





"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
		from ...onnx.config import PatchingSpec
		from ...tokenization_utils_base import PreTrainedTokenizerBase


A			      =	logging.get_logger(__name__)

A			      =	{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}




class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	'''longformer'''







					def __init__(    self						,      _UpperCAmelCase = 512						,      _UpperCAmelCase = 2						,      _UpperCAmelCase = 1						,      _UpperCAmelCase = 0						,      _UpperCAmelCase = 2						,      _UpperCAmelCase = 30522						,      _UpperCAmelCase = 768						,      _UpperCAmelCase = 12						,      _UpperCAmelCase = 12						,      _UpperCAmelCase = 3072						,      _UpperCAmelCase = "gelu"						,      _UpperCAmelCase = 0.1						,      _UpperCAmelCase = 0.1						,      _UpperCAmelCase = 512						,      _UpperCAmelCase = 2						,      _UpperCAmelCase = 0.0_2						,      _UpperCAmelCase = 1e-1_2						,      _UpperCAmelCase = False						,      **_UpperCAmelCase						,      ):
									super().__init__(pad_token_id=_UpperCAmelCase						,      **_UpperCAmelCase  )

									__a			:    Union[str, Any]      							=	attention_window
									__a			:    List[Any]      							=	sep_token_id
									__a			:    Optional[Any]      							=	bos_token_id
									__a			:    List[str]      							=	eos_token_id
									__a			:    List[Any]      							=	vocab_size
									__a			:    Optional[Any]      							=	hidden_size
									__a			:    Union[str, Any]      							=	num_hidden_layers
									__a			:    Optional[int]      							=	num_attention_heads
									__a			:    Optional[Any]      							=	hidden_act
									__a			:    Dict      							=	intermediate_size
									__a			:    Dict      							=	hidden_dropout_prob
									__a			:    Optional[int]      							=	attention_probs_dropout_prob
									__a			:    Tuple      							=	max_position_embeddings
									__a			:    List[Any]      							=	type_vocab_size
									__a			:    int      							=	initializer_range
									__a			:    Optional[int]      							=	layer_norm_eps
									__a			:    str      							=	onnx_export






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = "default"						,      _UpperCAmelCase = None  ):
									super().__init__(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  )
									__a			:    List[Any]      							=	True







					@property
					def 			_lowerCamelCase		(    self  ):
									if self.task == "multiple-choice":
													__a			:    List[str]      							=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
													__a			:    Tuple      							=	{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''global_attention_mask''', dynamic_axis),
									    ]  )







					@property
					def 			_lowerCamelCase		(    self  ):
									__a			:    str      							=	super().outputs
									if self.task == "default":
													__a			:    Optional[Any]      							=	{0: '''batch'''}
									return outputs







					@property
					def 			_lowerCamelCase		(    self  ):
									return 1e-4







					@property
					def 			_lowerCamelCase		(    self  ):
									# needs to be >= 14 to support tril operator
									return max(super().default_onnx_opset						,      14  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = -1						,      _UpperCAmelCase = -1						,      _UpperCAmelCase = False						,      _UpperCAmelCase = None						,      ):
									__a			:    Dict      							=	super().generate_dummy_inputs(
									    preprocessor=_UpperCAmelCase						,      batch_size=_UpperCAmelCase						,      seq_length=_UpperCAmelCase						,      is_pair=_UpperCAmelCase						,      framework=_UpperCAmelCase  )
									import torch

									# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
									# makes the export fail randomly
									__a			:    str      							=	torch.zeros_like(inputs['''input_ids''']  )
									# make every second token global
									__a			:    int      							=	1

									return inputs

"""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     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			'laion/clap-htsat-unfused'
         a__		=			tempfile.mkdtemp()



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



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



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         shutil.rmtree(self.tmpdirname   )



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			self.get_tokenizer()
         a__		=			self.get_feature_extractor()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

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

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

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



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			ClapProcessor(tokenizer=self.get_tokenizer()     ,				feature_extractor=self.get_feature_extractor()   )
         processor.save_pretrained(self.tmpdirname   )

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

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

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

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



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			floats_list((3, 1_0_0_0)   )

         a__		=			feature_extractor(__SCREAMING_SNAKE_CASE     ,				return_tensors='np'   )
         a__		=			processor(audios=__SCREAMING_SNAKE_CASE     ,				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 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			'This is a test string'

         a__		=			processor(text=__SCREAMING_SNAKE_CASE   )

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE   )

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



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

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

         a__		=			processor.batch_decode(__SCREAMING_SNAKE_CASE   )
         a__		=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )

         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

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


