



'''simple docstring'''






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 tensorflow as tf

   from transformers.models.swin.modeling_tf_swin import (
       TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFSwinForImageClassification,
       TFSwinForMaskedImageModeling,
       TFSwinModel,
   )


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_    ,a_=13    ,a_=32    ,a_=2    ,a_=3    ,a_=16    ,a_=[1, 2, 1]    ,a_=[2, 2, 4]    ,a_=2    ,a_=2.0    ,a_=True    ,a_=0.0    ,a_=0.0    ,a_=0.1    ,a_="gelu"    ,a_=False    ,a_=True    ,a_=0.02    ,a_=1E-5    ,a_=True    ,a_=None    ,a_=True    ,a_=10    ,a_=8    ,)						->  None:
        _UpperCAmelCase    :		int     	=					parent
        _UpperCAmelCase    :		Optional[int]     	=					batch_size
        _UpperCAmelCase    :		List[Any]     	=					image_size
        _UpperCAmelCase    :		Optional[int]     	=					patch_size
        _UpperCAmelCase    :		Union[str, Any]     	=					num_channels
        _UpperCAmelCase    :		Tuple     	=					embed_dim
        _UpperCAmelCase    :		Union[str, Any]     	=					depths
        _UpperCAmelCase    :		List[Any]     	=					num_heads
        _UpperCAmelCase    :		Optional[int]     	=					window_size
        _UpperCAmelCase    :		Tuple     	=					mlp_ratio
        _UpperCAmelCase    :		Optional[int]     	=					qkv_bias
        _UpperCAmelCase    :		Union[str, Any]     	=					hidden_dropout_prob
        _UpperCAmelCase    :		str     	=					attention_probs_dropout_prob
        _UpperCAmelCase    :		Optional[int]     	=					drop_path_rate
        _UpperCAmelCase    :		Tuple     	=					hidden_act
        _UpperCAmelCase    :		List[str]     	=					use_absolute_embeddings
        _UpperCAmelCase    :		int     	=					patch_norm
        _UpperCAmelCase    :		str     	=					layer_norm_eps
        _UpperCAmelCase    :		List[Any]     	=					initializer_range
        _UpperCAmelCase    :		Any     	=					is_training
        _UpperCAmelCase    :		Any     	=					scope
        _UpperCAmelCase    :		Optional[Any]     	=					use_labels
        _UpperCAmelCase    :		Dict     	=					type_sequence_label_size
        _UpperCAmelCase    :		str     	=					encoder_stride







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Any     	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        _UpperCAmelCase    :		str     	=					None
        if self.use_labels:
               _UpperCAmelCase    :		Dict     	=					ids_tensor([self.batch_size]    ,self.type_sequence_label_size				)

        _UpperCAmelCase    :		str     	=					self.get_config()

        return config, pixel_values, labels







 def _snake_case					(				self				)						->  Any:
        return SwinConfig(
            image_size=self.image_size    ,patch_size=self.patch_size    ,num_channels=self.num_channels    ,embed_dim=self.embed_dim    ,depths=self.depths    ,num_heads=self.num_heads    ,window_size=self.window_size    ,mlp_ratio=self.mlp_ratio    ,qkv_bias=self.qkv_bias    ,hidden_dropout_prob=self.hidden_dropout_prob    ,attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,drop_path_rate=self.drop_path_rate    ,hidden_act=self.hidden_act    ,use_absolute_embeddings=self.use_absolute_embeddings    ,path_norm=self.patch_norm    ,layer_norm_eps=self.layer_norm_eps    ,initializer_range=self.initializer_range    ,encoder_stride=self.encoder_stride    ,)







 def _snake_case					(				self    ,a_    ,a_    ,a_				)						->  Any:
        _UpperCAmelCase    :		List[str]     	=					TFSwinModel(config=a_				)
        _UpperCAmelCase    :		Optional[int]     	=					model(a_				)

        _UpperCAmelCase    :		Optional[int]     	=					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
        _UpperCAmelCase    :		str     	=					int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

        self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, expected_seq_len, expected_dim)				)







 def _snake_case					(				self    ,a_    ,a_    ,a_				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					TFSwinForMaskedImageModeling(config=a_				)
        _UpperCAmelCase    :		Any     	=					model(a_				)
        self.parent.assertEqual(
            result.logits.shape    ,(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

        # test greyscale images
        _UpperCAmelCase    :		Dict     	=					1
        _UpperCAmelCase    :		Optional[int]     	=					TFSwinForMaskedImageModeling(a_				)

        _UpperCAmelCase    :		Optional[int]     	=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
        _UpperCAmelCase    :		List[Any]     	=					model(a_				)
        self.parent.assertEqual(result.logits.shape    ,(self.batch_size, 1, self.image_size, self.image_size)				)







 def _snake_case					(				self    ,a_    ,a_    ,a_				)						->  Dict:
        _UpperCAmelCase    :		List[str]     	=					self.type_sequence_label_size
        _UpperCAmelCase    :		int     	=					TFSwinForImageClassification(a_				)
        _UpperCAmelCase    :		Optional[Any]     	=					model(a_    ,labels=a_				)
        self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)				)

        # test greyscale images
        _UpperCAmelCase    :		str     	=					1
        _UpperCAmelCase    :		List[Any]     	=					TFSwinForImageClassification(a_				)
        _UpperCAmelCase    :		Tuple     	=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
        _UpperCAmelCase    :		Union[str, Any]     	=					model(a_				)
        self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)				)







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		str     	=					self.prepare_config_and_inputs()
        _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		List[str]     	=					config_and_inputs
        _UpperCAmelCase    :		Dict     	=					{"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_tf
class 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    (
     (
         TFSwinModel,
         TFSwinForImageClassification,
         TFSwinForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 UpperCAmelCase		      =    (
     {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
     if is_tf_available()
     else {}
 )

 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		List[Any]     	=					TFSwinModelTester(self				)
        _UpperCAmelCase    :		Dict     	=					ConfigTester(self    ,config_class=a_    ,embed_dim=37				)







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







 def _snake_case					(				self				)						->  List[Any]:
        _UpperCAmelCase    :		Optional[int]     	=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_				)







 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		Dict     	=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*a_				)







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		Any     	=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*a_				)







 @unittest.skip(reason="""Swin does not use inputs_embeds"""				)
 def _snake_case					(				self				)						->  Dict:
        pass







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase	,_UpperCAmelCase    :		int     	=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _UpperCAmelCase    :		Optional[Any]     	=					model_class(a_				)
               self.assertIsInstance(model.get_input_embeddings()    ,tf.keras.layers.Layer				)
               _UpperCAmelCase    :		Any     	=					model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(a_    ,tf.keras.layers.Dense				)				)







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase	,_UpperCAmelCase    :		Optional[int]     	=					self.model_tester.prepare_config_and_inputs_for_common()

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

               _UpperCAmelCase    :		Tuple     	=					["""pixel_values"""]
               self.assertListEqual(arg_names[:1]    ,a_				)







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase	,_UpperCAmelCase    :		str     	=					self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase    :		str     	=					True

        for model_class in self.all_model_classes:
               _UpperCAmelCase    :		Dict     	=					True
               _UpperCAmelCase    :		Dict     	=					False
               _UpperCAmelCase    :		Any     	=					True
               _UpperCAmelCase    :		int     	=					model_class(a_				)
               _UpperCAmelCase    :		Optional[Any]     	=					model(**self._prepare_for_class(a_    ,a_				)				)
               _UpperCAmelCase    :		List[str]     	=					outputs.attentions
               _UpperCAmelCase    :		int     	=					len(self.model_tester.depths				)
               self.assertEqual(len(a_				)    ,a_				)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _UpperCAmelCase    :		str     	=					True
               _UpperCAmelCase    :		Optional[int]     	=					config.window_size**2
               _UpperCAmelCase    :		Optional[int]     	=					model_class(a_				)
               _UpperCAmelCase    :		List[Any]     	=					model(**self._prepare_for_class(a_    ,a_				)				)
               _UpperCAmelCase    :		List[Any]     	=					outputs.attentions
               self.assertEqual(len(a_				)    ,a_				)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]				)    ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,)
               _UpperCAmelCase    :		List[Any]     	=					len(a_				)

               # Check attention is always last and order is fine
               _UpperCAmelCase    :		Any     	=					True
               _UpperCAmelCase    :		Any     	=					True
               _UpperCAmelCase    :		Union[str, Any]     	=					model_class(a_				)
               _UpperCAmelCase    :		str     	=					model(**self._prepare_for_class(a_    ,a_				)				)

               if hasattr(self.model_tester    ,"""num_hidden_states_types"""				):
                      _UpperCAmelCase    :		Tuple     	=					self.model_tester.num_hidden_states_types
               else:
                      # also another +1 for reshaped_hidden_states
                      _UpperCAmelCase    :		Dict     	=					2
               self.assertEqual(out_len + added_hidden_states    ,len(a_				)				)

               _UpperCAmelCase    :		int     	=					outputs.attentions

               self.assertEqual(len(a_				)    ,a_				)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]				)    ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,)







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_				)						->  Union[str, Any]:
        _UpperCAmelCase    :		List[Any]     	=					model_class(a_				)
        _UpperCAmelCase    :		Dict     	=					model(**self._prepare_for_class(a_    ,a_				)				)
        _UpperCAmelCase    :		List[Any]     	=					outputs.hidden_states

        _UpperCAmelCase    :		List[str]     	=					getattr(
            self.model_tester    ,"""expected_num_hidden_layers"""    ,len(self.model_tester.depths				) + 1				)
        self.assertEqual(len(a_				)    ,a_				)

        # Swin has a different seq_length
        _UpperCAmelCase    :		Tuple     	=					to_atuple(config.patch_size				)

        _UpperCAmelCase    :		List[str]     	=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]				)    ,[num_patches, self.model_tester.embed_dim]    ,)

        _UpperCAmelCase    :		Union[str, Any]     	=					outputs.reshaped_hidden_states
        self.assertEqual(len(a_				)    ,a_				)

        _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		Optional[int]     	=					reshaped_hidden_states[0].shape

        _UpperCAmelCase    :		List[Any]     	=					tf.reshape(reshaped_hidden_states[0]    ,(batch_size, num_channels, height * width)				)
        _UpperCAmelCase    :		Optional[int]     	=					tf.transpose(a_    ,(0, 2, 1)				)

        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]				)    ,[num_patches, self.model_tester.embed_dim]    ,)







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase	,_UpperCAmelCase    :		List[Any]     	=					self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase    :		int     	=					to_atuple(self.model_tester.image_size				)

        for model_class in self.all_model_classes:
               _UpperCAmelCase    :		Tuple     	=					True
               self.check_hidden_states_output(a_    ,a_    ,a_    ,a_				)

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

               self.check_hidden_states_output(a_    ,a_    ,a_    ,a_				)







 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase	,_UpperCAmelCase    :		str     	=					self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase    :		Union[str, Any]     	=					3

        _UpperCAmelCase    :		Optional[int]     	=					to_atuple(self.model_tester.image_size				)
        _UpperCAmelCase    :		Dict     	=					to_atuple(config.patch_size				)

        _UpperCAmelCase    :		Optional[int]     	=					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        _UpperCAmelCase    :		int     	=					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
               _UpperCAmelCase    :		str     	=					True
               self.check_hidden_states_output(a_    ,a_    ,a_    ,(padded_height, padded_width)				)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _UpperCAmelCase    :		Any     	=					True
               self.check_hidden_states_output(a_    ,a_    ,a_    ,(padded_height, padded_width)				)







 @slow
 def _snake_case					(				self				)						->  Any:
        for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase    :		Any     	=					TFSwinModel.from_pretrained(a_				)
               self.assertIsNotNone(a_				)







@require_vision
@require_tf
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 @cached_property
 def _snake_case					(				self				)						->  Optional[Any]:
        return (
            AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""				)
            if is_vision_available()
            else None
        )







 @slow
 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		str     	=					TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""				)
        _UpperCAmelCase    :		Optional[int]     	=					self.default_image_processor

        _UpperCAmelCase    :		Dict     	=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
        _UpperCAmelCase    :		Tuple     	=					image_processor(images=a_    ,return_tensors="""tf"""				)

        # forward pass
        _UpperCAmelCase    :		Optional[Any]     	=					model(a_				)

        # verify the logits
        _UpperCAmelCase    :		Optional[int]     	=					tf.TensorShape((1, 1_000)				)
        self.assertEqual(outputs.logits.shape    ,a_				)
        _UpperCAmelCase    :		Any     	=					tf.constant([-0.0948, -0.6454, -0.0921]				)
        self.assertTrue(np.allclose(outputs.logits[0, :3]    ,a_    ,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()
snake_case__		:  List[str]     					=					logging.get_logger(__name__)


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

      return orig_key


def   _snake_case    (    _snake_case					:      List[str]      ):
      # transformer decoder self-attention layers
      for i in range(6      ):
            # read in weights + bias of input projection layer of self-attention
            lowerCAmelCase       :						str							= state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
            lowerCAmelCase       :						str							= 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
            lowerCAmelCase       :						Tuple							= in_proj_weight[:256, :]
            lowerCAmelCase       :						Optional[int]							= in_proj_bias[:256]
            lowerCAmelCase       :						int							= in_proj_weight[256:512, :]
            lowerCAmelCase       :						Optional[int]							= in_proj_bias[256:512]
            lowerCAmelCase       :						Any							= in_proj_weight[-256:, :]
            lowerCAmelCase       :						str							= in_proj_bias[-256:]


def   _snake_case    (    ):
      lowerCAmelCase       :						List[str]							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase       :						Dict							= Image.open(requests.get(_snake_case			, stream=_snake_case      ).raw      )

      return im


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

      # load image processor
      lowerCAmelCase       :						str							= DeformableDetrImageProcessor(format='''coco_detection'''      )

      # prepare image
      lowerCAmelCase       :						Tuple							= prepare_img()
      lowerCAmelCase       :						int							= image_processor(images=_snake_case			, return_tensors='''pt'''      )
      lowerCAmelCase       :						int							= encoding['''pixel_values''']

      logger.info('''Converting model...'''      )

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

      lowerCAmelCase       :						List[str]							= '''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(_snake_case      )
      # verify our conversion
      lowerCAmelCase       :						Dict							= model(pixel_values.to(_snake_case      )      )

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

      if single_scale:
            lowerCAmelCase       :						List[Any]							= torch.tensor(
                [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
            lowerCAmelCase       :						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:
            lowerCAmelCase       :						str							= torch.tensor(
                [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
            lowerCAmelCase       :						Tuple							= torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )

      if with_box_refine:
            lowerCAmelCase       :						Optional[Any]							= torch.tensor(
                [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
            lowerCAmelCase       :						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:
            lowerCAmelCase       :						Dict							= torch.tensor(
                [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
            lowerCAmelCase       :						Union[str, 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(_snake_case      )			, atol=1E-4      )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]			, expected_boxes.to(_snake_case      )			, 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(_snake_case      ).mkdir(exist_ok=_snake_case      )
      model.save_pretrained(_snake_case      )
      image_processor.save_pretrained(_snake_case      )

      # Push to hub
      if push_to_hub:
            lowerCAmelCase       :						Optional[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=_snake_case			, organization='''nielsr'''			, commit_message='''Add model'''      )


if __name__ == "__main__":
 snake_case__		:  Union[str, Any]     					=					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.'''
 )
 snake_case__		:  Union[str, Any]     					=					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,
 )


