
'''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()
lowercase    :							Optional[Any]      =					logging.get_logger(__name__)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=False	)       ->	int:
	_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'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"
		_snake_case   						=		[(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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A=False	)       ->	Union[str, Any]:
	for i in range(config.num_hidden_layers	):
		if base_model:
			_snake_case   						=		''
		else:
			_snake_case   						=		'vit.'
		# 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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	List[str]:
	_snake_case   						=		['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__A			,				__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Optional[int]:
	_snake_case   						=		dct.pop(__A	)
	_snake_case   						=		val







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Any:
	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)
	return im







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A=True	)       ->	Optional[int]:
	_snake_case   						=		ViTConfig()
	# patch_size
	if model_name[-1] == "8":
		_snake_case   						=		8
	# set labels if required
	if not base_model:
		_snake_case   						=		1_000
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'imagenet-1k-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}
	# size of the architecture
	if model_name in ["dino_vits8", "dino_vits16"]:
		_snake_case   						=		384
		_snake_case   						=		1_536
		_snake_case   						=		12
		_snake_case   						=		6

	# load original model from torch hub
	_snake_case   						=		torch.hub.load('facebookresearch/dino:main'			,				__A	)
	original_model.eval()

	# load state_dict of original model, remove and rename some keys
	_snake_case   						=		original_model.state_dict()
	if base_model:
		remove_classification_head_(__A	)
	_snake_case   						=		create_rename_keys(__A			,				base_model=__A	)
	for src, dest in rename_keys:
		rename_key(__A			,				__A			,				__A	)
	read_in_q_k_v(__A			,				__A			,				__A	)

	# load HuggingFace model
	if base_model:
		_snake_case   						=		ViTModel(__A			,				add_pooling_layer=__A	).eval()
	else:
		_snake_case   						=		ViTForImageClassification(__A	).eval()
	model.load_state_dict(__A	)

	# Check outputs on an image, prepared by ViTImageProcessor
	_snake_case   						=		ViTImageProcessor()
	_snake_case   						=		image_processor(images=prepare_img()			,				return_tensors='pt'	)
	_snake_case   						=		encoding['pixel_values']
	_snake_case   						=		model(__A	)

	if base_model:
		_snake_case   						=		original_model(__A	)
		assert torch.allclose(__A			,				outputs.last_hidden_state[:, 0, :]			,				atol=1e-1	)
	else:
		_snake_case   						=		original_model(__A	)
		assert logits.shape == outputs.logits.shape
		assert torch.allclose(__A			,				outputs.logits			,				atol=1e-3	)

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


if __name__ == "__main__":
					lowercase    :							List[str]      =					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)
					lowercase    :							Union[str, Any]      =					parser.parse_args()
					convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[Any]	    =    (DDPMScheduler,)


							def 				__lowercase				(			self       ,							**_a       )    ->    Any:
												_a	:    List[Any]         =		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''variance_type''': '''fixed_small''',
												    '''clip_sample''': True,
												}

												config.update(**_a       )
												return config


							def 				__lowercase				(			self       )    ->    Any:
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,							[0.002, 0.02, 0.2, 2]       ):
																	self.check_over_configs(beta_start=_a       ,							beta_end=_a       )


							def 				__lowercase				(			self       )    ->    List[str]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=_a       )


							def 				__lowercase				(			self       )    ->    str:
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												self.check_over_configs(thresholding=_a       )
												for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=_a       ,							prediction_type=_a       ,							sample_max_value=_a       ,							)


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=_a       )


							def 				__lowercase				(			self       )    ->    int:
												for t in [0, 5_0_0, 9_9_9]:
																	self.check_over_forward(time_step=_a       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    List[Any]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_0979       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1e-5


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    int         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    Optional[int]         =		len(_a       )

												_a	:    Optional[Any]         =		self.dummy_model()
												_a	:    str         =		self.dummy_sample_deter
												_a	:    Union[str, Any]         =		torch.manual_seed(0       )

												for t in reversed(range(_a       )       ):
																	# 1. predict noise residual
																	_a	:    str         =		model(_a       ,							_a       )

																	# 2. predict previous mean of sample x_t-1
																	_a	:    Optional[int]         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    List[Any]         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Optional[Any]         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 258.9606       ) < 1e-2
												assert abs(result_mean.item() - 0.3372       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Optional[int]         =		self.scheduler_classes[0]
												_a	:    Optional[Any]         =		self.get_scheduler_config(prediction_type='''v_prediction'''       )
												_a	:    Union[str, Any]         =		scheduler_class(**_a       )

												_a	:    Dict         =		len(_a       )

												_a	:    int         =		self.dummy_model()
												_a	:    Tuple         =		self.dummy_sample_deter
												_a	:    List[Any]         =		torch.manual_seed(0       )

												for t in reversed(range(_a       )       ):
																	# 1. predict noise residual
																	_a	:    Dict         =		model(_a       ,							_a       )

																	# 2. predict previous mean of sample x_t-1
																	_a	:    int         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    str         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Tuple         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 202.0296       ) < 1e-2
												assert abs(result_mean.item() - 0.2631       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    Union[str, Any]         =		self.scheduler_classes[0]
												_a	:    Tuple         =		self.get_scheduler_config()
												_a	:    Any         =		scheduler_class(**_a       )

												_a	:    Optional[Any]         =		[1_0_0, 8_7, 5_0, 1, 0]

												scheduler.set_timesteps(timesteps=_a       )

												_a	:    Optional[int]         =		scheduler.timesteps

												for i, timestep in enumerate(_a       ):
																	if i == len(_a       ) - 1:
																						_a	:    Dict         =		-1
																	else:
																						_a	:    Tuple         =		timesteps[i + 1]

																	_a	:    Optional[Any]         =		scheduler.previous_timestep(_a       )
																	_a	:    Optional[Any]         =		prev_t.item()

																	self.assertEqual(_a       ,							_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Tuple         =		scheduler_class(**_a       )

												_a	:    str         =		[1_0_0, 8_7, 5_0, 5_1, 0]

												with self.assertRaises(_a       ,							msg='''`custom_timesteps` must be in descending order.'''       ):
																	scheduler.set_timesteps(timesteps=_a       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    List[str]         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												_a	:    Union[str, Any]         =		[1_0_0, 8_7, 5_0, 1, 0]
												_a	:    Optional[Any]         =		len(_a       )

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




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    Union[str, Any]         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    str         =		[scheduler.config.num_train_timesteps]

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



