
"""simple docstring"""







from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import numpy as np
       import tensorflow as tf

       from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
       from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




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



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  self.config_class(**self.inputs_dict )
      self.parent.assertTrue(hasattr(a_     ,   """hidden_sizes""" ) )
      self.parent.assertTrue(hasattr(a_     ,   """neck_hidden_sizes""" ) )
      self.parent.assertTrue(hasattr(a_     ,   """num_attention_heads""" ) )



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : List[Any]     ,   a_       : List[Any]     ,   a_       : Any=13     ,   a_       : Union[str, Any]=32     ,   a_       : List[Any]=2     ,   a_       : Tuple=3     ,   a_       : List[Any]=6_40     ,   a_       : List[str]=4     ,   a_       : Optional[int]="silu"     ,   a_       : int=3     ,   a_       : Any=32     ,   a_       : int=0.1     ,   a_       : Any=0.1     ,   a_       : Dict=0.1     ,   a_       : Optional[Any]=0.02     ,   a_       : int=True     ,   a_       : List[Any]=True     ,   a_       : int=10     ,   a_       : Dict=None     ,   )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[int]    					=  parent
      UpperCAmelCase_						:    Dict    					=  batch_size
      UpperCAmelCase_						:    str    					=  image_size
      UpperCAmelCase_						:    Any    					=  patch_size
      UpperCAmelCase_						:    int    					=  num_channels
      UpperCAmelCase_						:    Any    					=  last_hidden_size
      UpperCAmelCase_						:    str    					=  num_attention_heads
      UpperCAmelCase_						:    Union[str, Any]    					=  hidden_act
      UpperCAmelCase_						:    str    					=  conv_kernel_size
      UpperCAmelCase_						:    Union[str, Any]    					=  output_stride
      UpperCAmelCase_						:    Any    					=  hidden_dropout_prob
      UpperCAmelCase_						:    Union[str, Any]    					=  attention_probs_dropout_prob
      UpperCAmelCase_						:    List[str]    					=  classifier_dropout_prob
      UpperCAmelCase_						:    List[Any]    					=  use_labels
      UpperCAmelCase_						:    Optional[int]    					=  is_training
      UpperCAmelCase_						:    Tuple    					=  num_labels
      UpperCAmelCase_						:    Tuple    					=  initializer_range
      UpperCAmelCase_						:    Dict    					=  scope




 def   a      ( self       : Dict )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

      UpperCAmelCase_						:    str    					=  None
      UpperCAmelCase_						:    List[str]    					=  None
      if self.use_labels:
           UpperCAmelCase_						:    Dict    					=  ids_tensor([self.batch_size]     ,   self.num_labels )
           UpperCAmelCase_						:    int    					=  ids_tensor([self.batch_size, self.image_size, self.image_size]     ,   self.num_labels )

      UpperCAmelCase_						:    Dict    					=  self.get_config()

      return config, pixel_values, labels, pixel_labels




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



      """simple docstring"""

      return MobileViTConfig(
          image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   num_attention_heads=self.num_attention_heads     ,   hidden_act=self.hidden_act     ,   conv_kernel_size=self.conv_kernel_size     ,   output_stride=self.output_stride     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   classifier_dropout_prob=self.classifier_dropout_prob     ,   initializer_range=self.initializer_range     ,   )




 def   a      ( self       : List[str]     ,   a_       : List[Any]     ,   a_       : str     ,   a_       : Union[str, Any]     ,   a_       : Any )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  TFMobileViTModel(config=a_ )
      UpperCAmelCase_						:    Any    					=  model(a_     ,   training=a_ )
      UpperCAmelCase_						:    List[str]    					=  self.image_size // self.output_stride
      self.parent.assertEqual(
          result.last_hidden_state.shape     ,   (self.batch_size, self.last_hidden_size, expected_height, expected_width) )




 def   a      ( self       : Any     ,   a_       : Dict     ,   a_       : int     ,   a_       : List[Any]     ,   a_       : Optional[Any] )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.num_labels
      UpperCAmelCase_						:    List[str]    					=  TFMobileViTForImageClassification(a_ )
      UpperCAmelCase_						:    int    					=  model(a_     ,   labels=a_     ,   training=a_ )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels) )




 def   a      ( self       : List[str]     ,   a_       : Optional[int]     ,   a_       : Optional[Any]     ,   a_       : List[Any]     ,   a_       : Dict )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  self.num_labels
      UpperCAmelCase_						:    Tuple    					=  TFMobileViTForSemanticSegmentation(a_ )
      UpperCAmelCase_						:    Optional[int]    					=  self.image_size // self.output_stride

      UpperCAmelCase_						:    Union[str, Any]    					=  model(a_     ,   training=a_ )
      self.parent.assertEqual(
          result.logits.shape     ,   (self.batch_size, self.num_labels, expected_height, expected_width) )

      UpperCAmelCase_						:    str    					=  model(a_     ,   labels=a_     ,   training=a_ )
      self.parent.assertEqual(
          result.logits.shape     ,   (self.batch_size, self.num_labels, expected_height, expected_width) )






 def   a      ( self       : Tuple )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.prepare_config_and_inputs()
      UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_						:    List[str]    					=  config_and_inputs
      UpperCAmelCase_						:    Any    					=  {"""pixel_values""": pixel_values}
      return config, inputs_dict



@require_tf
class 		UpperCAmelCase_	(lowerCamelCase_  , lowerCamelCase_  , unittest.TestCase						):
 """simple docstring"""




 UpperCamelCase_						:     List[str]				   =    (
     (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
     if is_tf_available()
     else ()
 )
 UpperCamelCase_						:     Optional[Any]				   =    (
     {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
     if is_tf_available()
     else {}
 )

 UpperCamelCase_						:     List[str]				   =    False
 UpperCamelCase_						:     Any				   =    False
 UpperCamelCase_						:     Optional[int]				   =    False
 UpperCamelCase_						:     Any				   =    False
 UpperCamelCase_						:     Optional[int]				   =    False




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



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  TFMobileViTModelTester(self )
      UpperCAmelCase_						:    Union[str, Any]    					=  TFMobileViTConfigTester(self     ,   config_class=a_     ,   has_text_modality=a_ )




 def   a      ( self       : List[str] )->					int:



      """simple docstring"""

      self.config_tester.run_common_tests()




 @unittest.skip(reason="""MobileViT does not use inputs_embeds""" )
 def   a      ( self       : Tuple )->					List[str]:



      """simple docstring"""

      pass




 @unittest.skip(reason="""MobileViT does not support input and output embeddings""" )
 def   a      ( self       : Tuple )->					Any:



      """simple docstring"""

      pass




 @unittest.skip(reason="""MobileViT does not output attentions""" )
 def   a      ( self       : Any )->					int:



      """simple docstring"""

      pass




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



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    Tuple    					=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCAmelCase_						:    List[Any]    					=  model_class(a_ )
           UpperCAmelCase_						:    str    					=  inspect.signature(model.call )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCAmelCase_						:    int    					=  [*signature.parameters.keys()]

           UpperCAmelCase_						:    Union[str, Any]    					=  ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]     ,   a_ )




 def   a      ( self       : Dict )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[int]    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*a_ )




 def   a      ( self       : int )->					List[str]:



      """simple docstring"""

      def check_hidden_states_output(a_       : Dict     ,   a_       : Union[str, Any]     ,   a_       : Tuple ):
           UpperCAmelCase_						:    Optional[Any]    					=  model_class(a_ )

           UpperCAmelCase_						:    List[str]    					=  model(**self._prepare_for_class(a_     ,   a_ ) )

           UpperCAmelCase_						:    Dict    					=  outputs.hidden_states

           UpperCAmelCase_						:    Union[str, Any]    					=  5
           self.assertEqual(len(a_ )     ,   a_ )

           # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
           # with the width and height being successively divided by 2.
           UpperCAmelCase_						:    List[Any]    					=  2
           for i in range(len(a_ ) ):
                self.assertListEqual(
                    list(hidden_states[i].shape[-2:] )     ,   [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]     ,   )
                divisor *= 2

           self.assertEqual(self.model_tester.output_stride     ,   divisor // 2 )

      UpperCAmelCase_					,UpperCAmelCase_						:    str    					=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCAmelCase_						:    Union[str, 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_						:    List[Any]    					=  True

           check_hidden_states_output(a_     ,   a_     ,   a_ )




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



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*a_ )




 def   a      ( self       : Dict )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*a_ )




 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0     ,   reason="""TF does not support backprop for grouped convolutions on CPU."""     ,   )
 def   a      ( self       : Any )->					Union[str, Any]:



      """simple docstring"""

      super().test_dataset_conversion()




 def   a      ( self       : Optional[Any]     ,   a_       : List[Any]     ,   a_       : Dict     ,   a_       : Tuple=2E-1     ,   a_       : Optional[Any]=2E-1 )->					Optional[Any]:



      """simple docstring"""

      self.assertTrue(np.allclose(a_     ,   a_     ,   atol=a_     ,   rtol=a_ ) )




 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0     ,   reason="""TF does not support backprop for grouped convolutions on CPU."""     ,   )
 @slow
 def   a      ( self       : str )->					int:



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    Any    					=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
           if model_class.__name__ != "TFMobileViTModel":
                UpperCAmelCase_						:    Tuple    					=  model_class(a_ )
                if getattr(a_     ,   """hf_compute_loss"""     ,   a_ ):
                     super().test_keras_fit()




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



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    str    					=  self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           # set an ignore index to correctly test the masked loss used in
           # `TFMobileViTForSemanticSegmentation`.
           if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                UpperCAmelCase_						:    Optional[int]    					=  5

           UpperCAmelCase_						:    Dict    					=  model_class(a_ )
           if getattr(a_     ,   """hf_compute_loss"""     ,   a_ ):
                # The number of elements in the loss should be the same as the number of elements in the label
                UpperCAmelCase_						:    List[str]    					=  self._prepare_for_class(inputs_dict.copy()     ,   a_     ,   return_labels=a_ )
                UpperCAmelCase_						:    Optional[Any]    					=  prepared_for_class[
                    sorted(prepared_for_class.keys() - inputs_dict.keys()     ,   reverse=a_ )[0]
                ]
                UpperCAmelCase_						:    List[Any]    					=  added_label.shape.as_list()[:1]

                # Test that model correctly compute the loss with kwargs
                UpperCAmelCase_						:    str    					=  self._prepare_for_class(inputs_dict.copy()     ,   a_     ,   return_labels=a_ )
                UpperCAmelCase_						:    Tuple    					=  {"""input_ids""", """pixel_values""", """input_features"""}
                UpperCAmelCase_						:    Union[str, Any]    					=  possible_input_names.intersection(set(a_ ) ).pop()
                UpperCAmelCase_						:    Union[str, Any]    					=  prepared_for_class.pop(a_ )

                UpperCAmelCase_						:    Optional[Any]    					=  model(a_     ,   **a_ )[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

                # Test that model correctly compute the loss when we mask some positions
                UpperCAmelCase_						:    Any    					=  self._prepare_for_class(inputs_dict.copy()     ,   a_     ,   return_labels=a_ )
                UpperCAmelCase_						:    Optional[int]    					=  {"""input_ids""", """pixel_values""", """input_features"""}
                UpperCAmelCase_						:    str    					=  possible_input_names.intersection(set(a_ ) ).pop()
                UpperCAmelCase_						:    List[Any]    					=  prepared_for_class.pop(a_ )
                if "labels" in prepared_for_class:
                     UpperCAmelCase_						:    List[Any]    					=  prepared_for_class["""labels"""].numpy()
                     if len(labels.shape ) > 1 and labels.shape[1] != 1:
                          # labels[0] = -100
                          UpperCAmelCase_						:    List[Any]    					=  tf.convert_to_tensor(a_ )
                          UpperCAmelCase_						:    Union[str, Any]    					=  model(a_     ,   **a_ )[0]
                          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
                          self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )

                # Test that model correctly compute the loss with a dict
                UpperCAmelCase_						:    List[str]    					=  self._prepare_for_class(inputs_dict.copy()     ,   a_     ,   return_labels=a_ )
                UpperCAmelCase_						:    List[Any]    					=  model(a_ )[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

                # Test that model correctly compute the loss with a tuple
                UpperCAmelCase_						:    Any    					=  self._prepare_for_class(inputs_dict.copy()     ,   a_     ,   return_labels=a_ )

                # Get keys that were added with the _prepare_for_class function
                UpperCAmelCase_						:    Tuple    					=  prepared_for_class.keys() - inputs_dict.keys()
                UpperCAmelCase_						:    str    					=  inspect.signature(model.call ).parameters
                UpperCAmelCase_						:    Union[str, Any]    					=  list(signature.keys() )

                # Create a dictionary holding the location of the tensors in the tuple
                UpperCAmelCase_						:    Union[str, Any]    					=  {0: input_name}
                for label_key in label_keys:
                     UpperCAmelCase_						:    Union[str, Any]    					=  signature_names.index(a_ )
                     UpperCAmelCase_						:    int    					=  label_key
                UpperCAmelCase_						:    Any    					=  sorted(tuple_index_mapping.items() )
                # Initialize a list with their default values, update the values and convert to a tuple
                UpperCAmelCase_						:    int    					=  []

                for name in signature_names:
                     if name != "kwargs":
                          list_input.append(signature[name].default )

                for index, value in sorted_tuple_index_mapping:
                     UpperCAmelCase_						:    List[str]    					=  prepared_for_class[value]

                UpperCAmelCase_						:    Tuple    					=  tuple(a_ )

                # Send to model
                UpperCAmelCase_						:    List[str]    					=  model(tuple_input[:-1] )[0]

                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )






 @slow
 def   a      ( self       : Any )->					Tuple:



      """simple docstring"""

      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase_						:    Any    					=  TFMobileViTModel.from_pretrained(a_ )
           self.assertIsNotNone(a_ )




def 			A_			(					)     ->							Optional[int]:


     """simple docstring"""







     UpperCAmelCase_						:    Union[str, Any]    					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
     return image



@require_tf
class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




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



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""" )

      UpperCAmelCase_						:    Optional[int]    					=  MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small""" )
      UpperCAmelCase_						:    int    					=  prepare_img()
      UpperCAmelCase_						:    Any    					=  image_processor(images=a_     ,   return_tensors="""tf""" )

      # forward pass
      UpperCAmelCase_						:    Optional[int]    					=  model(**a_     ,   training=a_ )

      # verify the logits
      UpperCAmelCase_						:    Union[str, Any]    					=  tf.TensorShape((1, 10_00) )
      self.assertEqual(outputs.logits.shape     ,   a_ )

      UpperCAmelCase_						:    Optional[Any]    					=  tf.constant([-1.9364, -1.2327, -0.4653] )

      tf.debugging.assert_near(outputs.logits[0, :3]     ,   a_     ,   atol=1E-4     ,   rtol=1E-04 )






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



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )

      UpperCAmelCase_						:    Dict    					=  MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )

      UpperCAmelCase_						:    Tuple    					=  prepare_img()
      UpperCAmelCase_						:    Tuple    					=  image_processor(images=a_     ,   return_tensors="""tf""" )

      # forward pass
      UpperCAmelCase_						:    List[Any]    					=  model(inputs.pixel_values     ,   training=a_ )
      UpperCAmelCase_						:    Union[str, Any]    					=  outputs.logits

      # verify the logits
      UpperCAmelCase_						:    Optional[int]    					=  tf.TensorShape((1, 21, 32, 32) )
      self.assertEqual(logits.shape     ,   a_ )

      UpperCAmelCase_						:    int    					=  tf.constant(
          [
              [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
              [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
              [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
          ] )

      tf.debugging.assert_near(logits[0, :3, :3, :3]     ,   a_     ,   rtol=1E-4     ,   atol=1E-4 )



"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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




def 			A_			(					lowercase      )     ->							List[Any]:


     """simple docstring"""







     if "backbone.0.body" in orig_key:
          UpperCAmelCase_						:    Dict    					=  orig_key.replace("""backbone.0.body"""						, """backbone.conv_encoder.model"""      )
     if "transformer" in orig_key:
          UpperCAmelCase_						:    List[Any]    					=  orig_key.replace("""transformer."""						, """"""      )
     if "norm1" in orig_key:
          if "encoder" in orig_key:
               UpperCAmelCase_						:    List[str]    					=  orig_key.replace("""norm1"""						, """self_attn_layer_norm"""      )
          else:
               UpperCAmelCase_						:    Any    					=  orig_key.replace("""norm1"""						, """encoder_attn_layer_norm"""      )
     if "norm2" in orig_key:
          if "encoder" in orig_key:
               UpperCAmelCase_						:    Any    					=  orig_key.replace("""norm2"""						, """final_layer_norm"""      )
          else:
               UpperCAmelCase_						:    Union[str, Any]    					=  orig_key.replace("""norm2"""						, """self_attn_layer_norm"""      )
     if "norm3" in orig_key:
          UpperCAmelCase_						:    Optional[Any]    					=  orig_key.replace("""norm3"""						, """final_layer_norm"""      )
     if "linear1" in orig_key:
          UpperCAmelCase_						:    Union[str, Any]    					=  orig_key.replace("""linear1"""						, """fc1"""      )
     if "linear2" in orig_key:
          UpperCAmelCase_						:    List[str]    					=  orig_key.replace("""linear2"""						, """fc2"""      )
     if "query_embed" in orig_key:
          UpperCAmelCase_						:    str    					=  orig_key.replace("""query_embed"""						, """query_position_embeddings"""      )
     if "cross_attn" in orig_key:
          UpperCAmelCase_						:    Optional[int]    					=  orig_key.replace("""cross_attn"""						, """encoder_attn"""      )

     return orig_key




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


     """simple docstring"""







     for i in range(6      ):
          # read in weights + bias of input projection layer of self-attention
          UpperCAmelCase_						:    int    					=  state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
          UpperCAmelCase_						:    List[Any]    					=  state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_bias'''      )
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_						:    List[Any]    					=  in_proj_weight[:256, :]
          UpperCAmelCase_						:    Union[str, Any]    					=  in_proj_bias[:256]
          UpperCAmelCase_						:    int    					=  in_proj_weight[256:512, :]
          UpperCAmelCase_						:    List[Any]    					=  in_proj_bias[256:512]
          UpperCAmelCase_						:    List[Any]    					=  in_proj_weight[-256:, :]
          UpperCAmelCase_						:    List[str]    					=  in_proj_bias[-256:]




def 			A_			(					)     ->							Any:


     """simple docstring"""







     UpperCAmelCase_						:    List[str]    					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCAmelCase_						:    Union[str, Any]    					=  Image.open(requests.get(lowercase						, stream=lowercase      ).raw      )

     return im




@torch.no_grad()
def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, )     ->							List[Any]:


     """simple docstring"""







     UpperCAmelCase_						:    Dict    					=  DeformableDetrConfig()
     # set config attributes
     if single_scale:
          UpperCAmelCase_						:    Optional[Any]    					=  1
     UpperCAmelCase_						:    Dict    					=  dilation
     UpperCAmelCase_						:    Optional[int]    					=  with_box_refine
     UpperCAmelCase_						:    str    					=  two_stage
     # set labels
     UpperCAmelCase_						:    Optional[int]    					=  91
     UpperCAmelCase_						:    Dict    					=  """huggingface/label-files"""
     UpperCAmelCase_						:    int    					=  """coco-detection-id2label.json"""
     UpperCAmelCase_						:    List[str]    					=  json.load(open(cached_download(hf_hub_url(lowercase						, lowercase						, repo_type="""dataset"""      )      )						, """r"""      )      )
     UpperCAmelCase_						:    List[Any]    					=  {int(lowercase      ): v for k, v in idalabel.items()}
     UpperCAmelCase_						:    List[Any]    					=  idalabel
     UpperCAmelCase_						:    int    					=  {v: k for k, v in idalabel.items()}

     # load image processor
     UpperCAmelCase_						:    Union[str, Any]    					=  DeformableDetrImageProcessor(format="""coco_detection"""      )

     # prepare image
     UpperCAmelCase_						:    Tuple    					=  prepare_img()
     UpperCAmelCase_						:    Optional[int]    					=  image_processor(images=lowercase						, return_tensors="""pt"""      )
     UpperCAmelCase_						:    List[str]    					=  encoding["""pixel_values"""]

     logger.info("""Converting model..."""      )

     # load original state dict
     UpperCAmelCase_						:    Union[str, Any]    					=  torch.load(lowercase						, map_location="""cpu"""      )["""model"""]
     # rename keys
     for key in state_dict.copy().keys():
          UpperCAmelCase_						:    List[str]    					=  state_dict.pop(lowercase      )
          UpperCAmelCase_						:    Optional[int]    					=  val
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase      )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCAmelCase_						:    Optional[int]    					=  """model."""
     for key in state_dict.copy().keys():
          if not key.startswith("""class_embed"""      ) and not key.startswith("""bbox_embed"""      ):
               UpperCAmelCase_						:    Dict    					=  state_dict.pop(lowercase      )
               UpperCAmelCase_						:    Tuple    					=  val
    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_						:    List[Any]    					=  DeformableDetrForObjectDetection(lowercase      )
     model.load_state_dict(lowercase      )
     model.eval()

     UpperCAmelCase_						:    Optional[Any]    					=  """cuda""" if torch.cuda.is_available() else """cpu"""
     model.to(lowercase      )
     # verify our conversion
     UpperCAmelCase_						:    Dict    					=  model(pixel_values.to(lowercase      )      )

     UpperCAmelCase_						:    Optional[int]    					=  torch.tensor(
         [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]      )
     UpperCAmelCase_						:    Dict    					=  torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]      )

     if single_scale:
          UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor(
              [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
          UpperCAmelCase_						:    Optional[int]    					=  torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]      )

     if single_scale and dilation:
          UpperCAmelCase_						:    Any    					=  torch.tensor(
              [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
          UpperCAmelCase_						:    List[str]    					=  torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )

     if with_box_refine:
          UpperCAmelCase_						:    Optional[int]    					=  torch.tensor(
              [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
          UpperCAmelCase_						:    List[str]    					=  torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]      )

     if with_box_refine and two_stage:
          UpperCAmelCase_						:    str    					=  torch.tensor(
              [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
          UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]      )

     print("""Logits:"""						, outputs.logits[0, :3, :3]      )

     assert torch.allclose(outputs.logits[0, :3, :3]						, expected_logits.to(lowercase      )						, atol=1E-4      )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]						, expected_boxes.to(lowercase      )						, atol=1E-4      )

     print("""Everything ok!"""      )

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

     # Push to hub
     if push_to_hub:
          UpperCAmelCase_						:    Any    					=  """deformable-detr"""
          model_name += "-single-scale" if single_scale else ""
          model_name += "-dc5" if dilation else ""
          model_name += "-with-box-refine" if with_box_refine else ""
          model_name += "-two-stage" if two_stage else ""
          print("""Pushing model to hub..."""      )
          model.push_to_hub(repo_path_or_name=lowercase						, organization="""nielsr"""						, commit_message="""Add model"""      )


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

       parser.add_argument(
           "--checkpoint_path",
           type=str,
           default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
           help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
       )
       parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
       parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
       parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
       parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=str,
           required=True,
           help="Path to the folder to output PyTorch model.",
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )
       lowercase_						    =					parser.parse_args()
       convert_deformable_detr_checkpoint(
           args.checkpoint_path,
           args.single_scale,
           args.dilation,
           args.with_box_refine,
           args.two_stage,
           args.pytorch_dump_folder_path,
           args.push_to_hub,
       )

