

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

@dataclass
class   __UpperCamelCase		:
	SCREAMING_SNAKE_CASE__      			=       42
	SCREAMING_SNAKE_CASE__      			=       field(default_factory=lowercase  )
	SCREAMING_SNAKE_CASE__      			=       field(default_factory=lowercase  )




	def  __A   (						self		:  Optional[Any]						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  List[str]						,		lowerCAmelCase		:  Optional[int]							):


		'''simple docstring'''







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




	def __call__(						self		:  Optional[int]						,		lowerCAmelCase		:  Optional[Any]							):


		'''simple docstring'''







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




	@property
	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		return list(filter(lambda lowerCAmelCase							: len(list(x.state_dict().keys()							)							) > 0						,		self.traced							)							)


@dataclass
class   __UpperCamelCase		:
	SCREAMING_SNAKE_CASE__      			=       42
	SCREAMING_SNAKE_CASE__      			=       42
	SCREAMING_SNAKE_CASE__      			=       0
	SCREAMING_SNAKE_CASE__      			=       field(default_factory=lowercase  )
	SCREAMING_SNAKE_CASE__      			=       field(default_factory=lowercase  )




	def __call__(						self		:  List[str]						,		lowerCAmelCase		:  List[str]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     Tracker(self.dest							)(lowerCAmelCase							).parametrized
		UpperCAmelCase_    			=     Tracker(self.src							)(lowerCAmelCase							).parametrized

		UpperCAmelCase_    			=     list(filter(lambda lowerCAmelCase							: type(lowerCAmelCase							) not in self.src_skip						,		lowerCAmelCase							)							)
		UpperCAmelCase_    			=     list(filter(lambda lowerCAmelCase							: type(lowerCAmelCase							) not in self.dest_skip						,		lowerCAmelCase							)							)

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

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



def  __lowerCAmelCase (  A    ,       A      ):
	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(SCREAMING_SNAKE_CASE_      )

	our_model.load_state_dict(SCREAMING_SNAKE_CASE_      )
	return our_model



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A    ,       A    ,       A = True    ,       ):
	print(F"Downloading weights for {name}..."      )
	UpperCAmelCase_    			=     cached_download(SCREAMING_SNAKE_CASE_      )
	print(F"Converting {name}..."      )
	UpperCAmelCase_    			=     torch.load(SCREAMING_SNAKE_CASE_      )["state_dict"]
	from_model.load_state_dict(SCREAMING_SNAKE_CASE_      )
	from_model.eval()
	with torch.no_grad():
		UpperCAmelCase_    			=     VanForImageClassification(SCREAMING_SNAKE_CASE_      ).eval()
		UpperCAmelCase_    			=     ModuleTransfer(src=SCREAMING_SNAKE_CASE_    ,       dest=SCREAMING_SNAKE_CASE_      )
		UpperCAmelCase_    			=     torch.randn((1, 3, 224, 224)      )
		module_transfer(SCREAMING_SNAKE_CASE_      )
		UpperCAmelCase_    			=     copy_parameters(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_      )

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

	UpperCAmelCase_    			=     name
	print(SCREAMING_SNAKE_CASE_      )

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

		# 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=SCREAMING_SNAKE_CASE_    ,       )

		print(F"Pushed {checkpoint_name}"      )



def  __lowerCAmelCase (  A    ,       A = None    ,       A = True      ):
	UpperCAmelCase_    			=     "imagenet-1k-id2label.json"
	UpperCAmelCase_    			=     1000

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

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

	UpperCAmelCase_    			=     partial(SCREAMING_SNAKE_CASE_    ,       num_labels=SCREAMING_SNAKE_CASE_    ,       idalabel=SCREAMING_SNAKE_CASE_    ,       labelaid=SCREAMING_SNAKE_CASE_      )

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


if __name__ == "__main__":
				_a:      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.""",
				)

				_a:      int								=     parser.parse_args()
				_a:      List[str]								=     args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				_a:      Optional[int]								=     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)


from string import ascii_uppercase



def  __lowerCAmelCase (  A    ,       A    ,       A = False    ,       A = ascii_uppercase      ):
	UpperCAmelCase_    			=     keyword.upper()
	UpperCAmelCase_    			=     plaintext.upper()
	UpperCAmelCase_    			=     set(A      )

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	UpperCAmelCase_    			=     []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(A      )
    # the number of those unique characters will determine the number of rows
	UpperCAmelCase_    			=     len(A      )

	# create a shifted version of the alphabet
	UpperCAmelCase_    			=     unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	UpperCAmelCase_    			=     [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0    ,       26    ,       A      )
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	UpperCAmelCase_    			=     {}
	UpperCAmelCase_    			=     0
	for column in range(A      ):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(A      ) <= column:
				break

			# map current letter to letter in modified alphabet
			UpperCAmelCase_    			=     row[column]
			letter_index += 1

	if verbose:
		print(A      )
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext      )


if __name__ == "__main__":
				# example use
				print(mixed_keyword("""college""", """UNIVERSITY"""))