



import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

snake_case__					:     Optional[int]    		=   collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
snake_case__					:     str    		=   """https://storage.googleapis.com/cvdf-datasets/mnist/"""



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			numpy.dtype(numpy.uintaa   ).newbyteorder(">"   )
	return numpy.frombuffer(bytestream.read(4   )  ,	dtype=_SCREAMING_SNAKE_CASE   )[0]



@deprecated(_SCREAMING_SNAKE_CASE  ,	"Please use tf.data to implement this functionality."   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	print("Extracting"  ,	f.name   )
	with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE   ) as bytestream:
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		if magic != 2_0_5_1:
			raise ValueError(
			    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)   )
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			bytestream.read(rows * cols * num_images   )
		__lowercase		       =			numpy.frombuffer(_SCREAMING_SNAKE_CASE  ,	dtype=numpy.uinta   )
		__lowercase		       =			data.reshape(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	1   )
		return data



@deprecated(_SCREAMING_SNAKE_CASE  ,	"Please use tf.one_hot on tensors."   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			labels_dense.shape[0]
	__lowercase		       =			numpy.arange(_SCREAMING_SNAKE_CASE   ) * num_classes
	__lowercase		       =			numpy.zeros((num_labels, num_classes)   )
	__lowercase		       =			1
	return labels_one_hot



@deprecated(_SCREAMING_SNAKE_CASE  ,	"Please use tf.data to implement this functionality."   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE=1_0   ):
	print("Extracting"  ,	f.name   )
	with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE   ) as bytestream:
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		if magic != 2_0_4_9:
			raise ValueError(
			    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)   )
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			bytestream.read(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			numpy.frombuffer(_SCREAMING_SNAKE_CASE  ,	dtype=numpy.uinta   )
		if one_hot:
			return _dense_to_one_hot(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		return labels

class   _A   :


						'''simple docstring'''





						@deprecated(
						    lowerCamelCase							,      "Please use alternatives such as official/mnist/_DataSet.py"
						    " from tensorflow/models."							,      )
						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Optional[Any]=False							,      lowerCamelCase	:							str=False							,      lowerCamelCase	:							str=dtypes.floataa							,      lowerCamelCase	:							Optional[int]=True							,      lowerCamelCase	:							int=None							,      ):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			random_seed.get_seed(lowerCamelCase	)
							# If op level seed is not set, use whatever graph level seed is returned
							numpy.random.seed(seeda if seed is None else seeda	)
							__lowercase		       =			dtypes.as_dtype(lowerCamelCase	).base_dtype
							if dtype not in (dtypes.uinta, dtypes.floataa):
								raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype	)
							if fake_data:
								__lowercase		       =			10_000
								__lowercase		       =			one_hot
							else:
								assert (
								    images.shape[0] == labels.shape[0]
								), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
								__lowercase		       =			images.shape[0]

								# Convert shape from [num examples, rows, columns, depth]
								# to [num examples, rows*columns] (assuming depth == 1)
								if reshape:
									assert images.shape[3] == 1
									__lowercase		       =			images.reshape(
									    images.shape[0]							,      images.shape[1] * images.shape[2]	)
								if dtype == dtypes.floataa:
									# Convert from [0, 255] -> [0.0, 1.0].
									__lowercase		       =			images.astype(numpy.floataa	)
									__lowercase		       =			numpy.multiply(lowerCamelCase							,      1.0 / 255.0	)
							__lowercase		       =			images
							__lowercase		       =			labels
							__lowercase		       =			0
							__lowercase		       =			0



						@property
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							return self._images



						@property
						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							return self._labels



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return self._num_examples



						@property
						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							return self._epochs_completed



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Dict=False							,      lowerCamelCase	:							List[str]=True	):






							'''simple docstring'''



							if fake_data:
								__lowercase		       =			[1] * 784
								__lowercase		       =			[1] + [0] * 9 if self.one_hot else 0
								return (
								    [fake_image for _ in range(lowerCamelCase	)],
								    [fake_label for _ in range(lowerCamelCase	)],
								)
							__lowercase		       =			self._index_in_epoch
							# Shuffle for the first epoch
							if self._epochs_completed == 0 and start == 0 and shuffle:
								__lowercase		       =			numpy.arange(self._num_examples	)
								numpy.random.shuffle(lowerCamelCase	)
								__lowercase		       =			self.images[perma]
								__lowercase		       =			self.labels[perma]
							# Go to the next epoch
							if start + batch_size > self._num_examples:
								# Finished epoch
								self._epochs_completed += 1
								# Get the rest examples in this epoch
								__lowercase		       =			self._num_examples - start
								__lowercase		       =			self._images[start : self._num_examples]
								__lowercase		       =			self._labels[start : self._num_examples]
								# Shuffle the data
								if shuffle:
									__lowercase		       =			numpy.arange(self._num_examples	)
									numpy.random.shuffle(lowerCamelCase	)
									__lowercase		       =			self.images[perm]
									__lowercase		       =			self.labels[perm]
								# Start next epoch
								__lowercase		       =			0
								__lowercase		       =			batch_size - rest_num_examples
								__lowercase		       =			self._index_in_epoch
								__lowercase		       =			self._images[start:end]
								__lowercase		       =			self._labels[start:end]
								return (
								    numpy.concatenate((images_rest_part, images_new_part)							,      axis=0	),
								    numpy.concatenate((labels_rest_part, labels_new_part)							,      axis=0	),
								)
							else:
								self._index_in_epoch += batch_size
								__lowercase		       =			self._index_in_epoch
								return self._images[start:end], self._labels[start:end]



@deprecated(_SCREAMING_SNAKE_CASE  ,	"Please write your own downloading logic."   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if not gfile.Exists(_SCREAMING_SNAKE_CASE   ):
		gfile.MakeDirs(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			os.path.join(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	if not gfile.Exists(_SCREAMING_SNAKE_CASE   ):
		urllib.request.urlretrieve(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  # noqa: S310
		with gfile.GFile(_SCREAMING_SNAKE_CASE   ) as f:
			__lowercase		       =			f.size()
		print("Successfully downloaded"  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"bytes."   )
	return filepath



@deprecated(
    _SCREAMING_SNAKE_CASE  ,	"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE=dtypes.floataa  ,	_SCREAMING_SNAKE_CASE=True  ,	_SCREAMING_SNAKE_CASE=5_0_0_0  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=DEFAULT_SOURCE_URL  ,	):
	if fake_data:

		def fake():
			return _DataSet(
			    []  ,	[]  ,	fake_data=_SCREAMING_SNAKE_CASE  ,	one_hot=_SCREAMING_SNAKE_CASE  ,	dtype=_SCREAMING_SNAKE_CASE  ,	seed=_SCREAMING_SNAKE_CASE   )

		__lowercase		       =			fake()
		__lowercase		       =			fake()
		__lowercase		       =			fake()
		return _Datasets(train=_SCREAMING_SNAKE_CASE  ,	validation=_SCREAMING_SNAKE_CASE  ,	test=_SCREAMING_SNAKE_CASE   )

	if not source_url:  # empty string check
		__lowercase		       =			DEFAULT_SOURCE_URL

	__lowercase		       =			"train-images-idx3-ubyte.gz"
	__lowercase		       =			"train-labels-idx1-ubyte.gz"
	__lowercase		       =			"t10k-images-idx3-ubyte.gz"
	__lowercase		       =			"t10k-labels-idx1-ubyte.gz"

	__lowercase		       =			_maybe_download(
	    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	source_url + train_images_file   )
	with gfile.Open(_SCREAMING_SNAKE_CASE  ,	"rb"   ) as f:
		__lowercase		       =			_extract_images(_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			_maybe_download(
	    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	source_url + train_labels_file   )
	with gfile.Open(_SCREAMING_SNAKE_CASE  ,	"rb"   ) as f:
		__lowercase		       =			_extract_labels(_SCREAMING_SNAKE_CASE  ,	one_hot=_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			_maybe_download(
	    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	source_url + test_images_file   )
	with gfile.Open(_SCREAMING_SNAKE_CASE  ,	"rb"   ) as f:
		__lowercase		       =			_extract_images(_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			_maybe_download(
	    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	source_url + test_labels_file   )
	with gfile.Open(_SCREAMING_SNAKE_CASE  ,	"rb"   ) as f:
		__lowercase		       =			_extract_labels(_SCREAMING_SNAKE_CASE  ,	one_hot=_SCREAMING_SNAKE_CASE   )

	if not 0 <= validation_size <= len(_SCREAMING_SNAKE_CASE   ):
		__lowercase		       =			(
		    "Validation size should be between 0 and "
		    F"""{len(_SCREAMING_SNAKE_CASE   )}. Received: {validation_size}."""
		)
		raise ValueError(_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			train_images[:validation_size]
	__lowercase		       =			train_labels[:validation_size]
	__lowercase		       =			train_images[validation_size:]
	__lowercase		       =			train_labels[validation_size:]

	__lowercase		       =			{"dtype": dtype, "reshape": reshape, "seed": seed}

	__lowercase		       =			_DataSet(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_DataSet(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_DataSet(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	**_SCREAMING_SNAKE_CASE   )

	return _Datasets(train=_SCREAMING_SNAKE_CASE  ,	validation=_SCREAMING_SNAKE_CASE  ,	test=_SCREAMING_SNAKE_CASE   )








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    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			SwinvaConfig()
	__lowercase		       =			swinva_name.split("_"   )

	__lowercase		       =			name_split[1]
	if "to" in name_split[3]:
		__lowercase		       =			int(name_split[3][-3:]   )
	else:
		__lowercase		       =			int(name_split[3]   )
	if "to" in name_split[2]:
		__lowercase		       =			int(name_split[2][-2:]   )
	else:
		__lowercase		       =			int(name_split[2][6:]   )

	if model_size == "tiny":
		__lowercase		       =			9_6
		__lowercase		       =			(2, 2, 6, 2)
		__lowercase		       =			(3, 6, 1_2, 2_4)
	elif model_size == "small":
		__lowercase		       =			9_6
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(3, 6, 1_2, 2_4)
	elif model_size == "base":
		__lowercase		       =			1_2_8
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(4, 8, 1_6, 3_2)
	else:
		__lowercase		       =			1_9_2
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(6, 1_2, 2_4, 4_8)

	if "to" in swinva_name:
		__lowercase		       =			(1_2, 1_2, 1_2, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		__lowercase		       =			2_1_8_4_1
		__lowercase		       =			"huggingface/label-files"
		__lowercase		       =			"imagenet-22k-id2label.json"
		__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
		__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}

	else:
		__lowercase		       =			1_0_0_0
		__lowercase		       =			"huggingface/label-files"
		__lowercase		       =			"imagenet-1k-id2label.json"
		__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
		__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}

	__lowercase		       =			img_size
	__lowercase		       =			num_classes
	__lowercase		       =			embed_dim
	__lowercase		       =			depths
	__lowercase		       =			num_heads
	__lowercase		       =			window_size

	return config



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	if "patch_embed.proj" in name:
		__lowercase		       =			name.replace("patch_embed.proj"  ,	"embeddings.patch_embeddings.projection"   )
	if "patch_embed.norm" in name:
		__lowercase		       =			name.replace("patch_embed.norm"  ,	"embeddings.norm"   )
	if "layers" in name:
		__lowercase		       =			"encoder." + name
	if "attn.proj" in name:
		__lowercase		       =			name.replace("attn.proj"  ,	"attention.output.dense"   )
	if "attn" in name:
		__lowercase		       =			name.replace("attn"  ,	"attention.self"   )
	if "norm1" in name:
		__lowercase		       =			name.replace("norm1"  ,	"layernorm_before"   )
	if "norm2" in name:
		__lowercase		       =			name.replace("norm2"  ,	"layernorm_after"   )
	if "mlp.fc1" in name:
		__lowercase		       =			name.replace("mlp.fc1"  ,	"intermediate.dense"   )
	if "mlp.fc2" in name:
		__lowercase		       =			name.replace("mlp.fc2"  ,	"output.dense"   )
	if "q_bias" in name:
		__lowercase		       =			name.replace("q_bias"  ,	"query.bias"   )
	if "k_bias" in name:
		__lowercase		       =			name.replace("k_bias"  ,	"key.bias"   )
	if "v_bias" in name:
		__lowercase		       =			name.replace("v_bias"  ,	"value.bias"   )
	if "cpb_mlp" in name:
		__lowercase		       =			name.replace("cpb_mlp"  ,	"continuous_position_bias_mlp"   )
	if name == "norm.weight":
		__lowercase		       =			"layernorm.weight"
	if name == "norm.bias":
		__lowercase		       =			"layernorm.bias"

	if "head" in name:
		__lowercase		       =			name.replace("head"  ,	"classifier"   )
	else:
		__lowercase		       =			"swinv2." + name

	return name



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	for key in orig_state_dict.copy().keys():
		__lowercase		       =			orig_state_dict.pop(_SCREAMING_SNAKE_CASE   )

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

			if "weight" in key:
				__lowercase		       =			val[:dim, :]
				__lowercase		       =			val[dim : dim * 2, :]
				__lowercase		       =			val[-dim:, :]
			else:
				__lowercase		       =			val[:dim]
				__lowercase		       =			val[
				    dim : dim * 2
				]
				__lowercase		       =			val[-dim:]
		else:
			__lowercase		       =			val

	return orig_state_dict



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			timm.create_model(_SCREAMING_SNAKE_CASE  ,	pretrained=_SCREAMING_SNAKE_CASE   )
	timm_model.eval()

	__lowercase		       =			get_swinva_config(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			SwinvaForImageClassification(_SCREAMING_SNAKE_CASE   )
	model.eval()

	__lowercase		       =			convert_state_dict(timm_model.state_dict()  ,	_SCREAMING_SNAKE_CASE   )
	model.load_state_dict(_SCREAMING_SNAKE_CASE   )

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

	__lowercase		       =			AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"  ,	"-"   )   )   )
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	__lowercase		       =			image_processor(images=_SCREAMING_SNAKE_CASE  ,	return_tensors="pt"   )

	__lowercase		       =			timm_model(inputs["pixel_values"]   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   ).logits

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

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

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

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


if __name__ == "__main__":
	snake_case__					:     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."""
	)

	snake_case__					:     str    		=   parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)



