import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
     from cookiecutter.main import cookiecutter

     snake_case__				= True
except ImportError:
     snake_case__				= False

snake_case__				= logging.get_logger(__name__)  # pylint: disable=invalid-name
def       __magic_name__(  __UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      return AddNewModelCommand(args.testing ,						args.testing_file ,						path=args.path		)




class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     @staticmethod
     def 		UpperCamelCase_						(					A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				parser.add_parser('''add-new-model'''						)
           add_new_model_parser.add_argument('''--testing'''				,				action='''store_true'''				,				help='''If in testing mode.'''						)
           add_new_model_parser.add_argument('''--testing_file'''				,				type=A_				,				help='''Configuration file on which to run.'''						)
           add_new_model_parser.add_argument(
               '''--path'''				,				type=A_				,				help='''Path to cookiecutter. Should only be used for testing purposes.'''						)
           add_new_model_parser.set_defaults(func=A_						)



     def __init__(					self				,				A_				,				A_				,				A_=None				,				*A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				testing
           _lowerCamelCase			    =				testing_file
           _lowerCamelCase			    =				path





     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           warnings.warn(
               '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
               '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
               '''checks, you should use `transformers-cli add-new-model-like` instead.'''						)
           if not _has_cookiecutter:
                 raise ImportError(
                     '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
                     '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''						)
           # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
           _lowerCamelCase			    =				[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
           if len(A_						) > 0:
                 raise ValueError(
                     '''Several directories starting with `cookiecutter-template-` in current working directory. '''
                     '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
                     '''change your working directory.'''						)

           _lowerCamelCase			    =				(
               Path(A_						).parent.parent.parent.parent if self._path is None else Path(self._path						).parent.parent
           )
           _lowerCamelCase			    =				path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

           # Execute cookiecutter
           if not self._testing:
                 cookiecutter(str(A_						)						)
           else:
                 with open(self._testing_file				,				'''r'''						) as configuration_file:
                       _lowerCamelCase			    =				json.load(A_						)

                 cookiecutter(
                     str(path_to_cookiecutter if self._path is None else self._path						)				,				no_input=A_				,				extra_context=A_				,				)

           _lowerCamelCase			    =				[directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

           # Retrieve configuration
           with open(directory + '''/configuration.json'''				,				'''r'''						) as configuration_file:
                 _lowerCamelCase			    =				json.load(A_						)

           _lowerCamelCase			    =				configuration['''lowercase_modelname''']
           _lowerCamelCase			    =				configuration['''generate_tensorflow_pytorch_and_flax''']
           os.remove(F'{directory}/configuration.json'						)

           _lowerCamelCase			    =				'''PyTorch''' in generate_tensorflow_pytorch_and_flax
           _lowerCamelCase			    =				'''TensorFlow''' in generate_tensorflow_pytorch_and_flax
           _lowerCamelCase			    =				'''Flax''' in generate_tensorflow_pytorch_and_flax

           _lowerCamelCase			    =				F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
           os.makedirs(A_				,				exist_ok=A_						)
           os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'				,				exist_ok=A_						)

           # Tests require submodules as they have parent imports
           with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'				,				'''w'''						):
                 pass

           shutil.move(
               F'{directory}/__init__.py'				,				F'{model_dir}/__init__.py'				,				)
           shutil.move(
               F'{directory}/configuration_{lowercase_model_name}.py'				,				F'{model_dir}/configuration_{lowercase_model_name}.py'				,				)

           def remove_copy_lines(A_						):
                 with open(A_				,				'''r'''						) as f:
                       _lowerCamelCase			    =				f.readlines()
                 with open(A_				,				'''w'''						) as f:
                       for line in lines:
                             if "# Copied from transformers." not in line:
                                   f.write(A_						)

           if output_pytorch:
                 if not self._testing:
                       remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'						)

                 shutil.move(
                     F'{directory}/modeling_{lowercase_model_name}.py'				,				F'{model_dir}/modeling_{lowercase_model_name}.py'				,				)

                 shutil.move(
                     F'{directory}/test_modeling_{lowercase_model_name}.py'				,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'				,				)
           else:
                 os.remove(F'{directory}/modeling_{lowercase_model_name}.py'						)
                 os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'						)

           if output_tensorflow:
                 if not self._testing:
                       remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'						)

                 shutil.move(
                     F'{directory}/modeling_tf_{lowercase_model_name}.py'				,				F'{model_dir}/modeling_tf_{lowercase_model_name}.py'				,				)

                 shutil.move(
                     F'{directory}/test_modeling_tf_{lowercase_model_name}.py'				,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'				,				)
           else:
                 os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'						)
                 os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'						)

           if output_flax:
                 if not self._testing:
                       remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'						)

                 shutil.move(
                     F'{directory}/modeling_flax_{lowercase_model_name}.py'				,				F'{model_dir}/modeling_flax_{lowercase_model_name}.py'				,				)

                 shutil.move(
                     F'{directory}/test_modeling_flax_{lowercase_model_name}.py'				,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'				,				)
           else:
                 os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'						)
                 os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'						)

           shutil.move(
               F'{directory}/{lowercase_model_name}.md'				,				F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'				,				)

           shutil.move(
               F'{directory}/tokenization_{lowercase_model_name}.py'				,				F'{model_dir}/tokenization_{lowercase_model_name}.py'				,				)

           shutil.move(
               F'{directory}/tokenization_fast_{lowercase_model_name}.py'				,				F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'				,				)

           from os import fdopen, remove
           from shutil import copymode, move
           from tempfile import mkstemp

           def replace(A_				,				A_				,				A_						):
                 # Create temp file
                 _lowerCamelCase    ,					_lowerCamelCase			    =				mkstemp()
                 _lowerCamelCase			    =				False
                 with fdopen(A_				,				'''w'''						) as new_file:
                       with open(A_						) as old_file:
                             for line in old_file:
                                   new_file.write(A_						)
                                   if line_to_copy_below in line:
                                         _lowerCamelCase			    =				True
                                         for line_to_copy in lines_to_copy:
                                               new_file.write(A_						)

                 if not line_found:
                       raise ValueError(F'Line {line_to_copy_below} was not found in file.'						)

                 # Copy the file permissions from the old file to the new file
                 copymode(A_				,				A_						)
                 # Remove original file
                 remove(A_						)
                 # Move new file
                 move(A_				,				A_						)

           def skip_units(A_						):
                 return (
                     ("generating PyTorch" in line and not output_pytorch)
                     or ("generating TensorFlow" in line and not output_tensorflow)
                     or ("generating Flax" in line and not output_flax)
                 )

           def replace_in_files(A_						):
                 with open(A_						) as datafile:
                       _lowerCamelCase			    =				[]
                       _lowerCamelCase			    =				False
                       _lowerCamelCase			    =				False
                       for line in datafile:
                             if "# To replace in: " in line and "##" not in line:
                                   _lowerCamelCase			    =				line.split('''"'''						)[1]
                                   _lowerCamelCase			    =				skip_units(A_						)
                             elif "# Below: " in line and "##" not in line:
                                   _lowerCamelCase			    =				line.split('''"'''						)[1]
                                   _lowerCamelCase			    =				skip_units(A_						)
                             elif "# End." in line and "##" not in line:
                                   if not skip_file and not skip_snippet:
                                         replace(A_				,				A_				,				A_						)

                                   _lowerCamelCase			    =				[]
                             elif "# Replace with" in line and "##" not in line:
                                   _lowerCamelCase			    =				[]
                             elif "##" not in line:
                                   lines_to_copy.append(A_						)

                 remove(A_						)

           replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'						)
           os.rmdir(A_						)
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
     from huggingface_hub import snapshot_download
     from pyctcdecode import BeamSearchDecoderCTC

     from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
     from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
     from transformers import WavaVecaForCTC



@require_pyctcdecode
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				'''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
           _lowerCamelCase			    =				dict(zip(A_				,				range(len(A_						)						)						)						)

           _lowerCamelCase			    =				{
               '''unk_token''': '''<unk>''',
               '''bos_token''': '''<s>''',
               '''eos_token''': '''</s>''',
           }
           _lowerCamelCase			    =				{
               '''feature_size''': 1,
               '''padding_value''': 0.0,
               '''sampling_rate''': 1_60_00,
               '''return_attention_mask''': False,
               '''do_normalize''': True,
           }

           _lowerCamelCase			    =				tempfile.mkdtemp()
           _lowerCamelCase			    =				os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES['''vocab_file''']						)
           _lowerCamelCase			    =				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'''						)

           # load decoder from hub
           _lowerCamelCase			    =				'''hf-internal-testing/ngram-beam-search-decoder'''



     def 		UpperCamelCase_						(					self				,				**A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.add_kwargs_tokens_map.copy()
           kwargs.update(A_						)
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,				**A_						)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,				**A_						)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				int:






           """simple docstring"""
           return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name				,				**A_						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           shutil.rmtree(self.tmpdirname						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           processor.save_pretrained(self.tmpdirname						)
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname						)

           # tokenizer
           self.assertEqual(processor.tokenizer.get_vocab()				,				tokenizer.get_vocab()						)
           self.assertIsInstance(processor.tokenizer				,				A_						)

           # feature extractor
           self.assertEqual(processor.feature_extractor.to_json_string()				,				feature_extractor.to_json_string()						)
           self.assertIsInstance(processor.feature_extractor				,				A_						)

           # decoder
           self.assertEqual(processor.decoder._alphabet.labels				,				decoder._alphabet.labels						)
           self.assertEqual(
               processor.decoder.model_container[decoder._model_key]._unigram_set				,				decoder.model_container[decoder._model_key]._unigram_set				,				)
           self.assertIsInstance(processor.decoder				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM(
               tokenizer=self.get_tokenizer()				,				feature_extractor=self.get_feature_extractor()				,				decoder=self.get_decoder()						)
           processor.save_pretrained(self.tmpdirname						)

           # make sure that error is thrown when decoder alphabet doesn't match
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained(
               self.tmpdirname				,				alpha=5.0				,				beta=3.0				,				score_boundary=-7.0				,				unk_score_offset=3						)

           # decoder
           self.assertEqual(processor.language_model.alpha				,				5.0						)
           self.assertEqual(processor.language_model.beta				,				3.0						)
           self.assertEqual(processor.language_model.score_boundary				,				-7.0						)
           self.assertEqual(processor.language_model.unk_score_offset				,				3						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_tokenizer()
           # add token to trigger raise
           tokenizer.add_tokens(['''xx''']						)
           with self.assertRaisesRegex(A_				,				'''include'''						):
                 WavaVecaProcessorWithLM(
                     tokenizer=A_				,				feature_extractor=self.get_feature_extractor()				,				decoder=self.get_decoder()						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				floats_list((3, 10_00)						)

           _lowerCamelCase			    =				feature_extractor(A_				,				return_tensors='''np'''						)
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				'''This is a test string'''

           _lowerCamelCase			    =				processor(text=A_						)

           _lowerCamelCase			    =				tokenizer(A_						)

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



     def 		UpperCamelCase_						(					self				,				A_=(2, 10, 16)				,				A_=77						) ->				Optional[Any]:






           """simple docstring"""
           np.random.seed(A_						)
           return np.random.rand(*A_						)



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






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				self._get_dummy_logits(shape=(10, 16)				,				seed=13						)

           _lowerCamelCase			    =				processor.decode(A_						)

           _lowerCamelCase			    =				decoder.decode_beams(A_						)[0]

           self.assertEqual(decoded_decoder[0]				,				decoded_processor.text						)
           self.assertEqual('''</s> <s> </s>'''				,				decoded_processor.text						)
           self.assertEqual(decoded_decoder[-2]				,				decoded_processor.logit_score						)
           self.assertEqual(decoded_decoder[-1]				,				decoded_processor.lm_score						)



     @parameterized.expand([[None], ['''fork'''], ['''spawn''']]						)
     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
           #       otherwise, the LM won't be available to the pool's sub-processes.
           # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
           if pool_context is None:
                 _lowerCamelCase			    =				processor.batch_decode(A_						)
           else:
                 with get_context(A_						).Pool() as pool:
                       _lowerCamelCase			    =				processor.batch_decode(A_				,				A_						)

           _lowerCamelCase			    =				list(A_						)

           with get_context('''fork'''						).Pool() as p:
                 _lowerCamelCase			    =				decoder.decode_beams_batch(A_				,				A_						)

           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				[], [], []
           for beams in decoded_beams:
                 texts_decoder.append(beams[0][0]						)
                 logit_scores_decoder.append(beams[0][-2]						)
                 lm_scores_decoder.append(beams[0][-1]						)

           self.assertListEqual(A_				,				decoded_processor.text						)
           self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']				,				decoded_processor.text						)
           self.assertListEqual(A_				,				decoded_processor.logit_score						)
           self.assertListEqual(A_				,				decoded_processor.lm_score						)



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				15
           _lowerCamelCase			    =				-20.0
           _lowerCamelCase			    =				-4.0

           _lowerCamelCase			    =				processor.batch_decode(
               A_				,				beam_width=A_				,				beam_prune_logp=A_				,				token_min_logp=A_				,				)
           _lowerCamelCase			    =				decoded_processor_out.text

           _lowerCamelCase			    =				list(A_						)

           with get_context('''fork'''						).Pool() as pool:
                 _lowerCamelCase			    =				decoder.decode_beams_batch(
                     A_				,				A_				,				beam_width=A_				,				beam_prune_logp=A_				,				token_min_logp=A_				,				)

           _lowerCamelCase			    =				[d[0][0] for d in decoded_decoder_out]
           _lowerCamelCase			    =				[d[0][2] for d in decoded_decoder_out]
           _lowerCamelCase			    =				[d[0][3] for d in decoded_decoder_out]

           self.assertListEqual(A_				,				A_						)
           self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']				,				A_						)

           self.assertTrue(np.array_equal(A_				,				decoded_processor_out.logit_score						)						)
           self.assertTrue(np.allclose([-20.054, -18.447]				,				A_				,				atol=1E-3						)						)

           self.assertTrue(np.array_equal(A_				,				decoded_processor_out.lm_score						)						)
           self.assertTrue(np.allclose([-15.554, -13.9474]				,				A_				,				atol=1E-3						)						)



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






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				2.0
           _lowerCamelCase			    =				5.0
           _lowerCamelCase			    =				-20.0
           _lowerCamelCase			    =				True

           _lowerCamelCase			    =				processor.batch_decode(
               A_				,				alpha=A_				,				beta=A_				,				unk_score_offset=A_				,				lm_score_boundary=A_				,				)
           _lowerCamelCase			    =				decoded_processor_out.text

           _lowerCamelCase			    =				list(A_						)
           decoder.reset_params(
               alpha=A_				,				beta=A_				,				unk_score_offset=A_				,				lm_score_boundary=A_				,				)

           with get_context('''fork'''						).Pool() as pool:
                 _lowerCamelCase			    =				decoder.decode_beams_batch(
                     A_				,				A_				,				)

           _lowerCamelCase			    =				[d[0][0] for d in decoded_decoder_out]

           self.assertListEqual(A_				,				A_						)
           self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']				,				A_						)
           _lowerCamelCase			    =				processor.decoder.model_container[processor.decoder._model_key]
           self.assertEqual(lm_model.alpha				,				2.0						)
           self.assertEqual(lm_model.beta				,				5.0						)
           self.assertEqual(lm_model.unk_score_offset				,				-20.0						)
           self.assertEqual(lm_model.score_boundary				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

           _lowerCamelCase			    =				processor.decoder.model_container[processor.decoder._model_key]
           _lowerCamelCase			    =				Path(language_model._kenlm_model.path.decode('''utf-8'''						)						).parent.parent.absolute()

           _lowerCamelCase			    =				os.listdir(A_						)
           _lowerCamelCase			    =				['''alphabet.json''', '''language_model''']

           downloaded_decoder_files.sort()
           expected_decoder_files.sort()

           # test that only decoder relevant files from
           # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
           # are downloaded and none of the rest (e.g. README.md, ...)
           self.assertListEqual(A_				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				snapshot_download('''hf-internal-testing/processor_with_lm'''						)

           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained(A_						)

           _lowerCamelCase			    =				processor.decoder.model_container[processor.decoder._model_key]
           _lowerCamelCase			    =				Path(language_model._kenlm_model.path.decode('''utf-8'''						)						).parent.parent.absolute()

           _lowerCamelCase			    =				os.listdir(A_						)
           _lowerCamelCase			    =				os.listdir(A_						)

           local_decoder_files.sort()
           expected_decoder_files.sort()

           # test that both decoder form hub and local files in cache are the same
           self.assertListEqual(A_				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

           _lowerCamelCase			    =				floats_list((3, 10_00)						)

           _lowerCamelCase			    =				processor_wavaveca(A_				,				return_tensors='''np'''						)
           _lowerCamelCase			    =				processor_auto(A_				,				return_tensors='''np'''						)

           for key in input_wavaveca.keys():
                 self.assertAlmostEqual(input_wavaveca[key].sum()				,				input_auto[key].sum()				,				delta=1E-2						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				processor_wavaveca.batch_decode(A_						)
           _lowerCamelCase			    =				processor_auto.batch_decode(A_						)

           self.assertListEqual(decoded_wavaveca.text				,				decoded_auto.text						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

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



     @staticmethod
     def 		UpperCamelCase_						(					A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				[d[key] for d in offsets]
           return retrieved_list



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






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				self._get_dummy_logits()[0]

           _lowerCamelCase			    =				processor.decode(A_				,				output_word_offsets=A_						)
           # check Wav2Vec2CTCTokenizerOutput keys for word
           self.assertEqual(len(outputs.keys()						)				,				4						)
           self.assertTrue('''text''' in outputs						)
           self.assertTrue('''word_offsets''' in outputs						)
           self.assertTrue(isinstance(A_				,				A_						)						)

           self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']				,				'''word'''						)						)				,				outputs.text						)
           self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']				,				'''word'''						)				,				['''<s>''', '''<s>''', '''</s>''']						)
           self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']				,				'''start_offset'''						)				,				[0, 2, 4]						)
           self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']				,				'''end_offset'''						)				,				[1, 3, 5]						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				processor.batch_decode(A_				,				output_word_offsets=A_						)

           # check Wav2Vec2CTCTokenizerOutput keys for word
           self.assertEqual(len(outputs.keys()						)				,				4						)
           self.assertTrue('''text''' in outputs						)
           self.assertTrue('''word_offsets''' in outputs						)
           self.assertTrue(isinstance(A_				,				A_						)						)

           self.assertListEqual(
               [''' '''.join(self.get_from_offsets(A_				,				'''word'''						)						) for o in outputs['''word_offsets''']]				,				outputs.text						)
           self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]				,				'''word'''						)				,				['''<s>''', '''<s>''', '''</s>''']						)
           self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]				,				'''start_offset'''						)				,				[0, 2, 4]						)
           self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]				,				'''end_offset'''						)				,				[1, 3, 5]						)





     @slow
     @require_torch
     @require_torchaudio
     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           import torch

           _lowerCamelCase			    =				load_dataset('''common_voice'''				,				'''en'''				,				split='''train'''				,				streaming=A_						)
           _lowerCamelCase			    =				ds.cast_column('''audio'''				,				datasets.Audio(sampling_rate=1_60_00						)						)
           _lowerCamelCase			    =				iter(A_						)
           _lowerCamelCase			    =				next(A_						)

           _lowerCamelCase			    =				AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''						)
           _lowerCamelCase			    =				WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''						)

           # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
           _lowerCamelCase			    =				processor(sample['''audio''']['''array''']				,				return_tensors='''pt'''						).input_values

           with torch.no_grad():
                 _lowerCamelCase			    =				model(A_						).logits.cpu().numpy()

           _lowerCamelCase			    =				processor.decode(logits[0]				,				output_word_offsets=A_						)

           _lowerCamelCase			    =				model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
           _lowerCamelCase			    =				[
               {
                   '''start_time''': d['''start_offset'''] * time_offset,
                   '''end_time''': d['''end_offset'''] * time_offset,
                   '''word''': d['''word'''],
               }
               for d in output['''word_offsets''']
           ]

           _lowerCamelCase			    =				'''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

           # output words
           self.assertEqual(''' '''.join(self.get_from_offsets(A_				,				'''word'''						)						)				,				A_						)
           self.assertEqual(''' '''.join(self.get_from_offsets(A_				,				'''word'''						)						)				,				output.text						)

           # output times
           _lowerCamelCase			    =				torch.tensor(self.get_from_offsets(A_				,				'''start_time'''						)						)
           _lowerCamelCase			    =				torch.tensor(self.get_from_offsets(A_				,				'''end_time'''						)						)

           # fmt: off
           _lowerCamelCase			    =				torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]						)
           _lowerCamelCase			    =				torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]						)
           # fmt: on

           self.assertTrue(torch.allclose(A_				,				A_				,				atol=0.01						)						)
           self.assertTrue(torch.allclose(A_				,				A_				,				atol=0.01						)						)