

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


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






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






def 							_lowerCAmelCase (      __magic_name__	:    Any	)							->     int:
						lowercase   :       Any								=['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(__magic_name__      ,   __magic_name__	)






def 							_lowerCAmelCase (      __magic_name__	:    Tuple      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    str	)							->     Union[str, Any]:
						lowercase   :       Optional[Any]								=dct.pop(__magic_name__	)
						lowercase   :       Optional[Any]								=val






def 							_lowerCAmelCase (      )							->     Dict:
						lowercase   :       List[Any]								='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase   :       List[str]								=Image.open(requests.get(__magic_name__      ,   stream=__magic_name__	).raw	)
						return im






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

						# load original model from torch hub
						lowercase   :       List[str]								=torch.hub.load('''facebookresearch/dino:main'''      ,   __magic_name__	)
						original_model.eval()

						# load state_dict of original model, remove and rename some keys
						lowercase   :       Dict								=original_model.state_dict()
						if base_model:
												remove_classification_head_(__magic_name__	)
						lowercase   :       Union[str, Any]								=create_rename_keys(__magic_name__      ,   base_model=__magic_name__	)
						for src, dest in rename_keys:
												rename_key(__magic_name__      ,   __magic_name__      ,   __magic_name__	)
						read_in_q_k_v(__magic_name__      ,   __magic_name__      ,   __magic_name__	)

						# load HuggingFace model
						if base_model:
												lowercase   :       List[str]								=ViTModel(__magic_name__      ,   add_pooling_layer=__magic_name__	).eval()
						else:
												lowercase   :       Dict								=ViTForImageClassification(__magic_name__	).eval()
						model.load_state_dict(__magic_name__	)

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

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

						Path(__magic_name__	).mkdir(exist_ok=__magic_name__	)
						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
						model.save_pretrained(__magic_name__	)
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
						image_processor.save_pretrained(__magic_name__	)


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'''





from collections import defaultdict






def 							_lowerCAmelCase (      __magic_name__	:    str      ,   __magic_name__	:    str	)							->     bool:
						lowercase   :       Optional[int]								=first_str.lower().strip()
						lowercase   :       Union[str, Any]								=second_str.lower().strip()

						# Remove whitespace
						lowercase   :       Optional[int]								=first_str.replace(''' '''      ,   ''''''	)
						lowercase   :       Optional[Any]								=second_str.replace(''' '''      ,   ''''''	)

						# Strings of different lengths are not anagrams
						if len(__magic_name__	) != len(__magic_name__	):
												return False

						# Default values for count should be 0
						lowercase   :       defaultdict[str, int]								=defaultdict(__magic_name__	)

						# For each character in input strings,
						# increment count in the corresponding
						for i in range(len(__magic_name__	)	):
												count[first_str[i]] += 1
												count[second_str[i]] -= 1

						return all(_count == 0 for _count in count.values()	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							UpperCamelCase_							=					input("""Enter the first string """).strip()
							UpperCamelCase_							=					input("""Enter the second string """).strip()

							UpperCamelCase_							=					check_anagrams(input_a, input_b)
							print(f'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')





