import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor
     from transformers.image_transforms import get_image_size



class 			UpperCamelCase					(			unittest.TestCase			):

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_=7					,					A_=3					,					A_=18					,					A_=30					,					A_=4_00					,					A_=True					,					A_=1 / 2_55					,					A_=True					,					A_=8					,					)			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					parent
           _lowerCamelCase           =					batch_size
           _lowerCamelCase           =					num_channels
           _lowerCamelCase           =					image_size
           _lowerCamelCase           =					min_resolution
           _lowerCamelCase           =					max_resolution
           _lowerCamelCase           =					do_rescale
           _lowerCamelCase           =					rescale_factor
           _lowerCamelCase           =					do_pad
           _lowerCamelCase           =					pad_size



     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           return {
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
               "pad_size": self.pad_size,
           }





     def 		UpperCamelCase_    (       self					,					A_=False					,					A_=False					,					A_=False    )			->					List[Any]:






           """simple docstring"""
           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
                 _lowerCamelCase           =					[]
                 for i in range(self.batch_size    ):
                       image_inputs.append(
                           np.random.randint(
                               2_55					,					size=(self.num_channels, self.max_resolution, self.max_resolution)					,					dtype=np.uinta    )    )
           else:
                 _lowerCamelCase           =					[]
                 for i in range(self.batch_size    ):
                       _lowerCamelCase           =					np.random.choice(np.arange(self.min_resolution					,					self.max_resolution    )					,					2    )
                       image_inputs.append(np.random.randint(2_55					,					size=(self.num_channels, width, height)					,					dtype=np.uinta    )    )

           if not numpify and not torchify:
                 # PIL expects the channel dimension as last dimension
                 _lowerCamelCase           =					[Image.fromarray(np.moveaxis(snake_case__					,					0					,					-1    )    ) for x in image_inputs]

           if torchify:
                 _lowerCamelCase           =					[torch.from_numpy(snake_case__    ) for x in image_inputs]

           return image_inputs





@require_torch
@require_vision
class 			UpperCamelCase					(			_lowercase ,	unittest.TestCase			):

     '''simple docstring'''

     A_					    =	SwinaSRImageProcessor if is_vision_available() else None



     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					SwinaSRImageProcessingTester(self    )



     @property
     def 		UpperCamelCase_    (       self    )			->					List[str]:






           """simple docstring"""
           return self.image_processor_tester.prepare_image_processor_dict()



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					self.image_processing_class(**self.image_processor_dict    )
           self.assertTrue(hasattr(snake_case__					,					'''do_rescale'''    )    )
           self.assertTrue(hasattr(snake_case__					,					'''rescale_factor'''    )    )
           self.assertTrue(hasattr(snake_case__					,					'''do_pad'''    )    )
           self.assertTrue(hasattr(snake_case__					,					'''pad_size'''    )    )



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






           """simple docstring"""
           pass



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






           """simple docstring"""
           _lowerCamelCase           =					get_image_size(snake_case__    )
           _lowerCamelCase           =					self.image_processor_tester.pad_size

           _lowerCamelCase           =					(old_height // size + 1) * size - old_height
           _lowerCamelCase           =					(old_width // size + 1) * size - old_width
           return old_height + pad_height, old_width + pad_width



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






           """simple docstring"""
           _lowerCamelCase           =					self.image_processing_class(**self.image_processor_dict    )
           # create random PIL images
           _lowerCamelCase           =					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__    )
           for image in image_inputs:
                 self.assertIsInstance(snake_case__					,					Image.Image    )

           # Test not batched input
           _lowerCamelCase           =					image_processor(image_inputs[0]					,					return_tensors='''pt'''    ).pixel_values
           _lowerCamelCase           =					self.calculate_expected_size(np.array(image_inputs[0]    )    )
           self.assertEqual(
               encoded_images.shape					,					(
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,					)



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






           """simple docstring"""
           _lowerCamelCase           =					self.image_processing_class(**self.image_processor_dict    )
           # create random numpy tensors
           _lowerCamelCase           =					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__					,					numpify=snake_case__    )
           for image in image_inputs:
                 self.assertIsInstance(snake_case__					,					np.ndarray    )

           # Test not batched input
           _lowerCamelCase           =					image_processor(image_inputs[0]					,					return_tensors='''pt'''    ).pixel_values
           _lowerCamelCase           =					self.calculate_expected_size(image_inputs[0]    )
           self.assertEqual(
               encoded_images.shape					,					(
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,					)





     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					self.image_processing_class(**self.image_processor_dict    )
           # create random PyTorch tensors
           _lowerCamelCase           =					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__					,					torchify=snake_case__    )
           for image in image_inputs:
                 self.assertIsInstance(snake_case__					,					torch.Tensor    )

           # Test not batched input
           _lowerCamelCase           =					image_processor(image_inputs[0]					,					return_tensors='''pt'''    ).pixel_values
           _lowerCamelCase           =					self.calculate_expected_size(image_inputs[0]    )
           self.assertEqual(
               encoded_images.shape					,					(
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,					)

"""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		:    List[str]			=       logging.get_logger(__name__)


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      str:


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

					return orig_key


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      List[str]:


					'''simple docstring'''
					# transformer decoder self-attention layers
					for i in range(6			):
										# read in weights + bias of input projection layer of self-attention
										__a:    Optional[Any]		     =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
										__a:    List[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
										__a:    Optional[int]		     =       in_proj_weight[:2_56, :]
										__a:    int		     =       in_proj_bias[:2_56]
										__a:    Tuple		     =       in_proj_weight[2_56:5_12, :]
										__a:    Union[str, Any]		     =       in_proj_bias[2_56:5_12]
										__a:    Optional[int]		     =       in_proj_weight[-2_56:, :]
										__a:    Any		     =       in_proj_bias[-2_56:]


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


					'''simple docstring'''
					__a:    List[Any]		     =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__a:    Tuple		     =       Image.open(requests.get(lowerCamelCase_						,					stream=lowerCamelCase_			).raw			)

					return im


@torch.no_grad()
def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					)  ->      Union[str, Any]:


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

					# load image processor
					__a:    Tuple		     =       DeformableDetrImageProcessor(format="""coco_detection"""			)

					# prepare image
					__a:    Tuple		     =       prepare_img()
					__a:    Optional[int]		     =       image_processor(images=lowerCamelCase_						,					return_tensors="""pt"""			)
					__a:    Any		     =       encoding["""pixel_values"""]

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

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

					__a:    List[str]		     =       """cuda""" if torch.cuda.is_available() else """cpu"""
					model.to(lowerCamelCase_			)
					# verify our conversion
					__a:    str		     =       model(pixel_values.to(lowerCamelCase_			)			)

					__a:    Union[str, Any]		     =       torch.tensor(
					    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]			)
					__a:    Union[str, Any]		     =       torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]			)

					if single_scale:
										__a:    Optional[Any]		     =       torch.tensor(
										    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]			)
										__a:    Any		     =       torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]			)

					if single_scale and dilation:
										__a:    Dict		     =       torch.tensor(
										    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]			)
										__a:    int		     =       torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]			)

					if with_box_refine:
										__a:    List[str]		     =       torch.tensor(
										    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]			)
										__a:    Optional[int]		     =       torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]			)

					if with_box_refine and two_stage:
										__a:    List[Any]		     =       torch.tensor(
										    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]			)
										__a:    Union[str, Any]		     =       torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]			)

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

					assert torch.allclose(outputs.logits[0, :3, :3]						,					expected_logits.to(lowerCamelCase_			)						,					atol=1e-4			)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,					expected_boxes.to(lowerCamelCase_			)						,					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(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
					model.save_pretrained(lowerCamelCase_			)
					image_processor.save_pretrained(lowerCamelCase_			)

					# Push to hub
					if push_to_hub:
										__a:    Dict		     =       """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=lowerCamelCase_						,					organization="""nielsr"""						,					commit_message="""Add model"""			)


if __name__ == "__main__":
			lowercase		:    int			=       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		:    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,
			)
