




import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		SwinConfig()
							lowerCAmelCase   =		swin_name.split('''_'''							)

							lowerCAmelCase   =		name_split[1]
							lowerCAmelCase   =		int(name_split[4]							)
							lowerCAmelCase   =		int(name_split[3][-1]							)

							if model_size == "tiny":
														lowerCAmelCase   =		96
														lowerCAmelCase   =		(2, 2, 6, 2)
														lowerCAmelCase   =		(3, 6, 12, 24)
							elif model_size == "small":
														lowerCAmelCase   =		96
														lowerCAmelCase   =		(2, 2, 18, 2)
														lowerCAmelCase   =		(3, 6, 12, 24)
							elif model_size == "base":
														lowerCAmelCase   =		128
														lowerCAmelCase   =		(2, 2, 18, 2)
														lowerCAmelCase   =		(4, 8, 16, 32)
							else:
														lowerCAmelCase   =		192
														lowerCAmelCase   =		(2, 2, 18, 2)
														lowerCAmelCase   =		(6, 12, 24, 48)

							if "in22k" in swin_name:
														lowerCAmelCase   =		21841
							else:
														lowerCAmelCase   =		1000
														lowerCAmelCase   =		'''huggingface/label-files'''
														lowerCAmelCase   =		'''imagenet-1k-id2label.json'''
														lowerCAmelCase   =		json.load(open(hf_hub_download(_snake_case						, _snake_case						, repo_type='''dataset'''							)						, '''r'''							)							)
														lowerCAmelCase   =		{int(_snake_case							): v for k, v in idalabel.items()}
														lowerCAmelCase   =		idalabel
														lowerCAmelCase   =		{v: k for k, v in idalabel.items()}

							lowerCAmelCase   =		img_size
							lowerCAmelCase   =		num_classes
							lowerCAmelCase   =		embed_dim
							lowerCAmelCase   =		depths
							lowerCAmelCase   =		num_heads
							lowerCAmelCase   =		window_size

							return config



def       UpperCAmelCase       (     _snake_case							):
							if "patch_embed.proj" in name:
														lowerCAmelCase   =		name.replace('''patch_embed.proj'''						, '''embeddings.patch_embeddings.projection'''							)
							if "patch_embed.norm" in name:
														lowerCAmelCase   =		name.replace('''patch_embed.norm'''						, '''embeddings.norm'''							)
							if "layers" in name:
														lowerCAmelCase   =		'''encoder.''' + name
							if "attn.proj" in name:
														lowerCAmelCase   =		name.replace('''attn.proj'''						, '''attention.output.dense'''							)
							if "attn" in name:
														lowerCAmelCase   =		name.replace('''attn'''						, '''attention.self'''							)
							if "norm1" in name:
														lowerCAmelCase   =		name.replace('''norm1'''						, '''layernorm_before'''							)
							if "norm2" in name:
														lowerCAmelCase   =		name.replace('''norm2'''						, '''layernorm_after'''							)
							if "mlp.fc1" in name:
														lowerCAmelCase   =		name.replace('''mlp.fc1'''						, '''intermediate.dense'''							)
							if "mlp.fc2" in name:
														lowerCAmelCase   =		name.replace('''mlp.fc2'''						, '''output.dense'''							)

							if name == "norm.weight":
														lowerCAmelCase   =		'''layernorm.weight'''
							if name == "norm.bias":
														lowerCAmelCase   =		'''layernorm.bias'''

							if "head" in name:
														lowerCAmelCase   =		name.replace('''head'''						, '''classifier'''							)
							else:
														lowerCAmelCase   =		'''swin.''' + name

							return name



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							for key in orig_state_dict.copy().keys():
														lowerCAmelCase   =		orig_state_dict.pop(_snake_case							)

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					lowerCAmelCase   =		key.split('''.'''							)
																					lowerCAmelCase   =		int(key_split[1]							)
																					lowerCAmelCase   =		int(key_split[3]							)
																					lowerCAmelCase   =		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												lowerCAmelCase   =		val[:dim, :]
																												lowerCAmelCase   =		val[
																												    dim : dim * 2, :
																												]
																												lowerCAmelCase   =		val[-dim:, :]
																					else:
																												lowerCAmelCase   =		val[
																												    :dim
																												]
																												lowerCAmelCase   =		val[
																												    dim : dim * 2
																												]
																												lowerCAmelCase   =		val[
																												    -dim:
																												]
														else:
																					lowerCAmelCase   =		val

							return orig_state_dict



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							lowerCAmelCase   =		timm.create_model(_snake_case						, pretrained=_snake_case							)
							timm_model.eval()

							lowerCAmelCase   =		get_swin_config(_snake_case							)
							lowerCAmelCase   =		SwinForImageClassification(_snake_case							)
							model.eval()

							lowerCAmelCase   =		convert_state_dict(timm_model.state_dict()						, _snake_case							)
							model.load_state_dict(_snake_case							)

							lowerCAmelCase   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''

							lowerCAmelCase   =		AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''						, '''-'''							)							)							)
							lowerCAmelCase   =		Image.open(requests.get(_snake_case						, stream=_snake_case							).raw							)
							lowerCAmelCase   =		image_processor(images=_snake_case						, return_tensors='''pt'''							)

							lowerCAmelCase   =		timm_model(inputs['''pixel_values''']							)
							lowerCAmelCase   =		model(**_snake_case							).logits

							assert torch.allclose(_snake_case						, _snake_case						, atol=1E-3							)

							print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""							)
							model.save_pretrained(_snake_case							)

							print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
							image_processor.save_pretrained(_snake_case							)


if __name__ == "__main__":
			UpperCAmelCase_						=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--swin_name""",
			    default="""swin_tiny_patch4_window7_224""",
			    type=str,
			    help="""Name of the Swin timm model you'd like to convert.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
			)

			UpperCAmelCase_						=parser.parse_args()
			convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							lowerCAmelCase   =		list(''' ''' + ascii_lowercase							)
							# what is our total sum of probabilities.
							lowerCAmelCase   =		sum(single_char_strings.values()							)

							# one length string
							lowerCAmelCase   =		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:
																					lowerCAmelCase   =		single_char_strings[ch]
																					lowerCAmelCase   =		my_str / all_sum
																					my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

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

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

    # 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       (     _snake_case							):
							lowerCAmelCase   =		Counter()  # type: ignore
							lowerCAmelCase   =		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(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							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()



