


'''simple docstring'''




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

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

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


UpperCamelCase__ :			Optional[Any]												=				logging.get_logger(__name__)

UpperCamelCase__ :			List[str]												=				list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
UpperCamelCase__ :			Any												=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 		_UpperCamelCase						:


			'''simple docstring'''

			_A   :	str    					=	field(
			    default=lowerCamelCase__					,      metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(lowerCamelCase__					)}					)
			_A   :	str    					=	field(
			    default=lowerCamelCase__					,      metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''}					)
			_A   :	int    					=	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   :	int    					=	field(
			    default=128					,      metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}					,      )
			_A   :	int    					=	field(
			    default=64					,      metadata={
			        '''help''': (
			            '''The maximum number of tokens for the question. Questions longer than this will '''
			            '''be truncated to this length.'''
			        )
			    }					,      )
			_A   :	int    					=	field(
			    default=30					,      metadata={
			        '''help''': (
			            '''The maximum length of an answer that can be generated. This is needed because the start '''
			            '''and end predictions are not conditioned on one another.'''
			        )
			    }					,      )
			_A   :	bool    					=	field(
			    default=lowerCamelCase__					,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)
			_A   :	bool    					=	field(
			    default=lowerCamelCase__					,      metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''}					)
			_A   :	float    					=	field(
			    default=0.0					,      metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}					)
			_A   :	int    					=	field(
			    default=20					,      metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}					)
			_A   :	int    					=	field(
			    default=0					,      metadata={
			        '''help''': (
			            '''language id of input for language-specific xlm models (see'''
			            ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
			        )
			    }					,      )
			_A   :	int    					=	field(default=1					,      metadata={'''help''': '''multiple threads for converting example to features'''}					)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[Any]    					=	'''train'''
			_A   :	Tuple    					=	'''dev'''






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	SquadDataTrainingArguments
			_A   :	List[SquadFeatures]
			_A   :	Split
			_A   :	bool

			def __init__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				SquadDataTrainingArguments							,  lowerCAmelCase__  :				PreTrainedTokenizer							,  lowerCAmelCase__  :				Optional[int] = None							,  lowerCAmelCase__  :				Union[str, Split] = Split.train							,  lowerCAmelCase__  :				Optional[bool] = False							,  lowerCAmelCase__  :				Optional[str] = None							,  lowerCAmelCase__  :				Optional[str] = "pt"							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	args
					__SCREAMING_SNAKE_CASE :	Dict					 =	is_language_sensitive
					__SCREAMING_SNAKE_CASE :	Tuple					 =	SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
					if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
							try:
									__SCREAMING_SNAKE_CASE :	List[str]					 =	Split[mode]
							except KeyError:
									raise KeyError("""mode is not a valid split name"""		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	mode
					# Load data features from cache or dataset file
					__SCREAMING_SNAKE_CASE :	str					 =	"""v2""" if args.version_2_with_negative else """v1"""
					__SCREAMING_SNAKE_CASE :	int					 =	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}_{version_tag}"							,  )

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	cached_features_file + """.lock"""
					with FileLock(lowerCAmelCase__		):
							if os.path.exists(lowerCAmelCase__		) and not args.overwrite_cache:
									__SCREAMING_SNAKE_CASE :	str					 =	time.time()
									__SCREAMING_SNAKE_CASE :	str					 =	torch.load(lowerCAmelCase__		)

									# Legacy cache files have only features, while new cache files
									# will have dataset and examples also.
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.old_features["""features"""]
									__SCREAMING_SNAKE_CASE :	int					 =	self.old_features.get("""dataset"""							,  lowerCAmelCase__		)
									__SCREAMING_SNAKE_CASE :	Any					 =	self.old_features.get("""examples"""							,  lowerCAmelCase__		)
									logger.info(
									    F"Loading features from cached file {cached_features_file} [took %.3f s]"							,  time.time() - start		)

									if self.dataset is None or self.examples is None:
											logger.warning(
											    F"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
											    """ future run"""		)
							else:
									if mode == Split.dev:
											__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.processor.get_dev_examples(args.data_dir		)
									else:
											__SCREAMING_SNAKE_CASE :	int					 =	self.processor.get_train_examples(args.data_dir		)

									__SCREAMING_SNAKE_CASE :	List[str]					 =	squad_convert_examples_to_features(
									    examples=self.examples							,  tokenizer=lowerCAmelCase__							,  max_seq_length=args.max_seq_length							,  doc_stride=args.doc_stride							,  max_query_length=args.max_query_length							,  is_training=mode == Split.train							,  threads=args.threads							,  return_dataset=lowerCAmelCase__							,  )

									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	time.time()
									torch.save(
									    {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples}							,  lowerCAmelCase__							,  )
									# ^ 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  :				Tuple		):





					"""simple docstring"""






					return len(self.features		)



			def __getitem__(							self  :				Dict							,  lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.features[i]

					__SCREAMING_SNAKE_CASE :	int					 =	torch.tensor(feature.input_ids							,  dtype=torch.long		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.tensor(feature.attention_mask							,  dtype=torch.long		)
					__SCREAMING_SNAKE_CASE :	str					 =	torch.tensor(feature.token_type_ids							,  dtype=torch.long		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.tensor(feature.cls_index							,  dtype=torch.long		)
					__SCREAMING_SNAKE_CASE :	Any					 =	torch.tensor(feature.p_mask							,  dtype=torch.float		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.tensor(feature.is_impossible							,  dtype=torch.float		)

					__SCREAMING_SNAKE_CASE :	Dict					 =	{
					    """input_ids""": input_ids,
					    """attention_mask""": attention_mask,
					    """token_type_ids""": token_type_ids,
					}

					if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
							del inputs["token_type_ids"]

					if self.args.model_type in ["xlnet", "xlm"]:
							inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask}		)
							if self.args.version_2_with_negative:
									inputs.update({"""is_impossible""": is_impossible}		)
							if self.is_language_sensitive:
									inputs.update({"""langs""": (torch.ones(input_ids.shape							,  dtype=torch.intaa		) * self.args.lang_id)}		)

					if self.mode == Split.train:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.tensor(feature.start_position							,  dtype=torch.long		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.tensor(feature.end_position							,  dtype=torch.long		)
							inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}		)

					return inputs




'''simple docstring'''




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        lowerCAmelCase_     (			_lowerCamelCase:     Tuple      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	SwinvaConfig()
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	swinva_name.split("""_"""      )

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	name_split[1]
		if "to" in name_split[3]:
				__SCREAMING_SNAKE_CASE :	Dict					 =	int(name_split[3][-3:]      )
		else:
				__SCREAMING_SNAKE_CASE :	str					 =	int(name_split[3]      )
		if "to" in name_split[2]:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	int(name_split[2][-2:]      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	int(name_split[2][6:]      )

		if model_size == "tiny":
				__SCREAMING_SNAKE_CASE :	Dict					 =	96
				__SCREAMING_SNAKE_CASE :	List[str]					 =	(2, 2, 6, 2)
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(3, 6, 12, 24)
		elif model_size == "small":
				__SCREAMING_SNAKE_CASE :	List[str]					 =	96
				__SCREAMING_SNAKE_CASE :	int					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	int					 =	(3, 6, 12, 24)
		elif model_size == "base":
				__SCREAMING_SNAKE_CASE :	int					 =	1_28
				__SCREAMING_SNAKE_CASE :	str					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(4, 8, 16, 32)
		else:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	1_92
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	Dict					 =	(6, 12, 24, 48)

		if "to" in swinva_name:
				__SCREAMING_SNAKE_CASE :	int					 =	(12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				__SCREAMING_SNAKE_CASE :	int					 =	2_18_41
				__SCREAMING_SNAKE_CASE :	str					 =	"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE :	List[str]					 =	"""imagenet-22k-id2label.json"""
				__SCREAMING_SNAKE_CASE :	List[str]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	idalabel
				__SCREAMING_SNAKE_CASE :	str					 =	{v: k for k, v in idalabel.items()}

		else:
				__SCREAMING_SNAKE_CASE :	str					 =	10_00
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE :	Any					 =	"""imagenet-1k-id2label.json"""
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
				__SCREAMING_SNAKE_CASE :	int					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	idalabel
				__SCREAMING_SNAKE_CASE :	Dict					 =	{v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE :	Any					 =	img_size
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	num_classes
		__SCREAMING_SNAKE_CASE :	int					 =	embed_dim
		__SCREAMING_SNAKE_CASE :	Dict					 =	depths
		__SCREAMING_SNAKE_CASE :	str					 =	num_heads
		__SCREAMING_SNAKE_CASE :	int					 =	window_size

		return config


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

		if "head" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""head""" ,   """classifier"""      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""swinv2.""" + name

		return name


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

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

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

		return orig_state_dict


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     int      ):
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	timm.create_model(_lowerCamelCase ,   pretrained=_lowerCamelCase      )
		timm_model.eval()

		__SCREAMING_SNAKE_CASE :	int					 =	get_swinva_config(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	SwinvaForImageClassification(_lowerCamelCase      )
		model.eval()

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	convert_state_dict(timm_model.state_dict() ,   _lowerCamelCase      )
		model.load_state_dict(_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	int					 =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

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

		__SCREAMING_SNAKE_CASE :	int					 =	timm_model(inputs["""pixel_values"""]      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model(**_lowerCamelCase      ).logits

		assert torch.allclose(_lowerCamelCase ,   _lowerCamelCase ,   atol=1E-3      )

		print(F"Saving model {swinva_name} to {pytorch_dump_folder_path}"      )
		model.save_pretrained(_lowerCamelCase      )

		print(F"Saving image processor to {pytorch_dump_folder_path}"      )
		image_processor.save_pretrained(_lowerCamelCase      )

		model.push_to_hub(
		    repo_path_or_name=Path(_lowerCamelCase ,   _lowerCamelCase      ) ,   organization="""nandwalritik""" ,   commit_message="""Add model""" ,   )


if __name__ == "__main__":
						UpperCamelCase__ :			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.'''
						)

						UpperCamelCase__ :			Optional[int]												=				parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)