

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


a_      =     logging.get_logger(__name__)


a_      =     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
a_      =     {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

a_      =     {
    'RUCAIBox/mvp': 1024,
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =VOCAB_FILES_NAMES
			lowerCamelCase__							     =PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__							     =['input_ids', 'attention_mask']
			lowerCamelCase__							     =MvpTokenizer
			def __init__(      self				:			int   ,   a				:			List[str]=None   ,   a				:			List[str]=None   ,   a				:			List[str]=None   ,   a				:			int="replace"   ,   a				:			Tuple="<s>"   ,   a				:			List[Any]="</s>"   ,   a				:			int="</s>"   ,   a				:			Optional[Any]="<s>"   ,   a				:			int="<unk>"   ,   a				:			int="<pad>"   ,   a				:			List[Any]="<mask>"   ,   a				:			Union[str, Any]=False   ,   a				:			str=True   ,   **a				:			Optional[int]   ,   )				->							int:

				"""simple docstring"""

				super().__init__(
				    a   ,   a   ,   tokenizer_file=a   ,   errors=a   ,   bos_token=a   ,   eos_token=a   ,   sep_token=a   ,   cls_token=a   ,   unk_token=a   ,   pad_token=a   ,   mask_token=a   ,   add_prefix_space=a   ,   trim_offsets=a   ,   **a   ,   )

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
				if pre_tok_state.get("add_prefix_space"   ,   a						) != add_prefix_space:
					SCREAMING_SNAKE_CASE :   Dict					  =							getattr(a   ,   pre_tok_state.pop("type"						)						)
					SCREAMING_SNAKE_CASE :   str					  =							add_prefix_space
					SCREAMING_SNAKE_CASE :   List[str]					  =							pre_tok_class(**a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				SCREAMING_SNAKE_CASE :   List[str]					  =							"post_processor"
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							getattr(self.backend_tokenizer   ,   a   ,   a						)
				if tokenizer_component_instance:
					SCREAMING_SNAKE_CASE :   Any					  =							json.loads(tokenizer_component_instance.__getstate__()						)

					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
					if "sep" in state:
						SCREAMING_SNAKE_CASE :   str					  =							tuple(state["sep"]						)
					if "cls" in state:
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							tuple(state["cls"]						)

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							False

					if state.get("add_prefix_space"   ,   a						) != add_prefix_space:
						SCREAMING_SNAKE_CASE :   List[str]					  =							add_prefix_space
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							True

					if state.get("trim_offsets"   ,   a						) != trim_offsets:
						SCREAMING_SNAKE_CASE :   str					  =							trim_offsets
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True

					if changes_to_apply:
						SCREAMING_SNAKE_CASE :   Tuple					  =							getattr(a   ,   state.pop("type"						)						)
						SCREAMING_SNAKE_CASE :   Tuple					  =							component_class(**a						)
						setattr(self.backend_tokenizer   ,   a   ,   a						)
			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				if self._mask_token is None:
					if self.verbose:
						logger.error("Using mask_token, but it is not set yet."						)
					return None
				return str(self._mask_token						)
			@mask_token.setter
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			Tuple						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else value
				SCREAMING_SNAKE_CASE :   str					  =							value
			def 				__UpperCamelCase			(      self				:			Tuple   ,   *a				:			List[Any]   ,   **a				:			List[str]						)				->							BatchEncoding:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							kwargs.get("is_split_into_words"   ,   a						)

				if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
					    "to use it with pretokenized inputs."						)

				return super()._batch_encode_plus(*a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   *a				:			Union[str, Any]   ,   **a				:			Dict						)				->							BatchEncoding:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							kwargs.get("is_split_into_words"   ,   a						)

				if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
					    "to use it with pretokenized inputs."						)

				return super()._encode_plus(*a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			str   ,   a				:			Optional[str] = None						)				->							Tuple[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self._tokenizer.model.save(a   ,   name=a						)
				return tuple(a						)
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			str   ,   a				:			Optional[Any]=None						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
					return output

				return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			List[int]   ,   a				:			Optional[List[int]] = None						)				->							List[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							[self.sep_token_id]
				SCREAMING_SNAKE_CASE :   Union[str, 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 + sep + token_ids_a + sep						) * [0]




"""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 					A_(unittest.TestCase      ):







			"""simple docstring"""



			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    'laion/clap-htsat-unfused'
						_lowerCamelCase					:					Optional[Any]	       =    tempfile.mkdtemp()
			def    _lowerCAmelCase		(	self				,   **A							):
						return RobertaTokenizer.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self				,   **A							):
						return ClapFeatureExtractor.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self							):
						shutil.rmtree(self.tmpdirname							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()

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

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

						_lowerCamelCase					:					Tuple	       =    self.get_tokenizer(bos_token='(BOS)'				,   eos_token='(EOS)'							)
						_lowerCamelCase					:					str	       =    self.get_feature_extractor(do_normalize=A				,   padding_value=1.0							)

						_lowerCamelCase					:					List[Any]	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[int]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Any	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

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

						_lowerCamelCase					:					str	       =    feature_extractor(A				,   return_tensors='np'							)
						_lowerCamelCase					:					List[str]	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    self.get_feature_extractor()
						_lowerCamelCase					:					str	       =    self.get_tokenizer()

						_lowerCamelCase					:					List[Any]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					List[str]	       =    'This is a test string'

						_lowerCamelCase					:					Any	       =    processor(text=A							)

						_lowerCamelCase					:					Optional[int]	       =    tokenizer(A							)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]				,   encoded_processor[key]							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Optional[int]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

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

						_lowerCamelCase					:					int	       =    processor.batch_decode(A							)
						_lowerCamelCase					:					int	       =    tokenizer.batch_decode(A							)

						self.assertListEqual(A				,   A							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					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'				,   )





