





'''simple docstring'''




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
a__				: Optional[int]									=    logging.get_logger()






@dataclass
class 							__snake_case    :
	__lowerCAmelCase						=     42
	__lowerCAmelCase						=     field(default_factory=__magic_name__       )
	__lowerCAmelCase						=     field(default_factory=__magic_name__       )
	__lowerCAmelCase						=     field(default_factory=__magic_name__       )





	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Optional[int]:
		snake_case__  					=      len(list(m.modules()		)		) == 1 or isinstance(UpperCamelCase_			,					nn.Convad		) or isinstance(UpperCamelCase_			,					nn.BatchNormad		)
		if has_not_submodules:
			self.traced.append(UpperCamelCase_		)
			snake_case__  					=      m





	def __call__(		self			,					UpperCamelCase_		)			->		Optional[int]:
		for name, m in self.module.named_modules():
			self.handles.append(m.register_forward_hook(partial(self._forward_hook			,					name=UpperCamelCase_		)		)		)
		self.module(UpperCamelCase_		)
		[x.remove() for x in self.handles]
		return self





	@property
	def      _snake_case      (		self		)			->		Optional[int]:
		# check the len of the state_dict keys to see if we have learnable params
		return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()		)		) > 0}






class 							__snake_case    (				nn.Module       ):





	def __init__(		self			,					UpperCamelCase_		)			->		int:
		super().__init__()

		snake_case__  					=      []
		# - get the stem
		feature_blocks.append(('conv1', model.stem)		)
		# - get all the feature blocks
		for k, v in model.trunk_output.named_children():
			assert k.startswith('block'		), F'''Unexpected layer name {k}'''
			snake_case__  					=      len(UpperCamelCase_		) + 1
			feature_blocks.append((F'''res{block_index}''', v)		)

		snake_case__  					=      nn.ModuleDict(UpperCamelCase_		)





	def      _snake_case      (		self			,					UpperCamelCase_		)			->		List[str]:
		return get_trunk_forward_outputs(
		    UpperCamelCase_			,					out_feat_keys=UpperCamelCase_			,					feature_blocks=self._feature_blocks			,					)






class 							__snake_case    (				__magic_name__       ):





	def      _snake_case      (		self		)			->		List[str]:
		return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]





def 	__lowerCamelCase (  UpperCAmelCase_       )      ->Dict[str, str]:
	snake_case__  					=      RegNetConfig(depths=[2, 7, 17, 1]				,							hidden_sizes=[8, 8, 8, 8]				,							groups_width=8       )
	if "in1k" in model_name:
		snake_case__  					=      RegNetForImageClassification(UpperCAmelCase_       )
	else:
		snake_case__  					=      RegNetModel(UpperCAmelCase_       )
	# create from model (with small weights)
	snake_case__  					=      FakeRegNetVisslWrapper(
	    RegNet(FakeRegNetParams(depth=27				,							group_width=10_10				,							w_a=17_44				,							w_a=620.83				,							w_m=2.52       )       )       )

	with torch.no_grad():
		snake_case__  					=      from_model.eval()
		snake_case__  					=      our_model.eval()

		snake_case__  					=      torch.randn((1, 3, 32, 32)       )
		# trace both
		snake_case__  					=      Tracker(UpperCAmelCase_       )
		snake_case__  					=      dest_tracker(UpperCAmelCase_       ).parametrized

		pprint(dest_tracker.nameamodule       )
		snake_case__  					=      Tracker(UpperCAmelCase_       )
		snake_case__  					=      src_tracker(UpperCAmelCase_       ).parametrized

	# convert the keys -> module dict to keys -> params
	def to_params_dict(UpperCAmelCase_       ):
		snake_case__  					=      OrderedDict()
		for name, module in dict_with_modules.items():
			for param_name, param in module.state_dict().items():
				snake_case__  					=      param
		return params_dict

	snake_case__  					=      {}

	snake_case__  					=      to_params_dict(UpperCAmelCase_       )
	snake_case__  					=      to_params_dict(UpperCAmelCase_       )

	for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()				,							dst_state_dict.items()       ):
		snake_case__  					=      dest_key
		logger.info(f'''{src_key} -> {dest_key}'''       )
	# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
	if "in1k" in model_name:
		snake_case__  					=      'classifier.1.weight'
		snake_case__  					=      'classifier.1.bias'

	return from_to_ours_keys




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_ = None				,							UpperCAmelCase_ = True       )      ->Union[str, Any]:
	snake_case__  					=      'imagenet-1k-id2label.json'
	snake_case__  					=      10_00

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

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

	snake_case__  					=      partial(UpperCAmelCase_				,							num_labels=UpperCAmelCase_				,							idalabel=UpperCAmelCase_				,							labelaid=UpperCAmelCase_       )

	snake_case__  					=      {
	    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]				,							hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,							groups_width=10_10       ),
	    # finetuned on imagenet
	    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]				,							hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,							groups_width=10_10       ),
	}

	# add seer weights logic
	def load_using_classy_vision(UpperCAmelCase_       ) -> Tuple[Dict, Dict]:
		snake_case__  					=      torch.hub.load_state_dict_from_url(UpperCAmelCase_				,							model_dir=str(UpperCAmelCase_       )				,							map_location='cpu'       )
		# check if we have a head, if yes add it
		snake_case__  					=      files['classy_state_dict']['base_model']['model']
		return model_state_dict["trunk"], model_state_dict["heads"]

	snake_case__  					=      {
	    'regnet-y-10b-seer': partial(
	        UpperCAmelCase_				,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'				,							),
	    'regnet-y-10b-seer-in1k': partial(
	        UpperCAmelCase_				,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'				,							),
	}

	snake_case__  					=      get_from_to_our_keys(UpperCAmelCase_       )

	if not (save_directory / f'''{model_name}.pth''').exists():
		logger.info('Loading original state_dict.'       )
		snake_case__					,     snake_case__  					=      names_to_from_model[model_name]()
		snake_case__  					=      from_state_dict_trunk
		if "in1k" in model_name:
			# add the head
			snake_case__  					=      {**from_state_dict_trunk, **from_state_dict_head}
		logger.info('Done!'       )

		snake_case__  					=      {}

		snake_case__  					=      list(from_state_dict.keys()       )
		snake_case__  					=      R'\.block.-part.'
		# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
		for key in from_state_dict.keys():
			# remove the weird "block[0,1]-part" from the key
			snake_case__  					=      re.sub(UpperCAmelCase_				,							''				,							UpperCAmelCase_       )
			# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
			snake_case__  					=      from_to_ours_keys[src_key]
			# store the parameter with our key
			snake_case__  					=      from_state_dict[key]
			not_used_keys.remove(UpperCAmelCase_       )
		# check that all keys have been updated
		assert len(UpperCAmelCase_       ) == 0, f'''Some keys where not used {','.join(UpperCAmelCase_       )}'''

		logger.info(f'''The following keys were not used: {','.join(UpperCAmelCase_       )}'''       )

		# save our state dict to disk
		torch.save(UpperCAmelCase_				,							save_directory / f'''{model_name}.pth'''       )

		del converted_state_dict
	else:
		logger.info('The state_dict was already stored on disk.'       )
	if push_to_hub:
		logger.info(f'''Token is {os.environ['HF_TOKEN']}'''       )
		logger.info('Loading our model.'       )
		# create our model
		snake_case__  					=      names_to_config[model_name]
		snake_case__  					=      RegNetModel
		if "in1k" in model_name:
			snake_case__  					=      RegNetForImageClassification
		snake_case__  					=      our_model_func(UpperCAmelCase_       )
		# place our model to the meta device (so remove all the weights)
		our_model.to(torch.device('meta'       )       )
		logger.info('Loading state_dict in our model.'       )
		# load state dict
		snake_case__  					=      our_model.state_dict().keys()
		PreTrainedModel._load_pretrained_model_low_mem(
		    UpperCAmelCase_				,							UpperCAmelCase_				,							[save_directory / f'''{model_name}.pth''']       )
		logger.info('Finally, pushing!'       )
		# push it to hub
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / model_name				,							commit_message='Add model'				,							output_dir=save_directory / model_name				,							)
		snake_case__  					=      3_84
		# we can use the convnext one
		snake_case__  					=      AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				,							size=UpperCAmelCase_       )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / model_name				,							commit_message='Add image processor'				,							output_dir=save_directory / model_name				,							)


if __name__ == "__main__":
			a__				: Tuple									=    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 regnet* architecture,'''
			        ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=Path,
			    required=True,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    default=True,
			    type=bool,
			    required=False,
			    help='''If True, push model and image processor to the hub.''',
			)

			a__				: List[str]									=    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)










'''simple docstring'''




import shutil
import tempfile
import unittest

import pytest

from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio

from .test_feature_extraction_whisper import floats_list


if is_speech_available():
			from transformers import WhisperFeatureExtractor, WhisperProcessor


a__				: Tuple									=    5_0358
a__				: str									=    5_0362






@require_torch
@require_torchaudio
@require_sentencepiece
class 							__snake_case    (				unittest.TestCase       ):





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      'openai/whisper-small.en'
		snake_case__  					=      tempfile.mkdtemp()





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[int]:
		return WhisperTokenizer.from_pretrained(self.checkpoint			,					**UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
		return WhisperFeatureExtractor.from_pretrained(self.checkpoint			,					**UpperCamelCase_		)





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





	def      _snake_case      (		self		)			->		Any:
		snake_case__  					=      self.get_tokenizer()
		snake_case__  					=      self.get_feature_extractor()

		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

		processor.save_pretrained(self.tmpdirname		)
		snake_case__  					=      WhisperProcessor.from_pretrained(self.tmpdirname		)

		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer.get_vocab()		)
		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)

		self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor.to_json_string()		)
		self.assertIsInstance(processor.feature_extractor			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
		processor.save_pretrained(self.tmpdirname		)

		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
		snake_case__  					=      self.get_feature_extractor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)

		snake_case__  					=      WhisperProcessor.from_pretrained(
		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)

		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)

		self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor_add_kwargs.to_json_string()		)
		self.assertIsInstance(processor.feature_extractor			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

		snake_case__  					=      floats_list((3, 1000)		)

		snake_case__  					=      feature_extractor(UpperCamelCase_			,					return_tensors='np'		)
		snake_case__  					=      processor(UpperCamelCase_			,					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      _snake_case      (		self		)			->		Union[str, Any]:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

		snake_case__  					=      'This is a test string'

		snake_case__  					=      processor(text=UpperCamelCase_		)

		snake_case__  					=      tokenizer(UpperCamelCase_		)

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





	def      _snake_case      (		self		)			->		Tuple:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

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





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
		snake_case__  					=      processor.get_decoder_prompt_ids(task='transcribe'			,					no_timestamps=UpperCamelCase_		)

		self.assertIsInstance(UpperCamelCase_			,					UpperCamelCase_		)
		for ids in forced_decoder_ids:
			self.assertIsInstance(UpperCamelCase_			,					(list, tuple)		)

		snake_case__  					=      [TRANSCRIBE, NOTIMESTAMPS]
		self.assertListEqual([ids[-1] for ids in forced_decoder_ids]			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
		snake_case__  					=      processor.get_prompt_ids('Mr. Quilter'		)
		snake_case__  					=      processor.tokenizer.decode(UpperCamelCase_		)

		self.assertListEqual(prompt_ids.tolist()			,					[5_0360, 1770, 13, 2264, 346, 353]		)
		self.assertEqual(UpperCamelCase_			,					'<|startofprev|> Mr. Quilter'		)





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
		snake_case__  					=      processor.get_prompt_ids(''		)
		snake_case__  					=      processor.tokenizer.decode(UpperCamelCase_		)

		self.assertListEqual(prompt_ids.tolist()			,					[5_0360, 220]		)
		self.assertEqual(UpperCamelCase_			,					'<|startofprev|> '		)





	def      _snake_case      (		self		)			->		int:
		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)

		def _test_prompt_error_raised_helper(UpperCamelCase_			,					UpperCamelCase_		):
			with pytest.raises(UpperCamelCase_		) as excinfo:
				processor.get_prompt_ids(UpperCamelCase_		)
			snake_case__  					=      F'''Encountered text in the prompt corresponding to disallowed special token: {special_token}.'''
			self.assertEqual(UpperCamelCase_			,					str(excinfo.value		)		)

		_test_prompt_error_raised_helper('<|startofprev|> test'			,					'<|startofprev|>'		)
		_test_prompt_error_raised_helper('test <|notimestamps|>'			,					'<|notimestamps|>'		)
		_test_prompt_error_raised_helper('test <|zh|> test <|transcribe|>'			,					'<|zh|>'		)



