



import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


A__  :  int								=					logging.get_logger(__name__)



@dataclass
class 				__snake_case						:
							_a			      = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()						)}						)
							_a			      = field(
							    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}						)
							_a			      = field(
							    default=128						,metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     List[str]									= self.task_name.lower()





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''train'''
							_a			      = '''dev'''
							_a			      = '''test'''





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = 42
							_a			      = 42
							_a			      = 42




							def __init__(       self     :       int				,				A_     :       GlueDataTrainingArguments				,				A_     :       PreTrainedTokenizerBase				,				A_     :       Optional[int] = None				,				A_     :       Union[str, Split] = Split.train				,				A_     :       Optional[str] = None				,				):
									warnings.warn(
									    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
									    '''library. You can have a look at this example script for pointers: '''
									    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''				,				A_				,				)
									lowerCAmelCase_			:     Dict									= args
									lowerCAmelCase_			:     Optional[int]									= glue_processors[args.task_name]()
									lowerCAmelCase_			:     Optional[Any]									= glue_output_modes[args.task_name]
									if isinstance(A_				,				A_):
											try:
													lowerCAmelCase_			:     Optional[Any]									= Split[mode]
											except KeyError:
													raise KeyError('''mode is not a valid split name''')
        # Load data features from cache or dataset file
									lowerCAmelCase_			:     Any									= os.path.join(
									    cache_dir if cache_dir is not None else args.data_dir				,				F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""				,				)
									lowerCAmelCase_			:     Optional[int]									= self.processor.get_labels()
									if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
									    "RobertaTokenizer",
									    "RobertaTokenizerFast",
									    "XLMRobertaTokenizer",
									    "BartTokenizer",
									    "BartTokenizerFast",
									):
											# HACK(label indices are swapped in RoBERTa pretrained model)
											lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= label_list[2], label_list[1]
									lowerCAmelCase_			:     Tuple									= label_list

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									lowerCAmelCase_			:     Union[str, Any]									= cached_features_file + '''.lock'''
									with FileLock(A_):
											if os.path.exists(A_) and not args.overwrite_cache:
													lowerCAmelCase_			:     str									= time.time()
													lowerCAmelCase_			:     int									= torch.load(A_)
													logger.info(
													    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""				,				time.time() - start)
											else:
													logger.info(F"""Creating features from dataset file at {args.data_dir}""")

													if mode == Split.dev:
															lowerCAmelCase_			:     Tuple									= self.processor.get_dev_examples(args.data_dir)
													elif mode == Split.test:
															lowerCAmelCase_			:     Tuple									= self.processor.get_test_examples(args.data_dir)
													else:
															lowerCAmelCase_			:     Optional[int]									= self.processor.get_train_examples(args.data_dir)
													if limit_length is not None:
															lowerCAmelCase_			:     Tuple									= examples[:limit_length]
													lowerCAmelCase_			:     Any									= glue_convert_examples_to_features(
													    A_				,				A_				,				max_length=args.max_seq_length				,				label_list=A_				,				output_mode=self.output_mode				,				)
													lowerCAmelCase_			:     Optional[int]									= time.time()
													torch.save(self.features				,				A_)
													# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
													logger.info(
													    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""")




							def __len__(       self     :       str):
									return len(self.features)




							def __getitem__(       self     :       List[Any]				,				A_     :       Dict):
									return self.features[i]







							def    UpperCAmelCase__			(       self     :       Optional[int]):
									return self.label_list










import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     List[str]									= SwinvaConfig()
		lowerCAmelCase_			:     List[str]									= swinva_name.split('''_''' )

		lowerCAmelCase_			:     str									= name_split[1]
		if "to" in name_split[3]:
				lowerCAmelCase_			:     List[Any]									= int(name_split[3][-3:] )
		else:
				lowerCAmelCase_			:     List[Any]									= int(name_split[3] )
		if "to" in name_split[2]:
				lowerCAmelCase_			:     List[str]									= int(name_split[2][-2:] )
		else:
				lowerCAmelCase_			:     int									= int(name_split[2][6:] )

		if model_size == "tiny":
				lowerCAmelCase_			:     Any									= 96
				lowerCAmelCase_			:     List[str]									= (2, 2, 6, 2)
				lowerCAmelCase_			:     Union[str, Any]									= (3, 6, 12, 24)
		elif model_size == "small":
				lowerCAmelCase_			:     List[str]									= 96
				lowerCAmelCase_			:     Any									= (2, 2, 18, 2)
				lowerCAmelCase_			:     Dict									= (3, 6, 12, 24)
		elif model_size == "base":
				lowerCAmelCase_			:     Union[str, Any]									= 128
				lowerCAmelCase_			:     List[Any]									= (2, 2, 18, 2)
				lowerCAmelCase_			:     Tuple									= (4, 8, 16, 32)
		else:
				lowerCAmelCase_			:     Optional[Any]									= 192
				lowerCAmelCase_			:     List[Any]									= (2, 2, 18, 2)
				lowerCAmelCase_			:     List[Any]									= (6, 12, 24, 48)

		if "to" in swinva_name:
				lowerCAmelCase_			:     Union[str, Any]									= (12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				lowerCAmelCase_			:     Optional[int]									= 21841
				lowerCAmelCase_			:     Any									= '''huggingface/label-files'''
				lowerCAmelCase_			:     Tuple									= '''imagenet-22k-id2label.json'''
				lowerCAmelCase_			:     Any									= json.load(open(hf_hub_download(__UpperCamelCase     ,__UpperCamelCase     ,repo_type='''dataset''' )     ,'''r''' ) )
				lowerCAmelCase_			:     Optional[Any]									= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				lowerCAmelCase_			:     str									= idalabel
				lowerCAmelCase_			:     List[str]									= {v: k for k, v in idalabel.items()}

		else:
				lowerCAmelCase_			:     Optional[int]									= 1000
				lowerCAmelCase_			:     Tuple									= '''huggingface/label-files'''
				lowerCAmelCase_			:     Union[str, Any]									= '''imagenet-1k-id2label.json'''
				lowerCAmelCase_			:     Dict									= json.load(open(hf_hub_download(__UpperCamelCase     ,__UpperCamelCase     ,repo_type='''dataset''' )     ,'''r''' ) )
				lowerCAmelCase_			:     int									= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				lowerCAmelCase_			:     List[str]									= idalabel
				lowerCAmelCase_			:     Optional[int]									= {v: k for k, v in idalabel.items()}

		lowerCAmelCase_			:     Optional[int]									= img_size
		lowerCAmelCase_			:     Dict									= num_classes
		lowerCAmelCase_			:     Dict									= embed_dim
		lowerCAmelCase_			:     Optional[Any]									= depths
		lowerCAmelCase_			:     Optional[int]									= num_heads
		lowerCAmelCase_			:     Dict									= window_size

		return config


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		if "patch_embed.proj" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''patch_embed.proj'''     ,'''embeddings.patch_embeddings.projection''' )
		if "patch_embed.norm" in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''patch_embed.norm'''     ,'''embeddings.norm''' )
		if "layers" in name:
				lowerCAmelCase_			:     int									= '''encoder.''' + name
		if "attn.proj" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''attn.proj'''     ,'''attention.output.dense''' )
		if "attn" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''attn'''     ,'''attention.self''' )
		if "norm1" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''norm1'''     ,'''layernorm_before''' )
		if "norm2" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''norm2'''     ,'''layernorm_after''' )
		if "mlp.fc1" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''mlp.fc1'''     ,'''intermediate.dense''' )
		if "mlp.fc2" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''mlp.fc2'''     ,'''output.dense''' )
		if "q_bias" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''q_bias'''     ,'''query.bias''' )
		if "k_bias" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''k_bias'''     ,'''key.bias''' )
		if "v_bias" in name:
				lowerCAmelCase_			:     int									= name.replace('''v_bias'''     ,'''value.bias''' )
		if "cpb_mlp" in name:
				lowerCAmelCase_			:     Any									= name.replace('''cpb_mlp'''     ,'''continuous_position_bias_mlp''' )
		if name == "norm.weight":
				lowerCAmelCase_			:     Dict									= '''layernorm.weight'''
		if name == "norm.bias":
				lowerCAmelCase_			:     Any									= '''layernorm.bias'''

		if "head" in name:
				lowerCAmelCase_			:     int									= name.replace('''head'''     ,'''classifier''' )
		else:
				lowerCAmelCase_			:     Union[str, Any]									= '''swinv2.''' + name

		return name


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		Optional[int] ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_			:     Optional[int]									= orig_state_dict.pop(__UpperCamelCase )

				if "mask" in key:
						continue
				elif "qkv" in key:
						lowerCAmelCase_			:     Dict									= key.split('''.''' )
						lowerCAmelCase_			:     Any									= int(key_split[1] )
						lowerCAmelCase_			:     Optional[int]									= int(key_split[3] )
						lowerCAmelCase_			:     Dict									= model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								lowerCAmelCase_			:     Optional[Any]									= val[:dim, :]
								lowerCAmelCase_			:     Any									= val[dim : dim * 2, :]
								lowerCAmelCase_			:     List[Any]									= val[-dim:, :]
						else:
								lowerCAmelCase_			:     Dict									= val[:dim]
								lowerCAmelCase_			:     Union[str, Any]									= val[
								    dim : dim * 2
								]
								lowerCAmelCase_			:     Dict									= val[-dim:]
				else:
						lowerCAmelCase_			:     Optional[Any]									= val

		return orig_state_dict


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Dict ):
		lowerCAmelCase_			:     Optional[Any]									= timm.create_model(__UpperCamelCase     ,pretrained=__UpperCamelCase )
		timm_model.eval()

		lowerCAmelCase_			:     List[str]									= get_swinva_config(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= SwinvaForImageClassification(__UpperCamelCase )
		model.eval()

		lowerCAmelCase_			:     str									= convert_state_dict(timm_model.state_dict()     ,__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )

		lowerCAmelCase_			:     List[Any]									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''

		lowerCAmelCase_			:     Tuple									= AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''     ,'''-''' ) ) )
		lowerCAmelCase_			:     Union[str, Any]									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw )
		lowerCAmelCase_			:     Optional[Any]									= image_processor(images=__UpperCamelCase     ,return_tensors='''pt''' )

		lowerCAmelCase_			:     List[str]									= timm_model(inputs['''pixel_values'''] )
		lowerCAmelCase_			:     Union[str, Any]									= model(**__UpperCamelCase ).logits

		assert torch.allclose(__UpperCamelCase     ,__UpperCamelCase     ,atol=1e-3 )

		print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
		model.save_pretrained(__UpperCamelCase )

		print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
		image_processor.save_pretrained(__UpperCamelCase )

		model.push_to_hub(
		    repo_path_or_name=Path(__UpperCamelCase     ,__UpperCamelCase )     ,organization='''nandwalritik'''     ,commit_message='''Add model'''     ,)


if __name__ == "__main__":
						A__  :  Union[str, Any]								=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swinv2_name''',
						    default='''swinv2_tiny_patch4_window8_256''',
						    type=str,
						    help='''Name of the Swinv2 timm model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)

						A__  :  Optional[Any]								=					parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





