




import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=False     ):
							SCREAMING_SNAKE_CASE_   	=    []
							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"deit.encoder.layer.{i}.layernorm_before.weight")     )
														rename_keys.append((F"blocks.{i}.norm1.bias", F"deit.encoder.layer.{i}.layernorm_before.bias")     )
														rename_keys.append((F"blocks.{i}.attn.proj.weight", F"deit.encoder.layer.{i}.attention.output.dense.weight")     )
														rename_keys.append((F"blocks.{i}.attn.proj.bias", F"deit.encoder.layer.{i}.attention.output.dense.bias")     )
														rename_keys.append((F"blocks.{i}.norm2.weight", F"deit.encoder.layer.{i}.layernorm_after.weight")     )
														rename_keys.append((F"blocks.{i}.norm2.bias", F"deit.encoder.layer.{i}.layernorm_after.bias")     )
														rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"deit.encoder.layer.{i}.intermediate.dense.weight")     )
														rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"deit.encoder.layer.{i}.intermediate.dense.bias")     )
														rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"deit.encoder.layer.{i}.output.dense.weight")     )
														rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"deit.encoder.layer.{i}.output.dense.bias")     )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('''cls_token''', '''deit.embeddings.cls_token'''),
							        ('''dist_token''', '''deit.embeddings.distillation_token'''),
							        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
							        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
							        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
							    ]     )

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''layernorm.weight'''),
														        ('''norm.bias''', '''layernorm.bias'''),
														        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
														        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
														    ]     )

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

							return rename_keys

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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    dct.pop(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    val

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE_   	=    Image.open(requests.get(_lowerCAmelCase  ,    stream=_lowerCAmelCase     ).raw     )
							return im

@torch.no_grad()
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    DeiTConfig()
							# all deit models have fine-tuned heads
							SCREAMING_SNAKE_CASE_   	=    False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							SCREAMING_SNAKE_CASE_   	=    1_0_0_0
							SCREAMING_SNAKE_CASE_   	=    '''huggingface/label-files'''
							SCREAMING_SNAKE_CASE_   	=    '''imagenet-1k-id2label.json'''
							SCREAMING_SNAKE_CASE_   	=    json.load(open(hf_hub_download(_lowerCAmelCase  ,    _lowerCAmelCase  ,    repo_type='''dataset'''     )  ,    '''r'''     )     )
							SCREAMING_SNAKE_CASE_   	=    {int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE_   	=    idalabel
							SCREAMING_SNAKE_CASE_   	=    {v: k for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE_   	=    int(deit_name[-6:-4]     )
							SCREAMING_SNAKE_CASE_   	=    int(deit_name[-3:]     )
							# size of the architecture
							if deit_name[9:].startswith('''tiny'''     ):
														SCREAMING_SNAKE_CASE_   	=    1_9_2
														SCREAMING_SNAKE_CASE_   	=    7_6_8
														SCREAMING_SNAKE_CASE_   	=    1_2
														SCREAMING_SNAKE_CASE_   	=    3
							elif deit_name[9:].startswith('''small'''     ):
														SCREAMING_SNAKE_CASE_   	=    3_8_4
														SCREAMING_SNAKE_CASE_   	=    1_5_3_6
														SCREAMING_SNAKE_CASE_   	=    1_2
														SCREAMING_SNAKE_CASE_   	=    6
							if deit_name[9:].startswith('''base'''     ):
														pass
							elif deit_name[4:].startswith('''large'''     ):
														SCREAMING_SNAKE_CASE_   	=    1_0_2_4
														SCREAMING_SNAKE_CASE_   	=    4_0_9_6
														SCREAMING_SNAKE_CASE_   	=    2_4
														SCREAMING_SNAKE_CASE_   	=    1_6

							# load original model from timm
							SCREAMING_SNAKE_CASE_   	=    timm.create_model(_lowerCAmelCase  ,    pretrained=_lowerCAmelCase     )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							SCREAMING_SNAKE_CASE_   	=    timm_model.state_dict()
							SCREAMING_SNAKE_CASE_   	=    create_rename_keys(_lowerCAmelCase  ,    _lowerCAmelCase     )
							for src, dest in rename_keys:
														rename_key(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							read_in_q_k_v(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )

							# load HuggingFace model
							SCREAMING_SNAKE_CASE_   	=    DeiTForImageClassificationWithTeacher(_lowerCAmelCase     ).eval()
							model.load_state_dict(_lowerCAmelCase     )

							# Check outputs on an image, prepared by DeiTImageProcessor
							SCREAMING_SNAKE_CASE_   	=    int(
							    (2_5_6 / 2_2_4) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							SCREAMING_SNAKE_CASE_   	=    DeiTImageProcessor(size=_lowerCAmelCase  ,    crop_size=config.image_size     )
							SCREAMING_SNAKE_CASE_   	=    image_processor(images=prepare_img()  ,    return_tensors='''pt'''     )
							SCREAMING_SNAKE_CASE_   	=    encoding['''pixel_values''']
							SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )

							SCREAMING_SNAKE_CASE_   	=    timm_model(_lowerCAmelCase     )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCAmelCase  ,    outputs.logits  ,    atol=1e-3     )

							Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
							print(F"Saving model {deit_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__":
			__SCREAMING_SNAKE_CASE 						=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--deit_name""",
			    default="""vit_deit_base_distilled_patch16_224""",
			    type=str,
			    help="""Name of the DeiT 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."""
			)

			__SCREAMING_SNAKE_CASE 						=parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    [], []
							while len(_lowerCAmelCase     ) > 1:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    min(_lowerCAmelCase     ), max(_lowerCAmelCase     )
														start.append(_lowerCAmelCase     )
														end.append(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
							end.reverse()
							return start + collection + end


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=input("""Enter numbers separated by a comma:\n""").strip()
			__SCREAMING_SNAKE_CASE 						=[int(item) for item in user_input.split(""",""")]
			print(*merge_sort(unsorted), sep=""",""")


