





from string import ascii_uppercase
def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase = False,      _lowercase = ascii_uppercase      ):


    '''simple docstring'''



    _lowercase     =	keyword.upper()
    _lowercase     =	plaintext.upper()
    _lowercase     =	set(lowercase__      )

    # 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(lowercase__      )
    # the number of those unique characters will determine the number of rows
    _lowercase     =	len(lowercase__      )

    # 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,      lowercase__      )
    ]

    # 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(lowercase__      ):
        for row in modified_alphabet:
            # if current row (the last one) is too short, break out of loop
            if len(lowercase__      ) <= column:
                break

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

    if verbose:
        print(lowercase__      )
    # 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()
lowerCamelCase										=  logging.get_logger(__name__)
def        UpperCAmelCase      (   _lowercase,      _lowercase=False,      _lowercase=False      ):


    '''simple docstring'''



    _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      (   _lowercase,      _lowercase,      _lowercase=False,      _lowercase=False      ):


    '''simple docstring'''



    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      (   _lowercase,      _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	dct.pop(_lowercase      )
    _lowercase     =	val
def        UpperCAmelCase      (   ):


    '''simple docstring'''



    _lowercase     =	'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase     =	Image.open(requests.get(_lowercase,      stream=_lowercase      ).raw      )
    return im
@torch.no_grad()
def        UpperCAmelCase      (   _lowercase,      _lowercase      ):


    '''simple docstring'''



    _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     =	21841
        _lowercase     =	'imagenet-22k-id2label.json'
        _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
        _lowercase     =	{int(_lowercase      ): 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[15027]
        _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(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
        _lowercase     =	{int(_lowercase      ): 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(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
        _lowercase     =	{int(_lowercase      ): 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(_lowercase,      map_location='cpu',      check_hash=_lowercase      )
    _lowercase     =	state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

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

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

    # Check outputs on an image
    if is_semantic:
        _lowercase     =	BeitImageProcessor(size=config.image_size,      do_center_crop=_lowercase      )
        _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=_lowercase      )
        _lowercase     =	prepare_img()

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

    _lowercase     =	model(_lowercase      )
    _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, 21841]      )
        _lowercase     =	torch.tensor([2.2_288, 2.4_671, 0.7_395]      )
        _lowercase     =	2397
    elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'      ):
        _lowercase     =	torch.Size([1, 21841]      )
        _lowercase     =	torch.tensor([1.6_881, -0.2_787, 0.5_901]      )
        _lowercase     =	2396
    elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'      ):
        _lowercase     =	torch.tensor([0.1_241, 0.0_798, -0.6_569]      )
        _lowercase     =	285
    elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'      ):
        _lowercase     =	torch.tensor([-1.2_385, -1.0_987, -1.0_108]      )
        _lowercase     =	281
    elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'      ):
        _lowercase     =	torch.tensor([-1.5_303, -0.9_484, -0.3_147]      )
        _lowercase     =	761
    elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'      ):
        _lowercase     =	torch.tensor([0.4_610, -0.0_928, 0.2_086]      )
        _lowercase     =	761
    elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'      ):
        _lowercase     =	torch.tensor([-0.4_804, 0.6_257, -0.1_837]      )
        _lowercase     =	761
    elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'      ):
        _lowercase     =	torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]      )
        _lowercase     =	761
    elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'      ):
        _lowercase     =	torch.tensor([-0.3_062, 0.7_261, 0.4_852]      )
        _lowercase     =	761
    elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'      ):
        _lowercase     =	(1, 150, 160, 160)
        _lowercase     =	torch.tensor(
            [
                [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
            ]      )
    elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'      ):
        _lowercase     =	(1, 150, 160, 160)
        _lowercase     =	torch.tensor(
            [
                [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
                [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
                [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
            ]      )
    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],      _lowercase,      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],      _lowercase,      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(_lowercase      ).mkdir(exist_ok=_lowercase      )
    print(f'''Saving model to {pytorch_dump_folder_path}'''      )
    model.save_pretrained(_lowercase      )
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
    image_processor.save_pretrained(_lowercase      )


if __name__ == "__main__":
   lowerCamelCase										=  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.'''
   )
   lowerCamelCase										=  parser.parse_args()
   convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
