

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
 import torch



class 						lowercase_					:



      def __init__(    self   :       List[str]    ,      A__   :       List[str]    ,      A__   :       int=14    ,      A__   :       Optional[Any]=7    ,      A__   :       Dict=True    ,      A__   :       Dict=True    ,      A__   :       Optional[Any]=False    ,      A__   :       str=True    ,      A__   :       List[Any]=99    ,      A__   :       Tuple=32    ,      A__   :       List[Any]=5    ,      A__   :       Union[str, Any]=4    ,      A__   :       Union[str, Any]=37    ,      A__   :       Any="gelu"    ,      A__   :       Optional[int]=0.1    ,      A__   :       Dict=0.1    ,      A__   :       List[str]=512    ,      A__   :       Optional[Any]=0.02    ,      )					->  List[Any]:
        _snake_case		=							parent
        _snake_case		=							batch_size
        _snake_case		=							seq_length
        _snake_case		=							is_training
        _snake_case		=							use_input_mask
        _snake_case		=							use_token_type_ids
        _snake_case		=							use_labels
        _snake_case		=							vocab_size
        _snake_case		=							hidden_size
        _snake_case		=							num_hidden_layers
        _snake_case		=							num_attention_heads
        _snake_case		=							intermediate_size
        _snake_case		=							hidden_act
        _snake_case		=							hidden_dropout_prob
        _snake_case		=							attention_probs_dropout_prob
        _snake_case		=							max_position_embeddings
        _snake_case		=							initializer_range
        _snake_case		=							None
        _snake_case		=							vocab_size - 1
        _snake_case		=							vocab_size - 1
        _snake_case		=							vocab_size - 1



      def 		UpperCamelCase_	(    self   :       List[str]	)					->  List[Any]:
        _snake_case		=							ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size	)

        _snake_case		=							None
        if self.use_input_mask:
          _snake_case		=							random_attention_mask([self.batch_size, self.seq_length]	)

        _snake_case		=							GPTaConfig(
            vocab_size=self.vocab_size    ,      n_embd=self.hidden_size    ,      n_layer=self.num_hidden_layers    ,      n_head=self.num_attention_heads    ,      n_positions=self.max_position_embeddings    ,      use_cache=lowerCamelCase__    ,      bos_token_id=self.bos_token_id    ,      eos_token_id=self.eos_token_id    ,      pad_token_id=self.pad_token_id    ,      )

        return (config, input_ids, input_mask)



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Tuple:
        _snake_case		=							self.prepare_config_and_inputs()
        _snake_case		=							config_and_inputs
        _snake_case		=							{"input_ids": input_ids, "attention_mask": attention_mask}
        return config, inputs_dict



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  Any:
        _snake_case		=							self.prepare_config_and_inputs()

        _snake_case		=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
        _snake_case		=							ids_tensor([self.batch_size, self.seq_length]    ,      vocab_size=2	)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )



      def 		UpperCamelCase_	(    self   :       Optional[Any]    ,      A__   :       Optional[Any]    ,      A__   :       Union[str, Any]    ,      A__   :       List[Any]    ,      A__   :       Optional[int]	)					->  str:
        _snake_case		=							20
        _snake_case		=							model_class_name(lowerCamelCase__	)

        _snake_case		=							model.init_cache(input_ids.shape[0]    ,      lowerCamelCase__	)
        _snake_case		=							jnp.ones((input_ids.shape[0], max_decoder_length)    ,      dtype='''i4'''	)

        _snake_case		=							jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)	)
        _snake_case		=							model(
            input_ids[:, :-1]    ,      attention_mask=lowerCamelCase__    ,      past_key_values=lowerCamelCase__    ,      position_ids=lowerCamelCase__    ,      )

        _snake_case		=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype='''i4'''	)
        _snake_case		=							model(
            input_ids[:, -1:]    ,      attention_mask=lowerCamelCase__    ,      past_key_values=outputs_cache.past_key_values    ,      position_ids=lowerCamelCase__    ,      )

        _snake_case		=							model(lowerCamelCase__	)

        _snake_case		=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3    ,      msg=f"""Max diff is {diff}"""	)







      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       Optional[int]    ,      A__   :       Tuple    ,      A__   :       int    ,      A__   :       int	)					->  str:
        _snake_case		=							20
        _snake_case		=							model_class_name(lowerCamelCase__	)

        _snake_case		=							jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]    ,      axis=-1    ,      )

        _snake_case		=							model.init_cache(input_ids.shape[0]    ,      lowerCamelCase__	)
        _snake_case		=							jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)	)

        _snake_case		=							model(
            input_ids[:, :-1]    ,      attention_mask=lowerCamelCase__    ,      past_key_values=lowerCamelCase__    ,      position_ids=lowerCamelCase__    ,      )
        _snake_case		=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype='''i4'''	)
        _snake_case		=							model(
            input_ids[:, -1:]    ,      past_key_values=outputs_cache.past_key_values    ,      attention_mask=lowerCamelCase__    ,      position_ids=lowerCamelCase__    ,      )

        _snake_case		=							model(lowerCamelCase__    ,      attention_mask=lowerCamelCase__	)

        _snake_case		=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3    ,      msg=f"""Max diff is {diff}"""	)



@require_flax
class 						lowercase_					(						UpperCAmelCase__     ,							UpperCAmelCase__     ,							unittest.TestCase  ):
      UpperCamelCase_    :	Optional[int]		   =       (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      UpperCamelCase_    :	Union[str, Any]		   =       (FlaxGPTaLMHeadModel,) if is_flax_available() else ()



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  int:
        _snake_case		=							FlaxGPTaModelTester(self	)



      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  Optional[Any]:
        for model_class_name in self.all_model_classes:
          _snake_case		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowerCamelCase__    ,      lowerCamelCase__    ,      lowerCamelCase__    ,      lowerCamelCase__	)



      def 		UpperCamelCase_	(    self   :       str	)					->  str:
        for model_class_name in self.all_model_classes:
          _snake_case		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              lowerCamelCase__    ,      lowerCamelCase__    ,      lowerCamelCase__    ,      lowerCamelCase__	)



      @slow
      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Any:
        _snake_case		=							GPTaTokenizer.from_pretrained('''gpt2'''    ,      pad_token='''</s>'''    ,      padding_side='''left'''	)
        _snake_case		=							tokenizer(['''Hello this is a long string''', '''Hey''']    ,      return_tensors='''np'''    ,      padding=lowerCamelCase__    ,      truncation=lowerCamelCase__	)

        _snake_case		=							FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''	)
        _snake_case		=							False
        _snake_case		=							model.config.eos_token_id

        _snake_case		=							jax.jit(model.generate	)

        _snake_case		=							jit_generate(inputs['''input_ids''']    ,      attention_mask=inputs['''attention_mask''']	).sequences

        _snake_case		=							tokenizer.batch_decode(lowerCamelCase__    ,      skip_special_tokens=lowerCamelCase__	)

        _snake_case		=							[
            "Hello this is a long string of words. I'm going to start with the first one.\n",
            "Hey, I'm not sure if I'm going to be able to do",
        ]

        self.assertListEqual(lowerCamelCase__    ,      lowerCamelCase__	)



      @is_pt_flax_cross_test
      def 		UpperCamelCase_	(    self   :       str	)					->  Optional[int]:
        _snake_case		=							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__	):
            # prepare inputs
            _snake_case		=							self._prepare_for_class(lowerCamelCase__    ,      lowerCamelCase__	)
            _snake_case		=							{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

            # load corresponding PyTorch class
            _snake_case		=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
            _snake_case		=							getattr(lowerCamelCase__    ,      lowerCamelCase__	)

            _snake_case		=							pt_inputs["input_ids"].shape
            _snake_case		=							np.random.randint(0    ,      seq_length - 1    ,      size=(batch_size,)	)
            for batch_idx, start_index in enumerate(lowerCamelCase__	):
              _snake_case		=							0
              _snake_case		=							1
              _snake_case		=							0
              _snake_case		=							1
            _snake_case		=							pt_model_class(lowerCamelCase__	).eval()
            _snake_case		=							model_class(lowerCamelCase__    ,      dtype=jnp.floataa	)

            _snake_case		=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,      lowerCamelCase__	)
            _snake_case		=							fx_state

            with torch.no_grad():
              _snake_case		=							pt_model(**lowerCamelCase__	).to_tuple()

            _snake_case		=							fx_model(**lowerCamelCase__	).to_tuple()
            self.assertEqual(len(lowerCamelCase__	)    ,      len(lowerCamelCase__	)    ,      '''Output lengths differ between Flax and PyTorch'''	)
            for fx_output, pt_output in zip(lowerCamelCase__    ,      lowerCamelCase__	):
              self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4e-2	)

            with tempfile.TemporaryDirectory() as tmpdirname:
              pt_model.save_pretrained(lowerCamelCase__	)
              _snake_case		=							model_class.from_pretrained(lowerCamelCase__    ,      from_pt=lowerCamelCase__	)

            _snake_case		=							fx_model_loaded(**lowerCamelCase__	).to_tuple()
            self.assertEqual(
                len(lowerCamelCase__	)    ,      len(lowerCamelCase__	)    ,      '''Output lengths differ between Flax and PyTorch'''	)
            for fx_output_loaded, pt_output in zip(lowerCamelCase__    ,      lowerCamelCase__	):
              self.assert_almost_equals(fx_output_loaded[:, -1]    ,      pt_output[:, -1].numpy()    ,      4e-2	)



      @is_pt_flax_cross_test
      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Union[str, Any]:
        _snake_case		=							self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__	):
            # prepare inputs
            _snake_case		=							self._prepare_for_class(lowerCamelCase__    ,      lowerCamelCase__	)
            _snake_case		=							{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

            # load corresponding PyTorch class
            _snake_case		=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
            _snake_case		=							getattr(lowerCamelCase__    ,      lowerCamelCase__	)

            _snake_case		=							pt_model_class(lowerCamelCase__	).eval()
            _snake_case		=							model_class(lowerCamelCase__    ,      dtype=jnp.floataa	)

            _snake_case		=							load_flax_weights_in_pytorch_model(lowerCamelCase__    ,      fx_model.params	)
            _snake_case		=							pt_inputs["input_ids"].shape
            _snake_case		=							np.random.randint(0    ,      seq_length - 1    ,      size=(batch_size,)	)
            for batch_idx, start_index in enumerate(lowerCamelCase__	):
              _snake_case		=							0
              _snake_case		=							1
              _snake_case		=							0
              _snake_case		=							1

            # make sure weights are tied in PyTorch
            pt_model.tie_weights()

            with torch.no_grad():
              _snake_case		=							pt_model(**lowerCamelCase__	).to_tuple()

            _snake_case		=							fx_model(**lowerCamelCase__	).to_tuple()
            self.assertEqual(len(lowerCamelCase__	)    ,      len(lowerCamelCase__	)    ,      '''Output lengths differ between Flax and PyTorch'''	)
            for fx_output, pt_output in zip(lowerCamelCase__    ,      lowerCamelCase__	):
              self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4e-2	)

            with tempfile.TemporaryDirectory() as tmpdirname:
              fx_model.save_pretrained(lowerCamelCase__	)
              _snake_case		=							pt_model_class.from_pretrained(lowerCamelCase__    ,      from_flax=lowerCamelCase__	)

            with torch.no_grad():
              _snake_case		=							pt_model_loaded(**lowerCamelCase__	).to_tuple()

            self.assertEqual(
                len(lowerCamelCase__	)    ,      len(lowerCamelCase__	)    ,      '''Output lengths differ between Flax and PyTorch'''	)
            for fx_output, pt_output in zip(lowerCamelCase__    ,      lowerCamelCase__	):
              self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4e-2	)







      @slow
      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Optional[int]:
        for model_class_name in self.all_model_classes:
          _snake_case		=							model_class_name.from_pretrained('''gpt2'''    ,      from_pt=lowerCamelCase__	)
          _snake_case		=							model(np.ones((1, 1)	)	)
          self.assertIsNotNone(lowerCamelCase__	)




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()
__A    	=			logging.get_logger(__name__)



def    snake_case_(_UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  if "backbone.0.body" in orig_key:
    _snake_case		=							orig_key.replace('''backbone.0.body'''				, '''backbone.conv_encoder.model'''  )
  if "transformer" in orig_key:
    _snake_case		=							orig_key.replace('''transformer.'''				, ''''''  )
  if "norm1" in orig_key:
    if "encoder" in orig_key:
      _snake_case		=							orig_key.replace('''norm1'''				, '''self_attn_layer_norm'''  )
    else:
      _snake_case		=							orig_key.replace('''norm1'''				, '''encoder_attn_layer_norm'''  )
  if "norm2" in orig_key:
    if "encoder" in orig_key:
      _snake_case		=							orig_key.replace('''norm2'''				, '''final_layer_norm'''  )
    else:
      _snake_case		=							orig_key.replace('''norm2'''				, '''self_attn_layer_norm'''  )
  if "norm3" in orig_key:
    _snake_case		=							orig_key.replace('''norm3'''				, '''final_layer_norm'''  )
  if "linear1" in orig_key:
    _snake_case		=							orig_key.replace('''linear1'''				, '''fc1'''  )
  if "linear2" in orig_key:
    _snake_case		=							orig_key.replace('''linear2'''				, '''fc2'''  )
  if "query_embed" in orig_key:
    _snake_case		=							orig_key.replace('''query_embed'''				, '''query_position_embeddings'''  )
  if "cross_attn" in orig_key:
    _snake_case		=							orig_key.replace('''cross_attn'''				, '''encoder_attn'''  )

  return orig_key



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  for i in range(6  ):
    # read in weights + bias of input projection layer of self-attention
    _snake_case		=							state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""  )
    _snake_case		=							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
    _snake_case		=							in_proj_weight[:256, :]
    _snake_case		=							in_proj_bias[:256]
    _snake_case		=							in_proj_weight[256:512, :]
    _snake_case		=							in_proj_bias[256:512]
    _snake_case		=							in_proj_weight[-256:, :]
    _snake_case		=							in_proj_bias[-256:]



def    snake_case_()							->			int:



  """simple docstring"""






  _snake_case		=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case		=							Image.open(requests.get(_UpperCamelCase				, stream=_UpperCamelCase  ).raw  )

  return im



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, )							->			Dict:



  """simple docstring"""






  _snake_case		=							DeformableDetrConfig()
  # set config attributes
  if single_scale:
    _snake_case		=							1
  _snake_case		=							dilation
  _snake_case		=							with_box_refine
  _snake_case		=							two_stage
  # set labels
  _snake_case		=							91
  _snake_case		=							'''huggingface/label-files'''
  _snake_case		=							'''coco-detection-id2label.json'''
  _snake_case		=							json.load(open(cached_download(hf_hub_url(_UpperCamelCase				, _UpperCamelCase				, repo_type='''dataset'''  )  )				, '''r'''  )  )
  _snake_case		=							{int(_UpperCamelCase  ): v for k, v in idalabel.items()}
  _snake_case		=							idalabel
  _snake_case		=							{v: k for k, v in idalabel.items()}

  # load image processor
  _snake_case		=							DeformableDetrImageProcessor(format='''coco_detection'''  )

  # prepare image
  _snake_case		=							prepare_img()
  _snake_case		=							image_processor(images=_UpperCamelCase				, return_tensors='''pt'''  )
  _snake_case		=							encoding['''pixel_values''']

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

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

  _snake_case		=							'''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(_UpperCamelCase  )
  # verify our conversion
  _snake_case		=							model(pixel_values.to(_UpperCamelCase  )  )

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

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

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

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


if __name__ == "__main__":
 __A    	=			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.'''
 )
 __A    	=			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,
 )

