"""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()
SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)





def    lowerCAmelCase__    (			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				List[str]=False					)					->				List[str]:





						"""simple docstring"""






						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"
												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    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				int=False					)					->				Optional[int]:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Optional[int]					)					->				Any:





						"""simple docstring"""






						snake_case								=			dct.pop(_UpperCamelCase					)
						snake_case								=			val





def    lowerCAmelCase__    (			)					->				Tuple:





						"""simple docstring"""






						snake_case								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						snake_case								=			Image.open(requests.get(_UpperCamelCase ,			stream=_UpperCamelCase					).raw					)
						return im





@torch.no_grad()
def    lowerCAmelCase__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				List[str]					)					->				Tuple:





						"""simple docstring"""






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

						# load original model from timm
						snake_case								=			timm.create_model(_UpperCamelCase ,			pretrained=_UpperCamelCase					)
						timm_model.eval()

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

						# load HuggingFace model
						snake_case								=			DeiTForImageClassificationWithTeacher(_UpperCamelCase					).eval()
						model.load_state_dict(_UpperCamelCase					)

						# Check outputs on an image, prepared by DeiTImageProcessor
						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
						snake_case								=			DeiTImageProcessor(size=_UpperCamelCase ,			crop_size=config.image_size					)
						snake_case								=			image_processor(images=prepare_img() ,			return_tensors='pt'					)
						snake_case								=			encoding['pixel_values']
						snake_case								=			model(_UpperCamelCase					)

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

						Path(_UpperCamelCase					).mkdir(exist_ok=_UpperCamelCase					)
						print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""					)
						model.save_pretrained(_UpperCamelCase					)
						print(f"""Saving image processor to {pytorch_dump_folder_path}"""					)
						image_processor.save_pretrained(_UpperCamelCase					)


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)



"""simple docstring"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					)					->				float:





						"""simple docstring"""






						if initial_intensity < 0:
												raise ValueError('The value of intensity cannot be negative'					)
												# handling of negative values of initial intensity
						if angle < 0 or angle > 3_6_0:
												raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'					)
												# handling of values out of allowed range
						return initial_intensity * (math.cos(math.radians(_UpperCamelCase					)					) ** 2)


if __name__ == "__main__":
					import doctest

					doctest.testmod(name="malus_law")


