





"""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()
a__					:     Union[str, Any]							=				logging.get_logger(__name__)





def 							a__			(  _UpperCAmelCase	)    ->  Optional[Any]:

 '''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 							a__			(  _UpperCAmelCase	)    ->  Optional[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[:2_5_6, :]
  snake_case__    							=				in_proj_bias[:2_5_6]
  snake_case__    							=				in_proj_weight[2_5_6:5_1_2, :]
  snake_case__    							=				in_proj_bias[2_5_6:5_1_2]
  snake_case__    							=				in_proj_weight[-2_5_6:, :]
  snake_case__    							=				in_proj_bias[-2_5_6:]





def 							a__			(  )    ->  Any:

 '''simple docstring'''






 snake_case__    							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
 snake_case__    							=				Image.open(requests.get(a_	,     stream=a_	).raw	)

 return im





@torch.no_grad()
def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     )    ->  Tuple:

 '''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__    							=				9_1
 snake_case__    							=				"huggingface/label-files"
 snake_case__    							=				"coco-detection-id2label.json"
 snake_case__    							=				json.load(open(cached_download(hf_hub_url(a_	,     a_	,     repo_type="dataset"	)	)	,     "r"	)	)
 snake_case__    							=				{int(a_	): 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=a_	,     return_tensors="pt"	)
 snake_case__    							=				encoding["pixel_values"]

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

 # load original state dict
 snake_case__    							=				torch.load(a_	,     map_location="cpu"	)["model"]
 # rename keys
 for key in state_dict.copy().keys():
  snake_case__    							=				state_dict.pop(a_	)
  snake_case__    							=				val
 # query, key and value matrices need special treatment
 read_in_q_k_v(a_	)
 # 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(a_	)
   snake_case__    							=				val
    # finally, create HuggingFace model and load state dict
 snake_case__    							=				DeformableDetrForObjectDetection(a_	)
 model.load_state_dict(a_	)
 model.eval()

 snake_case__    							=				"cuda" if torch.cuda.is_available() else "cpu"
 model.to(a_	)
 # verify our conversion
 snake_case__    							=				model(pixel_values.to(a_	)	)

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

 # 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=a_	,     organization="nielsr"	,     commit_message="Add model"	)


if __name__ == "__main__":
   a__					:     Dict							=				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__					:     Optional[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,
   )








"""simple docstring"""


import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import FlavaImageProcessor, FlavaProcessor
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )



@require_vision
class      lowercase_				(     unittest.TestCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				tempfile.mkdtemp()

        # fmt: off
        snake_case__    							=				["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
        # fmt: on
        snake_case__    							=				os.path.join(self.tmpdirname   ,					VOCAB_FILES_NAMES["vocab_file"]     )

        with open(self.vocab_file   ,					"w"   ,					encoding="utf-8"     ) as fp:
         fp.write("".join([x + "\n" for x in vocab_tokens]     )     )

        snake_case__    							=				{
            "image_mean": FLAVA_IMAGE_MEAN,
            "image_std": FLAVA_IMAGE_STD,
            "do_normalize": True,
            "do_resize": True,
            "size": 224,
            "do_center_crop": True,
            "crop_size": 224,
            "input_size_patches": 14,
            "total_mask_patches": 75,
            "mask_group_max_patches": None,
            "mask_group_min_patches": 16,
            "mask_group_min_aspect_ratio": 0.3,
            "mask_group_max_aspect_ratio": None,
            "codebook_do_resize": True,
            "codebook_size": 112,
            "codebook_do_center_crop": True,
            "codebook_crop_size": 112,
            "codebook_do_map_pixels": True,
            "codebook_do_normalize": True,
            "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
            "codebook_image_std": FLAVA_CODEBOOK_STD,
        }

        snake_case__    							=				os.path.join(self.tmpdirname   ,					__SCREAMING_SNAKE_CASE     )
        with open(self.image_processor_file   ,					"w"   ,					encoding="utf-8"     ) as fp:
         json.dump(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return BertTokenizer.from_pretrained(self.tmpdirname   ,					**__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return BertTokenizerFast.from_pretrained(self.tmpdirname   ,					**__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return FlavaImageProcessor.from_pretrained(self.tmpdirname   ,					**__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				[np.random.randint(255   ,					size=(3, 30, 400)   ,					dtype=np.uinta     )]

        snake_case__    							=				[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE   ,					0   ,					-1     )     ) for x in image_inputs]

        return image_inputs

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_tokenizer()
        snake_case__    							=				self.get_rust_tokenizer()
        snake_case__    							=				self.get_image_processor()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )
        processor_slow.save_pretrained(self.tmpdirname     )
        snake_case__    							=				FlavaProcessor.from_pretrained(self.tmpdirname   ,					use_fast=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )
        processor_fast.save_pretrained(self.tmpdirname     )
        snake_case__    							=				FlavaProcessor.from_pretrained(self.tmpdirname     )

        self.assertEqual(processor_slow.tokenizer.get_vocab()   ,					tokenizer_slow.get_vocab()     )
        self.assertEqual(processor_fast.tokenizer.get_vocab()   ,					tokenizer_fast.get_vocab()     )
        self.assertEqual(tokenizer_slow.get_vocab()   ,					tokenizer_fast.get_vocab()     )
        self.assertIsInstance(processor_slow.tokenizer   ,					__SCREAMING_SNAKE_CASE     )
        self.assertIsInstance(processor_fast.tokenizer   ,					__SCREAMING_SNAKE_CASE     )

        self.assertEqual(processor_slow.image_processor.to_json_string()   ,					image_processor.to_json_string()     )
        self.assertEqual(processor_fast.image_processor.to_json_string()   ,					image_processor.to_json_string()     )
        self.assertIsInstance(processor_slow.image_processor   ,					__SCREAMING_SNAKE_CASE     )
        self.assertIsInstance(processor_fast.image_processor   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				FlavaProcessor(tokenizer=self.get_tokenizer()   ,					image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case__    							=				self.get_tokenizer(bos_token="(BOS)"   ,					eos_token="(EOS)"     )
        snake_case__    							=				self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE   ,					padding_value=1.0     )

        snake_case__    							=				FlavaProcessor.from_pretrained(
            self.tmpdirname   ,					bos_token="(BOS)"   ,					eos_token="(EOS)"   ,					do_normalize=__SCREAMING_SNAKE_CASE   ,					padding_value=1.0     )

        self.assertEqual(processor.tokenizer.get_vocab()   ,					tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer   ,					__SCREAMING_SNAKE_CASE     )

        self.assertEqual(processor.image_processor.to_json_string()   ,					image_processor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.image_processor   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				self.prepare_image_inputs()

        snake_case__    							=				image_processor(__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )
        snake_case__    							=				processor(images=__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )

        for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()   ,					input_processor[key].sum()   ,					delta=1e-2     )

        # With rest of the args
        random.seed(1234     )
        snake_case__    							=				image_processor(
            __SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )
        random.seed(1234     )
        snake_case__    							=				processor(
            images=__SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )

        for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()   ,					input_processor[key].sum()   ,					delta=1e-2     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				"lower newer"

        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				tokenizer(__SCREAMING_SNAKE_CASE     )

        for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]   ,					encoded_processor[key]     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				"lower newer"
        snake_case__    							=				self.prepare_image_inputs()

        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE   ,					images=__SCREAMING_SNAKE_CASE     )

        self.assertListEqual(list(inputs.keys()     )   ,					["input_ids", "token_type_ids", "attention_mask", "pixel_values"]     )

        # add extra args
        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE   ,					images=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_SNAKE_CASE     )

        self.assertListEqual(
            list(inputs.keys()     )   ,					[
                "input_ids",
                "token_type_ids",
                "attention_mask",
                "pixel_values",
                "codebook_pixel_values",
                "bool_masked_pos",
            ]   ,					)

        # test if it raises when no input is passed
        with pytest.raises(__SCREAMING_SNAKE_CASE     ):
         processor()

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case__    							=				processor.batch_decode(__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     )

        self.assertListEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				"lower newer"
        snake_case__    							=				self.prepare_image_inputs()

        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE   ,					images=__SCREAMING_SNAKE_CASE     )

        self.assertListEqual(list(inputs.keys()     )   ,					processor.model_input_names     )

