
"""simple docstring"""



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()
_snake_case          =						logging.get_logger(__name__)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					str=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"""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"
														_lowerCAmelCase  	=			[(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 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Optional[int]=False						):






							"""simple docstring"""



							for i in range(config.num_hidden_layers						):
														if base_model:
																					_lowerCAmelCase  	=			""
														else:
																					_lowerCAmelCase  	=			"deit."
														# 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 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			dct.pop(_lowerCamelCase						)
							_lowerCAmelCase  	=			val






def 	__snake_case  (    ):






							"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Dict						):






							"""simple docstring"""



							_lowerCAmelCase  	=			DeiTConfig()
							# all deit models have fine-tuned heads
							_lowerCAmelCase  	=			False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							_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()}
							_lowerCAmelCase  	=			int(deit_name[-6:-4]						)
							_lowerCAmelCase  	=			int(deit_name[-3:]						)
							# size of the architecture
							if deit_name[9:].startswith('tiny'						):
														_lowerCAmelCase  	=			192
														_lowerCAmelCase  	=			768
														_lowerCAmelCase  	=			12
														_lowerCAmelCase  	=			3
							elif deit_name[9:].startswith('small'						):
														_lowerCAmelCase  	=			384
														_lowerCAmelCase  	=			1536
														_lowerCAmelCase  	=			12
														_lowerCAmelCase  	=			6
							if deit_name[9:].startswith('base'						):
														pass
							elif deit_name[4:].startswith('large'						):
														_lowerCAmelCase  	=			1024
														_lowerCAmelCase  	=			4096
														_lowerCAmelCase  	=			24
														_lowerCAmelCase  	=			16

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

							# load state_dict of original model, remove and rename some keys
							_lowerCAmelCase  	=			timm_model.state_dict()
							_lowerCAmelCase  	=			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
							_lowerCAmelCase  	=			DeiTForImageClassificationWithTeacher(_lowerCamelCase						).eval()
							model.load_state_dict(_lowerCamelCase						)

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

							_lowerCAmelCase  	=			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__":
			_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.'''
			)

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








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[0 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# initialize interval's left pointer and right pointer
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							for i in range(1     ,  len(SCREAMING_SNAKE_CASE						)						):
														# case when current index is inside the interval
														if i <= right_pointer:
																					_lowerCAmelCase  	=			min(right_pointer - i + 1     ,  z_result[i - left_pointer]						)
																					_lowerCAmelCase  	=			min_edge

														while go_next(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					z_result[i] += 1

														# if new index's result gives us more right interval,
														# we've to update left_pointer and right_pointer
														if i + z_result[i] - 1 > right_pointer:
																					_lowerCAmelCase	,		_lowerCAmelCase  	=			i, i + z_result[i] - 1

							return z_result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							return i + z_result[i] < len(SCREAMING_SNAKE_CASE						) and s[z_result[i]] == s[i + z_result[i]]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0
							# concatenate 'pattern' and 'input_str' and call z_function
							# with concatenated string
							_lowerCAmelCase  	=			z_function(pattern + input_str						)

							for val in z_result:
														# if value is greater then length of the pattern string
														# that means this index is starting position of substring
														# which is equal to pattern string
														if val >= len(SCREAMING_SNAKE_CASE						):
																					answer += 1

							return answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()






