






'''simple docstring'''


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_					:			Tuple     						=				logging.get_logger(__name__)
@dataclass
class  _SCREAMING_SNAKE_CASE				:


			'''simple docstring'''





			__a				:				nn.Module
			__a				:				List[nn.Module]         =       field(default_factory=SCREAMING_SNAKE_CASE						)
			__a				:				list         =       field(default_factory=SCREAMING_SNAKE_CASE						)






			def 					A   ( self				:    Optional[Any]			,   lowercase				:    Union[str, Any]			,   lowercase				:    Tensor			,   lowercase				:    Tensor      )				->     List[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			len(list(m.modules()      )      ) == 1 or isinstance(lowercase			,   nn.Convad      ) or isinstance(lowercase			,   nn.BatchNormad      )
									if has_not_submodules:
															if not isinstance(lowercase			,   lowercase      ):
																					self.traced.append(lowercase      )






			def __call__( self				:    Union[str, Any]			,   lowercase				:    Tensor      )				->     Tuple:

									'''simple docstring'''







									for m in self.module.modules():
															self.handles.append(m.register_forward_hook(self._forward_hook      )      )
									self.module(lowercase      )
									[x.remove() for x in self.handles]
									return self







			@property
			def 					A   ( self				:    str      )				->     List[Any]:

									'''simple docstring'''







									return list(filter(lambda lowercase      : len(list(x.state_dict().keys()      )      ) > 0			,   self.traced      )      )
@dataclass
class  _SCREAMING_SNAKE_CASE				:


			'''simple docstring'''





			__a				:				nn.Module
			__a				:				nn.Module
			__a				:				int         =       0
			__a				:				List         =       field(default_factory=SCREAMING_SNAKE_CASE						)
			__a				:				List         =       field(default_factory=SCREAMING_SNAKE_CASE						)






			def __call__( self				:    Optional[Any]			,   lowercase				:    Tensor      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			Tracker(self.dest      )(lowercase      ).parametrized
									UpperCamelCase__       =			Tracker(self.src      )(lowercase      ).parametrized

									UpperCamelCase__       =			list(filter(lambda lowercase      : type(lowercase      ) not in self.src_skip			,   lowercase      )      )
									UpperCamelCase__       =			list(filter(lambda lowercase      : type(lowercase      ) not in self.dest_skip			,   lowercase      )      )

									if len(lowercase      ) != len(lowercase      ):
															raise Exception(
															    f"Numbers of operations are different. Source module has {len(lowercase      )} operations while"
															    f" destination module has {len(lowercase      )}."      )

									for dest_m, src_m in zip(lowercase			,   lowercase      ):
															dest_m.load_state_dict(src_m.state_dict()      )
															if self.verbose == 1:
																					print(f"Transfered from={src_m} to={dest_m}"      )







def 						__magic_name__(			_A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			from_model.state_dict()
						UpperCamelCase__       =			our_model.state_dict()
						UpperCamelCase__       =			our_model.config
						UpperCamelCase__       =			[]
						for stage_idx in range(len(config.hidden_sizes       )       ):
												for block_id in range(config.depths[stage_idx]       ):
																		UpperCamelCase__       =			f"block{stage_idx + 1}.{block_id}.layer_scale_1"
																		UpperCamelCase__       =			f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

																		all_keys.append((from_key, to_key)       )
																		UpperCamelCase__       =			f"block{stage_idx + 1}.{block_id}.layer_scale_2"
																		UpperCamelCase__       =			f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

																		all_keys.append((from_key, to_key)       )

						for from_key, to_key in all_keys:
												UpperCamelCase__       =			from_state_dict.pop(_A       )

						our_model.load_state_dict(_A       )
						return our_model







def 						__magic_name__(			_A    ,   _A    ,   _A    ,   _A    ,   _A    ,   _A = True    ,   ):
						'''simple docstring'''





						print(f"Downloading weights for {name}..."       )
						UpperCamelCase__       =			cached_download(_A       )
						print(f"Converting {name}..."       )
						UpperCamelCase__       =			torch.load(_A       )["""state_dict"""]
						from_model.load_state_dict(_A       )
						from_model.eval()
						with torch.no_grad():
												UpperCamelCase__       =			VanForImageClassification(_A       ).eval()
												UpperCamelCase__       =			ModuleTransfer(src=_A    ,   dest=_A       )
												UpperCamelCase__       =			torch.randn((1, 3, 224, 224)       )
												module_transfer(_A       )
												UpperCamelCase__       =			copy_parameters(_A    ,   _A       )

						if not torch.allclose(from_model(_A       )    ,   our_model(_A       ).logits       ):
												raise ValueError("""The model logits don't match the original one."""       )

						UpperCamelCase__       =			name
						print(_A       )

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name    ,   commit_message="""Add model"""    ,   use_temp_dir=_A    ,   )

												# we can use the convnext one
												UpperCamelCase__       =			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""       )
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name    ,   commit_message="""Add image processor"""    ,   use_temp_dir=_A    ,   )

												print(f"Pushed {checkpoint_name}"       )







def 						__magic_name__(			_A    ,   _A = None    ,   _A = True       ):
						'''simple docstring'''





						UpperCamelCase__       =			"""imagenet-1k-id2label.json"""
						UpperCamelCase__       =			1000

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

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

						UpperCamelCase__       =			partial(_A    ,   num_labels=_A    ,   idalabel=_A    ,   labelaid=_A       )

						UpperCamelCase__       =			{
						    """van-tiny""": ImageNetPreTrainedConfig(
						        hidden_sizes=[32, 64, 160, 256]    ,   depths=[3, 3, 5, 2]    ,   mlp_ratios=[8, 8, 4, 4]    ,   ),
						    """van-small""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]    ,   depths=[2, 2, 4, 2]    ,   mlp_ratios=[8, 8, 4, 4]    ,   ),
						    """van-base""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]    ,   depths=[3, 3, 12, 3]    ,   mlp_ratios=[8, 8, 4, 4]    ,   ),
						    """van-large""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]    ,   depths=[3, 5, 27, 3]    ,   mlp_ratios=[8, 8, 4, 4]    ,   ),
						}

						UpperCamelCase__       =			{
						    """van-tiny""": van_tiny,
						    """van-small""": van_small,
						    """van-base""": van_base,
						    """van-large""": van_large,
						}

						UpperCamelCase__       =			{
						    """van-tiny""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
						    ),
						    """van-small""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
						    ),
						    """van-base""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
						    ),
						    """van-large""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    _A    ,   names_to_config[model_name]    ,   checkpoint=names_to_original_checkpoints[model_name]    ,   from_model=names_to_original_models[model_name]()    ,   save_directory=_A    ,   push_to_hub=_A    ,   )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    _A    ,   _A    ,   checkpoint=names_to_original_checkpoints[model_name]    ,   from_model=names_to_original_models[model_name]()    ,   save_directory=_A    ,   push_to_hub=_A    ,   )


if __name__ == "__main__":
					lowerCamelCase_					:			List[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 resnet* architecture,'''
					        ''' currently: van-tiny/small/base/large. 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(
					    '''--van_dir''',
					    required=True,
					    type=Path,
					    help=(
					        '''A path to VAN\'s original implementation directory. You can download from here:'''
					        ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
					    ),
					)
					parser.add_argument(
					    '''--push_to_hub''',
					    default=True,
					    type=bool,
					    required=False,
					    help='''If True, push model and image processor to the hub.''',
					)

					lowerCamelCase_					:			Dict     						=				parser.parse_args()
					lowerCamelCase_					:			Path     						=				args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					lowerCamelCase_					:			Any     						=				args.van_dir
					# append the path to the parents to maskformer dir
					sys.path.append(str(van_dir.parent))
					from van.models.van import van_base, van_large, van_small, van_tiny

					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
					from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

					from .test_feature_extraction_speechta import floats_list


lowerCamelCase_					:			List[str]     						=				get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_torch
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def 					A   ( self				:    Union[str, Any]      )				->     Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__       =			tempfile.mkdtemp()

									UpperCamelCase__       =			SpeechTaTokenizer(lowercase      )
									tokenizer.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			{
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 1_6_0_0_0,
									    """do_normalize""": False,
									    """num_mel_bins""": 8_0,
									    """hop_length""": 1_6,
									    """win_length""": 6_4,
									    """win_function""": """hann_window""",
									    """fmin""": 8_0,
									    """fmax""": 7_6_0_0,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

									UpperCamelCase__       =			os.path.join(self.tmpdirname			,   lowercase      )
									with open(self.feature_extraction_file			,   """w"""			,   encoding="""utf-8"""      ) as fp:
															fp.write(json.dumps(lowercase      ) + """\n"""      )






			def 					A   ( self				:    Union[str, Any]			,   **lowercase				:    List[Any]      )				->     Any:

									'''simple docstring'''







									return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,   **lowercase      )






			def 					A   ( self				:    str			,   **lowercase				:    Tuple      )				->     Optional[Any]:

									'''simple docstring'''







									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,   **lowercase      )






			def 					A   ( self				:    Union[str, Any]      )				->     str:

									'''simple docstring'''







									shutil.rmtree(self.tmpdirname      )






			def 					A   ( self				:    List[Any]      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_tokenizer()
									UpperCamelCase__       =			self.get_feature_extractor()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									processor.save_pretrained(self.tmpdirname      )
									UpperCamelCase__       =			SpeechTaProcessor.from_pretrained(self.tmpdirname      )

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

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






			def 					A   ( self				:    Tuple      )				->     Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=self.get_tokenizer()			,   feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			self.get_tokenizer(bos_token="""(BOS)"""			,   eos_token="""(EOS)"""      )
									UpperCamelCase__       =			self.get_feature_extractor(do_normalize=lowercase			,   padding_value=1.0      )

									UpperCamelCase__       =			SpeechTaProcessor.from_pretrained(
									    self.tmpdirname			,   bos_token="""(BOS)"""			,   eos_token="""(EOS)"""			,   do_normalize=lowercase			,   padding_value=1.0      )

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

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






			def 					A   ( self				:    List[Any]      )				->     Dict:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio=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				:    List[str]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio_target=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio_target=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				:    Optional[Any]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

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






			def 					A   ( self				:    List[Any]      )				->     int:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text_target=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

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






			def 					A   ( self				:    Any      )				->     Tuple:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

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

									UpperCamelCase__       =			processor.batch_decode(lowercase      )
									UpperCamelCase__       =			tokenizer.batch_decode(lowercase      )

									self.assertListEqual(lowercase			,   lowercase      )







			def 					A   ( self				:    List[Any]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

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