




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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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



def 				_A  (		lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   Union[str, Any]=False     ):
     """simple docstring"""

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

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ("cls_token", "vit.embeddings.cls_token"),
             ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
             ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
             ("pos_embed", "vit.embeddings.position_embeddings"),
         ]     )

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
              ]     )

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          lowerCAmelCase__            =					[(pair[0], pair[1][4:]) if pair[1].startswith("vit"     ) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("norm.weight", "vit.layernorm.weight"),
                  ("norm.bias", "vit.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]     )

     return rename_keys



def 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   str=False     ):
     """simple docstring"""

     for i in range(config.num_hidden_layers     ):
          if base_model:
               lowerCAmelCase__            =					""
          else:
               lowerCAmelCase__            =					"vit."
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          lowerCAmelCase__            =					state_dict.pop(F'blocks.{i}.attn.qkv.weight'     )
          lowerCAmelCase__            =					state_dict.pop(F'blocks.{i}.attn.qkv.bias'     )
          # next, add query, keys and values (in that order) to the state dict
          lowerCAmelCase__            =					in_proj_weight[
              : config.hidden_size, :
          ]
          lowerCAmelCase__            =					in_proj_bias[: config.hidden_size]
          lowerCAmelCase__            =					in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowerCAmelCase__            =					in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          lowerCAmelCase__            =					in_proj_weight[
              -config.hidden_size :, :
          ]
          lowerCAmelCase__            =					in_proj_bias[-config.hidden_size :]



def 				_A  (		lowerCAmelCase_	:   List[str]     ):
     """simple docstring"""

     lowerCAmelCase__            =					["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(lowerCAmelCase_  ,							lowerCAmelCase_     )



def 				_A  (		lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Union[str, Any]  ,							lowerCAmelCase_	:   int     ):
     """simple docstring"""

     lowerCAmelCase__            =					dct.pop(lowerCAmelCase_     )
     lowerCAmelCase__            =					val



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					"http://images.cocodataset.org/val2017/000000039769.jpg"
     lowerCAmelCase__            =					Image.open(requests.get(lowerCAmelCase_  ,							stream=lowerCAmelCase_     ).raw     )
     return im



@torch.no_grad()
def 				_A  (		lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   Any=True     ):
     """simple docstring"""

     lowerCAmelCase__            =					ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          lowerCAmelCase__            =					8
     # set labels if required
     if not base_model:
          lowerCAmelCase__            =					1000
          lowerCAmelCase__            =					"huggingface/label-files"
          lowerCAmelCase__            =					"imagenet-1k-id2label.json"
          lowerCAmelCase__            =					json.load(open(hf_hub_download(lowerCAmelCase_  ,							lowerCAmelCase_  ,							repo_type="dataset"     )  ,							"r"     )     )
          lowerCAmelCase__            =					{int(lowerCAmelCase_     ): v for k, v in idalabel.items()}
          lowerCAmelCase__            =					idalabel
          lowerCAmelCase__            =					{v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          lowerCAmelCase__            =					384
          lowerCAmelCase__            =					1536
          lowerCAmelCase__            =					12
          lowerCAmelCase__            =					6

     # load original model from torch hub
     lowerCAmelCase__            =					torch.hub.load("facebookresearch/dino:main"  ,							lowerCAmelCase_     )
     original_model.eval()

     # load state_dict of original model, remove and rename some keys
     lowerCAmelCase__            =					original_model.state_dict()
     if base_model:
          remove_classification_head_(lowerCAmelCase_     )
     lowerCAmelCase__            =					create_rename_keys(lowerCAmelCase_  ,							base_model=lowerCAmelCase_     )
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )
     read_in_q_k_v(lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )

     # load HuggingFace model
     if base_model:
          lowerCAmelCase__            =					ViTModel(lowerCAmelCase_  ,							add_pooling_layer=lowerCAmelCase_     ).eval()
     else:
          lowerCAmelCase__            =					ViTForImageClassification(lowerCAmelCase_     ).eval()
     model.load_state_dict(lowerCAmelCase_     )

     # Check outputs on an image, prepared by ViTImageProcessor
     lowerCAmelCase__            =					ViTImageProcessor()
     lowerCAmelCase__            =					image_processor(images=prepare_img()  ,							return_tensors="pt"     )
     lowerCAmelCase__            =					encoding["pixel_values"]
     lowerCAmelCase__            =					model(lowerCAmelCase_     )

     if base_model:
          lowerCAmelCase__            =					original_model(lowerCAmelCase_     )
          assert torch.allclose(lowerCAmelCase_  ,							outputs.last_hidden_state[:, 0, :]  ,							atol=1E-1     )
     else:
          lowerCAmelCase__            =					original_model(lowerCAmelCase_     )
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(lowerCAmelCase_  ,							outputs.logits  ,							atol=1E-3     )

     Path(lowerCAmelCase_     ).mkdir(exist_ok=lowerCAmelCase_     )
     print(F'Saving model {model_name} 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 __name__ == "__main__":
     UpperCamelCase								=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='dino_vitb16',
         type=str,
         help='Name of the model trained with DINO you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--base_model',
         action='store_true',
         help='Whether to only convert the base model (no projection head weights).',
     )

     parser.set_defaults(base_model=True)
     UpperCamelCase								=	parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    None:
					__lowerCAmelCase ,						__lowerCAmelCase						 =analyze_text(__lowerCamelCase						)
					__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(__lowerCamelCase						)  # 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(__lowerCamelCase						) / all_sum
																				my_sec_sum += prob * math.loga(__lowerCamelCase						)

    # 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        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    tuple[dict, dict]:
					__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(__lowerCamelCase						) - 1						):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings






def        __lowerCAmelCase		(	)	->    str:
					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()




