






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
 from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
 lowerCamelCase			:					Any         =	get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
 from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCamelCase			:					List[str]         =	128022
lowerCamelCase			:					Any         =	128028



@require_sentencepiece
class 							_UpperCamelCase			(a_							,		unittest.TestCase			):
      snake_case_  =							MaMaaaTokenizer
      snake_case_  =							False
      snake_case_  =							False
      snake_case_  =							True


      def  __UpperCAmelCase		(	self	)->	List[Any]:
           super().setUp()

           __lowerCAmelCase								=					["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
           __lowerCAmelCase								=					dict(zip(__UpperCamelCase					,						range(len(__UpperCamelCase	)	)	)	)
           __lowerCAmelCase								=					Path(self.tmpdirname	)
           save_json(__UpperCamelCase					,						save_dir / VOCAB_FILES_NAMES["vocab_file"]	)
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                copyfile(__UpperCamelCase					,						save_dir / VOCAB_FILES_NAMES["spm_file"]	)

           __lowerCAmelCase								=					MaMaaaTokenizer.from_pretrained(self.tmpdirname	)
           tokenizer.save_pretrained(self.tmpdirname	)


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Tuple:
           return MaMaaaTokenizer.from_pretrained(self.tmpdirname					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Dict:
           return (
               "This is a test",
               "This is a test",
           )


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					"</s>"
           __lowerCAmelCase								=					0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase	)					,						__UpperCamelCase	)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase	)					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					self.get_tokenizer()
           __lowerCAmelCase								=					list(tokenizer.get_vocab().keys()	)

           self.assertEqual(vocab_keys[0]					,						"</s>"	)
           self.assertEqual(vocab_keys[1]					,						"<unk>"	)
           self.assertEqual(vocab_keys[-1]					,						"<s>"	)
           self.assertEqual(len(__UpperCamelCase	)					,						tokenizer.vocab_size + len(tokenizer.get_added_vocab()	)	)


      @unittest.skip("Skip this test while all models are still to be uploaded."	)
      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           pass


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					tokenizer.tokenize("This is a test"	)
           self.assertListEqual(__UpperCamelCase					,						["▁This", "▁is", "▁a", "▁t", "est"]	)

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(__UpperCamelCase	)					,						[2, 3, 4, 5, 6]					,						)

           __lowerCAmelCase								=					tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]	)
           self.assertListEqual(__UpperCamelCase					,						["▁This", "▁is", "▁a", "▁t", "est"]	)

           __lowerCAmelCase								=					tokenizer.convert_tokens_to_string(__UpperCamelCase	)
           self.assertEqual(__UpperCamelCase					,						"This is a test"	)


      @slow
      def  __UpperCAmelCase		(	self	)->	List[Any]:
           # fmt: off
           __lowerCAmelCase								=					{"input_ids": [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__UpperCamelCase					,						model_name="facebook/m2m100_418M"					,						revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"					,						)



@require_torch
@require_sentencepiece
@require_tokenizers
class 							_UpperCamelCase			(unittest.TestCase			):
      snake_case_  =							"""facebook/m2m100_418M"""
      snake_case_  =							[
          """In my opinion, there are two levels of response from the French government.""",
          """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
      ]
      snake_case_  =							[
          """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
          """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
      ]

      # fmt: off
      snake_case_  =							[EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]


      @classmethod
      def  __UpperCAmelCase		(	cls	)->	Dict:
           __lowerCAmelCase								=					MaMaaaTokenizer.from_pretrained(
               cls.checkpoint_name					,						src_lang="en"					,						tgt_lang="fr"	)
           __lowerCAmelCase								=					1
           return cls


      def  __UpperCAmelCase		(	self	)->	List[str]:
           self.assertEqual(self.tokenizer.get_lang_id("ar"	)					,						1_2_8_0_0_6	)
           self.assertEqual(self.tokenizer.get_lang_id("en"	)					,						1_2_8_0_2_2	)
           self.assertEqual(self.tokenizer.get_lang_id("ro"	)					,						1_2_8_0_7_6	)
           self.assertEqual(self.tokenizer.get_lang_id("mr"	)					,						1_2_8_0_6_3	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					self.tokenizer.get_vocab()
           self.assertEqual(len(__UpperCamelCase	)					,						self.tokenizer.vocab_size	)
           self.assertEqual(vocab["<unk>"]					,						3	)
           self.assertIn(self.tokenizer.get_lang_token("en"	)					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					"en"
           __lowerCAmelCase								=					self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
           self.assertListEqual(self.expected_src_tokens					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	int:
           self.assertIn(__UpperCamelCase					,						self.tokenizer.all_special_ids	)
           # fmt: off
           __lowerCAmelCase								=					[FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
           # fmt: on
           __lowerCAmelCase								=					self.tokenizer.decode(__UpperCamelCase					,						skip_special_tokens=__UpperCamelCase	)
           __lowerCAmelCase								=					self.tokenizer.decode(generated_ids[1:]					,						skip_special_tokens=__UpperCamelCase	)
           self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)
           self.assertNotIn(self.tokenizer.eos_token					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					tempfile.mkdtemp()
           __lowerCAmelCase								=					self.tokenizer.lang_token_to_id
           self.tokenizer.save_pretrained(__UpperCamelCase	)
           __lowerCAmelCase								=					MaMaaaTokenizer.from_pretrained(__UpperCamelCase	)
           self.assertDictEqual(new_tok.lang_token_to_id					,						__UpperCamelCase	)


      @require_torch
      def  __UpperCAmelCase		(	self	)->	List[Any]:
           __lowerCAmelCase								=					"en"
           __lowerCAmelCase								=					"fr"

           __lowerCAmelCase								=					self.tokenizer(self.src_text					,						text_target=self.tgt_text					,						padding=__UpperCamelCase					,						return_tensors="pt"	)

           __lowerCAmelCase								=					shift_tokens_right(
               batch["labels"]					,						self.tokenizer.pad_token_id					,						self.tokenizer.eos_token_id	)

           for k in batch:
                __lowerCAmelCase								=					batch[k].tolist()
           # batch = {k: v.tolist() for k,v in batch.items()}
           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           # batch.decoder_inputs_ids[0][0] ==
           assert batch.input_ids[1][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == FR_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


      @require_torch
      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					"mr"
           self.assertListEqual(self.tokenizer.prefix_tokens					,						[self.tokenizer.get_lang_id("mr"	)]	)
           self.assertListEqual(self.tokenizer.suffix_tokens					,						[self.tokenizer.eos_token_id]	)

           __lowerCAmelCase								=					"zh"
           self.assertListEqual(self.tokenizer.prefix_tokens					,						[self.tokenizer.get_lang_id("zh"	)]	)
           self.assertListEqual(self.tokenizer.suffix_tokens					,						[self.tokenizer.eos_token_id]	)


      @require_torch
      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					"mr"
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens					,						[self.tokenizer.get_lang_id("mr"	)]	)
           self.assertListEqual(self.tokenizer.suffix_tokens					,						[self.tokenizer.eos_token_id]	)
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens					,						[self.tokenizer.get_lang_id(self.tokenizer.src_lang	)]	)

           __lowerCAmelCase								=					"zh"
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens					,						[self.tokenizer.get_lang_id("zh"	)]	)
           self.assertListEqual(self.tokenizer.suffix_tokens					,						[self.tokenizer.eos_token_id]	)
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens					,						[self.tokenizer.get_lang_id(self.tokenizer.src_lang	)]	)


      @require_torch
      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					self.tokenizer._build_translation_inputs("A test"					,						return_tensors="pt"					,						src_lang="en"					,						tgt_lang="ar"	)

           self.assertEqual(
               nested_simplify(__UpperCamelCase	)					,						{
                   # en_XX, A, test, EOS
                   "input_ids": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
                   "attention_mask": [[1, 1, 1, 1]],
                   # ar_AR
                   "forced_bos_token_id": 1_2_8_0_0_6,
               }					,						)














import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase			:					str         =	logging.get_logger(__name__)





def       __lowerCAmelCase      (						__snake_case       ):
     # initialize config
     if "resnet-50" in model_name:
          __lowerCAmelCase								=					ResNetConfig.from_pretrained("microsoft/resnet-50"       )
     elif "resnet-101" in model_name:
          __lowerCAmelCase								=					ResNetConfig.from_pretrained("microsoft/resnet-101"       )
     else:
          raise ValueError("Model name should include either resnet50 or resnet101"       )

     __lowerCAmelCase								=					DetrConfig(use_timm_backbone=__snake_case					, backbone_config=__snake_case       )

     # set label attributes
     __lowerCAmelCase								=					"panoptic" in model_name
     if is_panoptic:
          __lowerCAmelCase								=					250
     else:
          __lowerCAmelCase								=					91
          __lowerCAmelCase								=					"huggingface/label-files"
          __lowerCAmelCase								=					"coco-detection-id2label.json"
          __lowerCAmelCase								=					json.load(open(hf_hub_download(__snake_case					, __snake_case					, repo_type="dataset"       )					, "r"       )       )
          __lowerCAmelCase								=					{int(__snake_case       ): v for k, v in idalabel.items()}
          __lowerCAmelCase								=					idalabel
          __lowerCAmelCase								=					{v: k for k, v in idalabel.items()}

     return config, is_panoptic





def       __lowerCAmelCase      (						__snake_case       ):
     # here we list all keys to be renamed (original name on the left, our name on the right)
     __lowerCAmelCase								=					[]

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")       )
     rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")       )
     rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")       )
     rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")       )
     rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")       )
     # stages
     for stage_idx in range(len(config.backbone_config.depths       )       ):
          for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                        )       )
               # 3 convs
               for i in range(3       ):
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                        )       )
    # fmt: on

     for i in range(config.encoder_layers       ):
          # 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}.multihead_attn.out_proj.weight""",
                  F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
              )       )
          rename_keys.append(
              (
                  F"""transformer.decoder.layers.{i}.multihead_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""")       )

     # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
     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"),
         ]       )

     return rename_keys





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					state_dict.pop(__snake_case       )
     __lowerCAmelCase								=					val





def       __lowerCAmelCase      (						__snake_case					, __snake_case=False       ):
     __lowerCAmelCase								=					""
     if is_panoptic:
          __lowerCAmelCase								=					"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								=					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
          __lowerCAmelCase								=					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								=					in_proj_weight[:256, :]
          __lowerCAmelCase								=					in_proj_bias[:256]
          __lowerCAmelCase								=					in_proj_weight[256:512, :]
          __lowerCAmelCase								=					in_proj_bias[256:512]
          __lowerCAmelCase								=					in_proj_weight[-256:, :]
          __lowerCAmelCase								=					in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6       ):
          # read in weights + bias of input projection layer of self-attention
          __lowerCAmelCase								=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
          __lowerCAmelCase								=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""       )
          # next, add query, keys and values (in that order) to the state dict
          __lowerCAmelCase								=					in_proj_weight[:256, :]
          __lowerCAmelCase								=					in_proj_bias[:256]
          __lowerCAmelCase								=					in_proj_weight[256:512, :]
          __lowerCAmelCase								=					in_proj_bias[256:512]
          __lowerCAmelCase								=					in_proj_weight[-256:, :]
          __lowerCAmelCase								=					in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          __lowerCAmelCase								=					state_dict.pop(
              F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
          __lowerCAmelCase								=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""       )
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          __lowerCAmelCase								=					in_proj_weight_cross_attn[:256, :]
          __lowerCAmelCase								=					in_proj_bias_cross_attn[:256]
          __lowerCAmelCase								=					in_proj_weight_cross_attn[256:512, :]
          __lowerCAmelCase								=					in_proj_bias_cross_attn[256:512]
          __lowerCAmelCase								=					in_proj_weight_cross_attn[-256:, :]
          __lowerCAmelCase								=					in_proj_bias_cross_attn[-256:]





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

     return im





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case=None					, __snake_case=False       ):
     __lowerCAmelCase			, __lowerCAmelCase								=					get_detr_config(__snake_case       )

     # load original model from torch hub
     __lowerCAmelCase								=					{
         "detr-resnet-50": "detr_resnet50",
         "detr-resnet-101": "detr_resnet101",
     }
     logger.info(F"""Converting model {model_name}..."""       )
     __lowerCAmelCase								=					torch.hub.load("facebookresearch/detr"					, model_name_to_original_name[model_name]					, pretrained=__snake_case       ).eval()
     __lowerCAmelCase								=					detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(__snake_case       ):
          if is_panoptic:
               __lowerCAmelCase								=					"detr." + src
          rename_key(__snake_case					, __snake_case					, __snake_case       )
     # query, key and value matrices need special treatment
     read_in_q_k_v(__snake_case					, is_panoptic=__snake_case       )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     __lowerCAmelCase								=					"detr.model." if is_panoptic else "model."
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("detr"       )
                   and not key.startswith("class_labels_classifier"       )
                   and not key.startswith("bbox_predictor"       )
               ):
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
               elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
                    continue
               else:
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
          else:
               if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val

    # finally, create HuggingFace model and load state dict
     __lowerCAmelCase								=					DetrForSegmentation(__snake_case       ) if is_panoptic else DetrForObjectDetection(__snake_case       )
     model.load_state_dict(__snake_case       )
     model.eval()

     # verify our conversion on an image
     __lowerCAmelCase								=					"coco_panoptic" if is_panoptic else "coco_detection"
     __lowerCAmelCase								=					DetrImageProcessor(format=__snake_case       )

     __lowerCAmelCase								=					processor(images=prepare_img()					, return_tensors="pt"       )
     __lowerCAmelCase								=					encoding["pixel_values"]

     __lowerCAmelCase								=					detr(__snake_case       )
     __lowerCAmelCase								=					model(__snake_case       )

     assert torch.allclose(outputs.logits					, original_outputs["pred_logits"]					, atol=1E-3       )
     assert torch.allclose(outputs.pred_boxes					, original_outputs["pred_boxes"]					, atol=1E-3       )
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks					, original_outputs["pred_masks"]					, atol=1E-4       )
     print("Looks ok!"       )

     if pytorch_dump_folder_path is not None:
          # Save model and image processor
          logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
          Path(__snake_case       ).mkdir(exist_ok=__snake_case       )
          model.save_pretrained(__snake_case       )
          processor.save_pretrained(__snake_case       )

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info("Uploading PyTorch model and image processor to the hub..."       )
          model.push_to_hub(F"""nielsr/{model_name}"""       )
          processor.push_to_hub(F"""nielsr/{model_name}"""       )


if __name__ == "__main__":
 lowerCamelCase			:					Any         =	argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''detr-resnet-50''',
     type=str,
     choices=['''detr-resnet-50''', '''detr-resnet-101'''],
     help='''Name of the 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.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
 lowerCamelCase			:					int         =	parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






