





"""simple docstring"""




import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
a						: Union[str, Any]     	=			logging.get_logger(__name__)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->Any:






	'''simple docstring'''






	print("Loading config file..."					)

	def flatten_yaml_as_dict(_lowercase						:    str    ,       _lowercase						:    Tuple=""    ,       _lowercase						:    Optional[int]="."					):
		a      :		Union[str, Any]	=				[]
		for k, v in d.items():
			a      :		str	=				parent_key + sep + k if parent_key else k
			if isinstance(__UpperCamelCase    ,       collections.abc.MutableMapping					):
				items.extend(flatten_yaml_as_dict(__UpperCamelCase    ,       __UpperCamelCase    ,       sep=__UpperCamelCase					).items()					)
			else:
				items.append((new_key, v)					)
		return dict(__UpperCamelCase					)

	a      :		Optional[int]	=				argparse.Namespace()
	with open(__UpperCamelCase    ,       "r"					) as yaml_file:
		try:
			a      :		Optional[int]	=				yaml.load(__UpperCamelCase    ,       Loader=yaml.FullLoader					)

			a      :		str	=				flatten_yaml_as_dict(__UpperCamelCase					)
			for k, v in flat_cfg.items():
				setattr(__UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase					)
		except yaml.YAMLError as exc:
			logger.error("Error while loading config file: {}. Error message: {}".format(__UpperCamelCase    ,       str(__UpperCamelCase					)					)					)
	return config






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]    ,       _lowercase						:    Dict					) ->Tuple:






	'''simple docstring'''






	a      :		Optional[Any]	=				MobileViTVaConfig()

	a      :		Dict	=				False

	# dataset
	if task_name.startswith("imagenet1k_"					):
		a      :		str	=				1000
		if int(task_name.strip().split("_"					)[-1]					) == 384:
			a      :		Optional[int]	=				384
		else:
			a      :		Union[str, Any]	=				256
		a      :		Optional[int]	=				"imagenet-1k-id2label.json"
	elif task_name.startswith("imagenet21k_to_1k_"					):
		a      :		Optional[Any]	=				2_1000
		if int(task_name.strip().split("_"					)[-1]					) == 384:
			a      :		Optional[int]	=				384
		else:
			a      :		List[Any]	=				256
		a      :		List[str]	=				"imagenet-22k-id2label.json"
	elif task_name.startswith("ade20k_"					):
		a      :		Dict	=				151
		a      :		Any	=				512
		a      :		Union[str, Any]	=				"ade20k-id2label.json"
		a      :		Dict	=				True
	elif task_name.startswith("voc_"					):
		a      :		Tuple	=				21
		a      :		Union[str, Any]	=				512
		a      :		List[Any]	=				"pascal-voc-id2label.json"
		a      :		Optional[Any]	=				True

	# orig_config
	a      :		Tuple	=				load_orig_config_file(__UpperCamelCase					)
	assert getattr(__UpperCamelCase    ,       "model.classification.name"    ,       -1					) == "mobilevit_v2", "Invalid model"
	a      :		List[str]	=				getattr(__UpperCamelCase    ,       "model.classification.mitv2.width_multiplier"    ,       1.0					)
	assert (
	    getattr(__UpperCamelCase    ,       "model.classification.mitv2.attn_norm_layer"    ,       -1					) == "layer_norm_2d"
	), "Norm layers other than layer_norm_2d is not supported"
	a      :		Tuple	=				getattr(__UpperCamelCase    ,       "model.classification.activation.name"    ,       "swish"					)
	# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

	if is_segmentation_model:
		a      :		List[str]	=				getattr(__UpperCamelCase    ,       "model.segmentation.output_stride"    ,       16					)
		if "_deeplabv3" in task_name:
			a      :		List[str]	=				getattr(__UpperCamelCase    ,       "model.segmentation.deeplabv3.aspp_rates"    ,       [12, 24, 36]					)
			a      :		Optional[int]	=				getattr(__UpperCamelCase    ,       "model.segmentation.deeplabv3.aspp_out_channels"    ,       512					)
			a      :		str	=				getattr(__UpperCamelCase    ,       "model.segmentation.deeplabv3.aspp_dropout"    ,       0.1					)

		# id2label
	a      :		Optional[int]	=				"huggingface/label-files"
	a      :		Union[str, Any]	=				json.load(open(hf_hub_download(__UpperCamelCase    ,       __UpperCamelCase    ,       repo_type="dataset"					)    ,       "r"					)					)
	a      :		Any	=				{int(__UpperCamelCase					): v for k, v in idalabel.items()}
	a      :		Union[str, Any]	=				idalabel
	a      :		Union[str, Any]	=				{v: k for k, v in idalabel.items()}

	return config






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    Optional[Any]    ,       _lowercase						:    Tuple					) ->Union[str, Any]:






	'''simple docstring'''






	a      :		List[Any]	=				dct.pop(__UpperCamelCase					)
	a      :		Tuple	=				val






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    Union[str, Any]=False					) ->List[Any]:






	'''simple docstring'''






	if base_model:
		a      :		Dict	=				""
	else:
		a      :		Tuple	=				"mobilevitv2."

	a      :		Tuple	=				[]
	for k in state_dict.keys():
		if k[:8] == "encoder.":
			a      :		int	=				k[8:]
		else:
			a      :		Any	=				k

		if ".block." in k:
			a      :		List[Any]	=				k_new.replace(".block."    ,       "."					)
		if ".conv." in k:
			a      :		List[str]	=				k_new.replace(".conv."    ,       ".convolution."					)
		if ".norm." in k:
			a      :		Tuple	=				k_new.replace(".norm."    ,       ".normalization."					)

		if "conv_1." in k:
			a      :		Union[str, Any]	=				k_new.replace("conv_1."    ,       F"""{model_prefix}conv_stem."""					)
		for i in [1, 2]:
			if F"""layer_{i}.""" in k:
				a      :		Any	=				k_new.replace(F"""layer_{i}."""    ,       F"""{model_prefix}encoder.layer.{i-1}.layer."""					)
		if ".exp_1x1." in k:
			a      :		int	=				k_new.replace(".exp_1x1."    ,       ".expand_1x1."					)
		if ".red_1x1." in k:
			a      :		Optional[Any]	=				k_new.replace(".red_1x1."    ,       ".reduce_1x1."					)

		for i in [3, 4, 5]:
			if F"""layer_{i}.0.""" in k:
				a      :		int	=				k_new.replace(F"""layer_{i}.0."""    ,       F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""					)
			if F"""layer_{i}.1.local_rep.0.""" in k:
				a      :		Dict	=				k_new.replace(F"""layer_{i}.1.local_rep.0."""    ,       F"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""					)
			if F"""layer_{i}.1.local_rep.1.""" in k:
				a      :		Tuple	=				k_new.replace(F"""layer_{i}.1.local_rep.1."""    ,       F"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""					)

		for i in [3, 4, 5]:
			if i == 3:
				a      :		Tuple	=				[0, 1]
			elif i == 4:
				a      :		Tuple	=				[0, 1, 2, 3]
			elif i == 5:
				a      :		Union[str, Any]	=				[0, 1, 2]

			for j in j_in:
				if F"""layer_{i}.1.global_rep.{j}.""" in k:
					a      :		str	=				k_new.replace(
					    F"""layer_{i}.1.global_rep.{j}."""    ,       F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""					)
			if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
				a      :		Dict	=				k_new.replace(
				    F"""layer_{i}.1.global_rep.{j+1}."""    ,       F"""{model_prefix}encoder.layer.{i-1}.layernorm."""					)

			if F"""layer_{i}.1.conv_proj.""" in k:
				a      :		Optional[Any]	=				k_new.replace(F"""layer_{i}.1.conv_proj."""    ,       F"""{model_prefix}encoder.layer.{i-1}.conv_projection."""					)

		if "pre_norm_attn.0." in k:
			a      :		Dict	=				k_new.replace("pre_norm_attn.0."    ,       "layernorm_before."					)
		if "pre_norm_attn.1." in k:
			a      :		Tuple	=				k_new.replace("pre_norm_attn.1."    ,       "attention."					)
		if "pre_norm_ffn.0." in k:
			a      :		Optional[Any]	=				k_new.replace("pre_norm_ffn.0."    ,       "layernorm_after."					)
		if "pre_norm_ffn.1." in k:
			a      :		Dict	=				k_new.replace("pre_norm_ffn.1."    ,       "ffn.conv1."					)
		if "pre_norm_ffn.3." in k:
			a      :		str	=				k_new.replace("pre_norm_ffn.3."    ,       "ffn.conv2."					)

		if "classifier.1." in k:
			a      :		int	=				k_new.replace("classifier.1."    ,       "classifier."					)

		if "seg_head." in k:
			a      :		Any	=				k_new.replace("seg_head."    ,       "segmentation_head."					)
		if ".aspp_layer." in k:
			a      :		Dict	=				k_new.replace(".aspp_layer."    ,       "."					)
		if ".aspp_pool." in k:
			a      :		Dict	=				k_new.replace(".aspp_pool."    ,       "."					)

		rename_keys.append((k, k_new)					)
	return rename_keys






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Dict					) ->List[Any]:






	'''simple docstring'''






	a      :		Optional[Any]	=				[]
	for k in state_dict.keys():
		if k.startswith("seg_head.aux_head."					):
			keys_to_ignore.append(__UpperCamelCase					)
	for k in keys_to_ignore:
		state_dict.pop(__UpperCamelCase    ,       __UpperCamelCase					)






def       _SCREAMING_SNAKE_CASE		(	) ->Any:






	'''simple docstring'''






	a      :		Any	=				"http://images.cocodataset.org/val2017/000000039769.jpg"
	# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
	a      :		List[Any]	=				Image.open(requests.get(__UpperCamelCase    ,       stream=__UpperCamelCase					).raw					)
	return im






@torch.no_grad()
def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any    ,       _lowercase						:    int    ,       _lowercase						:    Any    ,       _lowercase						:    Dict					) ->Any:






	'''simple docstring'''






	a      :		Tuple	=				get_mobilevitva_config(__UpperCamelCase    ,       __UpperCamelCase					)

	# load original state_dict
	a      :		List[str]	=				torch.load(__UpperCamelCase    ,       map_location="cpu"					)

	# load huggingface model
	if task_name.startswith("ade20k_"					) or task_name.startswith("voc_"					):
		a      :		Optional[int]	=				MobileViTVaForSemanticSegmentation(__UpperCamelCase					).eval()
		a      :		List[str]	=				False
	else:
		a      :		Union[str, Any]	=				MobileViTVaForImageClassification(__UpperCamelCase					).eval()
		a      :		Union[str, Any]	=				False

	# remove and rename some keys of load the original model
	a      :		List[str]	=				checkpoint
	remove_unused_keys(__UpperCamelCase					)
	a      :		List[Any]	=				create_rename_keys(__UpperCamelCase    ,       base_model=__UpperCamelCase					)
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(__UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase					)

	# load modified state_dict
	model.load_state_dict(__UpperCamelCase					)

	# Check outputs on an image, prepared by MobileViTImageProcessor
	a      :		Union[str, Any]	=				MobileViTImageProcessor(crop_size=config.image_size    ,       size=config.image_size + 32					)
	a      :		Union[str, Any]	=				image_processor(images=prepare_img()    ,       return_tensors="pt"					)
	a      :		str	=				model(**__UpperCamelCase					)

	# verify classification model
	if task_name.startswith("imagenet"					):
		a      :		Tuple	=				outputs.logits
		a      :		int	=				logits.argmax(-1					).item()
		print("Predicted class:"    ,       model.config.idalabel[predicted_class_idx]					)
		if task_name.startswith("imagenet1k_256"					) and config.width_multiplier == 1.0:
			# expected_logits for base variant
			a      :		Union[str, Any]	=				torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]					)
			assert torch.allclose(logits[0, :3]    ,       __UpperCamelCase    ,       atol=1E-4					)

	Path(__UpperCamelCase					).mkdir(exist_ok=__UpperCamelCase					)
	print(F"""Saving model {task_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					)


if __name__ == "__main__":
		a						: str     	=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--task''',
		    default='''imagenet1k_256''',
		    type=str,
		    help=(
		        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
		        '''\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '''
		    ),
		    choices=[
		        '''imagenet1k_256''',
		        '''imagenet1k_384''',
		        '''imagenet21k_to_1k_256''',
		        '''imagenet21k_to_1k_384''',
		        '''ade20k_deeplabv3''',
		        '''voc_deeplabv3''',
		    ],
		)

		parser.add_argument(
		    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
		)
		parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
		)

		a						: int     	=			parser.parse_args()
		convert_mobilevitva_checkpoint(
		    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
		)










"""simple docstring"""


import argparse
import struct
import unittest


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     bytes  ):

     '''simple docstring'''
     __A									= data

     # Initialize hash values
     __A									= [
         0X6a_09e_667,
         0Xbb_67a_e85,
         0X3c_6ef_372,
         0Xa5_4ff_53a,
         0X51_0e5_27f,
         0X9b_056_88c,
         0X1f_83d_9ab,
         0X5b_e0c_d19,
     ]

     # Initialize round constants
     __A									= [
         0X42_8a2_f98,
         0X71_374_491,
         0Xb5_c0f_bcf,
         0Xe9_b5d_ba5,
         0X39_56c_25b,
         0X59_f11_1f1,
         0X92_3f8_2a4,
         0Xab_1c5_ed5,
         0Xd8_07a_a98,
         0X12_835_b01,
         0X24_318_5be,
         0X55_0c7_dc3,
         0X72_be5_d74,
         0X80_deb_1fe,
         0X9b_dc0_6a7,
         0Xc1_9bf_174,
         0Xe4_9b6_9c1,
         0Xef_be4_786,
         0X0f_c19_dc6,
         0X24_0ca_1cc,
         0X2d_e92_c6f,
         0X4a_748_4aa,
         0X5c_b0a_9dc,
         0X76_f98_8da,
         0X98_3e5_152,
         0Xa8_31c_66d,
         0Xb0_032_7c8,
         0Xbf_597_fc7,
         0Xc6_e00_bf3,
         0Xd5_a79_147,
         0X06_ca6_351,
         0X14_292_967,
         0X27_b70_a85,
         0X2e_1b2_138,
         0X4d_2c6_dfc,
         0X53_380_d13,
         0X65_0a7_354,
         0X76_6a0_abb,
         0X81_c2c_92e,
         0X92_722_c85,
         0Xa2_bfe_8a1,
         0Xa8_1a6_64b,
         0Xc2_4b8_b70,
         0Xc7_6c5_1a3,
         0Xd1_92e_819,
         0Xd6_990_624,
         0Xf4_0e3_585,
         0X10_6aa_070,
         0X19_a4c_116,
         0X1e_376_c08,
         0X27_487_74c,
         0X34_b0b_cb5,
         0X39_1c0_cb3,
         0X4e_d8a_a4a,
         0X5b_9cc_a4f,
         0X68_2e6_ff3,
         0X74_8f8_2ee,
         0X78_a56_36f,
         0X84_c87_814,
         0X8c_c70_208,
         0X90_bef_ffa,
         0Xa4_506_ceb,
         0Xbe_f9a_3f7,
         0Xc6_717_8f2,
     ]

     __A									= self.preprocessing(self.data  )
     self.final_hash()


   @staticmethod
   def 				_SCREAMING_SNAKE_CASE				(	_lowerCamelCase :     bytes  ):

     '''simple docstring'''
     __A									= b'''\x80''' + (b'''\x00''' * (63 - (len(_lowerCamelCase  ) + 8) % 64))
     __A									= struct.pack('''>Q''',       (len(_lowerCamelCase  ) * 8)  )
     return data + padding + big_endian_integer


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     # Convert into blocks of 64 bytes
     __A									= [
         self.preprocessed_data[x : x + 64]
         for x in range(0,       len(self.preprocessed_data  ),       64  )
     ]

     for block in self.blocks:
       # Convert the given block into a list of 4 byte integers
       __A									= list(struct.unpack('''>16L''',       _lowerCamelCase  )  )
       # add 48 0-ed integers
       words += [0] * 48

       __A			,    __A			,    __A			,    __A			,    __A			,    __A			,    __A			,    __A									= self.hashes

       for index in range(0,       64  ):
         if index > 15:
           # modify the zero-ed indexes at the end of the array
           __A									= (
               self.ror(words[index - 15],       7  )
               ^ self.ror(words[index - 15],       18  )
               ^ (words[index - 15] >> 3)
           )
           __A									= (
               self.ror(words[index - 2],       17  )
               ^ self.ror(words[index - 2],       19  )
               ^ (words[index - 2] >> 10)
           )

           __A									= (
               words[index - 16] + sa + words[index - 7] + sa
           ) % 0X100_000_000

         # Compression
         __A									= self.ror(_lowerCamelCase,       6  ) ^ self.ror(_lowerCamelCase,       11  ) ^ self.ror(_lowerCamelCase,       25  )
         __A									= (e & f) ^ ((~e & 0Xff_fff_fff) & g)
         __A									= (
             h + sa + ch + self.round_constants[index] + words[index]
         ) % 0X100_000_000
         __A									= self.ror(_lowerCamelCase,       2  ) ^ self.ror(_lowerCamelCase,       13  ) ^ self.ror(_lowerCamelCase,       22  )
         __A									= (a & b) ^ (a & c) ^ (b & c)
         __A									= (sa + maj) % 0X100_000_000

         __A			,    __A			,    __A			,    __A			,    __A			,    __A			,    __A			,    __A									= (
             g,
             f,
             e,
             ((d + tempa) % 0X100_000_000),
             c,
             b,
             a,
             ((tempa + tempa) % 0X100_000_000),
         )

       __A									= [a, b, c, d, e, f, g, h]

       # Modify final values
       __A									= [
           ((element + mutated_hash_values[index]) % 0X100_000_000)
           for index, element in enumerate(self.hashes  )
       ]

     __A									= ''''''.join([hex(_lowerCamelCase  )[2:].zfill(8  ) for value in self.hashes]  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     int,       _lowerCamelCase :     int  ):

     '''simple docstring'''
     return 0Xff_fff_fff & (value << (32 - rotations)) | (value >> rotations)





class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     import hashlib

     __A									= bytes('''Test String''',       '''utf-8'''  )
     self.assertEqual(SHAaaa(_lowerCamelCase  ).hash,       hashlib.shaaaa(_lowerCamelCase  ).hexdigest()  )





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  import doctest

  doctest.testmod()

  __A									= argparse.ArgumentParser()
  parser.add_argument(
      '''-s'''		,       '''--string'''		,       dest='''input_string'''		,       default='''Hello World!! Welcome to Cryptography'''		,       help='''Hash the string'''		,       )
  parser.add_argument(
      '''-f'''		,       '''--file'''		,       dest='''input_file'''		,       help='''Hash contents of a file''' )

  __A									= parser.parse_args()

  __A									= args.input_string

  # hash input should be a bytestring
  if args.input_file:
    with open(args.input_file		,       '''rb''' ) as f:
      __A									= f.read()
  else:
    __A									= bytes(__UpperCamelCase		,       '''utf-8''' )

  print(SHAaaa(__UpperCamelCase ).hash )


if __name__ == "__main__":
       main()




