import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def       __magic_name__(  __UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				SwinvaConfig()
      _lowerCamelCase			    =				swinva_name.split('''_'''		)

      _lowerCamelCase			    =				name_split[1]
      if "to" in name_split[3]:
            _lowerCamelCase			    =				int(name_split[3][-3:]		)
      else:
            _lowerCamelCase			    =				int(name_split[3]		)
      if "to" in name_split[2]:
            _lowerCamelCase			    =				int(name_split[2][-2:]		)
      else:
            _lowerCamelCase			    =				int(name_split[2][6:]		)

      if model_size == "tiny":
            _lowerCamelCase			    =				96
            _lowerCamelCase			    =				(2, 2, 6, 2)
            _lowerCamelCase			    =				(3, 6, 12, 24)
      elif model_size == "small":
            _lowerCamelCase			    =				96
            _lowerCamelCase			    =				(2, 2, 18, 2)
            _lowerCamelCase			    =				(3, 6, 12, 24)
      elif model_size == "base":
            _lowerCamelCase			    =				128
            _lowerCamelCase			    =				(2, 2, 18, 2)
            _lowerCamelCase			    =				(4, 8, 16, 32)
      else:
            _lowerCamelCase			    =				192
            _lowerCamelCase			    =				(2, 2, 18, 2)
            _lowerCamelCase			    =				(6, 12, 24, 48)

      if "to" in swinva_name:
            _lowerCamelCase			    =				(12, 12, 12, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            _lowerCamelCase			    =				2_1841
            _lowerCamelCase			    =				'''huggingface/label-files'''
            _lowerCamelCase			    =				'''imagenet-22k-id2label.json'''
            _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
            _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}

      else:
            _lowerCamelCase			    =				1000
            _lowerCamelCase			    =				'''huggingface/label-files'''
            _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
            _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
            _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}

      _lowerCamelCase			    =				img_size
      _lowerCamelCase			    =				num_classes
      _lowerCamelCase			    =				embed_dim
      _lowerCamelCase			    =				depths
      _lowerCamelCase			    =				num_heads
      _lowerCamelCase			    =				window_size

      return config
def       __magic_name__(  __UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      if "patch_embed.proj" in name:
            _lowerCamelCase			    =				name.replace('''patch_embed.proj''' ,						'''embeddings.patch_embeddings.projection'''		)
      if "patch_embed.norm" in name:
            _lowerCamelCase			    =				name.replace('''patch_embed.norm''' ,						'''embeddings.norm'''		)
      if "layers" in name:
            _lowerCamelCase			    =				'''encoder.''' + name
      if "attn.proj" in name:
            _lowerCamelCase			    =				name.replace('''attn.proj''' ,						'''attention.output.dense'''		)
      if "attn" in name:
            _lowerCamelCase			    =				name.replace('''attn''' ,						'''attention.self'''		)
      if "norm1" in name:
            _lowerCamelCase			    =				name.replace('''norm1''' ,						'''layernorm_before'''		)
      if "norm2" in name:
            _lowerCamelCase			    =				name.replace('''norm2''' ,						'''layernorm_after'''		)
      if "mlp.fc1" in name:
            _lowerCamelCase			    =				name.replace('''mlp.fc1''' ,						'''intermediate.dense'''		)
      if "mlp.fc2" in name:
            _lowerCamelCase			    =				name.replace('''mlp.fc2''' ,						'''output.dense'''		)
      if "q_bias" in name:
            _lowerCamelCase			    =				name.replace('''q_bias''' ,						'''query.bias'''		)
      if "k_bias" in name:
            _lowerCamelCase			    =				name.replace('''k_bias''' ,						'''key.bias'''		)
      if "v_bias" in name:
            _lowerCamelCase			    =				name.replace('''v_bias''' ,						'''value.bias'''		)
      if "cpb_mlp" in name:
            _lowerCamelCase			    =				name.replace('''cpb_mlp''' ,						'''continuous_position_bias_mlp'''		)
      if name == "norm.weight":
            _lowerCamelCase			    =				'''layernorm.weight'''
      if name == "norm.bias":
            _lowerCamelCase			    =				'''layernorm.bias'''

      if "head" in name:
            _lowerCamelCase			    =				name.replace('''head''' ,						'''classifier'''		)
      else:
            _lowerCamelCase			    =				'''swinv2.''' + name

      return name
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      for key in orig_state_dict.copy().keys():
            _lowerCamelCase			    =				orig_state_dict.pop(__UpperCAmelCase		)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  _lowerCamelCase			    =				key.split('''.'''		)
                  _lowerCamelCase			    =				int(key_split[1]		)
                  _lowerCamelCase			    =				int(key_split[3]		)
                  _lowerCamelCase			    =				model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        _lowerCamelCase			    =				val[:dim, :]
                        _lowerCamelCase			    =				val[dim : dim * 2, :]
                        _lowerCamelCase			    =				val[-dim:, :]
                  else:
                        _lowerCamelCase			    =				val[:dim]
                        _lowerCamelCase			    =				val[
                            dim : dim * 2
                        ]
                        _lowerCamelCase			    =				val[-dim:]
            else:
                  _lowerCamelCase			    =				val

      return orig_state_dict
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				timm.create_model(__UpperCAmelCase ,						pretrained=__UpperCAmelCase		)
      timm_model.eval()

      _lowerCamelCase			    =				get_swinva_config(__UpperCAmelCase		)
      _lowerCamelCase			    =				SwinvaForImageClassification(__UpperCAmelCase		)
      model.eval()

      _lowerCamelCase			    =				convert_state_dict(timm_model.state_dict() ,						__UpperCAmelCase		)
      model.load_state_dict(__UpperCAmelCase		)

      _lowerCamelCase			    =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''

      _lowerCamelCase			    =				AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''' ,						'''-'''		)		)		)
      _lowerCamelCase			    =				Image.open(requests.get(__UpperCAmelCase ,						stream=__UpperCAmelCase		).raw		)
      _lowerCamelCase			    =				image_processor(images=__UpperCAmelCase ,						return_tensors='''pt'''		)

      _lowerCamelCase			    =				timm_model(inputs['''pixel_values''']		)
      _lowerCamelCase			    =				model(**__UpperCAmelCase		).logits

      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase ,						atol=1E-3		)

      print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'		)
      model.save_pretrained(__UpperCAmelCase		)

      print(F'Saving image processor to {pytorch_dump_folder_path}'		)
      image_processor.save_pretrained(__UpperCAmelCase		)

      model.push_to_hub(
          repo_path_or_name=Path(__UpperCAmelCase ,						__UpperCAmelCase		) ,						organization='''nandwalritik''' ,						commit_message='''Add model''' ,						)


if __name__ == "__main__":
     UpperCamelCase__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swinv2_name',
         default='swinv2_tiny_patch4_window8_256',
         type=str,
         help='Name of the Swinv2 timm model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     UpperCamelCase__				= parser.parse_args()
     convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
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						)						)