

'''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    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



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

											SCREAMING_SNAKE_CASE_				=				{
											    'unk_token': '<unk>',
											    'bos_token': '<s>',
											    'eos_token': '</s>',
											}
											SCREAMING_SNAKE_CASE_				=				{
											    'feature_size': 1,
											    'padding_value': 0.0,
											    'sampling_rate': 16000,
											    'return_attention_mask': False,
											    'do_normalize': True,
											}

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

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

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



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



				def   a__					(    self,     **_lowercase       )     ->							Optional[int]:
											return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,     **_lowercase       )



				def   a__					(    self,     **_lowercase       )     ->							List[Any]:
											return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name,     **_lowercase       )



				def   a__					(    self       )     ->							Tuple:
											shutil.rmtree(self.tmpdirname       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_feature_extractor()
											SCREAMING_SNAKE_CASE_				=				self.get_decoder()

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM(tokenizer=_lowercase,     feature_extractor=_lowercase,     decoder=_lowercase       )

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

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

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

											# 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,     _lowercase       )



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				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
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											# add token to trigger raise
											tokenizer.add_tokens(['xx']       )
											with self.assertRaisesRegex(_lowercase,     'include'       ):
																		WavaVecaProcessorWithLM(
																		    tokenizer=_lowercase,     feature_extractor=self.get_feature_extractor(),     decoder=self.get_decoder()       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.get_feature_extractor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_decoder()

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM(tokenizer=_lowercase,     feature_extractor=_lowercase,     decoder=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				floats_list((3, 1000)       )

											SCREAMING_SNAKE_CASE_				=				feature_extractor(_lowercase,     return_tensors='np'       )
											SCREAMING_SNAKE_CASE_				=				processor(_lowercase,     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   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				self.get_feature_extractor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_decoder()

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM(tokenizer=_lowercase,     feature_extractor=_lowercase,     decoder=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				'This is a test string'

											SCREAMING_SNAKE_CASE_				=				processor(text=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				tokenizer(_lowercase       )

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



				def   a__					(    self,     _lowercase=(2, 10, 16),     _lowercase=77       )     ->							List[str]:
											np.random.seed(_lowercase       )
											return np.random.rand(*_lowercase       )



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.get_feature_extractor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_decoder()

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM(tokenizer=_lowercase,     feature_extractor=_lowercase,     decoder=_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				processor.decode(_lowercase       )

											SCREAMING_SNAKE_CASE_				=				decoder.decode_beams(_lowercase       )[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   a__					(    self,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				self.get_feature_extractor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_decoder()

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM(tokenizer=_lowercase,     feature_extractor=_lowercase,     decoder=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				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:
																		SCREAMING_SNAKE_CASE_				=				processor.batch_decode(_lowercase       )
											else:
																		with get_context(_lowercase       ).Pool() as pool:
																									SCREAMING_SNAKE_CASE_				=				processor.batch_decode(_lowercase,     _lowercase       )

											SCREAMING_SNAKE_CASE_				=				list(_lowercase       )

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

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



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				self.get_feature_extractor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_decoder()

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM(tokenizer=_lowercase,     feature_extractor=_lowercase,     decoder=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				self._get_dummy_logits()

											SCREAMING_SNAKE_CASE_				=				15
											SCREAMING_SNAKE_CASE_				=				-20.0
											SCREAMING_SNAKE_CASE_				=				-4.0

											SCREAMING_SNAKE_CASE_				=				processor.batch_decode(
											    _lowercase,     beam_width=_lowercase,     beam_prune_logp=_lowercase,     token_min_logp=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				decoded_processor_out.text

											SCREAMING_SNAKE_CASE_				=				list(_lowercase       )

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

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

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

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

											self.assertTrue(np.array_equal(_lowercase,     decoded_processor_out.lm_score       )       )
											self.assertTrue(np.allclose([-15.554, -13.9_474],     _lowercase,     atol=1E-3       )       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				self.get_feature_extractor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_decoder()

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM(tokenizer=_lowercase,     feature_extractor=_lowercase,     decoder=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				self._get_dummy_logits()

											SCREAMING_SNAKE_CASE_				=				2.0
											SCREAMING_SNAKE_CASE_				=				5.0
											SCREAMING_SNAKE_CASE_				=				-20.0
											SCREAMING_SNAKE_CASE_				=				True

											SCREAMING_SNAKE_CASE_				=				processor.batch_decode(
											    _lowercase,     alpha=_lowercase,     beta=_lowercase,     unk_score_offset=_lowercase,     lm_score_boundary=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				decoded_processor_out.text

											SCREAMING_SNAKE_CASE_				=				list(_lowercase       )
											decoder.reset_params(
											    alpha=_lowercase,     beta=_lowercase,     unk_score_offset=_lowercase,     lm_score_boundary=_lowercase,     )

											with get_context('fork'       ).Pool() as pool:
																		SCREAMING_SNAKE_CASE_				=				decoder.decode_beams_batch(
																		    _lowercase,     _lowercase,     )

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

											self.assertListEqual(_lowercase,     _lowercase       )
											self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>'],     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				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,     _lowercase       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'       )

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

											SCREAMING_SNAKE_CASE_				=				os.listdir(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				['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(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				snapshot_download('hf-internal-testing/processor_with_lm'       )

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM.from_pretrained(_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				os.listdir(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				os.listdir(_lowercase       )

											local_decoder_files.sort()
											expected_decoder_files.sort()

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



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'       )
											SCREAMING_SNAKE_CASE_				=				AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'       )

											SCREAMING_SNAKE_CASE_				=				floats_list((3, 1000)       )

											SCREAMING_SNAKE_CASE_				=				processor_wavaveca(_lowercase,     return_tensors='np'       )
											SCREAMING_SNAKE_CASE_				=				processor_auto(_lowercase,     return_tensors='np'       )

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

											SCREAMING_SNAKE_CASE_				=				self._get_dummy_logits()

											SCREAMING_SNAKE_CASE_				=				processor_wavaveca.batch_decode(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				processor_auto.batch_decode(_lowercase       )

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



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				self.get_feature_extractor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_decoder()

											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM(tokenizer=_lowercase,     feature_extractor=_lowercase,     decoder=_lowercase       )

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



				@staticmethod
				def   a__					(    _lowercase,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				[d[key] for d in offsets]
											return retrieved_list



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'       )
											SCREAMING_SNAKE_CASE_				=				self._get_dummy_logits()[0]

											SCREAMING_SNAKE_CASE_				=				processor.decode(_lowercase,     output_word_offsets=_lowercase       )
											# 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(_lowercase,     _lowercase       )       )

											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   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'       )
											SCREAMING_SNAKE_CASE_				=				self._get_dummy_logits()

											SCREAMING_SNAKE_CASE_				=				processor.batch_decode(_lowercase,     output_word_offsets=_lowercase       )

											# 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(_lowercase,     _lowercase       )       )

											self.assertListEqual(
											    [' '.join(self.get_from_offsets(_lowercase,     '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   a__					(    self       )     ->							List[str]:
											import torch

											SCREAMING_SNAKE_CASE_				=				load_dataset('common_voice',     'en',     split='train',     streaming=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				ds.cast_column('audio',     datasets.Audio(sampling_rate=16000       )       )
											SCREAMING_SNAKE_CASE_				=				iter(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				next(_lowercase       )

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

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

											SCREAMING_SNAKE_CASE_				=				processor.decode(logits[0],     output_word_offsets=_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				'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(_lowercase,     'word'       )       ),     _lowercase       )
											self.assertEqual(' '.join(self.get_from_offsets(_lowercase,     'word'       )       ),     output.text       )

											# output times
											SCREAMING_SNAKE_CASE_				=				torch.tensor(self.get_from_offsets(_lowercase,     'start_time'       )       )
											SCREAMING_SNAKE_CASE_				=				torch.tensor(self.get_from_offsets(_lowercase,     'end_time'       )       )

											# fmt: off
											SCREAMING_SNAKE_CASE_				=				torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]       )
											SCREAMING_SNAKE_CASE_				=				torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]       )
											# fmt: on

											self.assertTrue(torch.allclose(_lowercase,     _lowercase,     atol=0.01       )       )
											self.assertTrue(torch.allclose(_lowercase,     _lowercase,     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()
SCREAMING_SNAKE_CASE							:				Dict     	=			logging.get_logger()




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					str		,lowerCAmelCase__:					LevitConfig		,lowerCAmelCase__:					Path		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							print(F"""Converting {name}..."""   )

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

														from_model.eval()
														SCREAMING_SNAKE_CASE_				=				LevitForImageClassificationWithTeacher(lowerCAmelCase__   ).eval()
														SCREAMING_SNAKE_CASE_				=				OrderedDict()

														SCREAMING_SNAKE_CASE_				=				from_model.state_dict()
														SCREAMING_SNAKE_CASE_				=				list(from_model.state_dict().keys()   )
														SCREAMING_SNAKE_CASE_				=				list(our_model.state_dict().keys()   )
														print(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
														for i in range(len(lowerCAmelCase__   )   ):
																					SCREAMING_SNAKE_CASE_				=				weights[og_keys[i]]
														our_model.load_state_dict(lowerCAmelCase__   )

														SCREAMING_SNAKE_CASE_				=				torch.randn((2, 3, 224, 224)   )
														SCREAMING_SNAKE_CASE_				=				from_model(lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				our_model(lowerCAmelCase__   ).logits

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

							SCREAMING_SNAKE_CASE_				=				name
							print(lowerCAmelCase__   )

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

														print(F"""Pushed {checkpoint_name}"""   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					Path		,lowerCAmelCase__:					str = None		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				(1, num_labels)

							SCREAMING_SNAKE_CASE_				=				'huggingface/label-files'
							SCREAMING_SNAKE_CASE_				=				num_labels
							SCREAMING_SNAKE_CASE_				=				json.load(open(hf_hub_download(lowerCAmelCase__		,lowerCAmelCase__		,repo_type='dataset'   )		,'r'   )   )
							SCREAMING_SNAKE_CASE_				=				{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				idalabel
							SCREAMING_SNAKE_CASE_				=				{v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				partial(lowerCAmelCase__		,num_labels=lowerCAmelCase__		,idalabel=lowerCAmelCase__		,labelaid=lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							SCREAMING_SNAKE_CASE_				=				{
							    '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]		,lowerCAmelCase__		,names_to_config[model_name]		,lowerCAmelCase__		,lowerCAmelCase__   )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
							return config, expected_shape


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				str     	=			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",
							)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							SCREAMING_SNAKE_CASE							:				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)





