



"""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  A_     (	unittest.TestCase							):



						"""simple docstring"""






						@slow
						@unittest.skip('Model is not available.'   )
						def       UpperCAmelCase__	(   self					:Dict   )							->     Any:
												UpperCAmelCase			 =					'nvidia/megatron-gpt2-345m/'
												if "MYDIR" in os.environ:
																		UpperCAmelCase			 =					os.path.join(os.environ['MYDIR']  ,    lowercase_   )
												UpperCAmelCase			 =					GPTaLMHeadModel.from_pretrained(lowercase_   )
												model.to(lowercase_   )
												model.half()

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

												with torch.no_grad():
																		UpperCAmelCase			 =					model(lowercase_   ).logits

												UpperCAmelCase			 =					torch.Size((1, 9, 5_02_57)   )
												self.assertEqual(output.shape  ,    lowercase_   )

												UpperCAmelCase			 =					torch.tensor(
												    [
												        4.9414,
												        -0.2920,
												        -1.2148,
												        -4.0273,
												        -0.5161,
												        -5.2109,
												        -1.2412,
												        -1.8301,
												        -1.7734,
												        -4.7148,
												        -0.2317,
												        -1.0811,
												        -2.1777,
												        0.4141,
												        -3.7969,
												        -4.0586,
												        -2.5332,
												        -3.3809,
												        4.3867,
												    ]  ,    device=lowercase_  ,    dtype=torch.half  ,    )

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













"""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 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			'<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(' '			)
      lowercase				       =			dict(zip(_lowerCamelCase				,						range(len(_lowerCamelCase			)			)			)			)

      lowercase				       =			{
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      lowercase				       =			{
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 1_6_0_0_0,
          'return_attention_mask': False,
          'do_normalize': True,
      }

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

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







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      lowercase				       =			self.add_kwargs_tokens_map.copy()
      kwargs.update(_lowerCamelCase			)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_feature_extractor()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      processor.save_pretrained(self.tmpdirname			)
      lowercase				       =			WavaVecaProcessor.from_pretrained(self.tmpdirname			)

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

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			WavaVecaProcessor(tokenizer=self.get_tokenizer()				,						feature_extractor=self.get_feature_extractor()			)
      processor.save_pretrained(self.tmpdirname			)

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

      lowercase				       =			WavaVecaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

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

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			floats_list((3, 1_0_0_0)			)

      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(_lowerCamelCase				,						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      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			'This is a test string'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

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

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

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


