"""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        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







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




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					'''<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(''' '''  )
									lowerCamelCase__		:   Optional[int]  				=					dict(zip(__UpperCAmelCase      ,range(len(__UpperCAmelCase  )  )  )  )

									lowerCamelCase__		:   Union[str, Any]  				=					{
									    '''pad_token''': '''<pad>''',
									    '''unk_token''': '''<unk>''',
									    '''bos_token''': '''<s>''',
									    '''eos_token''': '''</s>''',
									}
									lowerCamelCase__		:   Union[str, Any]  				=					{
									    '''feature_size''': 1,
									    '''padding_value''': 0.0,
									    '''sampling_rate''': 1_60_00,
									    '''return_attention_mask''': False,
									    '''do_normalize''': True,
									}

									lowerCamelCase__		:   List[Any]  				=					tempfile.mkdtemp()
									lowerCamelCase__		:   Optional[Any]  				=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['''vocab_file''']  )
									lowerCamelCase__		:   Tuple  				=					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   lowercase_		(    self    :int      ,**__UpperCAmelCase    :Dict  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					self.add_kwargs_tokens_map.copy()
									kwargs.update(__UpperCAmelCase  )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,**__UpperCAmelCase  )







				def   lowercase_		(    self    :List[Any]      ,**__UpperCAmelCase    :int  )      ->					Tuple:




									"""simple docstring"""



									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,**__UpperCAmelCase  )







				def   lowercase_		(    self    :Optional[Any]  )      ->					int:




									"""simple docstring"""



									shutil.rmtree(self.tmpdirname  )







				def   lowercase_		(    self    :Optional[int]  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					self.get_tokenizer()
									lowerCamelCase__		:   Any  				=					self.get_feature_extractor()

									lowerCamelCase__		:   int  				=					WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,feature_extractor=__UpperCAmelCase  )

									processor.save_pretrained(self.tmpdirname  )
									lowerCamelCase__		:   Dict  				=					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   lowercase_		(    self    :Union[str, Any]  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					WavaVecaProcessor(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=__UpperCAmelCase      ,padding_value=1.0  )

									lowerCamelCase__		:   str  				=					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   lowercase_		(    self    :Any  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					self.get_feature_extractor()
									lowerCamelCase__		:   Dict  				=					self.get_tokenizer()

									lowerCamelCase__		:   Optional[Any]  				=					WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,feature_extractor=__UpperCAmelCase  )

									lowerCamelCase__		:   Union[str, Any]  				=					floats_list((3, 10_00)  )

									lowerCamelCase__		:   str  				=					feature_extractor(__UpperCAmelCase      ,return_tensors='''np'''  )
									lowerCamelCase__		:   List[str]  				=					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   lowercase_		(    self    :Union[str, Any]  )      ->					Tuple:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					self.get_feature_extractor()
									lowerCamelCase__		:   str  				=					self.get_tokenizer()

									lowerCamelCase__		:   str  				=					WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,feature_extractor=__UpperCAmelCase  )

									lowerCamelCase__		:   int  				=					'''This is a test string'''

									lowerCamelCase__		:   Tuple  				=					processor(text=__UpperCAmelCase  )

									lowerCamelCase__		:   Any  				=					tokenizer(__UpperCAmelCase  )

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







				def   lowercase_		(    self    :Optional[Any]  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Union[str, Any]  				=					self.get_feature_extractor()
									lowerCamelCase__		:   Union[str, Any]  				=					self.get_tokenizer()

									lowerCamelCase__		:   str  				=					WavaVecaProcessor(tokenizer=__UpperCAmelCase      ,feature_extractor=__UpperCAmelCase  )

									lowerCamelCase__		:   List[Any]  				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									lowerCamelCase__		:   Optional[Any]  				=					processor.batch_decode(__UpperCAmelCase  )
									lowerCamelCase__		:   Union[str, Any]  				=					tokenizer.batch_decode(__UpperCAmelCase  )

									self.assertListEqual(__UpperCAmelCase      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :List[Any]  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					self.get_feature_extractor()
									lowerCamelCase__		:   Optional[Any]  				=					self.get_tokenizer()

									lowerCamelCase__		:   str  				=					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'''      ,)







"""simple docstring"""




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				@unittest.skip('''Model is not available.'''  )
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''nvidia/megatron-gpt2-345m/'''
									if "MYDIR" in os.environ:
														lowerCamelCase__		:   str  				=					os.path.join(os.environ['''MYDIR''']      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					GPTaLMHeadModel.from_pretrained(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.half()

									lowerCamelCase__		:   Dict  				=					torch.tensor(
									    [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]      ,device=__UpperCAmelCase      ,dtype=torch.long      ,)

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase  ).logits

									lowerCamelCase__		:   List[str]  				=					torch.Size((1, 9, 5_02_57)  )
									self.assertEqual(output.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Tuple  				=					torch.tensor(
									    [
									        4.9_414,
									        -0.2_920,
									        -1.2_148,
									        -4.0_273,
									        -0.5_161,
									        -5.2_109,
									        -1.2_412,
									        -1.8_301,
									        -1.7_734,
									        -4.7_148,
									        -0.2_317,
									        -1.0_811,
									        -2.1_777,
									        0.4_141,
									        -3.7_969,
									        -4.0_586,
									        -2.5_332,
									        -3.3_809,
									        4.3_867,
									    ]      ,device=__UpperCAmelCase      ,dtype=torch.half      ,)

									for i in range(19  ):
														lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					8 * i // 17, 27_92 * i  # along the diagonal
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					output[0, r, c], expected_diag[i]
														lowerCamelCase__		:   Dict  				=					F"""row={r} col={c} computed={computed} expected={expected}"""
														self.assertAlmostEqual(__UpperCAmelCase      ,__UpperCAmelCase      ,delta=1E-4      ,msg=__UpperCAmelCase  )






