
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 				_lowerCamelCase      (       lowerCamelCase_:	List[Any]					,						lowerCamelCase_:	Any=False		):



				'''simple docstring'''







				A  :	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"""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"
								A  :	List[Any]				=					[(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 				_lowerCamelCase      (       lowerCamelCase_:	str					,						lowerCamelCase_:	int					,						lowerCamelCase_:	Optional[int]=False		):



				'''simple docstring'''







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





def 				_lowerCamelCase      (       lowerCamelCase_:	List[str]		):



				'''simple docstring'''







				A  :	Union[str, Any]				=					['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(_lowerCamelCase					,						_lowerCamelCase		)





def 				_lowerCamelCase      (       lowerCamelCase_:	str					,						lowerCamelCase_:	Any					,						lowerCamelCase_:	Optional[int]		):



				'''simple docstring'''







				A  :	int				=					dct.pop(_lowerCamelCase		)
				A  :	List[Any]				=					val





def 				_lowerCamelCase      (       ):



				'''simple docstring'''







				A  :	Optional[Any]				=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				A  :	Any				=					Image.open(requests.get(_lowerCamelCase					,						stream=_lowerCamelCase		).raw		)
				return im





@torch.no_grad()
def 				_lowerCamelCase      (       lowerCamelCase_:	str					,						lowerCamelCase_:	Optional[int]					,						lowerCamelCase_:	Dict=True		):



				'''simple docstring'''







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

				# load original model from torch hub
				A  :	str				=					torch.hub.load('''facebookresearch/dino:main'''					,						_lowerCamelCase		)
				original_model.eval()

				# load state_dict of original model, remove and rename some keys
				A  :	Dict				=					original_model.state_dict()
				if base_model:
								remove_classification_head_(_lowerCamelCase		)
				A  :	Dict				=					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:
								A  :	List[str]				=					ViTModel(_lowerCamelCase					,						add_pooling_layer=_lowerCamelCase		).eval()
				else:
								A  :	List[Any]				=					ViTForImageClassification(_lowerCamelCase		).eval()
				model.load_state_dict(_lowerCamelCase		)

				# Check outputs on an image, prepared by ViTImageProcessor
				A  :	Optional[int]				=					ViTImageProcessor()
				A  :	Dict				=					image_processor(images=prepare_img()					,						return_tensors='''pt'''		)
				A  :	Any				=					encoding['''pixel_values''']
				A  :	Optional[Any]				=					model(_lowerCamelCase		)

				if base_model:
								A  :	List[str]				=					original_model(_lowerCamelCase		)
								assert torch.allclose(_lowerCamelCase					,						outputs.last_hidden_state[:, 0, :]					,						atol=1e-1		)
				else:
								A  :	Optional[int]				=					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)






'''simple docstring'''

import string
from math import logaa





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     document.translate(
        str.maketrans(""		,			""		,			string.punctuation			)			).replace("\n"		,			""			)
    A__							=     document_without_punctuation.split(" "			)  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     corpus.lower().translate(
        str.maketrans(""		,			""		,			string.punctuation			)			)  # strip all punctuation and replace it with ''
    A__							=     corpus_without_punctuation.split("\n"			)
    A__							=     term.lower()
    return (len([doc for doc in docs if term in doc]			), len(_lowerCamelCase			))





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple=False			):
    if smoothing:
        if n == 0:
            raise ValueError("log10(0) is undefined."			)
        return round(1 + logaa(n / (1 + df)			)		,			3			)

    if df == 0:
        raise ZeroDivisionError("df must be > 0"			)
    elif n == 0:
        raise ValueError("log10(0) is undefined."			)
    return round(logaa(n / df			)		,			3			)





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    return round(tf * idf		,			3			)



