


"""simple docstring"""



import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MobileNetVaForImageClassification, MobileNetVaModel
  from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor







class _lowerCAmelCase				(      _lowercase	):







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      str      					=    self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(UpperCamelCase__						,	"tf_padding"						)						)
       self.parent.assertTrue(hasattr(UpperCamelCase__						,	"depth_multiplier"						)						)







class _lowerCAmelCase				:







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__=13						,	UpperCamelCase__=3						,	UpperCamelCase__=32						,	UpperCamelCase__=0.25						,	UpperCamelCase__=8						,	UpperCamelCase__=True						,	UpperCamelCase__=1024						,	UpperCamelCase__=32						,	UpperCamelCase__="relu6"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.02						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=10						,	UpperCamelCase__=None						,	)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    parent
       snake_case    :      List[str]      					=    batch_size
       snake_case    :      Optional[int]      					=    num_channels
       snake_case    :      Optional[int]      					=    image_size
       snake_case    :      Optional[Any]      					=    depth_multiplier
       snake_case    :      Union[str, Any]      					=    min_depth
       snake_case    :      Optional[Any]      					=    tf_padding
       snake_case    :      str      					=    int(last_hidden_size * depth_multiplier						)
       snake_case    :      Any      					=    output_stride
       snake_case    :      int      					=    hidden_act
       snake_case    :      Tuple      					=    classifier_dropout_prob
       snake_case    :      Dict      					=    use_labels
       snake_case    :      Tuple      					=    is_training
       snake_case    :      Optional[Any]      					=    num_labels
       snake_case    :      Union[str, Any]      					=    initializer_range
       snake_case    :      Optional[Any]      					=    scope







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

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       snake_case    :      Optional[Any]      					=    None
       snake_case    :      Dict      					=    None
       if self.use_labels:
           snake_case    :      Union[str, Any]      					=    ids_tensor([self.batch_size]						,	self.num_labels						)
           snake_case    :      Dict      					=    ids_tensor([self.batch_size, self.image_size, self.image_size]						,	self.num_labels						)

       snake_case    :      Dict      					=    self.get_config()

       return config, pixel_values, labels, pixel_labels







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       return MobileNetVaConfig(
           num_channels=self.num_channels						,	image_size=self.image_size						,	depth_multiplier=self.depth_multiplier						,	min_depth=self.min_depth						,	tf_padding=self.tf_padding						,	hidden_act=self.hidden_act						,	classifier_dropout_prob=self.classifier_dropout_prob						,	initializer_range=self.initializer_range						,	)







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

       '''simple docstring'''




       snake_case    :      List[str]      					=    MobileNetVaModel(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      List[str]      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(
           result.last_hidden_state.shape						,	(
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )						,	)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      List[str]      					=    self.num_labels
       snake_case    :      Optional[Any]      					=    MobileNetVaForImageClassification(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case    :      List[Any]      					=    model(UpperCamelCase__						,	labels=UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)						)







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

       '''simple docstring'''




       snake_case    :      str      					=    self.prepare_config_and_inputs()
       snake_case    :      int      					=    config_and_inputs
       snake_case    :      Optional[int]      					=    {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class _lowerCAmelCase				(      _lowercase    ,							_lowercase    ,							unittest.TestCase	):
   __UpperCAmelCase :       List[str]							  =  (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
   __UpperCAmelCase :       str							  =  (
       {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
       if is_torch_available()
       else {}
   )

   __UpperCAmelCase :       List[str]							  =  False
   __UpperCAmelCase :       List[Any]							  =  False
   __UpperCAmelCase :       Union[str, Any]							  =  False
   __UpperCAmelCase :       str							  =  False







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    MobileNetVaModelTester(self						)
       snake_case    :      List[str]      					=    MobileNetVaConfigTester(self						,	config_class=UpperCamelCase__						,	has_text_modality=UpperCamelCase__						)







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

       '''simple docstring'''




       self.config_tester.run_common_tests()







   @unittest.skip(reason="MobileNetV1 does not use inputs_embeds"						)
   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       pass







   @unittest.skip(reason="MobileNetV1 does not support input and output embeddings"						)
   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       pass







   @unittest.skip(reason="MobileNetV1 does not output attentions"						)
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       pass







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

       '''simple docstring'''




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

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

           snake_case    :      Dict      					=    ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]						,	UpperCamelCase__						)







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

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       def check_hidden_states_output(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						):
           snake_case    :      str      					=    model_class(UpperCamelCase__						)
           model.to(UpperCamelCase__						)
           model.eval()

           with torch.no_grad():
               snake_case    :      Union[str, Any]      					=    model(**self._prepare_for_class(UpperCamelCase__						,	UpperCamelCase__						)						)

           snake_case    :      List[Any]      					=    outputs.hidden_states

           snake_case    :      Tuple      					=    26
           self.assertEqual(len(UpperCamelCase__						)						,	UpperCamelCase__						)

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

       for model_class in self.all_model_classes:
           snake_case    :      List[Any]      					=    True
           check_hidden_states_output(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           snake_case    :      Optional[Any]      					=    True

           check_hidden_states_output(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)







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

       '''simple docstring'''




       snake_case    :      int      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__						)







   @slow
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case    :      Dict      					=    MobileNetVaModel.from_pretrained(UpperCamelCase__						)
           self.assertIsNotNone(UpperCamelCase__						)







def   __lowerCAmelCase						(						)	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      Optional[int]      					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
    return image







@require_torch
@require_vision
class _lowerCAmelCase				(      unittest.TestCase	):







   @cached_property
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       return (
           MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"						) if is_vision_available() else None
       )







   @slow
   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"						).to(UpperCamelCase__						)

       snake_case    :      Dict      					=    self.default_image_processor
       snake_case    :      int      					=    prepare_img()
       snake_case    :      List[Any]      					=    image_processor(images=UpperCamelCase__						,	return_tensors="pt"						).to(UpperCamelCase__						)

       # forward pass
       with torch.no_grad():
           snake_case    :      str      					=    model(**UpperCamelCase__						)

       # verify the logits
       snake_case    :      List[str]      					=    torch.Size((1, 1001)						)
       self.assertEqual(outputs.logits.shape						,	UpperCamelCase__						)

       snake_case    :      List[str]      					=    torch.tensor([-4.1739, -1.1233, 3.1205]						).to(UpperCamelCase__						)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]						,	UpperCamelCase__						,	atol=1e-4						)						)













'''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)

