


"""simple docstring"""



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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class _lowerCAmelCase				:
   __UpperCAmelCase :       Optional[Any]							  =  42

   # setable values
   __UpperCAmelCase :       Tuple							  =  42
   __UpperCAmelCase :       Optional[int]							  =  42
   __UpperCAmelCase :       Any							  =  None







   @classmethod
   def  lowerCamelCase		(	cls						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       return cls(common=UpperCamelCase__						,	init_noise_sigma=UpperCamelCase__						,	timesteps=UpperCamelCase__						)







@dataclass
class _lowerCAmelCase				(      _lowercase	):
   __UpperCAmelCase :       str							  =  42







class _lowerCAmelCase				(      _lowercase    ,							_lowercase	):
   __UpperCAmelCase :       Dict							  =  [e.name for e in FlaxKarrasDiffusionSchedulers]

   __UpperCAmelCase :       Any							  =  42







   @property
   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       return True







   @register_to_config
   def __init__(	self						,	UpperCamelCase__ = 1000						,	UpperCamelCase__ = 0.0001						,	UpperCamelCase__ = 0.02						,	UpperCamelCase__ = "linear"						,	UpperCamelCase__ = None						,	UpperCamelCase__ = "fixed_small"						,	UpperCamelCase__ = True						,	UpperCamelCase__ = "epsilon"						,	UpperCamelCase__ = jnp.floataa						,	)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Any      					=    dtype







   def  lowerCamelCase		(	self						,	UpperCamelCase__ = None						)	->		int:

       '''simple docstring'''




       if common is None:
           snake_case    :      Any      					=    CommonSchedulerState.create(self						)

       # standard deviation of the initial noise distribution
       snake_case    :      Optional[int]      					=    jnp.array(1.0						,	dtype=self.dtype						)

       snake_case    :      int      					=    jnp.arange(0						,	self.config.num_train_timesteps						).round()[::-1]

       return DDPMSchedulerState.create(
           common=UpperCamelCase__						,	init_noise_sigma=UpperCamelCase__						,	timesteps=UpperCamelCase__						,	)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		Optional[int]:

       '''simple docstring'''




       return sample







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = ()						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.config.num_train_timesteps // num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # rounding to avoid issues when num_inference_step is power of 3
       snake_case    :      Optional[Any]      					=    (jnp.arange(0						,	UpperCamelCase__						) * step_ratio).round()[::-1]

       return state.replace(
           num_inference_steps=UpperCamelCase__						,	timesteps=UpperCamelCase__						,	)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__=None						,	UpperCamelCase__=None						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    state.common.alphas_cumprod[t]
       snake_case    :      Union[str, Any]      					=    jnp.where(t > 0						,	state.common.alphas_cumprod[t - 1]						,	jnp.array(1.0						,	dtype=self.dtype						)						)

       # 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
       snake_case    :      List[Any]      					=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
           snake_case    :      Optional[int]      					=    self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small":
           snake_case    :      Tuple      					=    jnp.clip(UpperCamelCase__						,	a_min=1e-20						)
       # for rl-diffuser https://arxiv.org/abs/2205.09991
       elif variance_type == "fixed_small_log":
           snake_case    :      Union[str, Any]      					=    jnp.log(jnp.clip(UpperCamelCase__						,	a_min=1e-20						)						)
       elif variance_type == "fixed_large":
           snake_case    :      Union[str, Any]      					=    state.common.betas[t]
       elif variance_type == "fixed_large_log":
           # Glide max_log
           snake_case    :      Union[str, Any]      					=    jnp.log(state.common.betas[t]						)
       elif variance_type == "learned":
           return predicted_variance
       elif variance_type == "learned_range":
           snake_case    :      Optional[int]      					=    variance
           snake_case    :      List[Any]      					=    state.common.betas[t]
           snake_case    :      List[Any]      					=    (predicted_variance + 1) / 2
           snake_case    :      List[Any]      					=    frac * max_log + (1 - frac) * min_log

       return variance







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = True						,	)	->		Dict:

       '''simple docstring'''




       snake_case    :      int      					=    timestep

       if key is None:
           snake_case    :      Optional[int]      					=    jax.random.PRNGKey(0						)

       if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           snake_case    :      str      					=    jnp.split(UpperCamelCase__						,	sample.shape[1]						,	axis=1						)
       else:
           snake_case    :      Union[str, Any]      					=    None

       # 1. compute alphas, betas
       snake_case    :      str      					=    state.common.alphas_cumprod[t]
       snake_case    :      str      					=    jnp.where(t > 0						,	state.common.alphas_cumprod[t - 1]						,	jnp.array(1.0						,	dtype=self.dtype						)						)
       snake_case    :      str      					=    1 - alpha_prod_t
       snake_case    :      Union[str, Any]      					=    1 - alpha_prod_t_prev

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
           snake_case    :      str      					=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
           snake_case    :      Union[str, Any]      					=    model_output
       elif self.config.prediction_type == "v_prediction":
           snake_case    :      List[str]      					=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
       else:
           raise ValueError(
               F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
               " for the FlaxDDPMScheduler."						)

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
           snake_case    :      Dict      					=    jnp.clip(UpperCamelCase__						,	-1						,	1						)

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       snake_case    :      Optional[int]      					=    (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       snake_case    :      Optional[int]      					=    state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       snake_case    :      Dict      					=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
           snake_case    :      List[Any]      					=    jax.random.split(UpperCamelCase__						,	num=1						)
           snake_case    :      str      					=    jax.random.normal(UpperCamelCase__						,	shape=model_output.shape						,	dtype=self.dtype						)
           return (self._get_variance(UpperCamelCase__						,	UpperCamelCase__						,	predicted_variance=UpperCamelCase__						) ** 0.5) * noise

       snake_case    :      Optional[Any]      					=    jnp.where(t > 0						,	random_variance()						,	jnp.zeros(model_output.shape						,	dtype=self.dtype						)						)

       snake_case    :      Any      					=    pred_prev_sample + variance

       if not return_dict:
           return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase__						,	state=UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	)	->		Tuple:

       '''simple docstring'''




       return add_noise_common(state.common						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	)	->		Any:

       '''simple docstring'''




       return get_velocity_common(state.common						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)







   def __len__(	self						)	->		Optional[int]:

       '''simple docstring'''




       return self.config.num_train_timesteps













'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A	:			List[str]         =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A	:			Tuple         =[]
for i in range(6):
			# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
			)
			rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
			)
			rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
			rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
			# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
			)
			rename_keys.append(
			    (
			        F'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
			        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
			    )
			)
			rename_keys.append(
			    (
			        F'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
			        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
			    )
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

			# q, k, v projections in self/cross-attention in decoder for conditional DETR
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
			)
			# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
			)

			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
			)
			# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
			)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							lowerCamelCase__					:	List[Any]         =       state_dict.pop(UpperCamelCase      )
							lowerCamelCase__					:	Any         =       val





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Dict:
							lowerCamelCase__					:	Tuple         =       OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					lowerCamelCase__					:	List[str]         =       key.replace("""backbone.0.body"""				,			"""backbone.conv_encoder.model"""      )
																					lowerCamelCase__					:	Optional[int]         =       value
														else:
																					lowerCamelCase__					:	Any         =       value

							return new_state_dict





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase=False      )      ->       Dict:
							lowerCamelCase__					:	Optional[int]         =       """"""
							if is_panoptic:
														lowerCamelCase__					:	Dict         =       """conditional_detr."""

							# first: transformer encoder
							for i in range(6      ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														lowerCamelCase__					:	List[Any]         =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
														lowerCamelCase__					:	Union[str, Any]         =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''      )
														# next, add query, keys and values (in that order) to the state dict
														lowerCamelCase__					:	int         =       in_proj_weight[:256, :]
														lowerCamelCase__					:	Any         =       in_proj_bias[:256]
														lowerCamelCase__					:	str         =       in_proj_weight[256:512, :]
														lowerCamelCase__					:	Optional[int]         =       in_proj_bias[256:512]
														lowerCamelCase__					:	Dict         =       in_proj_weight[-256:, :]
														lowerCamelCase__					:	str         =       in_proj_bias[-256:]





def  SCREAMING_SNAKE_CASE_       ()      ->       List[Any]:
							lowerCamelCase__					:	Optional[int]         =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__					:	Optional[Any]         =       Image.open(requests.get(UpperCamelCase				,			stream=UpperCamelCase      ).raw      )

							return im





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	Optional[Any]         =       ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														lowerCamelCase__					:	Any         =       """resnet101"""
							if "dc5" in model_name:
														lowerCamelCase__					:	Optional[int]         =       True
							lowerCamelCase__					:	int         =       """panoptic""" in model_name
							if is_panoptic:
														lowerCamelCase__					:	List[str]         =       250
							else:
														lowerCamelCase__					:	int         =       91
														lowerCamelCase__					:	int         =       """huggingface/label-files"""
														lowerCamelCase__					:	List[str]         =       """coco-detection-id2label.json"""
														lowerCamelCase__					:	Optional[Any]         =       json.load(open(hf_hub_download(UpperCamelCase				,			UpperCamelCase				,			repo_type="""dataset"""      )				,			"""r"""      )      )
														lowerCamelCase__					:	Any         =       {int(UpperCamelCase      ): v for k, v in idalabel.items()}
														lowerCamelCase__					:	str         =       idalabel
														lowerCamelCase__					:	List[str]         =       {v: k for k, v in idalabel.items()}

							# load image processor
							lowerCamelCase__					:	Optional[int]         =       """coco_panoptic""" if is_panoptic else """coco_detection"""
							lowerCamelCase__					:	int         =       ConditionalDetrImageProcessor(format=UpperCamelCase      )

							# prepare image
							lowerCamelCase__					:	List[str]         =       prepare_img()
							lowerCamelCase__					:	int         =       image_processor(images=UpperCamelCase				,			return_tensors="""pt"""      )
							lowerCamelCase__					:	Optional[Any]         =       encoding["""pixel_values"""]

							logger.info(f'''Converting model {model_name}...'''      )

							# load original model from torch hub
							lowerCamelCase__					:	List[Any]         =       torch.hub.load("""DeppMeng/ConditionalDETR"""				,			UpperCamelCase				,			pretrained=UpperCamelCase      ).eval()
							lowerCamelCase__					:	Dict         =       conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					lowerCamelCase__					:	Optional[Any]         =       """conditional_detr.""" + src
														rename_key(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
							lowerCamelCase__					:	Dict         =       rename_backbone_keys(UpperCamelCase      )
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase				,			is_panoptic=UpperCamelCase      )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							lowerCamelCase__					:	Dict         =       """conditional_detr.model.""" if is_panoptic else """model."""
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("""conditional_detr"""      )
																					    and not key.startswith("""class_labels_classifier"""      )
																					    and not key.startswith("""bbox_predictor"""      )
																					):
																												lowerCamelCase__					:	int         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Union[str, Any]         =       val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												lowerCamelCase__					:	List[str]         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Any         =       val
																					elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
																												continue
																					else:
																												lowerCamelCase__					:	int         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Tuple         =       val
														else:
																					if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
																												lowerCamelCase__					:	Union[str, Any]         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Dict         =       val
    # finally, create HuggingFace model and load state dict
							lowerCamelCase__					:	Tuple         =       ConditionalDetrForSegmentation(UpperCamelCase      ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase      )
							model.load_state_dict(UpperCamelCase      )
							model.eval()
							model.push_to_hub(repo_id=UpperCamelCase				,			organization="""DepuMeng"""				,			commit_message="""Add model"""      )
							# verify our conversion
							lowerCamelCase__					:	Optional[Any]         =       conditional_detr(UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       model(UpperCamelCase      )
							assert torch.allclose(outputs.logits				,			original_outputs["""pred_logits"""]				,			atol=1E-4      )
							assert torch.allclose(outputs.pred_boxes				,			original_outputs["""pred_boxes"""]				,			atol=1E-4      )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks				,			original_outputs["""pred_masks"""]				,			atol=1E-4      )

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


if __name__ == "__main__":
			_A	:			List[Any]         =argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''',
			    default='''conditional_detr_resnet50''',
			    type=str,
			    help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
			)
			_A	:			Optional[Any]         =parser.parse_args()
			convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

