






"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list


class 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"""<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z""".split(""" """			)
     UpperCAmelCase__ 						=				dict(zip(__UpperCamelCase   ,			range(len(__UpperCamelCase			)			)			)			)

     UpperCAmelCase__ 						=				{
         """pad_token""": """<pad>""",
         """unk_token""": """<unk>""",
         """bos_token""": """<s>""",
         """eos_token""": """</s>""",
     }
     UpperCAmelCase__ 						=				{
         """feature_size""": 1,
         """padding_value""": 0.0,
         """sampling_rate""": 16000,
         """return_attention_mask""": False,
         """do_normalize""": True,
     }

     UpperCAmelCase__ 						=				tempfile.mkdtemp()
     UpperCAmelCase__ 						=				os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES["""vocab_file"""]			)
     UpperCAmelCase__ 						=				os.path.join(self.tmpdirname   ,			__UpperCamelCase			)
     with open(self.vocab_file   ,			"""w"""   ,			encoding="""utf-8"""			) as fp:
      fp.write(json.dumps(__UpperCamelCase			) + """\n"""			)

     with open(self.feature_extraction_file   ,			"""w"""   ,			encoding="""utf-8"""			) as fp:
      fp.write(json.dumps(__UpperCamelCase			) + """\n"""			)


    def 		A__      (      self   ,			**__lowercase			):
     UpperCAmelCase__ 						=				self.add_kwargs_tokens_map.copy()
     kwargs.update(__UpperCamelCase			)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,			**__UpperCamelCase			)


    def 		A__      (      self   ,			**__lowercase			):
     return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   ,			**__UpperCamelCase			)


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


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.get_tokenizer()
     UpperCAmelCase__ 						=				self.get_feature_extractor()

     UpperCAmelCase__ 						=				WavaVecaProcessor(tokenizer=__UpperCamelCase   ,			feature_extractor=__UpperCamelCase			)

     processor.save_pretrained(self.tmpdirname			)
     UpperCAmelCase__ 						=				WavaVecaProcessor.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			):
     UpperCAmelCase__ 						=				WavaVecaProcessor(tokenizer=self.get_tokenizer()   ,			feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     UpperCAmelCase__ 						=				self.get_tokenizer(bos_token="""(BOS)"""   ,			eos_token="""(EOS)"""			)
     UpperCAmelCase__ 						=				self.get_feature_extractor(do_normalize=__UpperCamelCase   ,			padding_value=1.0			)

     UpperCAmelCase__ 						=				WavaVecaProcessor.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			):
     UpperCAmelCase__ 						=				self.get_feature_extractor()
     UpperCAmelCase__ 						=				self.get_tokenizer()

     UpperCAmelCase__ 						=				WavaVecaProcessor(tokenizer=__UpperCamelCase   ,			feature_extractor=__UpperCamelCase			)

     UpperCAmelCase__ 						=				floats_list((3, 1000)			)

     UpperCAmelCase__ 						=				feature_extractor(__UpperCamelCase   ,			return_tensors="""np"""			)
     UpperCAmelCase__ 						=				processor(__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			):
     UpperCAmelCase__ 						=				self.get_feature_extractor()
     UpperCAmelCase__ 						=				self.get_tokenizer()

     UpperCAmelCase__ 						=				WavaVecaProcessor(tokenizer=__UpperCamelCase   ,			feature_extractor=__UpperCamelCase			)

     UpperCAmelCase__ 						=				"""This is a test string"""

     UpperCAmelCase__ 						=				processor(text=__UpperCamelCase			)

     UpperCAmelCase__ 						=				tokenizer(__UpperCamelCase			)

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


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.get_feature_extractor()
     UpperCAmelCase__ 						=				self.get_tokenizer()

     UpperCAmelCase__ 						=				WavaVecaProcessor(tokenizer=__UpperCamelCase   ,			feature_extractor=__UpperCamelCase			)

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

     UpperCAmelCase__ 						=				processor.batch_decode(__UpperCamelCase			)
     UpperCAmelCase__ 						=				tokenizer.batch_decode(__UpperCamelCase			)

     self.assertListEqual(__UpperCamelCase   ,			__UpperCamelCase			)






    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.get_feature_extractor()
     UpperCAmelCase__ 						=				self.get_tokenizer()

     UpperCAmelCase__ 						=				WavaVecaProcessor(tokenizer=__UpperCamelCase   ,			feature_extractor=__UpperCamelCase			)

     self.assertListEqual(
         processor.model_input_names   ,			feature_extractor.model_input_names   ,			msg="""`processor` and `feature_extractor` model input names do not match"""   ,			)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: int												=	"splinter"







	def __init__( self:			str	,			__UpperCamelCase:			Any=3_05_22	,			__UpperCamelCase:			List[Any]=7_68	,			__UpperCamelCase:			str=12	,			__UpperCamelCase:			Optional[Any]=12	,			__UpperCamelCase:			Optional[int]=30_72	,			__UpperCamelCase:			List[str]="gelu"	,			__UpperCamelCase:			Union[str, Any]=0.1	,			__UpperCamelCase:			Dict=0.1	,			__UpperCamelCase:			str=5_12	,			__UpperCamelCase:			Dict=2	,			__UpperCamelCase:			str=0.02	,			__UpperCamelCase:			Tuple=1E-12	,			__UpperCamelCase:			Optional[int]=True	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Optional[int]=1_04	,			**__UpperCamelCase:			Tuple	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_act
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     initializer_range
						__magic_name__				=     type_vocab_size
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     use_cache
						__magic_name__				=     question_token_id




