



'''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()
lowerCAmelCase	:		str				=   logging.get_logger(__name__)



def   __lowerCAmelCase					(							lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						List[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   __lowerCAmelCase					(							lowerCamelCase   :						Tuple				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						Union[str, Any]=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   __lowerCAmelCase					(							lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						Tuple				,		lowerCamelCase   :						Optional[Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  dct.pop(__lowercase					)
	__lowerCAmelCase												=  val



def   __lowerCAmelCase					(							):






	'''simple docstring'''




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



@torch.no_grad()
def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Union[str, Any]					):






	'''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												=  10_00
	__lowerCAmelCase												=  'huggingface/label-files'
	__lowerCAmelCase												=  'imagenet-1k-id2label.json'
	__lowerCAmelCase												=  json.load(open(hf_hub_download(__lowercase				,		__lowercase				,		repo_type="dataset"					)				,		"r"					)					)
	__lowerCAmelCase												=  {int(__lowercase					): 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												=  1_92
		__lowerCAmelCase												=  7_68
		__lowerCAmelCase												=  12
		__lowerCAmelCase												=  3
	elif deit_name[9:].startswith("small"					):
		__lowerCAmelCase												=  3_84
		__lowerCAmelCase												=  15_36
		__lowerCAmelCase												=  12
		__lowerCAmelCase												=  6
	if deit_name[9:].startswith("base"					):
		pass
	elif deit_name[4:].startswith("large"					):
		__lowerCAmelCase												=  10_24
		__lowerCAmelCase												=  40_96
		__lowerCAmelCase												=  24
		__lowerCAmelCase												=  16

	# load original model from timm
	__lowerCAmelCase												=  timm.create_model(__lowercase				,		pretrained=__lowercase					)
	timm_model.eval()

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

	# load HuggingFace model
	__lowerCAmelCase												=  DeiTForImageClassificationWithTeacher(__lowercase					).eval()
	model.load_state_dict(__lowercase					)

	# Check outputs on an image, prepared by DeiTImageProcessor
	__lowerCAmelCase												=  int(
	    (2_56 / 2_24) * 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=__lowercase				,		crop_size=config.image_size					)
	__lowerCAmelCase												=  image_processor(images=prepare_img()				,		return_tensors="pt"					)
	__lowerCAmelCase												=  encoding['pixel_values']
	__lowerCAmelCase												=  model(__lowercase					)

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

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


if __name__ == "__main__":
	lowerCAmelCase	:		Union[str, Any]				=   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.'''
	)

	lowerCAmelCase	:		Optional[int]				=   parser.parse_args()
	convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  self.scheduler_classes[0](**UpperCamelCase     )

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

						self.assertEqual(output_a.shape			,				sample.shape     )
						self.assertEqual(output_a.shape			,				output_a.shape     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )