


"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor







@require_vision
class _lowerCAmelCase				(      unittest.TestCase	):







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

       '''simple docstring'''




       snake_case    :      Dict      					=    tempfile.mkdtemp()

       # fmt: off
       snake_case    :      List[Any]      					=    ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
       # fmt: on
       snake_case    :      List[Any]      					=    dict(zip(UpperCamelCase__						,	range(len(UpperCamelCase__						)						)						)						)
       snake_case    :      Optional[int]      					=    ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
       snake_case    :      List[Any]      					=    {"""unk_token""": """<unk>"""}

       snake_case    :      Union[str, Any]      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]						)
       snake_case    :      str      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["merges_file"]						)
       with open(self.vocab_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write(json.dumps(UpperCamelCase__						) + "\n"						)
       with open(self.merges_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write("\n".join(UpperCamelCase__						)						)

       snake_case    :      Union[str, Any]      					=    {
           """do_resize""": True,
           """size""": 20,
           """do_center_crop""": True,
           """crop_size""": 18,
           """do_normalize""": True,
           """image_mean""": [0.48145466, 0.4578275, 0.40821073],
           """image_std""": [0.26862954, 0.26130258, 0.27577711],
       }
       snake_case    :      Dict      					=    os.path.join(self.tmpdirname						,	UpperCamelCase__						)
       with open(self.image_processor_file						,	"w"						,	encoding="utf-8"						) as fp:
           json.dump(UpperCamelCase__						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




       return CLIPTokenizer.from_pretrained(self.tmpdirname						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		List[Any]:

       '''simple docstring'''




       return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       return ViTImageProcessor.from_pretrained(self.tmpdirname						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       shutil.rmtree(self.tmpdirname						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




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

       snake_case    :      int      					=    [Image.fromarray(np.moveaxis(UpperCamelCase__						,	0						,	-1						)						) for x in image_inputs]

       return image_inputs







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.get_tokenizer()
       snake_case    :      List[str]      					=    self.get_rust_tokenizer()
       snake_case    :      Dict      					=    self.get_image_processor()

       snake_case    :      Any      					=    CLIPSegProcessor(tokenizer=UpperCamelCase__						,	image_processor=UpperCamelCase__						)
       processor_slow.save_pretrained(self.tmpdirname						)
       snake_case    :      Tuple      					=    CLIPSegProcessor.from_pretrained(self.tmpdirname						,	use_fast=UpperCamelCase__						)

       snake_case    :      Dict      					=    CLIPSegProcessor(tokenizer=UpperCamelCase__						,	image_processor=UpperCamelCase__						)
       processor_fast.save_pretrained(self.tmpdirname						)
       snake_case    :      List[Any]      					=    CLIPSegProcessor.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						,	UpperCamelCase__						)
       self.assertIsInstance(processor_fast.tokenizer						,	UpperCamelCase__						)

       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						,	UpperCamelCase__						)
       self.assertIsInstance(processor_fast.image_processor						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      int      					=    CLIPSegProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

       snake_case    :      int      					=    self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"						)
       snake_case    :      str      					=    self.get_image_processor(do_normalize=UpperCamelCase__						,	padding_value=1.0						)

       snake_case    :      List[str]      					=    CLIPSegProcessor.from_pretrained(
           self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=UpperCamelCase__						,	padding_value=1.0						)

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

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







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_image_processor()
       snake_case    :      Tuple      					=    self.get_tokenizer()

       snake_case    :      int      					=    CLIPSegProcessor(tokenizer=UpperCamelCase__						,	image_processor=UpperCamelCase__						)

       snake_case    :      Tuple      					=    self.prepare_image_inputs()

       snake_case    :      Dict      					=    image_processor(UpperCamelCase__						,	return_tensors="np"						)
       snake_case    :      Optional[int]      					=    processor(images=UpperCamelCase__						,	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  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      str      					=    self.get_image_processor()
       snake_case    :      Optional[int]      					=    self.get_tokenizer()

       snake_case    :      Dict      					=    CLIPSegProcessor(tokenizer=UpperCamelCase__						,	image_processor=UpperCamelCase__						)

       snake_case    :      Optional[Any]      					=    """lower newer"""

       snake_case    :      Optional[int]      					=    processor(text=UpperCamelCase__						)

       snake_case    :      Union[str, Any]      					=    tokenizer(UpperCamelCase__						)

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







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.get_image_processor()
       snake_case    :      List[str]      					=    self.get_tokenizer()

       snake_case    :      Tuple      					=    CLIPSegProcessor(tokenizer=UpperCamelCase__						,	image_processor=UpperCamelCase__						)

       snake_case    :      List[str]      					=    """lower newer"""
       snake_case    :      Optional[Any]      					=    self.prepare_image_inputs()

       snake_case    :      Optional[int]      					=    processor(text=UpperCamelCase__						,	images=UpperCamelCase__						)

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

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







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      int      					=    self.get_image_processor()
       snake_case    :      Optional[Any]      					=    self.get_tokenizer()

       snake_case    :      Optional[Any]      					=    CLIPSegProcessor(tokenizer=UpperCamelCase__						,	image_processor=UpperCamelCase__						)

       snake_case    :      Optional[Any]      					=    self.prepare_image_inputs()
       snake_case    :      List[Any]      					=    self.prepare_image_inputs()

       snake_case    :      Optional[int]      					=    processor(images=UpperCamelCase__						,	visual_prompt=UpperCamelCase__						)

       self.assertListEqual(list(inputs.keys()						)						,	["pixel_values", "conditional_pixel_values"]						)

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







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_image_processor()
       snake_case    :      List[str]      					=    self.get_tokenizer()

       snake_case    :      str      					=    CLIPSegProcessor(tokenizer=UpperCamelCase__						,	image_processor=UpperCamelCase__						)

       snake_case    :      List[Any]      					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       snake_case    :      List[Any]      					=    processor.batch_decode(UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    tokenizer.batch_decode(UpperCamelCase__						)

       self.assertListEqual(UpperCamelCase__						,	UpperCamelCase__						)













'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_A	:			Tuple         =[]
for i in range(6):
			# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
			)
			rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
			)
			rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
			rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
			# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
			)
			rename_keys.append(
			    (
			        F'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
			        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
			    )
			)
			rename_keys.append(
			    (
			        F'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
			        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
			    )
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

			# q, k, v projections in self/cross-attention in decoder for conditional DETR
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
			)
			# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
			)

			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
			)
			# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
			)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							lowerCamelCase__					:	List[Any]         =       state_dict.pop(UpperCamelCase      )
							lowerCamelCase__					:	Any         =       val





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Dict:
							lowerCamelCase__					:	Tuple         =       OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					lowerCamelCase__					:	List[str]         =       key.replace("""backbone.0.body"""				,			"""backbone.conv_encoder.model"""      )
																					lowerCamelCase__					:	Optional[int]         =       value
														else:
																					lowerCamelCase__					:	Any         =       value

							return new_state_dict





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase=False      )      ->       Dict:
							lowerCamelCase__					:	Optional[int]         =       """"""
							if is_panoptic:
														lowerCamelCase__					:	Dict         =       """conditional_detr."""

							# first: transformer encoder
							for i in range(6      ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														lowerCamelCase__					:	List[Any]         =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
														lowerCamelCase__					:	Union[str, Any]         =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''      )
														# next, add query, keys and values (in that order) to the state dict
														lowerCamelCase__					:	int         =       in_proj_weight[:256, :]
														lowerCamelCase__					:	Any         =       in_proj_bias[:256]
														lowerCamelCase__					:	str         =       in_proj_weight[256:512, :]
														lowerCamelCase__					:	Optional[int]         =       in_proj_bias[256:512]
														lowerCamelCase__					:	Dict         =       in_proj_weight[-256:, :]
														lowerCamelCase__					:	str         =       in_proj_bias[-256:]





def  SCREAMING_SNAKE_CASE_       ()      ->       List[Any]:
							lowerCamelCase__					:	Optional[int]         =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__					:	Optional[Any]         =       Image.open(requests.get(UpperCamelCase				,			stream=UpperCamelCase      ).raw      )

							return im





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	Optional[Any]         =       ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														lowerCamelCase__					:	Any         =       """resnet101"""
							if "dc5" in model_name:
														lowerCamelCase__					:	Optional[int]         =       True
							lowerCamelCase__					:	int         =       """panoptic""" in model_name
							if is_panoptic:
														lowerCamelCase__					:	List[str]         =       250
							else:
														lowerCamelCase__					:	int         =       91
														lowerCamelCase__					:	int         =       """huggingface/label-files"""
														lowerCamelCase__					:	List[str]         =       """coco-detection-id2label.json"""
														lowerCamelCase__					:	Optional[Any]         =       json.load(open(hf_hub_download(UpperCamelCase				,			UpperCamelCase				,			repo_type="""dataset"""      )				,			"""r"""      )      )
														lowerCamelCase__					:	Any         =       {int(UpperCamelCase      ): v for k, v in idalabel.items()}
														lowerCamelCase__					:	str         =       idalabel
														lowerCamelCase__					:	List[str]         =       {v: k for k, v in idalabel.items()}

							# load image processor
							lowerCamelCase__					:	Optional[int]         =       """coco_panoptic""" if is_panoptic else """coco_detection"""
							lowerCamelCase__					:	int         =       ConditionalDetrImageProcessor(format=UpperCamelCase      )

							# prepare image
							lowerCamelCase__					:	List[str]         =       prepare_img()
							lowerCamelCase__					:	int         =       image_processor(images=UpperCamelCase				,			return_tensors="""pt"""      )
							lowerCamelCase__					:	Optional[Any]         =       encoding["""pixel_values"""]

							logger.info(f'''Converting model {model_name}...'''      )

							# load original model from torch hub
							lowerCamelCase__					:	List[Any]         =       torch.hub.load("""DeppMeng/ConditionalDETR"""				,			UpperCamelCase				,			pretrained=UpperCamelCase      ).eval()
							lowerCamelCase__					:	Dict         =       conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					lowerCamelCase__					:	Optional[Any]         =       """conditional_detr.""" + src
														rename_key(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
							lowerCamelCase__					:	Dict         =       rename_backbone_keys(UpperCamelCase      )
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase				,			is_panoptic=UpperCamelCase      )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							lowerCamelCase__					:	Dict         =       """conditional_detr.model.""" if is_panoptic else """model."""
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("""conditional_detr"""      )
																					    and not key.startswith("""class_labels_classifier"""      )
																					    and not key.startswith("""bbox_predictor"""      )
																					):
																												lowerCamelCase__					:	int         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Union[str, Any]         =       val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												lowerCamelCase__					:	List[str]         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Any         =       val
																					elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
																												continue
																					else:
																												lowerCamelCase__					:	int         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Tuple         =       val
														else:
																					if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
																												lowerCamelCase__					:	Union[str, Any]         =       state_dict.pop(UpperCamelCase      )
																												lowerCamelCase__					:	Dict         =       val
    # finally, create HuggingFace model and load state dict
							lowerCamelCase__					:	Tuple         =       ConditionalDetrForSegmentation(UpperCamelCase      ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase      )
							model.load_state_dict(UpperCamelCase      )
							model.eval()
							model.push_to_hub(repo_id=UpperCamelCase				,			organization="""DepuMeng"""				,			commit_message="""Add model"""      )
							# verify our conversion
							lowerCamelCase__					:	Optional[Any]         =       conditional_detr(UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       model(UpperCamelCase      )
							assert torch.allclose(outputs.logits				,			original_outputs["""pred_logits"""]				,			atol=1E-4      )
							assert torch.allclose(outputs.pred_boxes				,			original_outputs["""pred_boxes"""]				,			atol=1E-4      )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks				,			original_outputs["""pred_masks"""]				,			atol=1E-4      )

							# 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      )


if __name__ == "__main__":
			_A	:			List[Any]         =argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''',
			    default='''conditional_detr_resnet50''',
			    type=str,
			    help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
			)
			_A	:			Optional[Any]         =parser.parse_args()
			convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

