
"""simple docstring"""




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 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			List[str]       =     '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
         _lowercase:			Optional[int]       =     dict(zip(A_ ,				range(len(A_       )       )       )       )

         _lowercase:			Tuple       =     {
             '''unk_token''': '''<unk>''',
             '''bos_token''': '''<s>''',
             '''eos_token''': '''</s>''',
         }
         _lowercase:			Dict       =     {
             '''feature_size''': 1,
             '''padding_value''': 0.0,
             '''sampling_rate''': 1_6000,
             '''return_attention_mask''': False,
             '''do_normalize''': True,
         }

         _lowercase:			Any       =     tempfile.mkdtemp()
         _lowercase:			Optional[Any]       =     os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['''vocab_file''']       )
         _lowercase:			List[str]       =     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
         _lowercase:			Any       =     '''hf-internal-testing/ngram-beam-search-decoder'''
       def 							lowercase_							(     self ,				**A_       )     ->	Any:





         """simple docstring"""




         _lowercase:			str       =     self.add_kwargs_tokens_map.copy()
         kwargs.update(A_       )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,				**A_       )
       def 							lowercase_							(     self ,				**A_       )     ->	Tuple:





         """simple docstring"""




         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,				**A_       )
       def 							lowercase_							(     self ,				**A_       )     ->	int:





         """simple docstring"""




         return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,				**A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         shutil.rmtree(self.tmpdirname       )
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Any       =     self.get_tokenizer()
         _lowercase:			List[str]       =     self.get_feature_extractor()
         _lowercase:			Any       =     self.get_decoder()

         _lowercase:			int       =     WavaVecaProcessorWithLM(tokenizer=A_ ,				feature_extractor=A_ ,				decoder=A_       )

         processor.save_pretrained(self.tmpdirname       )
         _lowercase:			List[str]       =     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 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     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
         _lowercase:			str       =     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 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




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





         """simple docstring"""




         _lowercase:			Any       =     self.get_feature_extractor()
         _lowercase:			Any       =     self.get_tokenizer()
         _lowercase:			Dict       =     self.get_decoder()

         _lowercase:			List[str]       =     WavaVecaProcessorWithLM(tokenizer=A_ ,				feature_extractor=A_ ,				decoder=A_       )

         _lowercase:			Optional[int]       =     floats_list((3, 1000)       )

         _lowercase:			Any       =     feature_extractor(A_ ,				return_tensors='''np'''       )
         _lowercase:			Union[str, Any]       =     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 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			int       =     self.get_feature_extractor()
         _lowercase:			str       =     self.get_tokenizer()
         _lowercase:			Union[str, Any]       =     self.get_decoder()

         _lowercase:			Dict       =     WavaVecaProcessorWithLM(tokenizer=A_ ,				feature_extractor=A_ ,				decoder=A_       )

         _lowercase:			Optional[int]       =     '''This is a test string'''

         _lowercase:			List[Any]       =     processor(text=A_       )

         _lowercase:			Union[str, Any]       =     tokenizer(A_       )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key] ,				encoded_processor[key]       )
       def 							lowercase_							(     self ,				A_=(2, 10, 16) ,				A_=77       )     ->	Any:





         """simple docstring"""




         np.random.seed(A_       )
         return np.random.rand(*A_       )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Any       =     self.get_feature_extractor()
         _lowercase:			str       =     self.get_tokenizer()
         _lowercase:			Optional[int]       =     self.get_decoder()

         _lowercase:			Tuple       =     WavaVecaProcessorWithLM(tokenizer=A_ ,				feature_extractor=A_ ,				decoder=A_       )

         _lowercase:			Optional[int]       =     self._get_dummy_logits(shape=(10, 16) ,				seed=13       )

         _lowercase:			Tuple       =     processor.decode(A_       )

         _lowercase:			Optional[Any]       =     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 							lowercase_							(     self ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.get_feature_extractor()
         _lowercase:			Any       =     self.get_tokenizer()
         _lowercase:			str       =     self.get_decoder()

         _lowercase:			List[Any]       =     WavaVecaProcessorWithLM(tokenizer=A_ ,				feature_extractor=A_ ,				decoder=A_       )

         _lowercase:			Optional[Any]       =     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:
           _lowercase:			Union[str, Any]       =     processor.batch_decode(A_       )
         else:
           with get_context(A_       ).Pool() as pool:
             _lowercase:			Any       =     processor.batch_decode(A_ ,				A_       )

         _lowercase:			Any       =     list(A_       )

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

         _lowercase ,				_lowercase ,				_lowercase:			List[Any]       =     [], [], []
         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 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.get_feature_extractor()
         _lowercase:			List[Any]       =     self.get_tokenizer()
         _lowercase:			Union[str, Any]       =     self.get_decoder()

         _lowercase:			Optional[int]       =     WavaVecaProcessorWithLM(tokenizer=A_ ,				feature_extractor=A_ ,				decoder=A_       )

         _lowercase:			Tuple       =     self._get_dummy_logits()

         _lowercase:			Optional[Any]       =     15
         _lowercase:			Tuple       =     -20.0
         _lowercase:			str       =     -4.0

         _lowercase:			int       =     processor.batch_decode(
             A_ ,				beam_width=A_ ,				beam_prune_logp=A_ ,				token_min_logp=A_ ,				)
         _lowercase:			int       =     decoded_processor_out.text

         _lowercase:			Union[str, Any]       =     list(A_       )

         with get_context('''fork'''       ).Pool() as pool:
           _lowercase:			List[str]       =     decoder.decode_beams_batch(
               A_ ,				A_ ,				beam_width=A_ ,				beam_prune_logp=A_ ,				token_min_logp=A_ ,				)

         _lowercase:			Union[str, Any]       =     [d[0][0] for d in decoded_decoder_out]
         _lowercase:			List[Any]       =     [d[0][2] for d in decoded_decoder_out]
         _lowercase:			List[str]       =     [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([-20.0_54, -18.4_47] ,				A_ ,				atol=1E-3       )       )

         self.assertTrue(np.array_equal(A_ ,				decoded_processor_out.lm_score       )       )
         self.assertTrue(np.allclose([-15.5_54, -13.94_74] ,				A_ ,				atol=1E-3       )       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Tuple       =     self.get_feature_extractor()
         _lowercase:			Any       =     self.get_tokenizer()
         _lowercase:			Union[str, Any]       =     self.get_decoder()

         _lowercase:			List[Any]       =     WavaVecaProcessorWithLM(tokenizer=A_ ,				feature_extractor=A_ ,				decoder=A_       )

         _lowercase:			Any       =     self._get_dummy_logits()

         _lowercase:			int       =     2.0
         _lowercase:			Optional[int]       =     5.0
         _lowercase:			Tuple       =     -20.0
         _lowercase:			Tuple       =     True

         _lowercase:			List[Any]       =     processor.batch_decode(
             A_ ,				alpha=A_ ,				beta=A_ ,				unk_score_offset=A_ ,				lm_score_boundary=A_ ,				)
         _lowercase:			Tuple       =     decoded_processor_out.text

         _lowercase:			int       =     list(A_       )
         decoder.reset_params(
             alpha=A_ ,				beta=A_ ,				unk_score_offset=A_ ,				lm_score_boundary=A_ ,				)

         with get_context('''fork'''       ).Pool() as pool:
           _lowercase:			List[str]       =     decoder.decode_beams_batch(
               A_ ,				A_ ,				)

         _lowercase:			Optional[int]       =     [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_       )
         _lowercase:			Tuple       =     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 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''       )

         _lowercase:			List[Any]       =     processor.decoder.model_container[processor.decoder._model_key]
         _lowercase:			List[str]       =     Path(language_model._kenlm_model.path.decode('''utf-8'''       )       ).parent.parent.absolute()

         _lowercase:			List[str]       =     os.listdir(A_       )
         _lowercase:			Dict       =     ['''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 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Any       =     snapshot_download('''hf-internal-testing/processor_with_lm'''       )

         _lowercase:			Optional[Any]       =     WavaVecaProcessorWithLM.from_pretrained(A_       )

         _lowercase:			Union[str, Any]       =     processor.decoder.model_container[processor.decoder._model_key]
         _lowercase:			Dict       =     Path(language_model._kenlm_model.path.decode('''utf-8'''       )       ).parent.parent.absolute()

         _lowercase:			int       =     os.listdir(A_       )
         _lowercase:			Any       =     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 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			Optional[int]       =     WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''       )
         _lowercase:			List[str]       =     AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''       )

         _lowercase:			str       =     floats_list((3, 1000)       )

         _lowercase:			List[Any]       =     processor_wavaveca(A_ ,				return_tensors='''np'''       )
         _lowercase:			Optional[Any]       =     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       )

         _lowercase:			Tuple       =     self._get_dummy_logits()

         _lowercase:			List[str]       =     processor_wavaveca.batch_decode(A_       )
         _lowercase:			List[Any]       =     processor_auto.batch_decode(A_       )

         self.assertListEqual(decoded_wavaveca.text ,				decoded_auto.text       )
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			str       =     self.get_feature_extractor()
         _lowercase:			Union[str, Any]       =     self.get_tokenizer()
         _lowercase:			Optional[Any]       =     self.get_decoder()

         _lowercase:			str       =     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 							lowercase_							(     A_ ,				A_       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			List[Any]       =     [d[key] for d in offsets]
         return retrieved_list
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Optional[int]       =     WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''       )
         _lowercase:			List[Any]       =     self._get_dummy_logits()[0]

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





         """simple docstring"""




         _lowercase:			List[str]       =     WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''       )
         _lowercase:			Optional[Any]       =     self._get_dummy_logits()

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





         """simple docstring"""




         import torch

         _lowercase:			List[Any]       =     load_dataset('''common_voice''' ,				'''en''' ,				split='''train''' ,				streaming=A_       )
         _lowercase:			Union[str, Any]       =     ds.cast_column('''audio''' ,				datasets.Audio(sampling_rate=1_6000       )       )
         _lowercase:			Dict       =     iter(A_       )
         _lowercase:			str       =     next(A_       )

         _lowercase:			int       =     AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''       )
         _lowercase:			int       =     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
         _lowercase:			Tuple       =     processor(sample['''audio''']['''array'''] ,				return_tensors='''pt'''       ).input_values

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

         _lowercase:			Optional[Any]       =     processor.decode(logits[0] ,				output_word_offsets=A_       )

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

         _lowercase:			Union[str, Any]       =     '''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
         _lowercase:			Union[str, Any]       =     torch.tensor(self.get_from_offsets(A_ ,				'''start_time'''       )       )
         _lowercase:			List[Any]       =     torch.tensor(self.get_from_offsets(A_ ,				'''end_time'''       )       )

         # fmt: off
         _lowercase:			Union[str, Any]       =     torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]       )
         _lowercase:			Tuple       =     torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]       )
         # fmt: on

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


"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A__   :					List[Any]							=   logging.get_logger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = True						):






  """simple docstring"""



  print(f'''Converting {name}...'''						)

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        _lowercase:			int       =     timm.create_model('''levit_128s''' ,				pretrained=_UpperCamelCase						)
      else:
        _lowercase:			int       =     timm.create_model('''levit_128''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 192:
      _lowercase:			str       =     timm.create_model('''levit_192''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 256:
      _lowercase:			int       =     timm.create_model('''levit_256''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 384:
      _lowercase:			Dict       =     timm.create_model('''levit_384''' ,				pretrained=_UpperCamelCase						)

    from_model.eval()
    _lowercase:			Any       =     LevitForImageClassificationWithTeacher(_UpperCamelCase						).eval()
    _lowercase:			Union[str, Any]       =     OrderedDict()

    _lowercase:			Optional[Any]       =     from_model.state_dict()
    _lowercase:			List[Any]       =     list(from_model.state_dict().keys()						)
    _lowercase:			int       =     list(our_model.state_dict().keys()						)
    print(len(_UpperCamelCase						) ,				len(_UpperCamelCase						)						)
    for i in range(len(_UpperCamelCase						)						):
      _lowercase:			Any       =     weights[og_keys[i]]
    our_model.load_state_dict(_UpperCamelCase						)

    _lowercase:			int       =     torch.randn((2, 3, 224, 224)						)
    _lowercase:			List[str]       =     from_model(_UpperCamelCase						)
    _lowercase:			Optional[int]       =     our_model(_UpperCamelCase						).logits

  assert torch.allclose(_UpperCamelCase ,				_UpperCamelCase						), "The model logits don't match the original one."

  _lowercase:			Union[str, Any]       =     name
  print(_UpperCamelCase						)

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name						)
    _lowercase:			Dict       =     LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name						)

    print(f'''Pushed {checkpoint_name}'''						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase = None ,				_UpperCamelCase = True						):






  """simple docstring"""



  _lowercase:			int       =     '''imagenet-1k-id2label.json'''
  _lowercase:			int       =     1_000
  _lowercase:			Tuple       =     (1, num_labels)

  _lowercase:			Dict       =     '''huggingface/label-files'''
  _lowercase:			Optional[int]       =     num_labels
  _lowercase:			List[str]       =     json.load(open(hf_hub_download(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset'''						) ,				'''r'''						)						)
  _lowercase:			List[str]       =     {int(_UpperCamelCase						): v for k, v in idalabel.items()}

  _lowercase:			int       =     idalabel
  _lowercase:			Tuple       =     {v: k for k, v in idalabel.items()}

  _lowercase:			Tuple       =     partial(_UpperCamelCase ,				num_labels=_UpperCamelCase ,				idalabel=_UpperCamelCase ,				labelaid=_UpperCamelCase						)

  _lowercase:			Union[str, Any]       =     {
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  _lowercase:			int       =     {
      '''levit-128S''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384] ,				num_attention_heads=[4, 6, 8] ,				depths=[2, 3, 4] ,				key_dim=[16, 16, 16] ,				drop_path_rate=0 ,				),
      '''levit-128''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384] ,				num_attention_heads=[4, 8, 12] ,				depths=[4, 4, 4] ,				key_dim=[16, 16, 16] ,				drop_path_rate=0 ,				),
      '''levit-192''': ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384] ,				num_attention_heads=[3, 5, 6] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0 ,				),
      '''levit-256''': ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512] ,				num_attention_heads=[4, 6, 8] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0 ,				),
      '''levit-384''': ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768] ,				num_attention_heads=[6, 9, 12] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0.1 ,				),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				names_to_config[model_name] ,				_UpperCamelCase ,				_UpperCamelCase						)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)
  return config, expected_shape


if __name__ == "__main__":
  A__   :					Any							=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default=None,
      type=str,
      help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='levit-dump-folder/',
      type=Path,
      required=False,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
  parser.add_argument(
      '--no-push_to_hub',
      dest='push_to_hub',
      action='store_false',
      help='Do not push model and image processor to the hub',
  )

  A__   :					Optional[Any]							=   parser.parse_args()
  A__   :					Path							=   args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
