
import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_						=						get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE				(		snake_case,     unittest.TestCase				):
				lowerCamelCase_	  =  PegasusTokenizer
				lowerCamelCase_	  =  PegasusTokenizerFast
				lowerCamelCase_	  =  True
				lowerCamelCase_	  =  True





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								super().setUp()

								# We have a SentencePiece fixture for testing
								A  :	Optional[Any]				=					PegasusTokenizer(A_				)
								tokenizer.save_pretrained(self.tmpdirname				)





				@cached_property
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								return PegasusTokenizer.from_pretrained('''google/pegasus-large'''				)





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]       ,   **snake_case_			:		Tuple				):


								"""simple docstring"""

								return PegasusTokenizer.from_pretrained(self.tmpdirname       ,   **A_				)





				def 							_UpperCAmelCase    (		self			:		Optional[int]       ,   snake_case_			:		int				):


								"""simple docstring"""

								return ("This is a test", "This is a test")





				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								A  :	List[Any]				=					'''</s>'''
								A  :	Tuple				=					1

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





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A  :	str				=					list(self.get_tokenizer().get_vocab().keys()				)

								self.assertEqual(vocab_keys[0]       ,   '''<pad>'''				)
								self.assertEqual(vocab_keys[1]       ,   '''</s>'''				)
								self.assertEqual(vocab_keys[-1]       ,   '''v'''				)
								self.assertEqual(len(A_				)       ,   1103				)





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								self.assertEqual(self.get_tokenizer().vocab_size       ,   1103				)





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	Dict				=					self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
								A  :	str				=					self.tokenizer_class.from_pretrained(self.tmpdirname				)
								A  :	Dict				=					(
								    '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
								    ''' </s> <pad> <pad> <pad>'''
								)
								A  :	Optional[Any]				=					rust_tokenizer([raw_input_str]       ,   return_tensors=A_       ,   add_special_tokens=A_				).input_ids[0]
								A  :	List[str]				=					py_tokenizer([raw_input_str]       ,   return_tensors=A_       ,   add_special_tokens=A_				).input_ids[0]
								self.assertListEqual(A_       ,   A_				)





				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								A  :	Optional[int]				=					self._large_tokenizer
								# <mask_1> masks whole sentence while <mask_2> masks single word
								A  :	List[Any]				=					'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
								A  :	Tuple				=					[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
								A  :	Union[str, Any]				=					tokenizer([raw_input_str]       ,   return_tensors=A_				).input_ids[0]
								self.assertListEqual(A_       ,   A_				)





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A  :	int				=					self._large_tokenizer
								# The tracebacks for the following asserts are **better** without messages or self.assertEqual
								assert tokenizer.vocab_size == 9_6103
								assert tokenizer.pad_token_id == 0
								assert tokenizer.eos_token_id == 1
								assert tokenizer.offset == 103
								assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
								assert tokenizer.unk_token == "<unk>"
								assert tokenizer.model_max_length == 1024
								A  :	Union[str, Any]				=					'''To ensure a smooth flow of bank resolutions.'''
								A  :	str				=					[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
								A  :	Optional[int]				=					tokenizer([raw_input_str]       ,   return_tensors=A_				).input_ids[0]
								self.assertListEqual(A_       ,   A_				)
								assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





				@require_torch
				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	int				=					['''This is going to be way too long.''' * 150, '''short example''']
								A  :	str				=					['''not super long but more than 5 tokens''', '''tiny''']
								A  :	Tuple				=					self._large_tokenizer(A_       ,   padding=A_       ,   truncation=A_       ,   return_tensors='''pt'''				)
								A  :	List[str]				=					self._large_tokenizer(
								    text_target=A_       ,   max_length=5       ,   padding=A_       ,   truncation=A_       ,   return_tensors='''pt'''				)

								assert batch.input_ids.shape == (2, 1024)
								assert batch.attention_mask.shape == (2, 1024)
								assert targets["input_ids"].shape == (2, 5)
								assert len(A_				) == 2  # input_ids, attention_mask.






				@slow
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								A  :	List[str]				=					{'''input_ids''': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], '''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, 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, 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=A_       ,   model_name='''google/bigbird-pegasus-large-arxiv'''       ,   revision='''ba85d0851d708441f91440d509690f1ab6353415'''       ,   )



@require_sentencepiece
@require_tokenizers
class      _SCREAMING_SNAKE_CASE				(		snake_case,     unittest.TestCase				):
				lowerCamelCase_	  =  PegasusTokenizer
				lowerCamelCase_	  =  PegasusTokenizerFast
				lowerCamelCase_	  =  True
				lowerCamelCase_	  =  True





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								super().setUp()

								# We have a SentencePiece fixture for testing
								A  :	List[str]				=					PegasusTokenizer(A_       ,   offset=0       ,   mask_token_sent=A_       ,   mask_token='''[MASK]'''				)
								tokenizer.save_pretrained(self.tmpdirname				)





				@cached_property
				def 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''				)





				def 							_UpperCAmelCase    (		self			:		Any       ,   **snake_case_			:		Optional[Any]				):


								"""simple docstring"""

								return PegasusTokenizer.from_pretrained(self.tmpdirname       ,   **A_				)





				def 							_UpperCAmelCase    (		self			:		Optional[int]       ,   snake_case_			:		str				):


								"""simple docstring"""

								return ("This is a test", "This is a test")





				def 							_UpperCAmelCase    (		self			:		List[Any]				):


								"""simple docstring"""

								A  :	Optional[int]				=					self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
								A  :	Any				=					self.tokenizer_class.from_pretrained(self.tmpdirname				)
								A  :	Dict				=					(
								    '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
								    ''' <pad> <pad> <pad>'''
								)
								A  :	Dict				=					rust_tokenizer([raw_input_str]       ,   return_tensors=A_       ,   add_special_tokens=A_				).input_ids[0]
								A  :	Dict				=					py_tokenizer([raw_input_str]       ,   return_tensors=A_       ,   add_special_tokens=A_				).input_ids[0]
								self.assertListEqual(A_       ,   A_				)





				@require_torch
				def 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					['''This is going to be way too long.''' * 1000, '''short example''']
								A  :	Tuple				=					['''not super long but more than 5 tokens''', '''tiny''']
								A  :	List[str]				=					self._large_tokenizer(A_       ,   padding=A_       ,   truncation=A_       ,   return_tensors='''pt'''				)
								A  :	List[Any]				=					self._large_tokenizer(
								    text_target=A_       ,   max_length=5       ,   padding=A_       ,   truncation=A_       ,   return_tensors='''pt'''				)

								assert batch.input_ids.shape == (2, 4096)
								assert batch.attention_mask.shape == (2, 4096)
								assert targets["input_ids"].shape == (2, 5)
								assert len(A_				) == 2  # input_ids, attention_mask.






				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								A  :	Any				=					(
								    '''This is an example string that is used to test the original TF implementation against the HF'''
								    ''' implementation'''
								)

								A  :	List[Any]				=					self._large_tokenizer(A_				).input_ids

								self.assertListEqual(
								    A_       ,   [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]       ,   )



"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__						: Optional[Any]    =				logging.get_logger(__name__)





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      SwinConfig(
			    embed_dim=192,			depths=(2, 2, 18, 2),			num_heads=(6, 12, 24, 48),			window_size=12,			out_features=["""stage2""", """stage3""", """stage4"""],			)

			__lowerCamelCase          =      DetaConfig(
			    backbone_config=lowercase__,			num_queries=900,			encoder_ffn_dim=2048,			decoder_ffn_dim=2048,			num_feature_levels=5,			assign_first_stage=lowercase__,			with_box_refine=lowercase__,			two_stage=lowercase__,			)

			# set labels
			__lowerCamelCase          =      """huggingface/label-files"""
			if "o365" in model_name:
						__lowerCamelCase          =      366
						__lowerCamelCase          =      """object365-id2label.json"""
			else:
						__lowerCamelCase          =      91
						__lowerCamelCase          =      """coco-detection-id2label.json"""

			__lowerCamelCase          =      num_labels
			__lowerCamelCase          =      json.load(open(cached_download(hf_hub_url(lowercase__,			lowercase__,			repo_type="""dataset"""     )     ),			"""r"""     )     )
			__lowerCamelCase          =      {int(lowercase__     ): v for k, v in idalabel.items()}
			__lowerCamelCase          =      idalabel
			__lowerCamelCase          =      {v: k for k, v in idalabel.items()}

			return config





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      []

			# stem
			# fmt: off
			rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")     )
			# stages
			for i in range(len(config.backbone_config.depths     )     ):
						for j in range(config.backbone_config.depths[i]     ):
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')     )

						if i < 3:
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')     )

			rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")     )
			rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")     )
			rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")     )
			rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")     )
			rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")     )
			rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")     )

			# transformer encoder
			for i in range(config.encoder_layers     ):
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')     )
						rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')     )

			# transformer decoder
			for i in range(config.decoder_layers     ):
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')     )
						rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')     )

			# fmt: on

			return rename_keys





def       a_ (	lowercase__ :Optional[Any],			lowercase__ :str,			lowercase__ :Optional[int]     ):
			__lowerCamelCase          =      dct.pop(lowercase__     )
			__lowerCamelCase          =      val





def       a_ (	lowercase__ :List[Any],			lowercase__ :Dict     ):
			__lowerCamelCase          =      [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
			for i in range(len(backbone_config.depths     )     ):
						__lowerCamelCase          =      num_features[i]
						for j in range(backbone_config.depths[i]     ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									__lowerCamelCase          =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'     )
									__lowerCamelCase          =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'     )
									# next, add query, keys and values (in that order) to the state dict
									__lowerCamelCase          =      in_proj_weight[:dim, :]
									__lowerCamelCase          =      in_proj_bias[: dim]
									__lowerCamelCase          =      in_proj_weight[
									    dim : dim * 2, :
									]
									__lowerCamelCase          =      in_proj_bias[
									    dim : dim * 2
									]
									__lowerCamelCase          =      in_proj_weight[
									    -dim :, :
									]
									__lowerCamelCase          =      in_proj_bias[-dim :]
									# fmt: on





def       a_ (	lowercase__ :int,			lowercase__ :Optional[int]     ):
			# transformer decoder self-attention layers
			__lowerCamelCase          =      config.d_model
			for i in range(config.decoder_layers     ):
						# read in weights + bias of input projection layer of self-attention
						__lowerCamelCase          =      state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
						__lowerCamelCase          =      state_dict.pop(f'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[:hidden_size, :]
						__lowerCamelCase          =      in_proj_bias[:hidden_size]
						__lowerCamelCase          =      in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						__lowerCamelCase          =      in_proj_bias[hidden_size : hidden_size * 2]
						__lowerCamelCase          =      in_proj_weight[-hidden_size:, :]
						__lowerCamelCase          =      in_proj_bias[-hidden_size:]





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

			return im





@torch.no_grad()
def       a_ (	lowercase__ :Optional[int],			lowercase__ :Any,			lowercase__ :Dict     ):
			__lowerCamelCase          =      get_deta_config(lowercase__     )

			# load original state dict
			if model_name == "deta-swin-large":
						__lowerCamelCase          =      hf_hub_download(repo_id="""nielsr/deta-checkpoints""",			filename="""adet_swin_ft.pth"""     )
			elif model_name == "deta-swin-large-o365":
						__lowerCamelCase          =      hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""",			filename="""deta_swin_pt_o365.pth"""     )
			else:
						raise ValueError(f'Model name {model_name} not supported'     )

			__lowerCamelCase          =      torch.load(lowercase__,			map_location="""cpu"""     )["""model"""]

			# original state dict
			for name, param in state_dict.items():
						print(lowercase__,			param.shape     )

			# rename keys
			__lowerCamelCase          =      create_rename_keys(lowercase__     )
			for src, dest in rename_keys:
						rename_key(lowercase__,			lowercase__,			lowercase__     )
			read_in_swin_q_k_v(lowercase__,			config.backbone_config     )
			read_in_decoder_q_k_v(lowercase__,			lowercase__     )

			# fix some prefixes
			for key in state_dict.copy().keys():
						if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
									__lowerCamelCase          =      state_dict.pop(lowercase__     )
									__lowerCamelCase          =      val
						if "input_proj" in key:
									__lowerCamelCase          =      state_dict.pop(lowercase__     )
									__lowerCamelCase          =      val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									__lowerCamelCase          =      state_dict.pop(lowercase__     )
									__lowerCamelCase          =      val

    # finally, create HuggingFace model and load state dict
			__lowerCamelCase          =      DetaForObjectDetection(lowercase__     )
			model.load_state_dict(lowercase__     )
			model.eval()

			__lowerCamelCase          =      """cuda""" if torch.cuda.is_available() else """cpu"""
			model.to(lowercase__     )

			# load image processor
			__lowerCamelCase          =      DetaImageProcessor(format="""coco_detection"""     )

			# verify our conversion on image
			__lowerCamelCase          =      prepare_img()
			__lowerCamelCase          =      processor(images=lowercase__,			return_tensors="""pt"""     )
			__lowerCamelCase          =      encoding["""pixel_values"""]
			__lowerCamelCase          =      model(pixel_values.to(lowercase__     )     )

			# verify logits
			print("""Logits:""",			outputs.logits[0, :3, :3]     )
			print("""Boxes:""",			outputs.pred_boxes[0, :3, :3]     )
			if model_name == "deta-swin-large":
						__lowerCamelCase          =      torch.tensor(
						    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]     )
						__lowerCamelCase          =      torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]     )
			elif model_name == "deta-swin-large-o365":
						__lowerCamelCase          =      torch.tensor(
						    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]     )
						__lowerCamelCase          =      torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]     )
			assert torch.allclose(outputs.logits[0, :3, :3],			expected_logits.to(lowercase__     ),			atol=1e-4     )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3],			expected_boxes.to(lowercase__     ),			atol=1e-4     )
			print("""Everything ok!"""     )

			if pytorch_dump_folder_path:
						# Save model and processor
						logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'     )
						Path(lowercase__     ).mkdir(exist_ok=lowercase__     )
						model.save_pretrained(lowercase__     )
						processor.save_pretrained(lowercase__     )

			# Push to hub
			if push_to_hub:
						print("""Pushing model and processor to hub..."""     )
						model.push_to_hub(f'jozhang97/{model_name}'     )
						processor.push_to_hub(f'jozhang97/{model_name}'     )


if __name__ == "__main__":
					__magic_name__						: Union[str, Any]    =				argparse.ArgumentParser()

					parser.add_argument(
					    '--model_name',
					    type=str,
					    default='deta-swin-large',
					    choices=['deta-swin-large', 'deta-swin-large-o365'],
					    help='Name of the 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 or not to push the converted model to the 🤗 hub.'
					)
					__magic_name__						: str    =				parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




