


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__			 =						get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

snake_case__			 =						25_0004
snake_case__			 =						25_0020


@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase_ (       _a			,unittest.TestCase):
						lowerCamelCase_         =     MBartTokenizer
						lowerCamelCase_         =     MBartTokenizerFast
						lowerCamelCase_         =     True
						lowerCamelCase_         =     True




						def  _snake_case					(	self						:   Any						)      ->Optional[int]:






									"""simple docstring"""






									super().setUp()

									# We have a SentencePiece fixture for testing
									a__ :Any						  =    MBartTokenizer(__A   ,     keep_accents=__A						)
									tokenizer.save_pretrained(self.tmpdirname						)




						def  _snake_case					(	self						:   Dict						)      ->List[str]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    MBartTokenizer(__A   ,     keep_accents=__A						)

									a__ :Optional[int]						  =    tokenizer.tokenize("This is a test"						)
									self.assertListEqual(__A   ,     ["▁This", "▁is", "▁a", "▁t", "est"]						)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(__A						)   ,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,     )

									a__ :List[Any]						  =    tokenizer.tokenize("I was born in 92000, and this is falsé."						)
									self.assertListEqual(
									    __A   ,     [
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]   ,     )
									a__ :int						  =    tokenizer.convert_tokens_to_ids(__A						)
									self.assertListEqual(
									    __A   ,     [
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
									    ]   ,     )

									a__ :int						  =    tokenizer.convert_ids_to_tokens(__A						)
									self.assertListEqual(
									    __A   ,     [
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]   ,     )






						def  _snake_case					(	self						:   Dict						)      ->int:






									"""simple docstring"""






									if not self.test_slow_tokenizer:
												# as we don't have a slow version, we can't compare the outputs between slow and fast versions
												return

									a__ :Any						  =    (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
															a__ :Tuple						  =    self.rust_tokenizer_class.from_pretrained(__A   ,     **__A						)
															a__ :str						  =    self.tokenizer_class.from_pretrained(__A   ,     **__A						)

															a__ :List[Any]						  =    tempfile.mkdtemp()

															a__ :List[str]						  =    tokenizer_r.save_pretrained(__A						)
															a__ :Optional[int]						  =    tokenizer_p.save_pretrained(__A						)

															# Checks it save with the same files + the tokenizer.json file for the fast one
															self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files						)						)
															a__ :Union[str, Any]						  =    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f						)
															self.assertSequenceEqual(__A   ,     __A						)

															# Checks everything loads correctly in the same way
															a__ :List[Any]						  =    tokenizer_r.from_pretrained(__A						)
															a__ :Optional[Any]						  =    tokenizer_p.from_pretrained(__A						)

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__A   ,     __A						)						)
																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

															shutil.rmtree(__A						)

															# Save tokenizer rust, legacy_format=True
															a__ :Optional[int]						  =    tempfile.mkdtemp()

															a__ :Any						  =    tokenizer_r.save_pretrained(__A   ,     legacy_format=__A						)
															a__ :Tuple						  =    tokenizer_p.save_pretrained(__A						)

															# Checks it save with the same files
															self.assertSequenceEqual(__A   ,     __A						)

															# Checks everything loads correctly in the same way
															a__ :str						  =    tokenizer_r.from_pretrained(__A						)
															a__ :Dict						  =    tokenizer_p.from_pretrained(__A						)

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__A   ,     __A						)						)

															shutil.rmtree(__A						)

															# Save tokenizer rust, legacy_format=False
															a__ :Tuple						  =    tempfile.mkdtemp()

															a__ :Dict						  =    tokenizer_r.save_pretrained(__A   ,     legacy_format=__A						)
															a__ :Tuple						  =    tokenizer_p.save_pretrained(__A						)

															# Checks it saved the tokenizer.json file
															self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files						)						)

															# Checks everything loads correctly in the same way
															a__ :str						  =    tokenizer_r.from_pretrained(__A						)
															a__ :Union[str, Any]						  =    tokenizer_p.from_pretrained(__A						)

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__A   ,     __A						)						)

															shutil.rmtree(__A						)






@require_torch
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase_ (       unittest.TestCase):
						lowerCamelCase_         =     'facebook/mbart-large-en-ro'
						lowerCamelCase_         =     [
						    ' UN Chief Says There Is No Military Solution in Syria',
						    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
						]
						lowerCamelCase_         =     [
						    'Şeful ONU declară că nu există o soluţie militară în Siria',
						    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
						    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
						    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
						]
						lowerCamelCase_         =     [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]




						@classmethod
						def  _snake_case					(	cls						:   str						)      ->List[str]:






									"""simple docstring"""






									a__ :MBartTokenizer						  =    MBartTokenizer.from_pretrained(
									    cls.checkpoint_name   ,     src_lang="en_XX"   ,     tgt_lang="ro_RO"						)
									a__ :List[str]						  =    1
									return cls




						def  _snake_case					(	self						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]   ,     250001						)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]   ,     250004						)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]   ,     250020						)




						def  _snake_case					(	self						:   int						)      ->Tuple:






									"""simple docstring"""






									a__ :Dict						  =    self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
									self.assertListEqual(self.expected_src_tokens   ,     __A						)




						def  _snake_case					(	self						:   int						)      ->List[Any]:






									"""simple docstring"""






									self.assertIn(__A   ,     self.tokenizer.all_special_ids						)
									a__ :Any						  =    [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
									a__ :Tuple						  =    self.tokenizer.decode(__A   ,     skip_special_tokens=__A						)
									a__ :List[Any]						  =    self.tokenizer.decode(generated_ids[1:]   ,     skip_special_tokens=__A						)
									self.assertEqual(__A   ,     __A						)
									self.assertNotIn(self.tokenizer.eos_token   ,     __A						)




						def  _snake_case					(	self						:   Optional[int]						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Dict						  =    ["this is gunna be a long sentence " * 20]
									assert isinstance(src_text[0]   ,     __A						)
									a__ :str						  =    10
									a__ :Optional[Any]						  =    self.tokenizer(__A   ,     max_length=__A   ,     truncation=__A						).input_ids[0]
									self.assertEqual(ids[-2]   ,     2						)
									self.assertEqual(ids[-1]   ,     __A						)
									self.assertEqual(len(__A						)   ,     __A						)




						def  _snake_case					(	self						:   Optional[int]						)      ->Union[str, Any]:






									"""simple docstring"""






									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]						)   ,     [250026, 250001]						)




						def  _snake_case					(	self						:   Dict						)      ->List[str]:






									"""simple docstring"""






									a__ :List[Any]						  =    tempfile.mkdtemp()
									a__ :Union[str, Any]						  =    self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(__A						)
									a__ :Optional[Any]						  =    MBartTokenizer.from_pretrained(__A						)
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,     __A						)




						@require_torch
						def  _snake_case					(	self						:   str						)      ->Any:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.tokenizer(self.src_text   ,     text_target=self.tgt_text   ,     padding=__A   ,     return_tensors="pt"						)
									a__ :Optional[Any]						  =    shift_tokens_right(batch["labels"]   ,     self.tokenizer.pad_token_id						)

									# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
									assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
									assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
									assert batch.decoder_input_ids[1][-1] == 2
									assert batch.labels[1][-2:].tolist() == [2, RO_CODE]




						@require_torch
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Tuple						  =    self.tokenizer(
									    self.src_text   ,     text_target=self.tgt_text   ,     padding=__A   ,     truncation=__A   ,     max_length=len(self.expected_src_tokens						)   ,     return_tensors="pt"   ,     )

									a__ :Dict						  =    shift_tokens_right(batch["labels"]   ,     self.tokenizer.pad_token_id						)

									self.assertIsInstance(__A   ,     __A						)

									self.assertEqual((2, 14)   ,     batch.input_ids.shape						)
									self.assertEqual((2, 14)   ,     batch.attention_mask.shape						)
									a__ :Optional[int]						  =    batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens   ,     __A						)
									self.assertEqual(2   ,     batch.decoder_input_ids[0, -1]						)  # EOS
									# Test that special tokens are reset
									self.assertEqual(self.tokenizer.prefix_tokens   ,     []						)
									self.assertEqual(self.tokenizer.suffix_tokens   ,     [self.tokenizer.eos_token_id, EN_CODE]						)




						def  _snake_case					(	self						:   List[Any]						)      ->Dict:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.tokenizer(self.src_text   ,     padding=__A   ,     truncation=__A   ,     max_length=3   ,     return_tensors="pt"						)
									a__ :int						  =    self.tokenizer(
									    text_target=self.tgt_text   ,     padding=__A   ,     truncation=__A   ,     max_length=10   ,     return_tensors="pt"						)
									a__ :Any						  =    targets["input_ids"]
									a__ :Optional[int]						  =    shift_tokens_right(__A   ,     self.tokenizer.pad_token_id						)

									self.assertEqual(batch.input_ids.shape[1]   ,     3						)
									self.assertEqual(batch.decoder_input_ids.shape[1]   ,     10						)






						@require_torch
						def  _snake_case					(	self						:   Optional[int]						)      ->Tuple:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    self.tokenizer._build_translation_inputs(
									    "A test"   ,     return_tensors="pt"   ,     src_lang="en_XX"   ,     tgt_lang="ar_AR"						)

									self.assertEqual(
									    nested_simplify(__A						)   ,     {
									        # A, test, EOS, en_XX
									        "input_ids": [[62, 3034, 2, 250004]],
									        "attention_mask": [[1, 1, 1, 1]],
									        # ar_AR
									        "forced_bos_token_id": 250001,
									    }   ,     )






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()
snake_case__			 =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__			 =						[]
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 				lowerCamelCase__	(      a	:   Any	,  a	:   Any	,  a	:   List[Any]				)    ->	Any:





			"""simple docstring"""
			a__ :Optional[Any]						  =    state_dict.pop(a				)
			a__ :Tuple						  =    val


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	str:





			"""simple docstring"""
			a__ :Any						  =    OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									a__ :List[Any]						  =    key.replace("backbone.0.body"	,  "backbone.conv_encoder.model"				)
									a__ :List[Any]						  =    value
						else:
									a__ :Dict						  =    value

			return new_state_dict


def 				lowerCamelCase__	(      a	:   str	,  a	:   Dict=False				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Optional[int]						  =    ""
			if is_panoptic:
						a__ :str						  =    "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)
						a__ :str						  =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
						a__ :Optional[int]						  =    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
						a__ :int						  =    in_proj_weight[:256, :]
						a__ :List[str]						  =    in_proj_bias[:256]
						a__ :List[str]						  =    in_proj_weight[256:512, :]
						a__ :Optional[Any]						  =    in_proj_bias[256:512]
						a__ :Dict						  =    in_proj_weight[-256:, :]
						a__ :Tuple						  =    in_proj_bias[-256:]


def 				lowerCamelCase__	(      )    ->	Optional[Any]:





			"""simple docstring"""
			a__ :str						  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__ :Optional[Any]						  =    Image.open(requests.get(a	,  stream=a				).raw				)

			return im


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Union[str, Any]				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Optional[int]						  =    ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						a__ :str						  =    "resnet101"
			if "dc5" in model_name:
						a__ :Any						  =    True
			a__ :Optional[int]						  =    "panoptic" in model_name
			if is_panoptic:
						a__ :Union[str, Any]						  =    250
			else:
						a__ :str						  =    91
						a__ :Dict						  =    "huggingface/label-files"
						a__ :Optional[Any]						  =    "coco-detection-id2label.json"
						a__ :Dict						  =    json.load(open(hf_hub_download(a	,  a	,  repo_type="dataset"				)	,  "r"				)				)
						a__ :str						  =    {int(a				): v for k, v in idalabel.items()}
						a__ :int						  =    idalabel
						a__ :Dict						  =    {v: k for k, v in idalabel.items()}

			# load image processor
			a__ :Union[str, Any]						  =    "coco_panoptic" if is_panoptic else "coco_detection"
			a__ :Any						  =    ConditionalDetrImageProcessor(format=a				)

			# prepare image
			a__ :int						  =    prepare_img()
			a__ :Optional[int]						  =    image_processor(images=a	,  return_tensors="pt"				)
			a__ :int						  =    encoding["pixel_values"]

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

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


if __name__ == "__main__":
		snake_case__			 =						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.'''
		)
		snake_case__			 =						parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


