


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				):
					def 			lowerCamelCase__(				self       :Dict				) ->   Any:
												a__					=					'| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
												a__					=					dict(zip(__snake_case      ,range(len(__snake_case				)				)				)				)

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

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

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

												# load decoder from hub
												a__					=					'hf-internal-testing/ngram-beam-search-decoder'
					def 			lowerCamelCase__(				self       :Dict      ,**__snake_case       :Union[str, Any]				) ->   List[Any]:
												a__					=					self.add_kwargs_tokens_map.copy()
												kwargs.update(__snake_case				)
												return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :str      ,**__snake_case       :List[Any]				) ->   str:
												return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :List[str]      ,**__snake_case       :Any				) ->   str:
												return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												shutil.rmtree(self.tmpdirname				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Dict:
												a__					=					self.get_tokenizer()
												a__					=					self.get_feature_extractor()
												a__					=					self.get_decoder()

												a__					=					WavaVecaProcessorWithLM(tokenizer=__snake_case      ,feature_extractor=__snake_case      ,decoder=__snake_case				)

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

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

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

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

												a__					=					WavaVecaProcessorWithLM(tokenizer=__snake_case      ,feature_extractor=__snake_case      ,decoder=__snake_case				)

												a__					=					floats_list((3, 10_00)				)

												a__					=					feature_extractor(__snake_case      ,return_tensors='np'				)
												a__					=					processor(__snake_case      ,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 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												a__					=					self.get_feature_extractor()
												a__					=					self.get_tokenizer()
												a__					=					self.get_decoder()

												a__					=					WavaVecaProcessorWithLM(tokenizer=__snake_case      ,feature_extractor=__snake_case      ,decoder=__snake_case				)

												a__					=					'This is a test string'

												a__					=					processor(text=__snake_case				)

												a__					=					tokenizer(__snake_case				)

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]      ,encoded_processor[key]				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :List[Any]=(2, 10, 16)      ,__snake_case       :List[Any]=77				) ->   Union[str, Any]:
												np.random.seed(__snake_case				)
												return np.random.rand(*__snake_case				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[int]:
												a__					=					self.get_feature_extractor()
												a__					=					self.get_tokenizer()
												a__					=					self.get_decoder()

												a__					=					WavaVecaProcessorWithLM(tokenizer=__snake_case      ,feature_extractor=__snake_case      ,decoder=__snake_case				)

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

												a__					=					processor.decode(__snake_case				)

												a__					=					decoder.decode_beams(__snake_case				)[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 			lowerCamelCase__(				self       :Any      ,__snake_case       :List[str]				) ->   Optional[int]:
												a__					=					self.get_feature_extractor()
												a__					=					self.get_tokenizer()
												a__					=					self.get_decoder()

												a__					=					WavaVecaProcessorWithLM(tokenizer=__snake_case      ,feature_extractor=__snake_case      ,decoder=__snake_case				)

												a__					=					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:
																			a__					=					processor.batch_decode(__snake_case				)
												else:
																			with get_context(__snake_case				).Pool() as pool:
																										a__					=					processor.batch_decode(__snake_case      ,__snake_case				)

												a__					=					list(__snake_case				)

												with get_context('fork'				).Pool() as p:
																			a__					=					decoder.decode_beams_batch(__snake_case      ,__snake_case				)

												a__				, a__				, a__					=					[], [], []
												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(__snake_case      ,decoded_processor.text				)
												self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']      ,decoded_processor.text				)
												self.assertListEqual(__snake_case      ,decoded_processor.logit_score				)
												self.assertListEqual(__snake_case      ,decoded_processor.lm_score				)
					def 			lowerCamelCase__(				self       :str				) ->   Any:
												a__					=					self.get_feature_extractor()
												a__					=					self.get_tokenizer()
												a__					=					self.get_decoder()

												a__					=					WavaVecaProcessorWithLM(tokenizer=__snake_case      ,feature_extractor=__snake_case      ,decoder=__snake_case				)

												a__					=					self._get_dummy_logits()

												a__					=					15
												a__					=					-20.0
												a__					=					-4.0

												a__					=					processor.batch_decode(
												    __snake_case      ,beam_width=__snake_case      ,beam_prune_logp=__snake_case      ,token_min_logp=__snake_case      ,)
												a__					=					decoded_processor_out.text

												a__					=					list(__snake_case				)

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

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

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

												self.assertTrue(np.array_equal(__snake_case      ,decoded_processor_out.logit_score				)				)
												self.assertTrue(np.allclose([-20.0_54, -18.4_47]      ,__snake_case      ,atol=1E-3				)				)

												self.assertTrue(np.array_equal(__snake_case      ,decoded_processor_out.lm_score				)				)
												self.assertTrue(np.allclose([-15.5_54, -13.94_74]      ,__snake_case      ,atol=1E-3				)				)
					def 			lowerCamelCase__(				self       :Dict				) ->   List[str]:
												a__					=					self.get_feature_extractor()
												a__					=					self.get_tokenizer()
												a__					=					self.get_decoder()

												a__					=					WavaVecaProcessorWithLM(tokenizer=__snake_case      ,feature_extractor=__snake_case      ,decoder=__snake_case				)

												a__					=					self._get_dummy_logits()

												a__					=					2.0
												a__					=					5.0
												a__					=					-20.0
												a__					=					True

												a__					=					processor.batch_decode(
												    __snake_case      ,alpha=__snake_case      ,beta=__snake_case      ,unk_score_offset=__snake_case      ,lm_score_boundary=__snake_case      ,)
												a__					=					decoded_processor_out.text

												a__					=					list(__snake_case				)
												decoder.reset_params(
												    alpha=__snake_case      ,beta=__snake_case      ,unk_score_offset=__snake_case      ,lm_score_boundary=__snake_case      ,)

												with get_context('fork'				).Pool() as pool:
																			a__					=					decoder.decode_beams_batch(
																			    __snake_case      ,__snake_case      ,)

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

												self.assertListEqual(__snake_case      ,__snake_case				)
												self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']      ,__snake_case				)
												a__					=					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      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[Any]:
												a__					=					WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'				)

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

												a__					=					os.listdir(__snake_case				)
												a__					=					['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(__snake_case      ,__snake_case				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   List[str]:
												a__					=					snapshot_download('hf-internal-testing/processor_with_lm'				)

												a__					=					WavaVecaProcessorWithLM.from_pretrained(__snake_case				)

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

												a__					=					os.listdir(__snake_case				)
												a__					=					os.listdir(__snake_case				)

												local_decoder_files.sort()
												expected_decoder_files.sort()

												# test that both decoder form hub and local files in cache are the same
												self.assertListEqual(__snake_case      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'				)
												a__					=					AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'				)

												a__					=					floats_list((3, 10_00)				)

												a__					=					processor_wavaveca(__snake_case      ,return_tensors='np'				)
												a__					=					processor_auto(__snake_case      ,return_tensors='np'				)

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

												a__					=					self._get_dummy_logits()

												a__					=					processor_wavaveca.batch_decode(__snake_case				)
												a__					=					processor_auto.batch_decode(__snake_case				)

												self.assertListEqual(decoded_wavaveca.text      ,decoded_auto.text				)
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[Any]:
												a__					=					self.get_feature_extractor()
												a__					=					self.get_tokenizer()
												a__					=					self.get_decoder()

												a__					=					WavaVecaProcessorWithLM(tokenizer=__snake_case      ,feature_extractor=__snake_case      ,decoder=__snake_case				)

												self.assertListEqual(
												    processor.model_input_names      ,feature_extractor.model_input_names      ,msg='`processor` and `feature_extractor` model input names do not match'      ,)
					@staticmethod
					def 			lowerCamelCase__(				__snake_case       :Optional[int]      ,__snake_case       :Optional[Any]				) ->   Any:
												a__					=					[d[key] for d in offsets]
												return retrieved_list
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'				)
												a__					=					self._get_dummy_logits()[0]

												a__					=					processor.decode(__snake_case      ,output_word_offsets=__snake_case				)
												# 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(__snake_case      ,__snake_case				)				)

												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 			lowerCamelCase__(				self       :Dict				) ->   Optional[Any]:
												a__					=					WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'				)
												a__					=					self._get_dummy_logits()

												a__					=					processor.batch_decode(__snake_case      ,output_word_offsets=__snake_case				)

												# 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(__snake_case      ,__snake_case				)				)

												self.assertListEqual(
												    [' '.join(self.get_from_offsets(__snake_case      ,'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 			lowerCamelCase__(				self       :List[str]				) ->   Optional[int]:
												import torch

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

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

												with torch.no_grad():
																			a__					=					model(__snake_case				).logits.cpu().numpy()

												a__					=					processor.decode(logits[0]      ,output_word_offsets=__snake_case				)

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

												a__					=					'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(__snake_case      ,'word'				)				)      ,__snake_case				)
												self.assertEqual(' '.join(self.get_from_offsets(__snake_case      ,'word'				)				)      ,output.text				)

												# output times
												a__					=					torch.tensor(self.get_from_offsets(__snake_case      ,'start_time'				)				)
												a__					=					torch.tensor(self.get_from_offsets(__snake_case      ,'end_time'				)				)

												# fmt: off
												a__					=					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]				)
												a__					=					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(__snake_case      ,__snake_case      ,atol=0.01				)				)
												self.assertTrue(torch.allclose(__snake_case      ,__snake_case      ,atol=0.01				)				)








import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case							:	Union[str, Any]					      =				logging.get_logger(__name__)

snake_case							:	Tuple					      =				torch.device('''cpu''')






def 							__lowercase       (			):
							a__					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
							return im






def 							__lowercase       (			__lowerCAmelCase							:    List[str]       ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    str       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    int       ):
							a__					=					[]
							for k in state_dict.keys():
														a__					=					k
														if ".pwconv" in k:
																					a__					=					k_new.replace('.pwconv' ,   '.point_wise_conv'       )
														if ".dwconv" in k:
																					a__					=					k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
														if ".Proj." in k:
																					a__					=					k_new.replace('.Proj.' ,   '.proj.'       )
														if "patch_embed" in k_new:
																					a__					=					k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
														if "network" in k_new:
																					a__					=					k_new.split('.'       )
																					if ls[2].isdigit():
																												a__					=					'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
																					else:
																												a__					=					k_new.replace('network' ,   'swiftformer.encoder.network'       )
														rename_keys.append((k, k_new)       )
							return rename_keys






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							a__					=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__					=					1_0_0_0
							a__					=					'huggingface/label-files'
							a__					=					'imagenet-1k-id2label.json'
							a__					=					json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type='dataset'       ) ,   'r'       )       )
							a__					=					{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
							a__					=					idalabel
							a__					=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														a__					=					[3, 3, 6, 4]
														a__					=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														a__					=					[3, 3, 9, 6]
														a__					=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														a__					=					[4, 3, 1_0, 5]
														a__					=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														a__					=					[4, 4, 1_2, 6]
														a__					=					[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'       ):
																					a__					=					torch.hub.load_state_dict_from_url(__lowerCAmelCase ,   map_location='cpu' ,   check_hash=__lowerCAmelCase       )
														else:
																					a__					=					torch.load(__lowerCAmelCase ,   map_location='cpu'       )
							a__					=					checkpoint

							a__					=					create_rename_keys(__lowerCAmelCase       )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

							# load HuggingFace model
							a__					=					SwiftFormerForImageClassification(__lowerCAmelCase       ).eval()
							hf_model.load_state_dict(__lowerCAmelCase       )

							# prepare test inputs
							a__					=					prepare_img()
							a__					=					ViTImageProcessor.from_pretrained('preprocessor_config'       )
							a__					=					processor(images=__lowerCAmelCase ,   return_tensors='pt'       )

							# compare outputs from both models
							a__					=					get_expected_output(__lowerCAmelCase       )
							a__					=					hf_model(inputs['pixel_values']       ).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]       )
							assert torch.allclose(hf_logits[0, 0:5] ,   __lowerCAmelCase ,   atol=1E-3       )

							Path(__lowerCAmelCase       ).mkdir(exist_ok=__lowerCAmelCase       )
							print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'       )
							hf_model.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	int					      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swiftformer_name''',
						    default='''swiftformer_xs''',
						    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
						    type=str,
						    help='''Name of the SwiftFormer model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''./converted_outputs/''',
						    type=str,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

						snake_case							:	Optional[int]					      =				parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




