






'''simple docstring'''

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()
_A				:    str     						=logging.get_logger(__name__)







def 						__UpperCamelCase				(							_lowercase,				_lowercase=False,				_lowercase=False					) ->			Any:
	_lowercase					:       str       =		'''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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase=False,				_lowercase=False					) ->			Union[str, Any]:







	for i in range(config.num_hidden_layers					):
		_lowercase					:       Optional[Any]       =		'''backbone.''' if is_semantic else ''''''
		# queries, keys and values
		_lowercase					:       str       =		state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''					)
		_lowercase					:       Dict       =		state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''					)
		_lowercase					:       Dict       =		state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''					)

		_lowercase					:       Dict       =		in_proj_weight[
		    : config.hidden_size, :
		]
		_lowercase					:       int       =		q_bias
		_lowercase					:       Optional[int]       =		in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		_lowercase					:       Optional[Any]       =		in_proj_weight[
		    -config.hidden_size :, :
		]
		_lowercase					:       Tuple       =		v_bias

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

		_lowercase					:       Dict       =		gamma_a
		_lowercase					:       str       =		gamma_a







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Tuple:
	_lowercase					:       int       =		dct.pop(__UpperCamelCase					)
	_lowercase					:       Any       =		val







def 						__UpperCamelCase				(							) ->			Any:
	_lowercase					:       Dict       =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	_lowercase					:       int       =		Image.open(requests.get(__UpperCamelCase,				stream=__UpperCamelCase					).raw					)
	return im







@torch.no_grad()
def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase=False					) ->			str:
	_lowercase					:       List[str]       =		False if '''rvlcdip''' in checkpoint_url else True
	_lowercase					:       int       =		BeitConfig(use_absolute_position_embeddings=__UpperCamelCase,				use_mask_token=__UpperCamelCase					)

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

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

	# load state_dict of original model, remove and rename some keys
	_lowercase					:       int       =		torch.hub.load_state_dict_from_url(__UpperCamelCase,				map_location='cpu'					)['''model''']

	_lowercase					:       List[str]       =		create_rename_keys(__UpperCamelCase,				has_lm_head=__UpperCamelCase					)
	for src, dest in rename_keys:
		rename_key(__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase					)
	read_in_q_k_v(__UpperCamelCase,				__UpperCamelCase,				has_lm_head=__UpperCamelCase					)

	# load HuggingFace model
	_lowercase					:       Optional[Any]       =		BeitForMaskedImageModeling(__UpperCamelCase					) if has_lm_head else BeitForImageClassification(__UpperCamelCase					)
	model.eval()
	model.load_state_dict(__UpperCamelCase					)

	# Check outputs on an image
	_lowercase					:       Optional[int]       =		BeitImageProcessor(
	    size=config.image_size,				resample=PILImageResampling.BILINEAR,				do_center_crop=__UpperCamelCase					)
	_lowercase					:       Optional[Any]       =		prepare_img()

	_lowercase					:       Any       =		image_processor(images=__UpperCamelCase,				return_tensors='pt'					)
	_lowercase					:       List[str]       =		encoding['''pixel_values''']

	_lowercase					:       Union[str, Any]       =		model(__UpperCamelCase					)
	_lowercase					:       Dict       =		outputs.logits

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

	Path(__UpperCamelCase					).mkdir(exist_ok=__UpperCamelCase					)
	print(f'''Saving model 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 push_to_hub:
		if has_lm_head:
			_lowercase					:       int       =		'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
		else:
			_lowercase					:       Dict       =		'''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(__UpperCamelCase,				__UpperCamelCase					),				organization='nielsr',				commit_message='Add image processor',				use_temp_dir=__UpperCamelCase,				)
		model.push_to_hub(
		    repo_path_or_name=Path(__UpperCamelCase,				__UpperCamelCase					),				organization='nielsr',				commit_message='Add model',				use_temp_dir=__UpperCamelCase,				)


if __name__ == "__main__":
		_A				:    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''',
		)
		_A				:    str     						=parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 						__UpperCamelCase				(							_lowercase					) ->			None:
	_lowercase  ,    _lowercase					:       List[Any]       =		analyze_text(_lowercase					)
	_lowercase					:       Any       =		list(' ' + ascii_lowercase					)
	# what is our total sum of probabilities.
	_lowercase					:       Union[str, Any]       =		sum(single_char_strings.values()					)

	# one length string
	_lowercase					:       Union[str, Any]       =		0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			_lowercase					:       Any       =		single_char_strings[ch]
			_lowercase					:       int       =		my_str / all_sum
			my_fir_sum += prob * math.loga(_lowercase					)  # entropy formula.

    # print entropy
	print(f'''{round(-1 * my_fir_sum					):.1f}'''					)

	# two len string
	_lowercase					:       str       =		sum(two_char_strings.values()					)
	_lowercase					:       str       =		0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			_lowercase					:       Optional[Any]       =		cha + cha
			if sequence in two_char_strings:
				_lowercase					:       int       =		two_char_strings[sequence]
				_lowercase					:       Optional[int]       =		int(_lowercase					) / all_sum
				my_sec_sum += prob * math.loga(_lowercase					)

    # print second entropy
	print(f'''{round(-1 * my_sec_sum					):.1f}'''					)

	# print the difference between them
	print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'''					)







def 						__UpperCamelCase				(							_lowercase					) ->			tuple[dict, dict]:
	_lowercase					:       Optional[Any]       =		Counter()  # type: ignore
	_lowercase					:       List[Any]       =		Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0,				len(_lowercase					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings







def 						__UpperCamelCase				(							) ->			List[Any]:
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
		main()
