





'''simple docstring'''





import inspect
import unittest

from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
  from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=13,	__magic_name__=32,	__magic_name__=3,	__magic_name__=4,	__magic_name__=[10, 20, 30, 40],	__magic_name__=[2, 2, 3, 2],	__magic_name__=True,	__magic_name__=True,	__magic_name__=37,	__magic_name__="gelu",	__magic_name__=10,	__magic_name__=0.02,	__magic_name__=["stage2", "stage3", "stage4"],	__magic_name__=[2, 3, 4],	__magic_name__=None,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   parent
       UpperCamelCase__							:       List[Any]      =   batch_size
       UpperCamelCase__							:       List[str]      =   image_size
       UpperCamelCase__							:       Optional[int]      =   num_channels
       UpperCamelCase__							:       Optional[Any]      =   num_stages
       UpperCamelCase__							:       Optional[int]      =   hidden_sizes
       UpperCamelCase__							:       Optional[Any]      =   depths
       UpperCamelCase__							:       int      =   is_training
       UpperCamelCase__							:       Any      =   use_labels
       UpperCamelCase__							:       Optional[Any]      =   intermediate_size
       UpperCamelCase__							:       Optional[int]      =   hidden_act
       UpperCamelCase__							:       Union[str, Any]      =   num_labels
       UpperCamelCase__							:       Dict      =   initializer_range
       UpperCamelCase__							:       Dict      =   out_features
       UpperCamelCase__							:       List[str]      =   out_indices
       UpperCamelCase__							:       int      =   scope




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCamelCase__							:       Optional[Any]      =   None
       if self.use_labels:
           UpperCamelCase__							:       List[Any]      =   ids_tensor([self.batch_size],	self.num_labels							)

       UpperCamelCase__							:       List[Any]      =   self.get_config()
       return config, pixel_values, labels




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       return ConvNextConfig(
           num_channels=self.num_channels,	hidden_sizes=self.hidden_sizes,	depths=self.depths,	num_stages=self.num_stages,	hidden_act=self.hidden_act,	is_decoder=_a,	initializer_range=self.initializer_range,	out_features=self.out_features,	out_indices=self.out_indices,	num_labels=self.num_labels,	)




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






       """simple docstring"""
       UpperCamelCase__							:       int      =   ConvNextModel(config=_a							)
       model.to(_a							)
       model.eval()
       UpperCamelCase__							:       Union[str, Any]      =   model(_a							)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   ConvNextForImageClassification(_a							)
       model.to(_a							)
       model.eval()
       UpperCamelCase__							:       Dict      =   model(_a,	labels=_a							)
       self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   ConvNextBackbone(config=_a							)
       model.to(_a							)
       model.eval()
       UpperCamelCase__							:       List[str]      =   model(_a							)

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps							),	len(config.out_features							)							)
       self.parent.assertListEqual(list(result.feature_maps[0].shape							),	[self.batch_size, self.hidden_sizes[1], 4, 4]							)

       # verify channels
       self.parent.assertEqual(len(model.channels							),	len(config.out_features							)							)
       self.parent.assertListEqual(model.channels,	config.hidden_sizes[1:]							)

       # verify backbone works with out_features=None
       UpperCamelCase__							:       Dict      =   None
       UpperCamelCase__							:       Dict      =   ConvNextBackbone(config=_a							)
       model.to(_a							)
       model.eval()
       UpperCamelCase__							:       Optional[Any]      =   model(_a							)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps							),	1							)
       self.parent.assertListEqual(list(result.feature_maps[0].shape							),	[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

       # verify channels
       self.parent.assertEqual(len(model.channels							),	1							)
       self.parent.assertListEqual(model.channels,	[config.hidden_sizes[-1]]							)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   self.prepare_config_and_inputs()
       UpperCamelCase__							:       Tuple      =   config_and_inputs
       UpperCamelCase__							:       Tuple      =   {"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_torch
class 					lowercase__ (			UpperCamelCase__    ,       UpperCamelCase__    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							int     	=	(
       (
           ConvNextModel,
           ConvNextForImageClassification,
           ConvNextBackbone,
       )
       if is_torch_available()
       else ()
   )
   a		:							Union[str, Any]     	=	(
       {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
       if is_torch_available()
       else {}
   )

   a		:							List[str]     	=	True
   a		:							Optional[int]     	=	False
   a		:							str     	=	False
   a		:							Tuple     	=	False
   a		:							Dict     	=	False




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   ConvNextModelTester(self							)
       UpperCamelCase__							:       Optional[int]      =   ConfigTester(self,	config_class=_a,	has_text_modality=_a,	hidden_size=37							)




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






       """simple docstring"""
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()




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






       """simple docstring"""
       return




   @unittest.skip(reason='''ConvNext does not use inputs_embeds'''							)
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       pass




   @unittest.skip(reason='''ConvNext does not support input and output embeddings'''							)
   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       pass




   @unittest.skip(reason='''ConvNext does not use feedforward chunking'''							)
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       pass




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






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__							:       List[str]      =   ["""pixel_values"""]
           self.assertListEqual(arg_names[:1],	_a							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a							)




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






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*_a							)




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






       """simple docstring"""
       def check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Any      =   model_class(_a							)
           model.to(_a							)
           model.eval()

           with torch.no_grad():
               UpperCamelCase__							:       str      =   model(**self._prepare_for_class(_a,	_a							)							)

           UpperCamelCase__							:       Optional[Any]      =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           UpperCamelCase__							:       Dict      =   self.model_tester.num_stages
           self.assertEqual(len(_a							),	expected_num_stages + 1							)

           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							),	[self.model_tester.image_size // 4, self.model_tester.image_size // 4],	)

       UpperCamelCase__							:       Union[str, Any]      =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

           check_hidden_states_output(_a,	_a,	_a							)




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






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_a							)




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






       """simple docstring"""
       for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__							:       Optional[Any]      =   ConvNextModel.from_pretrained(_a							)
           self.assertIsNotNone(_a							)




def 	lowerCAmelCase_					(						)   ->			Dict:
    UpperCamelCase__							:       str      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
    return image






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

   '''simple docstring'''


   @cached_property
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''							) if is_vision_available() else None




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






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''							).to(_a							)

       UpperCamelCase__							:       Optional[Any]      =   self.default_image_processor
       UpperCamelCase__							:       Union[str, Any]      =   prepare_img()
       UpperCamelCase__							:       int      =   image_processor(images=_a,	return_tensors='''pt'''							).to(_a							)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__							:       Tuple      =   model(**_a							)

       # verify the logits
       UpperCamelCase__							:       Tuple      =   torch.Size((1, 1000)							)
       self.assertEqual(outputs.logits.shape,	_a							)

       UpperCamelCase__							:       str      =   torch.tensor([-0.0260, -0.4739, 0.1911]							).to(_a							)

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






@require_torch
class 					lowercase__ (			unittest.TestCase    ,       UpperCamelCase__       ):

   '''simple docstring'''


   a		:							Optional[int]     	=	(ConvNextBackbone,) if is_torch_available() else ()
   a		:							Optional[int]     	=	ConvNextConfig

   a		:							str     	=	False




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






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   ConvNextModelTester(self							)








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()
UpperCAmelCase_  							=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_  							=				[]
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 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Dict   )   ->			Dict:
    UpperCamelCase__							:       List[str]      =   state_dict.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   val




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

    return new_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   ''''''
    if is_panoptic:
        UpperCamelCase__							:       List[Any]      =   '''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)
        UpperCamelCase__							:       List[Any]      =   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
        UpperCamelCase__							:       int      =   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
        UpperCamelCase__							:       Any      =   in_proj_weight[:256, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[:256]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[256:512, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[256:512]
        UpperCamelCase__							:       Tuple      =   in_proj_weight[-256:, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[-256:]




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Any      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Optional[int]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return im




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

    # load image processor
    UpperCamelCase__							:       int      =   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCamelCase__							:       str      =   ConditionalDetrImageProcessor(format=__UpperCAmelCase   )

    # prepare image
    UpperCamelCase__							:       Tuple      =   prepare_img()
    UpperCamelCase__							:       List[str]      =   image_processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Union[str, Any]      =   encoding['''pixel_values''']

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

    # load original model from torch hub
    UpperCamelCase__							:       Union[str, Any]      =   torch.hub.load('''DeppMeng/ConditionalDETR'''	,     __UpperCAmelCase	,     pretrained=__UpperCAmelCase   ).eval()
    UpperCamelCase__							:       Optional[int]      =   conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            UpperCamelCase__							:       List[str]      =   '''conditional_detr.''' + src
        rename_key(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       str      =   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
    UpperCamelCase__							:       List[str]      =   '''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'''   )
            ):
                UpperCamelCase__							:       Tuple      =   state_dict.pop(__UpperCAmelCase   )
                UpperCamelCase__							:       List[Any]      =   val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                UpperCamelCase__							:       Any      =   state_dict.pop(__UpperCAmelCase   )
                UpperCamelCase__							:       Dict      =   val
            elif key.startswith('''bbox_attention'''   ) or key.startswith('''mask_head'''   ):
                continue
            else:
                UpperCamelCase__							:       List[Any]      =   state_dict.pop(__UpperCAmelCase   )
                UpperCamelCase__							:       Optional[int]      =   val
        else:
            if not key.startswith('''class_labels_classifier'''   ) and not key.startswith('''bbox_predictor'''   ):
                UpperCamelCase__							:       List[str]      =   state_dict.pop(__UpperCAmelCase   )
                UpperCamelCase__							:       Optional[int]      =   val
    # finally, create HuggingFace model and load state dict
    UpperCamelCase__							:       str      =   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
    UpperCamelCase__							:       str      =   conditional_detr(__UpperCAmelCase   )
    UpperCamelCase__							:       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__":
  UpperCAmelCase_  							=				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.'
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

