


'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase     :						Optional[Any]	=     logging.get_logger(__name__)

__lowercase     :						List[str]	=     '▁'

__lowercase     :						str	=     {'vocab_file': 'sentencepiece.bpe.model'}

__lowercase     :						Union[str, Any]	=     {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

__lowercase     :						List[str]	=     {
    'facebook/xglm-564M': 20_48,
}





class 							__UpperCamelCase    (		A_ ):
	A_           =   VOCAB_FILES_NAMES
	A_           =   PRETRAINED_VOCAB_FILES_MAP
	A_           =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_           =   ["input_ids", "attention_mask"]




	def __init__(       self						,    __a						,    __a="<s>"						,    __a="</s>"						,    __a="</s>"						,    __a="<s>"						,    __a="<unk>"						,    __a="<pad>"						,    __a = None						,    **__a						,    ):

						'''simple docstring'''





						__a      :	List[Any]    =  {} if sp_model_kwargs is None else sp_model_kwargs

						# Compatibility with the original tokenizer
						__a      :	Optional[int]    =  7
						__a      :	List[str]    =  [f"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

						__a      :	Union[str, Any]    =  kwargs.get('additional_special_tokens'						,    []							)
						kwargs["additional_special_tokens"] += [
						    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
						]

						super().__init__(
						    bos_token=snake_case__						,    eos_token=snake_case__						,    unk_token=snake_case__						,    sep_token=snake_case__						,    cls_token=snake_case__						,    pad_token=snake_case__						,    sp_model_kwargs=self.sp_model_kwargs						,    **snake_case__						,    )

						__a      :	Any    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(str(snake_case__							)							)
						__a      :	str    =  vocab_file

						# Original fairseq vocab and spm vocab must be "aligned":
						# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
						# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
						# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
						# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

						# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
						__a      :	int    =  1

						# Mimic fairseq token-to-id alignment for the first 4 token
						__a      :	Any    =  {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

						__a      :	Tuple    =  len(self.sp_model							)
						__a      :	Any    =  {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
						self.fairseq_tokens_to_ids.update(snake_case__							)

						__a      :	List[str]    =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}




	def __getstate__(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.__dict__.copy()
						__a      :	str    =  None
						__a      :	Union[str, Any]    =  self.sp_model.serialized_model_proto()
						return state




	def __setstate__(       self						,    __a							):

						'''simple docstring'''





						__a      :	int    =  d

						# for backward compatibility
						if not hasattr(self						,    'sp_model_kwargs'							):
											__a      :	List[str]    =  {}

						__a      :	str    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						if token_ids_a is None:
											return [self.sep_token_id] + token_ids_a
						__a      :	Tuple    =  [self.sep_token_id]
						return sep + token_ids_a + sep + sep + token_ids_a




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None						,    __a = False							):

						'''simple docstring'''





						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=snake_case__						,    token_ids_a=snake_case__						,    already_has_special_tokens=snake_case__							)

						if token_ids_a is None:
											return [1] + ([0] * len(snake_case__							))
						return [1] + ([0] * len(snake_case__							)) + [1, 1] + ([0] * len(snake_case__							))




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						__a      :	List[str]    =  [self.sep_token_id]

						if token_ids_a is None:
											return len(sep + token_ids_a							) * [0]
						return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  {self.convert_ids_to_tokens(snake_case__							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						return self.sp_model.encode(snake_case__						,    out_type=snake_case__							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
						__a      :	List[Any]    =  self.sp_model.PieceToId(snake_case__							)

						# Need to return unknown token if the SP model returned 0
						return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[Any]    =  "".join(snake_case__							).replace(snake_case__						,    ' '							).strip()
						return out_string





	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





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

						if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case__							) and os.path.isfile(self.vocab_file							):
											copyfile(self.vocab_file						,    snake_case__							)
						elif not os.path.isfile(self.vocab_file							):
											with open(snake_case__						,    'wb'							) as fi:
																__a      :	Optional[Any]    =  self.sp_model.serialized_model_proto()
																fi.write(snake_case__							)

						return (out_vocab_file,)







'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_feature_extractor()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  ClapProcessor.from_pretrained(self.tmpdirname							)

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

						self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  ClapProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

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

						self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  processor(audios=__a						,    return_tensors='np'							)

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1E-2							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Union[str, Any]    =  'This is a test string'

						__a      :	Union[str, Any]    =  processor(text=__a							)

						__a      :	Tuple    =  tokenizer(__a							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

						__a      :	List[str]    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_feature_extractor()
						__a      :	Optional[int]    =  self.get_tokenizer()

						__a      :	int    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						self.assertListEqual(
						    processor.model_input_names[2:]						,    feature_extractor.model_input_names						,    msg='`processor` and `feature_extractor` model input names do not match'						,    )



