






from __future__ import annotations






def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						list[str] | None = None       ,       lowerCAmelCase_       :						dict[str, float] | None = None       ,       lowerCAmelCase_       :						bool = False       ,       ):
							__lowercase      :							Any				=     cipher_alphabet or [chr(lowerCAmelCase_      ) for i in range(97       ,       123      )]

							# If the argument is None or the user provided an empty dictionary
							if not frequencies_dict:
														# Frequencies of letters in the english language (how much they show up)
														__lowercase      :							Union[str, Any]				=     {
														    """a""": 0.08_497,
														    """b""": 0.01_492,
														    """c""": 0.02_202,
														    """d""": 0.04_253,
														    """e""": 0.11_162,
														    """f""": 0.02_228,
														    """g""": 0.02_015,
														    """h""": 0.06_094,
														    """i""": 0.07_546,
														    """j""": 0.00_153,
														    """k""": 0.01_292,
														    """l""": 0.04_025,
														    """m""": 0.02_406,
														    """n""": 0.06_749,
														    """o""": 0.07_507,
														    """p""": 0.01_929,
														    """q""": 0.00_095,
														    """r""": 0.07_587,
														    """s""": 0.06_327,
														    """t""": 0.09_356,
														    """u""": 0.02_758,
														    """v""": 0.00_978,
														    """w""": 0.02_560,
														    """x""": 0.00_150,
														    """y""": 0.01_994,
														    """z""": 0.00_077,
														}
							else:
														# Custom frequencies dictionary
														__lowercase      :							Dict				=     frequencies_dict

							if not case_sensitive:
														__lowercase      :							Any				=     ciphertext.lower()

							# Chi squared statistic values
							__lowercase      :							dict[int, tuple[float, str]]				=     {}

							# cycle through all of the shifts
							for shift in range(len(lowerCAmelCase_      )      ):
														__lowercase      :							str				=     """"""

														# decrypt the message with the shift
														for letter in ciphertext:
																					try:
																												# Try to index the letter in the alphabet
																												__lowercase      :							Union[str, Any]				=     (alphabet_letters.index(letter.lower()      ) - shift) % len(
																												    lowerCAmelCase_      )
																												decrypted_with_shift += (
																												    alphabet_letters[new_key].upper()
																												    if case_sensitive and letter.isupper()
																												    else alphabet_letters[new_key]
																												)
																					except ValueError:
																												# Append the character if it isn't in the alphabet
																												decrypted_with_shift += letter

														__lowercase      :							Dict				=     0.0

														# Loop through each letter in the decoded message with the shift
														for letter in decrypted_with_shift:
																					if case_sensitive:
																												__lowercase      :							Optional[Any]				=     letter.lower()
																												if letter in frequencies:
																																			# Get the amount of times the letter occurs in the message
																																			__lowercase      :							Dict				=     decrypted_with_shift.lower().count(lowerCAmelCase_      )

																																			# Get the excepcted amount of times the letter should appear based
																																			# on letter frequencies
																																			__lowercase      :							Optional[int]				=     frequencies[letter] * occurrences

																																			# Complete the chi squared statistic formula
																																			__lowercase      :							Optional[Any]				=     ((occurrences - expected) ** 2) / expected

																																			# Add the margin of error to the total chi squared statistic
																																			chi_squared_statistic += chi_letter_value
																					else:
																												if letter.lower() in frequencies:
																																			# Get the amount of times the letter occurs in the message
																																			__lowercase      :							Any				=     decrypted_with_shift.count(lowerCAmelCase_      )

																																			# Get the excepcted amount of times the letter should appear based
																																			# on letter frequencies
																																			__lowercase      :							int				=     frequencies[letter] * occurrences

																																			# Complete the chi squared statistic formula
																																			__lowercase      :							str				=     ((occurrences - expected) ** 2) / expected

																																			# Add the margin of error to the total chi squared statistic
																																			chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
														__lowercase      :							Dict				=     (
														    chi_squared_statistic,
														    decrypted_with_shift,
														)

							# Get the most likely cipher by finding the cipher with the smallest chi squared
							# statistic
							def chi_squared_statistic_values_sorting_key(lowerCAmelCase_       :						int      ) -> tuple[float, str]:
														return chi_squared_statistic_values[key]

							__lowercase      :							int				=     min(
							    lowerCAmelCase_       ,       key=lowerCAmelCase_       ,       )

							# Get all the data from the most likely cipher (key, decoded message)
							(
							    __lowercase			
							)      :							Dict				=     chi_squared_statistic_values[most_likely_cipher]

							# Return the data on the most likely shift
							return (
							    most_likely_cipher,
							    most_likely_cipher_chi_squared_value,
							    decoded_most_likely_cipher,
							)







import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
lowerCamelCase : List[Any]						   =       logging.get_logger(__name__)
def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						str=False       ,       lowerCAmelCase_       :						Any=False      ):
							__lowercase      :							Any				=     """backbone.""" if is_semantic else """"""

							__lowercase      :							Optional[Any]				=     []
							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"""),
							        (F"{prefix}pos_embed", """beit.embeddings.position_embeddings"""),
							    ]      )

							if has_lm_head:
														# mask token + layernorm
														rename_keys.extend(
														    [
														        ("""mask_token""", """beit.embeddings.mask_token"""),
														        ("""norm.weight""", """layernorm.weight"""),
														        ("""norm.bias""", """layernorm.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       snake_case_		(     lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						Any=False       ,       lowerCAmelCase_       :						List[Any]=False      ):
							for i in range(config.num_hidden_layers      ):
														__lowercase      :							Tuple				=     """backbone.""" if is_semantic else """"""
														# queries, keys and values
														__lowercase      :							int				=     state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"      )
														__lowercase      :							Dict				=     state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"      )
														__lowercase      :							int				=     state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"      )

														__lowercase      :							List[str]				=     in_proj_weight[
														    : config.hidden_size, :
														]
														__lowercase      :							Union[str, Any]				=     q_bias
														__lowercase      :							Any				=     in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowercase      :							Union[str, Any]				=     in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowercase      :							str				=     v_bias

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

														__lowercase      :							List[str]				=     gamma_a
														__lowercase      :							Optional[int]				=     gamma_a
def       snake_case_		(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						int      ):
							__lowercase      :							Tuple				=     dct.pop(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     val
def       snake_case_		(     ):
							__lowercase      :							Optional[int]				=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							__lowercase      :							Any				=     Image.open(requests.get(lowerCAmelCase_       ,       stream=lowerCAmelCase_      ).raw      )
							return im
@torch.no_grad()
def       snake_case_		(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Optional[int]=False      ):
							__lowercase      :							Dict				=     False if """rvlcdip""" in checkpoint_url else True
							__lowercase      :							Tuple				=     BeitConfig(use_absolute_position_embeddings=lowerCAmelCase_       ,       use_mask_token=lowerCAmelCase_      )

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														__lowercase      :							Union[str, Any]				=     1024
														__lowercase      :							Optional[int]				=     4096
														__lowercase      :							List[Any]				=     24
														__lowercase      :							Dict				=     16

							# labels
							if "rvlcdip" in checkpoint_url:
														__lowercase      :							Optional[int]				=     16
														__lowercase      :							Any				=     """huggingface/label-files"""
														__lowercase      :							Union[str, Any]				=     """rvlcdip-id2label.json"""
														__lowercase      :							List[str]				=     json.load(open(hf_hub_download(lowerCAmelCase_       ,       lowerCAmelCase_       ,       repo_type="""dataset"""      )       ,       """r"""      )      )
														__lowercase      :							Optional[int]				=     {int(lowerCAmelCase_      ): v for k, v in idalabel.items()}
														__lowercase      :							Union[str, Any]				=     idalabel
														__lowercase      :							Optional[Any]				=     {v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							__lowercase      :							Optional[int]				=     torch.hub.load_state_dict_from_url(lowerCAmelCase_       ,       map_location="""cpu"""      )["""model"""]

							__lowercase      :							Union[str, Any]				=     create_rename_keys(lowerCAmelCase_       ,       has_lm_head=lowerCAmelCase_      )
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )
							read_in_q_k_v(lowerCAmelCase_       ,       lowerCAmelCase_       ,       has_lm_head=lowerCAmelCase_      )

							# load HuggingFace model
							__lowercase      :							Dict				=     BeitForMaskedImageModeling(lowerCAmelCase_      ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_      )
							model.eval()
							model.load_state_dict(lowerCAmelCase_      )

							# Check outputs on an image
							__lowercase      :							List[str]				=     BeitImageProcessor(
							    size=config.image_size       ,       resample=PILImageResampling.BILINEAR       ,       do_center_crop=lowerCAmelCase_      )
							__lowercase      :							List[str]				=     prepare_img()

							__lowercase      :							Optional[Any]				=     image_processor(images=lowerCAmelCase_       ,       return_tensors="""pt"""      )
							__lowercase      :							Optional[int]				=     encoding["""pixel_values"""]

							__lowercase      :							str				=     model(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     outputs.logits

							# verify logits
							__lowercase      :							str				=     [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
							assert logits.shape == torch.Size(lowerCAmelCase_      ), "Shape of logits not as expected"

							Path(lowerCAmelCase_      ).mkdir(exist_ok=lowerCAmelCase_      )
							print(F"Saving model to {pytorch_dump_folder_path}"      )
							model.save_pretrained(lowerCAmelCase_      )
							print(F"Saving image processor to {pytorch_dump_folder_path}"      )
							image_processor.save_pretrained(lowerCAmelCase_      )

							if push_to_hub:
														if has_lm_head:
																					__lowercase      :							Optional[Any]				=     """dit-base""" if """base""" in checkpoint_url else """dit-large"""
														else:
																					__lowercase      :							Tuple				=     """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
														image_processor.push_to_hub(
														    repo_path_or_name=Path(lowerCAmelCase_       ,       lowerCAmelCase_      )       ,       organization="""nielsr"""       ,       commit_message="""Add image processor"""       ,       use_temp_dir=lowerCAmelCase_       ,       )
														model.push_to_hub(
														    repo_path_or_name=Path(lowerCAmelCase_       ,       lowerCAmelCase_      )       ,       organization="""nielsr"""       ,       commit_message="""Add model"""       ,       use_temp_dir=lowerCAmelCase_       ,       )


if __name__ == "__main__":
	lowerCamelCase : List[str]						   =       argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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.'''
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	)
	lowerCamelCase : List[str]						   =       parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)