	self.assertEqual(encoding.offset_mapping[0],    (0, len(A      ))      )
																	self.assertEqual(
																	    encoding.offset_mapping[1],    (len(A      ), len(A      ) + 1 + len(A      )),    )

																	lowerCamelCase					:			Optional[int]									= self.rust_tokenizer_class.from_pretrained(
																	    A,    use_fast=A,    add_prefix_space=A,    trim_offsets=A      )
																	lowerCamelCase					:			Tuple									= tokenizer_r(A,    return_offsets_mapping=A,    add_special_tokens=A      )
																	self.assertEqual(encoding.offset_mapping[0],    (0, len(A      ))      )
																	self.assertEqual(
																	    encoding.offset_mapping[1],    (len(A      ), len(A      ) + 1 + len(A      )),    )

																	lowerCamelCase					:			Tuple									= F''' {text}'''

																	# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																	#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																	# )
																	# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																	# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																	# self.assertEqual(
																	#     encoding.offset_mapping[1],
																	#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																	# )

																	lowerCamelCase					:			Any									= self.rust_tokenizer_class.from_pretrained(
																	    A,    use_fast=A,    add_prefix_space=A,    trim_offsets=A      )
																	lowerCamelCase					:			Tuple									= tokenizer_r(A,    return_offsets_mapping=A,    add_special_tokens=A      )
																	self.assertEqual(encoding.offset_mapping[0],    (1, 1 + len(A      ))      )
																	self.assertEqual(
																	    encoding.offset_mapping[1],    (1 + len(A      ) + 1, 1 + len(A      ) + 1 + len(A      )),    )

																	lowerCamelCase					:			Tuple									= self.rust_tokenizer_class.from_pretrained(
																	    A,    use_fast=A,    add_prefix_space=A,    trim_offsets=A      )
																	lowerCamelCase					:			int									= tokenizer_r(A,    return_offsets_mapping=A,    add_special_tokens=A      )
																	self.assertEqual(encoding.offset_mapping[0],    (0, 1 + len(A      ))      )
																	self.assertEqual(
																	    encoding.offset_mapping[1],    (1 + len(A      ), 1 + len(A      ) + 1 + len(A      )),    )

																	lowerCamelCase					:			Optional[Any]									= self.rust_tokenizer_class.from_pretrained(
																	    A,    use_fast=A,    add_prefix_space=A,    trim_offsets=A      )
																	lowerCamelCase					:			Any									= tokenizer_r(A,    return_offsets_mapping=A,    add_special_tokens=A      )
																	self.assertEqual(encoding.offset_mapping[0],    (0, 1 + len(A      ))      )
																	self.assertEqual(
																	    encoding.offset_mapping[1],    (1 + len(A      ), 1 + len(A      ) + 1 + len(A      )),    )


'''simple docstring'''






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


A			      =      logging.get_logger(__name__)




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''AutoTokenizer'''
					_lowerCAmelCase			     =	['''tokenizer''']

					_lowerCAmelCase			     =	{
					    '''semantic_prompt''': 1,
					    '''coarse_prompt''': 2,
					    '''fine_prompt''': 2,
					}
					def __init__(     self,    A,    A=None      ):

									"""simple docstring"""


									super().__init__(A      )

									lowerCamelCase					:			Union[str, Any]									= speaker_embeddings
					@classmethod
					def     UpperCAmelCase_							(     cls,    A,    A="speaker_embeddings_path.json",    **A      ):

									"""simple docstring"""


									if speaker_embeddings_dict_path is not None:
													lowerCamelCase					:			List[Any]									= 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`.'''      )
																	lowerCamelCase					:			Union[str, Any]									= None
													else:
																	with open(A      ) as speaker_embeddings_json:
																					lowerCamelCase					:			Union[str, Any]									= json.load(A      )
									else:
													lowerCamelCase					:			Dict									= None

									lowerCamelCase					:			int									= AutoTokenizer.from_pretrained(A,    **A      )

									return cls(tokenizer=A,    speaker_embeddings=A      )
					def     UpperCAmelCase_							(     self,    A,    A="speaker_embeddings_path.json",    A="speaker_embeddings",    A = False,    **A,    ):

									"""simple docstring"""


									if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(A,    A,    'v2'      ),    exist_ok=A      )

													lowerCamelCase					:			Any									= {}

													lowerCamelCase					:			Tuple									= save_directory

													for prompt_key in self.speaker_embeddings:
																	if prompt_key != "repo_or_path":
																					lowerCamelCase					:			Optional[int]									= self._load_voice_preset(A      )

																					lowerCamelCase					:			Optional[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,    )
																									lowerCamelCase					:			Tuple									= os.path.join(A,    F'''{prompt_key}_{key}.npy'''      )

																					lowerCamelCase					:			Optional[Any]									= tmp_dict

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

									super().save_pretrained(A,    A,    **A      )
					def     UpperCAmelCase_							(     self,    A = None,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.speaker_embeddings[voice_preset]

									lowerCamelCase					:			Optional[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}].'''      )

													lowerCamelCase					:			Optional[Any]									= 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.'''      )

													lowerCamelCase					:			Tuple									= np.load(A      )

									return voice_preset_dict
					def     UpperCAmelCase_							(     self,    A = None      ):

									"""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,    A=None,    A=None,    A="pt",    A=256,    A=False,    A=True,    A=False,    **A,    ):

									"""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
													):
																	lowerCamelCase					:			Dict									= self._load_voice_preset(A      )

													else:
																	if isinstance(A,    A      ) and not voice_preset.endswith('.npz'      ):
																					lowerCamelCase					:			List[Any]									= voice_preset + '.npz'

																	lowerCamelCase					:			Tuple									= np.load(A      )

									if voice_preset is not None:
													self._validate_voice_preset_dict(A,    **A      )
													lowerCamelCase					:			List[Any]									= BatchFeature(data=A,    tensor_type=A      )

									lowerCamelCase					:			str									= 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:
													lowerCamelCase					:			Dict									= voice_preset

									return encoded_text
