

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  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											snake_case_							=    'laion/clap-htsat-unfused'
											snake_case_							=    tempfile.mkdtemp()




				def     a							(					self      ,   **snake_case       ):
											return RobertaTokenizer.from_pretrained(self.checkpoint      ,   **UpperCAmelCase_       )




				def     a							(					self      ,   **snake_case       ):
											return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,   **UpperCAmelCase_       )




				def     a							(					self       ):
											shutil.rmtree(self.tmpdirname       )




				def     a							(					self       ):
											snake_case_							=    self.get_tokenizer()
											snake_case_							=    self.get_feature_extractor()

											snake_case_							=    ClapProcessor(tokenizer=UpperCAmelCase_      ,   feature_extractor=UpperCAmelCase_       )

											processor.save_pretrained(self.tmpdirname       )
											snake_case_							=    ClapProcessor.from_pretrained(self.tmpdirname       )

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

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




				def     a							(					self       ):
											snake_case_							=    ClapProcessor(tokenizer=self.get_tokenizer()      ,   feature_extractor=self.get_feature_extractor()       )
											processor.save_pretrained(self.tmpdirname       )

											snake_case_							=    self.get_tokenizer(bos_token='(BOS)'      ,   eos_token='(EOS)'       )
											snake_case_							=    self.get_feature_extractor(do_normalize=UpperCAmelCase_      ,   padding_value=1.0       )

											snake_case_							=    ClapProcessor.from_pretrained(
											    self.tmpdirname      ,   bos_token='(BOS)'      ,   eos_token='(EOS)'      ,   do_normalize=UpperCAmelCase_      ,   padding_value=1.0       )

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

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




				def     a							(					self       ):
											snake_case_							=    self.get_feature_extractor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ClapProcessor(tokenizer=UpperCAmelCase_      ,   feature_extractor=UpperCAmelCase_       )

											snake_case_							=    floats_list((3, 1000)       )

											snake_case_							=    feature_extractor(UpperCAmelCase_      ,   return_tensors='np'       )
											snake_case_							=    processor(audios=UpperCAmelCase_      ,   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     a							(					self       ):
											snake_case_							=    self.get_feature_extractor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ClapProcessor(tokenizer=UpperCAmelCase_      ,   feature_extractor=UpperCAmelCase_       )

											snake_case_							=    'This is a test string'

											snake_case_							=    processor(text=UpperCAmelCase_       )

											snake_case_							=    tokenizer(UpperCAmelCase_       )

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




				def     a							(					self       ):
											snake_case_							=    self.get_feature_extractor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ClapProcessor(tokenizer=UpperCAmelCase_      ,   feature_extractor=UpperCAmelCase_       )

											snake_case_							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_							=    processor.batch_decode(UpperCAmelCase_       )
											snake_case_							=    tokenizer.batch_decode(UpperCAmelCase_       )

											self.assertListEqual(UpperCAmelCase_      ,   UpperCAmelCase_       )




				def     a							(					self       ):
											snake_case_							=    self.get_feature_extractor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ClapProcessor(tokenizer=UpperCAmelCase_      ,   feature_extractor=UpperCAmelCase_       )

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






import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {"""vocab_file""": """vocab.txt"""}

__snake_case								=  {
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

__snake_case								=  {
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}






def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					with open(UpperCamelCase_	,	'r'		) as f:
										SCREAMING_SNAKE_CASE__													= f.read().splitlines()
										return [l.strip() for l in lines]




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Tuple				       =VOCAB_FILES_NAMES
	A__    :   Union[str, Any]				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   Any				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   Any				       =["""input_ids""", """attention_mask"""]
	def __init__(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple="<unk>"       ,   UpperCAmelCase_							:   Optional[Any]="<cls>"       ,   UpperCAmelCase_							:   List[Any]="<pad>"       ,   UpperCAmelCase_							:   Optional[int]="<mask>"       ,   UpperCAmelCase_							:   Optional[int]="<eos>"       ,   **UpperCAmelCase_							:   Optional[int]       ,   ):
						super().__init__(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= load_vocab_file(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= dict(enumerate(self.all_tokens							)							)
						SCREAMING_SNAKE_CASE__													= {tok: ind for ind, tok in enumerate(self.all_tokens							)}
						SCREAMING_SNAKE_CASE__													= unk_token
						SCREAMING_SNAKE_CASE__													= cls_token
						SCREAMING_SNAKE_CASE__													= pad_token
						SCREAMING_SNAKE_CASE__													= mask_token
						SCREAMING_SNAKE_CASE__													= eos_token
						SCREAMING_SNAKE_CASE__													= self.all_tokens
						self._create_trie(self.unique_no_split_tokens							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Tuple       ,   **UpperCAmelCase_							:   List[Any]							):
						return text.split()
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   Optional[Any]=False							):
						return len(self._id_to_token							)
	def      A_				(			self							:   Union[str, Any]							):
						return {token: i for i, token in enumerate(self.all_tokens							)}
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						SCREAMING_SNAKE_CASE__													= [self.eos_token_id]  # No sep token in ESM vocabulary
						if token_ids_a is None:
											if self.eos_token_id is None:
																return cls + token_ids_a
											else:
																return cls + token_ids_a + sep
						elif self.eos_token_id is None:
											raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'							)
						return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   List       ,   UpperCAmelCase_							:   Optional[List] = None       ,   UpperCAmelCase_							:   bool = False							):
						if already_has_special_tokens:
											if token_ids_a is not None:
																raise ValueError(
																    'You should not supply a second sequence if the provided sequence of '
																    'ids is already formatted with special tokens for the model.'							)

											return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
						SCREAMING_SNAKE_CASE__													= [1] + ([0] * len(UpperCAmelCase_							)) + [1]
						if token_ids_a is not None:
											mask += [0] * len(UpperCAmelCase_							) + [1]
						return mask
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= os.path.join(UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'							)
						with open(UpperCAmelCase_       ,   'w'							) as f:
											f.write('\n'.join(self.all_tokens							)							)
						return (vocab_file,)
	@property
	def      A_				(			self							:   int							):
						return self.get_vocab_size(with_added_tokens=UpperCAmelCase_							)





	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Union[List[str], List[AddedToken]]       ,   UpperCAmelCase_							:   bool = False							):
						return super()._add_tokens(UpperCAmelCase_       ,   special_tokens=UpperCAmelCase_							)





