
'''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 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Tuple=13  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Dict=6  ,	UpperCamelCase__				:			Union[str, Any]=17  ,	UpperCamelCase__				:			str=23  ,	UpperCamelCase__				:			Union[str, Any]=11  ,	UpperCamelCase__				:			Dict=True  ,	)						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						parent
				snake_case  :       int     			=						batch_size
				snake_case  :       int     			=						seq_length
				snake_case  :       str     			=						act_dim
				snake_case  :       List[str]     			=						state_dim
				snake_case  :       List[str]     			=						hidden_size
				snake_case  :       List[Any]     			=						max_length
				snake_case  :       int     			=						is_training



		def       lowerCAmelCase				(      self				:			Dict      )						->			str:






				"""simple docstring"""







				snake_case  :       List[str]     			=						floats_tensor((self.batch_size, self.seq_length, self.state_dim)      )
				snake_case  :       List[Any]     			=						floats_tensor((self.batch_size, self.seq_length, self.act_dim)      )
				snake_case  :       List[Any]     			=						floats_tensor((self.batch_size, self.seq_length, 1)      )
				snake_case  :       Optional[Any]     			=						floats_tensor((self.batch_size, self.seq_length, 1)      )
				snake_case  :       Tuple     			=						ids_tensor((self.batch_size, self.seq_length)  ,	vocab_size=1000      )
				snake_case  :       List[str]     			=						random_attention_mask((self.batch_size, self.seq_length)      )

				snake_case  :       int     			=						self.get_config()

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



		def       lowerCAmelCase				(      self				:			Any      )						->			List[str]:






				"""simple docstring"""







				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       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Any  ,	)						->			Dict:






				"""simple docstring"""







				snake_case  :       Tuple     			=						DecisionTransformerModel(config=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				model.eval()
				snake_case  :       Optional[int]     			=						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       lowerCAmelCase				(      self				:			Dict      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Dict     			=						self.prepare_config_and_inputs()
				(
				    (
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,
				)  :       List[str]     			=						config_and_inputs
				snake_case  :       Dict     			=						{
				    '''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 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						(DecisionTransformerModel,) if is_torch_available() else ()
		lowerCamelCase     							=						()
		lowerCamelCase     							=						{"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

		# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						DecisionTransformerModelTester(self      )
				snake_case  :       List[str]     			=						ConfigTester(self  ,	config_class=UpperCamelCase__  ,	hidden_size=37      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			int:






				"""simple docstring"""







				self.config_tester.run_common_tests()



		def       lowerCAmelCase				(      self				:			Any      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCamelCase__      )



		@slow
		def       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case  :       str     			=						DecisionTransformerModel.from_pretrained(UpperCamelCase__      )
						self.assertIsNotNone(UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Any      )						->			List[Any]:






				"""simple docstring"""







				snake_case			,snake_case  :       List[Any]     			=						self.model_tester.prepare_config_and_inputs_for_common()

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

						snake_case  :       Optional[Any]     			=						[
						    '''states''',
						    '''actions''',
						    '''rewards''',
						    '''returns_to_go''',
						    '''timesteps''',
						    '''attention_mask''',
						]

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







@require_torch
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Any     			=						2  # number of steps of autoregressive prediction we will perform
				snake_case  :       Dict     			=						10  # defined by the RL environment, may be normalized
				snake_case  :       int     			=						DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''      )
				snake_case  :       Optional[int]     			=						model.to(UpperCamelCase__      )
				snake_case  :       str     			=						model.config
				torch.manual_seed(0      )
				snake_case  :       int     			=						torch.randn(1  ,	1  ,	config.state_dim      ).to(device=UpperCamelCase__  ,	dtype=torch.floataa      )  # env.reset()

				snake_case  :       List[Any]     			=						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__      )

				snake_case  :       Union[str, Any]     			=						torch.tensor(UpperCamelCase__  ,	device=UpperCamelCase__  ,	dtype=torch.floataa      ).reshape(1  ,	1  ,	1      )
				snake_case  :       str     			=						state
				snake_case  :       Optional[Any]     			=						torch.zeros(1  ,	0  ,	config.act_dim  ,	device=UpperCamelCase__  ,	dtype=torch.floataa      )
				snake_case  :       Optional[Any]     			=						torch.zeros(1  ,	0  ,	device=UpperCamelCase__  ,	dtype=torch.floataa      )
				snake_case  :       Any     			=						torch.tensor(0  ,	device=UpperCamelCase__  ,	dtype=torch.long      ).reshape(1  ,	1      )

				for step in range(UpperCamelCase__      ):
						snake_case  :       List[str]     			=						torch.cat([actions, torch.zeros(1  ,	1  ,	config.act_dim  ,	device=UpperCamelCase__      )]  ,	dim=1      )
						snake_case  :       Optional[Any]     			=						torch.cat([rewards, torch.zeros(1  ,	1  ,	device=UpperCamelCase__      )]  ,	dim=1      )

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

						with torch.no_grad():
								snake_case			,snake_case			,snake_case  :       int     			=						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      )      )
						snake_case			,snake_case			,snake_case			,snake_case  :       Union[str, Any]     			=						(  # env.step(action)
						    torch.randn(1  ,	1  ,	config.state_dim      ).to(device=UpperCamelCase__  ,	dtype=torch.floataa      ),
						    1.0,
						    False,
						    {},
						)

						snake_case  :       List[str]     			=						action_pred[0, -1]
						snake_case  :       Optional[int]     			=						torch.cat([states, state]  ,	dim=1      )
						snake_case  :       List[Any]     			=						returns_to_go[0, -1] - reward
						snake_case  :       str     			=						torch.cat([returns_to_go, pred_return.reshape(1  ,	1  ,	1      )]  ,	dim=1      )
						snake_case  :       Tuple     			=						torch.cat(
						    [timesteps, torch.ones((1, 1)  ,	device=UpperCamelCase__  ,	dtype=torch.long      ) * (step + 1)]  ,	dim=1      )




'''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__						       =      logging.get_logger(__name__)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Tuple:


		'''simple docstring'''


		if "resnet-50" in model_name:
				snake_case  :       int     			=						ResNetConfig.from_pretrained('''microsoft/resnet-50'''			)
		elif "resnet-101" in model_name:
				snake_case  :       Any     			=						ResNetConfig.from_pretrained('''microsoft/resnet-101'''			)
		else:
				raise ValueError('''Model name should include either resnet50 or resnet101'''			)

		snake_case  :       Optional[Any]     			=						DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__ ,       backbone_config=SCREAMING_SNAKE_CASE__			)

		# set label attributes
		snake_case  :       Tuple     			=						'''panoptic''' in model_name
		if is_panoptic:
				snake_case  :       Tuple     			=						250
		else:
				snake_case  :       Any     			=						91
				snake_case  :       str     			=						'''huggingface/label-files'''
				snake_case  :       Optional[Any]     			=						'''coco-detection-id2label.json'''
				snake_case  :       List[str]     			=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       repo_type='''dataset'''			) ,       '''r'''			)			)
				snake_case  :       Optional[Any]     			=						{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
				snake_case  :       str     			=						idalabel
				snake_case  :       List[Any]     			=						{v: k for k, v in idalabel.items()}

		return config, is_panoptic




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       str     			=						[]

		# stem
		# fmt: off
		rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')			)
		rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')			)
		rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')			)
		rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')			)
		rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')			)
		# stages
		for stage_idx in range(len(config.backbone_config.depths			)			):
				for layer_idx in range(config.backbone_config.depths[stage_idx]			):
						# shortcut
						if layer_idx == 0:
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
								    )			)
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
								    )			)
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
								    )			)
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
								    )			)
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
								    )			)
						# 3 convs
						for i in range(3			):
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
								    )			)
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
								    )			)
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
								    )			)
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
								    )			)
								rename_keys.append(
								    (
								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
								    )			)
    # fmt: on

		for i in range(config.encoder_layers			):
				# 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}.multihead_attn.out_proj.weight',
				        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
				    )			)
				rename_keys.append(
				    (
				        F'transformer.decoder.layers.{i}.multihead_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')			)

		# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
		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'''),
		    ]			)

		return rename_keys




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       List[str]     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
		snake_case  :       List[Any]     			=						val




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=False			)  ->							List[Any]:


		'''simple docstring'''


		snake_case  :       Union[str, Any]     			=						''''''
		if is_panoptic:
				snake_case  :       Optional[Any]     			=						'''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)
				snake_case  :       Dict     			=						state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'			)
				snake_case  :       Optional[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
				snake_case  :       Union[str, Any]     			=						in_proj_weight[:256, :]
				snake_case  :       Any     			=						in_proj_bias[:256]
				snake_case  :       Dict     			=						in_proj_weight[256:512, :]
				snake_case  :       Tuple     			=						in_proj_bias[256:512]
				snake_case  :       Optional[int]     			=						in_proj_weight[-256:, :]
				snake_case  :       Optional[int]     			=						in_proj_bias[-256:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6			):
				# read in weights + bias of input projection layer of self-attention
				snake_case  :       Union[str, Any]     			=						state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
				snake_case  :       List[str]     			=						state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'			)
				# next, add query, keys and values (in that order) to the state dict
				snake_case  :       Union[str, Any]     			=						in_proj_weight[:256, :]
				snake_case  :       str     			=						in_proj_bias[:256]
				snake_case  :       Optional[int]     			=						in_proj_weight[256:512, :]
				snake_case  :       Optional[int]     			=						in_proj_bias[256:512]
				snake_case  :       Optional[Any]     			=						in_proj_weight[-256:, :]
				snake_case  :       Tuple     			=						in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				snake_case  :       Union[str, Any]     			=						state_dict.pop(
				    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'			)
				snake_case  :       str     			=						state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'			)
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				snake_case  :       Optional[Any]     			=						in_proj_weight_cross_attn[:256, :]
				snake_case  :       List[Any]     			=						in_proj_bias_cross_attn[:256]
				snake_case  :       Union[str, Any]     			=						in_proj_weight_cross_attn[256:512, :]
				snake_case  :       Any     			=						in_proj_bias_cross_attn[256:512]
				snake_case  :       List[str]     			=						in_proj_weight_cross_attn[-256:, :]
				snake_case  :       Tuple     			=						in_proj_bias_cross_attn[-256:]




def    _UpperCamelCase						(       )  ->							Dict:


		'''simple docstring'''


		snake_case  :       Optional[Any]     			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		snake_case  :       Any     			=						Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,       stream=SCREAMING_SNAKE_CASE__			).raw			)

		return im




@torch.no_grad()
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None ,       SCREAMING_SNAKE_CASE__=False			)  ->							Dict:


		'''simple docstring'''


		snake_case			,snake_case  :       Tuple     			=						get_detr_config(SCREAMING_SNAKE_CASE__			)

		# load original model from torch hub
		snake_case  :       Union[str, Any]     			=						{
		    '''detr-resnet-50''': '''detr_resnet50''',
		    '''detr-resnet-101''': '''detr_resnet101''',
		}
		logger.info(F'Converting model {model_name}...'			)
		snake_case  :       Union[str, Any]     			=						torch.hub.load('''facebookresearch/detr''' ,       model_name_to_original_name[model_name] ,       pretrained=SCREAMING_SNAKE_CASE__			).eval()
		snake_case  :       Optional[int]     			=						detr.state_dict()
		# rename keys
		for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__			):
				if is_panoptic:
						snake_case  :       Union[str, Any]     			=						'''detr.''' + src
				rename_key(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		# query, key and value matrices need special treatment
		read_in_q_k_v(SCREAMING_SNAKE_CASE__ ,       is_panoptic=SCREAMING_SNAKE_CASE__			)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		snake_case  :       List[str]     			=						'''detr.model.''' if is_panoptic else '''model.'''
		for key in state_dict.copy().keys():
				if is_panoptic:
						if (
						    key.startswith('''detr'''			)
						    and not key.startswith('''class_labels_classifier'''			)
						    and not key.startswith('''bbox_predictor'''			)
						):
								snake_case  :       Optional[Any]     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
								snake_case  :       Optional[int]     			=						val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								snake_case  :       Dict     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
								snake_case  :       List[Any]     			=						val
						elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
								continue
						else:
								snake_case  :       int     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
								snake_case  :       Optional[int]     			=						val
				else:
						if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
								snake_case  :       Dict     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
								snake_case  :       Any     			=						val

    # finally, create HuggingFace model and load state dict
		snake_case  :       Optional[Any]     			=						DetrForSegmentation(SCREAMING_SNAKE_CASE__			) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__			)
		model.load_state_dict(SCREAMING_SNAKE_CASE__			)
		model.eval()

		# verify our conversion on an image
		snake_case  :       List[str]     			=						'''coco_panoptic''' if is_panoptic else '''coco_detection'''
		snake_case  :       Optional[int]     			=						DetrImageProcessor(format=SCREAMING_SNAKE_CASE__			)

		snake_case  :       int     			=						processor(images=prepare_img() ,       return_tensors='''pt'''			)
		snake_case  :       Optional[int]     			=						encoding['''pixel_values''']

		snake_case  :       str     			=						detr(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[int]     			=						model(SCREAMING_SNAKE_CASE__			)

		assert torch.allclose(outputs.logits ,       original_outputs['''pred_logits'''] ,       atol=1E-3			)
		assert torch.allclose(outputs.pred_boxes ,       original_outputs['''pred_boxes'''] ,       atol=1E-3			)
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks ,       original_outputs['''pred_masks'''] ,       atol=1E-4			)
		print('''Looks ok!'''			)

		if pytorch_dump_folder_path is not None:
				# Save model and image processor
				logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'			)
				Path(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
				model.save_pretrained(SCREAMING_SNAKE_CASE__			)
				processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

		if push_to_hub:
				# Upload model and image processor to the hub
				logger.info('''Uploading PyTorch model and image processor to the hub...'''			)
				model.push_to_hub(F'nielsr/{model_name}'			)
				processor.push_to_hub(F'nielsr/{model_name}'			)


if __name__ == "__main__":
						lowercase__						       =      argparse.ArgumentParser()

						parser.add_argument(
						    "--model_name",
						    default="detr-resnet-50",
						    type=str,
						    choices=["detr-resnet-50", "detr-resnet-101"],
						    help="Name of the 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."
						)
						parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
						lowercase__						       =      parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


