

"""simple docstring"""





import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import DecisionTransformerModel
   from transformers.models.decision_transformer.modeling_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			int=1_3,					UpperCAmelCase__							:			int=7,					UpperCAmelCase__							:			Optional[Any]=6,					UpperCAmelCase__							:			List[str]=1_7,					UpperCAmelCase__							:			List[str]=2_3,					UpperCAmelCase__							:			Any=1_1,					UpperCAmelCase__							:			Dict=True,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						act_dim
    __lowercase								=						state_dim
    __lowercase								=						hidden_size
    __lowercase								=						max_length
    __lowercase								=						is_training


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						floats_tensor((self.batch_size, self.seq_length, self.state_dim)	)
    __lowercase								=						floats_tensor((self.batch_size, self.seq_length, self.act_dim)	)
    __lowercase								=						floats_tensor((self.batch_size, self.seq_length, 1)	)
    __lowercase								=						floats_tensor((self.batch_size, self.seq_length, 1)	)
    __lowercase								=						ids_tensor((self.batch_size, self.seq_length),					vocab_size=1_0_0_0	)
    __lowercase								=						random_attention_mask((self.batch_size, self.seq_length)	)

    __lowercase								=						self.get_config()

    return (
        config,
        states,
        actions,
        rewards,
        returns_to_go,
        timesteps,
        attention_mask,
    )


 def 					_lowercase (			self							:			Dict	):
    return DecisionTransformerConfig(
        batch_size=self.batch_size,					seq_length=self.seq_length,					act_dim=self.act_dim,					state_dim=self.state_dim,					hidden_size=self.hidden_size,					max_length=self.max_length,					)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[Any],					):
    __lowercase								=						DecisionTransformerModel(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)

    self.parent.assertEqual(result.state_preds.shape,					states.shape	)
    self.parent.assertEqual(result.action_preds.shape,					actions.shape	)
    self.parent.assertEqual(result.return_preds.shape,					returns_to_go.shape	)
    self.parent.assertEqual(
        result.last_hidden_state.shape,					(self.batch_size, self.seq_length * 3, self.hidden_size)	)  # seq length *3 as there are 3 modelities: states, returns and actions



 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs
    __lowercase								=						{
        "states": states,
        "actions": actions,
        "rewards": rewards,
        "returns_to_go": returns_to_go,
        "timesteps": timesteps,
        "attention_mask": attention_mask,
    }
    return config, inputs_dict







@require_torch
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	(DecisionTransformerModel,) if is_torch_available() else ()
 __UpperCAmelCase	:					int   		=	()
 __UpperCAmelCase	:					Tuple   		=	{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

 # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
 __UpperCAmelCase	:					str   		=	False

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __UpperCAmelCase	:					str   		=	False
 __UpperCAmelCase	:					str   		=	False
 __UpperCAmelCase	:					int   		=	False
 __UpperCAmelCase	:					Any   		=	False
 __UpperCAmelCase	:					Union[str, Any]   		=	False
 __UpperCAmelCase	:					List[Any]   		=	False
 __UpperCAmelCase	:					Any   		=	False
 __UpperCAmelCase	:					Any   		=	False
 __UpperCAmelCase	:					Optional[int]   		=	False


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						DecisionTransformerModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			str	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 @slow
 def 					_lowercase (			self							:			int	):
    for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						DecisionTransformerModel.from_pretrained(UpperCAmelCase__	)
       self.assertIsNotNone(UpperCAmelCase__	)



 def 					_lowercase (			self							:			Any	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=						model_class(UpperCAmelCase__	)
       __lowercase								=						inspect.signature(model.forward	)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowercase								=						[*signature.parameters.keys()]

       __lowercase								=						[
           "states",
           "actions",
           "rewards",
           "returns_to_go",
           "timesteps",
           "attention_mask",
       ]

       self.assertListEqual(arg_names[: len(UpperCAmelCase__	)],					UpperCAmelCase__	)







@require_torch
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						2  # number of steps of autoregressive prediction we will perform
    __lowercase								=						1_0  # defined by the RL environment, may be normalized
    __lowercase								=						DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"	)
    __lowercase								=						model.to(UpperCAmelCase__	)
    __lowercase								=						model.config
    torch.manual_seed(0	)
    __lowercase								=						torch.randn(1,					1,					config.state_dim	).to(device=UpperCAmelCase__,					dtype=torch.floataa	)  # env.reset()

    __lowercase								=						torch.tensor(
        [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]],					device=UpperCAmelCase__	)

    __lowercase								=						torch.tensor(UpperCAmelCase__,					device=UpperCAmelCase__,					dtype=torch.floataa	).reshape(1,					1,					1	)
    __lowercase								=						state
    __lowercase								=						torch.zeros(1,					0,					config.act_dim,					device=UpperCAmelCase__,					dtype=torch.floataa	)
    __lowercase								=						torch.zeros(1,					0,					device=UpperCAmelCase__,					dtype=torch.floataa	)
    __lowercase								=						torch.tensor(0,					device=UpperCAmelCase__,					dtype=torch.long	).reshape(1,					1	)

    for step in range(UpperCAmelCase__	):
       __lowercase								=						torch.cat([actions, torch.zeros(1,					1,					config.act_dim,					device=UpperCAmelCase__	)],					dim=1	)
       __lowercase								=						torch.cat([rewards, torch.zeros(1,					1,					device=UpperCAmelCase__	)],					dim=1	)

       __lowercase								=						torch.ones(1,					states.shape[1]	).to(dtype=torch.long,					device=states.device	)

       with torch.no_grad():
          __lowercase		,__lowercase		,__lowercase								=						model(
              states=UpperCAmelCase__,					actions=UpperCAmelCase__,					rewards=UpperCAmelCase__,					returns_to_go=UpperCAmelCase__,					timesteps=UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					)

       self.assertEqual(action_pred.shape,					actions.shape	)
       self.assertTrue(torch.allclose(action_pred[0, -1],					expected_outputs[step],					atol=1E-4	)	)
       __lowercase		,__lowercase		,__lowercase		,__lowercase								=						(  # env.step(action)
           torch.randn(1,					1,					config.state_dim	).to(device=UpperCAmelCase__,					dtype=torch.floataa	),
           1.0,
           False,
           {},
       )

       __lowercase								=						action_pred[0, -1]
       __lowercase								=						torch.cat([states, state],					dim=1	)
       __lowercase								=						returns_to_go[0, -1] - reward
       __lowercase								=						torch.cat([returns_to_go, pred_return.reshape(1,					1,					1	)],					dim=1	)
       __lowercase								=						torch.cat(
           [timesteps, torch.ones((1, 1),					device=UpperCAmelCase__,					dtype=torch.long	) * (step + 1)],					dim=1	)












'''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)

