






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class       snake_case__ (				SCREAMING_SNAKE_CASE_):

       '''simple docstring'''
       lowerCamelCase				:					Optional[Any]								=			"""xlm-roberta-xl"""



       def __init__(				self      ,    a__=25_08_80      ,    a__=25_60      ,    a__=36      ,    a__=32      ,    a__=1_02_40      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=5_14      ,    a__=1      ,    a__=0.02      ,    a__=1e-05      ,    a__=1      ,    a__=0      ,    a__=2      ,    a__="absolute"      ,    a__=True      ,    a__=None      ,    **a__      ,    )			->   List[Any]:






            '''simple docstring'''





            super().__init__(pad_token_id=a__      ,    bos_token_id=a__      ,    eos_token_id=a__      ,    **a__     )
            __snake_case      :List[str] 							=    vocab_size
            __snake_case      :Union[str, Any] 							=    hidden_size
            __snake_case      :Union[str, Any] 							=    num_hidden_layers
            __snake_case      :List[Any] 							=    num_attention_heads
            __snake_case      :Tuple 							=    hidden_act
            __snake_case      :Union[str, Any] 							=    intermediate_size
            __snake_case      :int 							=    hidden_dropout_prob
            __snake_case      :Optional[int] 							=    attention_probs_dropout_prob
            __snake_case      :List[Any] 							=    max_position_embeddings
            __snake_case      :List[Any] 							=    type_vocab_size
            __snake_case      :Dict 							=    initializer_range
            __snake_case      :List[str] 							=    layer_norm_eps
            __snake_case      :Any 							=    position_embedding_type
            __snake_case      :Tuple 							=    use_cache
            __snake_case      :List[Any] 							=    classifier_dropout




class       snake_case__ (				SCREAMING_SNAKE_CASE_):

       '''simple docstring'''



       @property
       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            if self.task == "multiple-choice":
                 __snake_case      :Optional[Any] 							=    {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                 __snake_case      :Any 							=    {0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                ]     )







"""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'				,   )





