





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 			__lowerCAmelCase				(					A__			):


	'''simple docstring'''





	_A       	=							42
	_A       	=							42


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int      ,						_lowercase    :   Tuple=0.999      ,						_lowercase    :   str="cosine"      ,						)      ->       Optional[int]:




		'''simple docstring'''







		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_lowercase    :   int ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(_lowercase    :   Optional[Any] ):
						return math.exp(t * -12.0 )

		else:
				raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )

		lowercase__				:			Optional[int]            =		[]
		for i in range(_lowercase ):
				lowercase__				:			List[Any]            =		i / num_diffusion_timesteps
				lowercase__				:			Optional[Any]            =		(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(_lowercase ) / alpha_bar_fn(_lowercase )      ,						_lowercase ) )
		return torch.tensor(_lowercase      ,						dtype=torch.floataa )





class 			__lowerCAmelCase				(					A__      ,						A__			):


	'''simple docstring'''





	_A       	=							1

	@register_to_config
	def __init__(	self:	str,				lowerCamelCase_:	int = 1000,				lowerCamelCase_:	float = 0.0_0_0_1,				lowerCamelCase_:	float = 0.0_2,				lowerCamelCase_:	str = "linear",				lowerCamelCase_:	float = 0.0,				lowerCamelCase_:	Optional[np.ndarray] = None,				lowerCamelCase_:	bool = True,				):
			if trained_betas is not None:
					lowercase__				:			Optional[int]            =		torch.from_numpy(_lowerCAmelCase )
			elif beta_schedule == "linear":
					lowercase__				:			List[str]            =		torch.linspace(_lowerCAmelCase,				_lowerCAmelCase,				_lowerCAmelCase,				dtype=torch.floataa )
			elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					lowercase__				:			Optional[int]            =		(
					    torch.linspace(beta_start**0.5,				beta_end**0.5,				_lowerCAmelCase,				dtype=torch.floataa ) ** 2
					)
			elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					lowercase__				:			Tuple            =		betas_for_alpha_bar(_lowerCAmelCase )
			elif beta_schedule == "sigmoid":
					# GeoDiff sigmoid schedule
					lowercase__				:			List[str]            =		torch.linspace(-6,				6,				_lowerCAmelCase )
					lowercase__				:			Dict            =		torch.sigmoid(_lowerCAmelCase ) * (beta_end - beta_start) + beta_start
			else:
					raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )

			lowercase__				:			Tuple            =		1.0 - self.betas
			lowercase__				:			Optional[int]            =		torch.cumprod(self.alphas,				dim=0 )
			lowercase__				:			Dict            =		torch.tensor(1.0 )

			lowercase__				:			str            =		torch.tensor(1.0 )

			# standard deviation of the initial noise distribution
			lowercase__				:			Any            =		1.0

			# setable values
			lowercase__				:			List[Any]            =		None
			lowercase__				:			Optional[Any]            =		torch.from_numpy(np.arange(0,				_lowerCAmelCase )[::-1].copy() )

			lowercase__				:			Union[str, Any]            =		eta

	def       snake_case__(	self:	Any,				lowerCamelCase_:	torch.FloatTensor,				lowerCamelCase_:	Optional[int] = None ):
			return sample

	def       snake_case__(	self:	Union[str, Any],				lowerCamelCase_:	int,				lowerCamelCase_:	int = 10,				lowerCamelCase_:	int = 10,				lowerCamelCase_:	Union[str, torch.device] = None,				):
			lowercase__				:			List[str]            =		min(self.config.num_train_timesteps,				_lowerCAmelCase )
			lowercase__				:			List[str]            =		num_inference_steps

			lowercase__				:			Optional[Any]            =		[]

			lowercase__				:			str            =		{}
			for j in range(0,				num_inference_steps - jump_length,				_lowerCAmelCase ):
					lowercase__				:			List[str]            =		jump_n_sample - 1

			lowercase__				:			Dict            =		num_inference_steps
			while t >= 1:
					lowercase__				:			List[str]            =		t - 1
					timesteps.append(_lowerCAmelCase )

					if jumps.get(_lowerCAmelCase,				0 ) > 0:
							lowercase__				:			Dict            =		jumps[t] - 1
							for _ in range(_lowerCAmelCase ):
									lowercase__				:			Optional[Any]            =		t + 1
									timesteps.append(_lowerCAmelCase )

			lowercase__				:			Tuple            =		np.array(_lowerCAmelCase ) * (self.config.num_train_timesteps // self.num_inference_steps)
			lowercase__				:			List[Any]            =		torch.from_numpy(_lowerCAmelCase ).to(_lowerCAmelCase )

	def       snake_case__(	self:	Dict,				lowerCamelCase_:	Optional[Any] ):
			lowercase__				:			Optional[int]            =		t - self.config.num_train_timesteps // self.num_inference_steps

			lowercase__				:			Optional[int]            =		self.alphas_cumprod[t]
			lowercase__				:			Optional[int]            =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
			lowercase__				:			Union[str, Any]            =		1 - alpha_prod_t
			lowercase__				:			Tuple            =		1 - alpha_prod_t_prev

			# For t > 0, compute predicted variance βt (see formula (6) and (7) from
			# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
			# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
			# variance to pred_sample
			# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
			# without eta.
			# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
			lowercase__				:			Any            =		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

			return variance

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	torch.FloatTensor,				lowerCamelCase_:	int,				lowerCamelCase_:	torch.FloatTensor,				lowerCamelCase_:	torch.FloatTensor,				lowerCamelCase_:	torch.FloatTensor,				lowerCamelCase_:	Optional[torch.Generator] = None,				lowerCamelCase_:	bool = True,				):
			lowercase__				:			Dict            =		timestep
			lowercase__				:			List[Any]            =		timestep - self.config.num_train_timesteps // self.num_inference_steps

			# 1. compute alphas, betas
			lowercase__				:			Dict            =		self.alphas_cumprod[t]
			lowercase__				:			Any            =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
			lowercase__				:			Union[str, Any]            =		1 - alpha_prod_t

			# 2. compute predicted original sample from predicted noise also called
			# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
			lowercase__				:			int            =		(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

			# 3. Clip "predicted x_0"
			if self.config.clip_sample:
					lowercase__				:			Dict            =		torch.clamp(_lowerCAmelCase,				-1,				1 )

			# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
			# substitute formula (7) in the algorithm coming from DDPM paper
			# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
			# DDIM schedule gives the same results as DDPM with eta = 1.0
			# Noise is being reused in 7. and 8., but no impact on quality has
			# been observed.

			# 5. Add noise
			lowercase__				:			Tuple            =		model_output.device
			lowercase__				:			Union[str, Any]            =		randn_tensor(model_output.shape,				generator=_lowerCAmelCase,				device=_lowerCAmelCase,				dtype=model_output.dtype )
			lowercase__				:			Optional[int]            =		self.eta * self._get_variance(_lowerCAmelCase ) ** 0.5

			lowercase__				:			Tuple            =		0
			if t > 0 and self.eta > 0:
					lowercase__				:			Union[str, Any]            =		std_dev_t * noise

			# 6. compute "direction pointing to x_t" of formula (12)
			# from https://arxiv.org/pdf/2010.02502.pdf
			lowercase__				:			int            =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

			# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			lowercase__				:			str            =		alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

			# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
			lowercase__				:			Optional[int]            =		(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

			# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
			lowercase__				:			Optional[int]            =		mask * prev_known_part + (1.0 - mask) * prev_unknown_part

			if not return_dict:
					return (
					    pred_prev_sample,
					    pred_original_sample,
					)

			return RePaintSchedulerOutput(prev_sample=_lowerCAmelCase,				pred_original_sample=_lowerCAmelCase )

	def       snake_case__(	self:	int,				lowerCamelCase_:	Optional[Any],				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Optional[Any]=None ):
			lowercase__				:			Optional[int]            =		self.config.num_train_timesteps // self.num_inference_steps

			for i in range(_lowerCAmelCase ):
					lowercase__				:			Optional[int]            =		self.betas[timestep + i]
					if sample.device.type == "mps":
							# randn does not work reproducibly on mps
							lowercase__				:			int            =		randn_tensor(sample.shape,				dtype=sample.dtype,				generator=_lowerCAmelCase )
							lowercase__				:			str            =		noise.to(sample.device )
					else:
							lowercase__				:			Optional[Any]            =		randn_tensor(sample.shape,				generator=_lowerCAmelCase,				device=sample.device,				dtype=sample.dtype )

					# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
					lowercase__				:			List[str]            =		(1 - beta) ** 0.5 * sample + beta**0.5 * noise

			return sample

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	torch.FloatTensor,				lowerCamelCase_:	torch.FloatTensor,				lowerCamelCase_:	torch.IntTensor,				):
			raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.' )





	def __len__(	self:	List[Any] ):
			return self.config.num_train_timesteps










import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase:  Tuple   =   logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str )      ->       List[Any]:




		'''simple docstring'''
		if "backbone.0.body" in orig_key:
				lowercase__				:			Optional[int]            =		orig_key.replace('backbone.0.body'      ,						'backbone.conv_encoder.model' )
		if "transformer" in orig_key:
				lowercase__				:			Any            =		orig_key.replace('transformer.'      ,						'' )
		if "norm1" in orig_key:
				if "encoder" in orig_key:
						lowercase__				:			str            =		orig_key.replace('norm1'      ,						'self_attn_layer_norm' )
				else:
						lowercase__				:			int            =		orig_key.replace('norm1'      ,						'encoder_attn_layer_norm' )
		if "norm2" in orig_key:
				if "encoder" in orig_key:
						lowercase__				:			Any            =		orig_key.replace('norm2'      ,						'final_layer_norm' )
				else:
						lowercase__				:			Optional[int]            =		orig_key.replace('norm2'      ,						'self_attn_layer_norm' )
		if "norm3" in orig_key:
				lowercase__				:			Union[str, Any]            =		orig_key.replace('norm3'      ,						'final_layer_norm' )
		if "linear1" in orig_key:
				lowercase__				:			List[str]            =		orig_key.replace('linear1'      ,						'fc1' )
		if "linear2" in orig_key:
				lowercase__				:			int            =		orig_key.replace('linear2'      ,						'fc2' )
		if "query_embed" in orig_key:
				lowercase__				:			str            =		orig_key.replace('query_embed'      ,						'query_position_embeddings' )
		if "cross_attn" in orig_key:
				lowercase__				:			int            =		orig_key.replace('cross_attn'      ,						'encoder_attn' )

		return orig_key


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Tuple )      ->       Optional[int]:




		'''simple docstring'''
		# transformer decoder self-attention layers
		for i in range(6 ):
				# read in weights + bias of input projection layer of self-attention
				lowercase__				:			int            =		state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight""" )
				lowercase__				:			List[Any]            =		state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias""" )
				# next, add query, keys and values (in that order) to the state dict
				lowercase__				:			str            =		in_proj_weight[:256, :]
				lowercase__				:			Tuple            =		in_proj_bias[:256]
				lowercase__				:			List[str]            =		in_proj_weight[256:512, :]
				lowercase__				:			str            =		in_proj_bias[256:512]
				lowercase__				:			Optional[int]            =		in_proj_weight[-256:, :]
				lowercase__				:			str            =		in_proj_bias[-256:]


def 			SCREAMING_SNAKE_CASE__     (					)      ->       Tuple:




		'''simple docstring'''
		lowercase__				:			str            =		'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowercase__				:			Optional[int]            =		Image.open(requests.get(_lowercase      ,						stream=_lowercase ).raw )

		return im


@torch.no_grad()
def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Dict      ,						_lowercase    :   Tuple      ,						_lowercase    :   List[Any]      ,						_lowercase    :   Optional[Any]      ,						_lowercase    :   List[str]      ,						_lowercase    :   List[str]      ,						_lowercase    :   Union[str, Any]      ,						)      ->       List[str]:




		'''simple docstring'''
		lowercase__				:			List[str]            =		DeformableDetrConfig()
		# set config attributes
		if single_scale:
				lowercase__				:			List[str]            =		1
		lowercase__				:			Dict            =		dilation
		lowercase__				:			int            =		with_box_refine
		lowercase__				:			Optional[int]            =		two_stage
		# set labels
		lowercase__				:			Optional[Any]            =		91
		lowercase__				:			Any            =		'huggingface/label-files'
		lowercase__				:			List[Any]            =		'coco-detection-id2label.json'
		lowercase__				:			Any            =		json.load(open(cached_download(hf_hub_url(_lowercase      ,						_lowercase      ,						repo_type='dataset' ) )      ,						'r' ) )
		lowercase__				:			Union[str, Any]            =		{int(_lowercase ): v for k, v in idalabel.items()}
		lowercase__				:			Tuple            =		idalabel
		lowercase__				:			Optional[int]            =		{v: k for k, v in idalabel.items()}

		# load image processor
		lowercase__				:			Tuple            =		DeformableDetrImageProcessor(format='coco_detection' )

		# prepare image
		lowercase__				:			List[Any]            =		prepare_img()
		lowercase__				:			Optional[int]            =		image_processor(images=_lowercase      ,						return_tensors='pt' )
		lowercase__				:			int            =		encoding['pixel_values']

		logger.info('Converting model...' )

		# load original state dict
		lowercase__				:			Dict            =		torch.load(_lowercase      ,						map_location='cpu' )['model']
		# rename keys
		for key in state_dict.copy().keys():
				lowercase__				:			int            =		state_dict.pop(_lowercase )
				lowercase__				:			Optional[Any]            =		val
		# query, key and value matrices need special treatment
		read_in_q_k_v(_lowercase )
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		lowercase__				:			List[str]            =		'model.'
		for key in state_dict.copy().keys():
				if not key.startswith('class_embed' ) and not key.startswith('bbox_embed' ):
						lowercase__				:			Optional[Any]            =		state_dict.pop(_lowercase )
						lowercase__				:			Dict            =		val
    # finally, create HuggingFace model and load state dict
		lowercase__				:			Tuple            =		DeformableDetrForObjectDetection(_lowercase )
		model.load_state_dict(_lowercase )
		model.eval()

		lowercase__				:			str            =		'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(_lowercase )
		# verify our conversion
		lowercase__				:			Dict            =		model(pixel_values.to(_lowercase ) )

		lowercase__				:			List[Any]            =		torch.tensor(
		    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]] )
		lowercase__				:			str            =		torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]] )

		if single_scale:
				lowercase__				:			Any            =		torch.tensor(
				    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]] )
				lowercase__				:			Any            =		torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]] )

		if single_scale and dilation:
				lowercase__				:			Optional[int]            =		torch.tensor(
				    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]] )
				lowercase__				:			Dict            =		torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]] )

		if with_box_refine:
				lowercase__				:			List[Any]            =		torch.tensor(
				    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]] )
				lowercase__				:			int            =		torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]] )

		if with_box_refine and two_stage:
				lowercase__				:			Union[str, Any]            =		torch.tensor(
				    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]] )
				lowercase__				:			List[Any]            =		torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]] )

		print('Logits:'      ,						outputs.logits[0, :3, :3] )

		assert torch.allclose(outputs.logits[0, :3, :3]      ,						expected_logits.to(_lowercase )      ,						atol=1e-4 )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,						expected_boxes.to(_lowercase )      ,						atol=1e-4 )

		print('Everything ok!' )

		# Save model and image processor
		logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
		Path(_lowercase ).mkdir(exist_ok=_lowercase )
		model.save_pretrained(_lowercase )
		image_processor.save_pretrained(_lowercase )

		# Push to hub
		if push_to_hub:
				lowercase__				:			Any            =		'deformable-detr'
				model_name += "-single-scale" if single_scale else ""
				model_name += "-dc5" if dilation else ""
				model_name += "-with-box-refine" if with_box_refine else ""
				model_name += "-two-stage" if two_stage else ""
				print('Pushing model to hub...' )
				model.push_to_hub(repo_path_or_name=_lowercase      ,						organization='nielsr'      ,						commit_message='Add model' )


if __name__ == "__main__":
			__UpperCamelCase:  List[str]   =   argparse.ArgumentParser()

			parser.add_argument(
			    """--checkpoint_path""",
			    type=str,
			    default="""/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth""",
			    help="""Path to Pytorch checkpoint (.pth file) you'd like to convert.""",
			)
			parser.add_argument("""--single_scale""", action="""store_true""", help="""Whether to set config.num_features_levels = 1.""")
			parser.add_argument("""--dilation""", action="""store_true""", help="""Whether to set config.dilation=True.""")
			parser.add_argument("""--with_box_refine""", action="""store_true""", help="""Whether to set config.with_box_refine=True.""")
			parser.add_argument("""--two_stage""", action="""store_true""", help="""Whether to set config.two_stage=True.""")
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to the folder to output PyTorch model.""",
			)
			parser.add_argument(
			    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
			)
			__UpperCamelCase:  List[str]   =   parser.parse_args()
			convert_deformable_detr_checkpoint(
			    args.checkpoint_path,
			    args.single_scale,
			    args.dilation,
			    args.with_box_refine,
			    args.two_stage,
			    args.pytorch_dump_folder_path,
			    args.push_to_hub,
			)


