



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 			__snake_case   ( unittest.TestCase):







							'''simple docstring'''






							def _a   (				self		):
											a__					   =		"""<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(""" """		)
											a__					   =		dict(zip(a_	,	range(len(a_		)		)		)		)

											a__					   =		{
											    """pad_token""": """<pad>""",
											    """unk_token""": """<unk>""",
											    """bos_token""": """<s>""",
											    """eos_token""": """</s>""",
											}
											a__					   =		{
											    """feature_size""": 1,
											    """padding_value""": 0.0,
											    """sampling_rate""": 16_000,
											    """return_attention_mask""": False,
											    """do_normalize""": True,
											}

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

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


							def _a   (				self	,	**a_		):
											a__					   =		self.add_kwargs_tokens_map.copy()
											kwargs.update(a_		)
											return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,	**a_		)


							def _a   (				self	,	**a_		):
											return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,	**a_		)


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


							def _a   (				self		):
											a__					   =		self.get_tokenizer()
											a__					   =		self.get_feature_extractor()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

											processor.save_pretrained(self.tmpdirname		)
											a__					   =		WavaVecaProcessor.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 _a   (				self		):
											a__					   =		WavaVecaProcessor(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=a_	,	padding_value=1.0		)

											a__					   =		WavaVecaProcessor.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 _a   (				self		):
											a__					   =		self.get_feature_extractor()
											a__					   =		self.get_tokenizer()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

											a__					   =		floats_list((3, 1_000)		)

											a__					   =		feature_extractor(a_	,	return_tensors="""np"""		)
											a__					   =		processor(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 _a   (				self		):
											a__					   =		self.get_feature_extractor()
											a__					   =		self.get_tokenizer()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

											a__					   =		"""This is a test string"""

											a__					   =		processor(text=a_		)

											a__					   =		tokenizer(a_		)

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


							def _a   (				self		):
											a__					   =		self.get_feature_extractor()
											a__					   =		self.get_tokenizer()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

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

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

											self.assertListEqual(a_	,	a_		)







							def _a   (				self		):
											a__					   =		self.get_feature_extractor()
											a__					   =		self.get_tokenizer()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

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


import torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, Any]						      =    (inputs > threshold).type(inputs.type()     )
        if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             a	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Optional[Any]						      =    0
        a	:     List[Any]						      =    1
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Tuple						      =    0
        a	:     List[str]						      =    1
        return mask