
from string import ascii_uppercase






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = False			,							__SCREAMING_SNAKE_CASE = ascii_uppercase							):
	lowercase					  =   keyword.upper()
	lowercase					  =   plaintext.upper()
	lowercase					  =   set(__SCREAMING_SNAKE_CASE							)

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

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

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

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

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

	if verbose:
		print(__SCREAMING_SNAKE_CASE							)
	# 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'''))



import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase     				=			logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=False			,							__SCREAMING_SNAKE_CASE=False							):
	lowercase					  =   'backbone.' if is_semantic else ''

	lowercase					  =   []
	for i in range(config.num_hidden_layers							):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')							)
		rename_keys.append(
		    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')							)
		rename_keys.append(
		    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')							)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        (F'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
	        (F'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
	        (F'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
	    ]							)

	if has_lm_head:
		# mask token + shared relative position bias + layernorm
		rename_keys.extend(
		    [
		        ('mask_token', 'beit.embeddings.mask_token'),
		        (
		            'rel_pos_bias.relative_position_bias_table',
		            'beit.encoder.relative_position_bias.relative_position_bias_table',
		        ),
		        (
		            'rel_pos_bias.relative_position_index',
		            'beit.encoder.relative_position_bias.relative_position_index',
		        ),
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		    ]							)
	elif is_semantic:
		# semantic segmentation classification heads
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]							)
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
		        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]							)

	return rename_keys






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=False			,							__SCREAMING_SNAKE_CASE=False							):
	for i in range(config.num_hidden_layers							):
		lowercase					  =   'backbone.' if is_semantic else ''
		# queries, keys and values
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''							)
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''							)
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''							)

		lowercase					  =   in_proj_weight[
		    : config.hidden_size, :
		]
		lowercase					  =   q_bias
		lowercase					  =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowercase					  =   in_proj_weight[
		    -config.hidden_size :, :
		]
		lowercase					  =   v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''							)
		lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''							)

		lowercase					  =   gamma_a
		lowercase					  =   gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''							)
			lowercase					  =   state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''							)

			lowercase					  =   table
			lowercase					  =   index






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   dct.pop(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   val






def 							UpperCAmelCase_		(							):
	lowercase					  =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowercase					  =   Image.open(requests.get(__SCREAMING_SNAKE_CASE			,							stream=__SCREAMING_SNAKE_CASE							).raw							)
	return im






@torch.no_grad()
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   BeitConfig()
	lowercase					  =   False
	lowercase					  =   False
	lowercase					  =   'huggingface/label-files'
	# set config parameters based on URL
	if checkpoint_url[-9:-4] == "pt22k":
		# masked image modeling
		lowercase					  =   True
		lowercase					  =   True
		lowercase					  =   True
	elif checkpoint_url[-9:-4] == "ft22k":
		# intermediate fine-tuning on ImageNet-22k
		lowercase					  =   True
		lowercase					  =   2_1841
		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()}
		# this dataset contains 21843 labels but the model only has 21841
		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
		del idalabel[9205]
		del idalabel[1_5027]
		lowercase					  =   idalabel
		lowercase					  =   {v: k for k, v in idalabel.items()}
	elif checkpoint_url[-8:-4] == "to1k":
		# fine-tuning on ImageNet-1k
		lowercase					  =   True
		lowercase					  =   1000
		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()}
		if "384" in checkpoint_url:
			lowercase					  =   384
		if "512" in checkpoint_url:
			lowercase					  =   512
	elif "ade20k" in checkpoint_url:
		# fine-tuning
		lowercase					  =   True
		lowercase					  =   150
		lowercase					  =   'ade20k-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					  =   640
		lowercase					  =   True
	else:
		raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''							)

	# size of the architecture
	if "base" in checkpoint_url:
		pass
	elif "large" in checkpoint_url:
		lowercase					  =   1024
		lowercase					  =   4096
		lowercase					  =   24
		lowercase					  =   16
		if "ade20k" in checkpoint_url:
			lowercase					  =   640
			lowercase					  =   [7, 11, 15, 23]
	else:
		raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'							)

	# load state_dict of original model, remove and rename some keys
	lowercase					  =   torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE			,							map_location='cpu'			,							check_hash=__SCREAMING_SNAKE_CASE							)
	lowercase					  =   state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

	lowercase					  =   create_rename_keys(__SCREAMING_SNAKE_CASE			,							has_lm_head=__SCREAMING_SNAKE_CASE			,							is_semantic=__SCREAMING_SNAKE_CASE							)
	for src, dest in rename_keys:
		rename_key(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	read_in_q_k_v(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							has_lm_head=__SCREAMING_SNAKE_CASE			,							is_semantic=__SCREAMING_SNAKE_CASE							)
	if is_semantic:
		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
			lowercase					  =   state_dict.pop(__SCREAMING_SNAKE_CASE							)
			if key.startswith('backbone.fpn'							):
				lowercase					  =   key.replace('backbone.fpn'			,							'fpn'							)
			lowercase					  =   val

    # load HuggingFace model
	if checkpoint_url[-9:-4] == "pt22k":
		lowercase					  =   BeitForMaskedImageModeling(__SCREAMING_SNAKE_CASE							)
	elif "ade20k" in checkpoint_url:
		lowercase					  =   BeitForSemanticSegmentation(__SCREAMING_SNAKE_CASE							)
	else:
		lowercase					  =   BeitForImageClassification(__SCREAMING_SNAKE_CASE							)
	model.eval()
	model.load_state_dict(__SCREAMING_SNAKE_CASE							)

	# Check outputs on an image
	if is_semantic:
		lowercase					  =   BeitImageProcessor(size=config.image_size			,							do_center_crop=__SCREAMING_SNAKE_CASE							)
		lowercase					  =   load_dataset('hf-internal-testing/fixtures_ade20k'			,							split='test'							)
		lowercase					  =   Image.open(ds[0]['file']							)
	else:
		lowercase					  =   BeitImageProcessor(
		    size=config.image_size			,							resample=PILImageResampling.BILINEAR			,							do_center_crop=__SCREAMING_SNAKE_CASE							)
		lowercase					  =   prepare_img()

	lowercase					  =   image_processor(images=__SCREAMING_SNAKE_CASE			,							return_tensors='pt'							)
	lowercase					  =   encoding['pixel_values']

	lowercase					  =   model(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   outputs.logits

	# verify logits
	lowercase					  =   torch.Size([1, 1000]							)
	if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'							):
		lowercase					  =   torch.Size([1, 196, 8192]							)
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'							):
		lowercase					  =   torch.Size([1, 196, 8192]							)
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'							):
		lowercase					  =   torch.Size([1, 2_1841]							)
		lowercase					  =   torch.tensor([2.22_88, 2.46_71, 0.73_95]							)
		lowercase					  =   2397
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'							):
		lowercase					  =   torch.Size([1, 2_1841]							)
		lowercase					  =   torch.tensor([1.68_81, -0.27_87, 0.59_01]							)
		lowercase					  =   2396
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'							):
		lowercase					  =   torch.tensor([0.12_41, 0.07_98, -0.65_69]							)
		lowercase					  =   285
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([-1.23_85, -1.09_87, -1.01_08]							)
		lowercase					  =   281
	elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([-1.53_03, -0.94_84, -0.31_47]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'							):
		lowercase					  =   torch.tensor([0.46_10, -0.09_28, 0.20_86]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([-0.48_04, 0.62_57, -0.18_37]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'							):
		lowercase					  =   torch.tensor([-0.30_62, 0.72_61, 0.48_52]							)
		lowercase					  =   761
	elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'							):
		lowercase					  =   (1, 150, 160, 160)
		lowercase					  =   torch.tensor(
		    [
		        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
		        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
		        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
		    ]							)
	elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'							):
		lowercase					  =   (1, 150, 160, 160)
		lowercase					  =   torch.tensor(
		    [
		        [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
		        [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
		        [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
		    ]							)
	else:
		raise ValueError('Can\'t verify logits as model is not supported'							)

	assert logits.shape == expected_shape, "Shape of logits not as expected"
	if not has_lm_head:
		if is_semantic:
			assert torch.allclose(
			    logits[0, :3, :3, :3]			,							__SCREAMING_SNAKE_CASE			,							atol=1e-3							), "First elements of logits not as expected"
		else:
			print('Predicted class idx:'			,							logits.argmax(-1							).item()							)
			assert torch.allclose(
			    logits[0, :3]			,							__SCREAMING_SNAKE_CASE			,							atol=1e-3							), "First elements of logits not as expected"
			assert logits.argmax(-1							).item() == expected_class_idx, "Predicted class index not as expected"

	Path(__SCREAMING_SNAKE_CASE							).mkdir(exist_ok=__SCREAMING_SNAKE_CASE							)
	print(F'''Saving model 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							)


if __name__ == "__main__":
	UpperCAmelCase     				=			argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
	    type=str,
	    help='''URL to the original PyTorch checkpoint (.pth file).''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
	)
	UpperCAmelCase     				=			parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

