


'''simple docstring'''

from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
	from transformers import (
	    TFAutoModel,
	    TFAutoModelForCausalLM,
	    TFAutoModelForMaskedLM,
	    TFAutoModelForPreTraining,
	    TFAutoModelForQuestionAnswering,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSequenceClassification,
	    TFAutoModelForTableQuestionAnswering,
	    TFAutoModelForTokenClassification,
	    TFAutoModelWithLMHead,
	    TFBertForMaskedLM,
	    TFBertForPreTraining,
	    TFBertForQuestionAnswering,
	    TFBertForSequenceClassification,
	    TFBertModel,
	    TFFunnelBaseModel,
	    TFFunnelModel,
	    TFGPTaLMHeadModel,
	    TFRobertaForMaskedLM,
	    TFTaForConditionalGeneration,
	    TFTapasForQuestionAnswering,
	)
	from transformers.models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_MASKED_LM_MAPPING,
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    TF_MODEL_MAPPING,
	)
	from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		int	=	'new-model'


if is_tf_available():


	class       a__				( __A      ):







					"""simple docstring"""



					__UpperCamelCase    :		Any	=	NewModelConfig


@require_tf
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				@slow
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''bert-base-cased'''
										__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
										self.assertIsNotNone(__lowercase		)
										self.assertIsInstance(__lowercase       ,			__lowercase		)

										__lowerCAmelCase							       =				TFAutoModel.from_pretrained(__lowercase		)
										self.assertIsNotNone(__lowercase		)
										self.assertIsInstance(__lowercase       ,			__lowercase		)




				@slow
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''bert-base-cased'''
										__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
										self.assertIsNotNone(__lowercase		)
										self.assertIsInstance(__lowercase       ,			__lowercase		)

										__lowerCAmelCase							       =				TFAutoModelForPreTraining.from_pretrained(__lowercase		)
										self.assertIsNotNone(__lowercase		)
										self.assertIsInstance(__lowercase       ,			__lowercase		)




				@slow
				def 					_snake_case  (self		):
										for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				TFAutoModelForCausalLM.from_pretrained(__lowercase		)
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				TFAutoModelForCausalLM.from_pretrained(__lowercase       ,			output_loading_info=__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)




				@slow
				def 					_snake_case  (self		):
										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				TFAutoModelWithLMHead.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)




				@slow
				def 					_snake_case  (self		):
										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				TFAutoModelForMaskedLM.from_pretrained(__lowercase		)
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				TFAutoModelForMaskedLM.from_pretrained(__lowercase       ,			output_loading_info=__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)




				@slow
				def 					_snake_case  (self		):
										for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				TFAutoModelForSeqaSeqLM.from_pretrained(__lowercase		)
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				TFAutoModelForSeqaSeqLM.from_pretrained(__lowercase       ,			output_loading_info=__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)




				@slow
				def 					_snake_case  (self		):
										# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										for model_name in ["bert-base-uncased"]:
																__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				TFAutoModelForSequenceClassification.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)




				@slow
				def 					_snake_case  (self		):
										# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										for model_name in ["bert-base-uncased"]:
																__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				TFAutoModelForQuestionAnswering.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)




				@slow
				@require_tensorflow_probability
				def 					_snake_case  (self		):
										for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
																__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				TFAutoModelForTableQuestionAnswering.from_pretrained(__lowercase		)
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				TFAutoModelForTableQuestionAnswering.from_pretrained(
																    __lowercase       ,			output_loading_info=__lowercase		)
																self.assertIsNotNone(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				TFAutoModelWithLMHead.from_pretrained(__lowercase		)
										self.assertIsInstance(__lowercase       ,			__lowercase		)
										self.assertEqual(model.num_parameters()       ,			1_44_10		)
										self.assertEqual(model.num_parameters(only_trainable=__lowercase		)       ,			1_44_10		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				TFAutoModelWithLMHead.from_pretrained(__lowercase		)
										self.assertIsInstance(__lowercase       ,			__lowercase		)
										self.assertEqual(model.num_parameters()       ,			1_44_10		)
										self.assertEqual(model.num_parameters(only_trainable=__lowercase		)       ,			1_44_10		)




				def 					_snake_case  (self		):
										# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
										__lowerCAmelCase							       =				TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''		)
										self.assertIsInstance(__lowercase       ,			__lowercase		)

										__lowerCAmelCase							       =				copy.deepcopy(model.config		)
										__lowerCAmelCase							       =				['''FunnelBaseModel''']
										__lowerCAmelCase							       =				TFAutoModel.from_config(__lowercase		)
										self.assertIsInstance(__lowercase       ,			__lowercase		)

										with tempfile.TemporaryDirectory() as tmp_dir:
																model.save_pretrained(__lowercase		)
																__lowerCAmelCase							       =				TFAutoModel.from_pretrained(__lowercase		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)




				def 					_snake_case  (self		):
										try:
																AutoConfig.register('''new-model'''       ,			__lowercase		)

																__lowerCAmelCase							       =				[
																    TFAutoModel,
																    TFAutoModelForCausalLM,
																    TFAutoModelForMaskedLM,
																    TFAutoModelForPreTraining,
																    TFAutoModelForQuestionAnswering,
																    TFAutoModelForSequenceClassification,
																    TFAutoModelForTokenClassification,
																]

																for auto_class in auto_classes:
																						with self.subTest(auto_class.__name__		):
																												# Wrong config class will raise an error
																												with self.assertRaises(__lowercase		):
																																		auto_class.register(__lowercase       ,			__lowercase		)
																												auto_class.register(__lowercase       ,			__lowercase		)
																												# Trying to register something existing in the Transformers library will raise an error
																												with self.assertRaises(__lowercase		):
																																		auto_class.register(__lowercase       ,			__lowercase		)

																												# Now that the config is registered, it can be used as any other config with the auto-API
																												__lowerCAmelCase							       =				BertModelTester(self		).get_config()
																												__lowerCAmelCase							       =				NewModelConfig(**tiny_config.to_dict()		)
																												__lowerCAmelCase							       =				auto_class.from_config(__lowercase		)
																												self.assertIsInstance(__lowercase       ,			__lowercase		)

																												with tempfile.TemporaryDirectory() as tmp_dir:
																																		model.save_pretrained(__lowercase		)
																																		__lowerCAmelCase							       =				auto_class.from_pretrained(__lowercase		)
																																		self.assertIsInstance(__lowercase       ,			__lowercase		)

										finally:
																if "new-model" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["new-model"]
																for mapping in (
																    TF_MODEL_MAPPING,
																    TF_MODEL_FOR_PRETRAINING_MAPPING,
																    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
																    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
																    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
																    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
																    TF_MODEL_FOR_MASKED_LM_MAPPING,
																):
																						if NewModelConfig in mapping._extra_content:
																												del mapping._extra_content[NewModelConfig]




				def 					_snake_case  (self		):
										with self.assertRaisesRegex(
										    __lowercase       ,			'''bert-base is not a local folder and is not a valid model identifier'''		):
																__lowerCAmelCase							       =				TFAutoModel.from_pretrained('''bert-base'''		)




				def 					_snake_case  (self		):
										with self.assertRaisesRegex(
										    __lowercase       ,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
																__lowerCAmelCase							       =				TFAutoModel.from_pretrained(__lowercase       ,			revision='''aaaaaa'''		)




				def 					_snake_case  (self		):
										with self.assertRaisesRegex(
										    __lowercase       ,			'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''       ,			):
																__lowerCAmelCase							       =				TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''		)




				def 					_snake_case  (self		):
										with self.assertRaisesRegex(__lowercase       ,			'''Use `from_pt=True` to load this model'''		):
																__lowerCAmelCase							       =				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''		)




				def 					_snake_case  (self		):
										# Make sure we have cached the model.
										__lowerCAmelCase							       =				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
										with RequestCounter() as counter:
																__lowerCAmelCase							       =				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
																self.assertEqual(counter.get_request_count       ,			0		)
																self.assertEqual(counter.head_request_count       ,			1		)
																self.assertEqual(counter.other_request_count       ,			0		)

										# With a sharded checkpoint
										__lowerCAmelCase							       =				TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''		)
										with RequestCounter() as counter:
																__lowerCAmelCase							       =				TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''		)
																self.assertEqual(counter.get_request_count       ,			0		)
																self.assertEqual(counter.head_request_count       ,			1		)
																self.assertEqual(counter.other_request_count       ,			0		)





'''simple docstring'''

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

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

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


logging.set_verbosity_info()
_UpperCAmelCase				: Optional[int]							    =				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase				: Any							    =				[]
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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
						__lowerCAmelCase							       =				val

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		__lowerCAmelCase							       =				key.replace('''backbone.0.body''',				'''backbone.conv_encoder.model''')
																		__lowerCAmelCase							       =				value
												else:
																		__lowerCAmelCase							       =				value

						return new_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				''''''
						if is_panoptic:
												__lowerCAmelCase							       =				'''conditional_detr.'''

						# first: transformer encoder
						for i in range(6):
												# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
												__lowerCAmelCase							       =				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[:2_5_6, :]
												__lowerCAmelCase							       =				in_proj_bias[:2_5_6]
												__lowerCAmelCase							       =				in_proj_weight[2_5_6:5_1_2, :]
												__lowerCAmelCase							       =				in_proj_bias[2_5_6:5_1_2]
												__lowerCAmelCase							       =				in_proj_weight[-2_5_6:, :]
												__lowerCAmelCase							       =				in_proj_bias[-2_5_6:]

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

						return im

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				ConditionalDetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												__lowerCAmelCase							       =				'''resnet101'''
						if "dc5" in model_name:
												__lowerCAmelCase							       =				True
						__lowerCAmelCase							       =				'''panoptic''' in model_name
						if is_panoptic:
												__lowerCAmelCase							       =				2_5_0
						else:
												__lowerCAmelCase							       =				9_1
												__lowerCAmelCase							       =				'''huggingface/label-files'''
												__lowerCAmelCase							       =				'''coco-detection-id2label.json'''
												__lowerCAmelCase							       =				json.load(open(hf_hub_download(lowerCamelCase,				lowerCamelCase,				repo_type='''dataset'''),				'''r'''))
												__lowerCAmelCase							       =				{int(lowerCamelCase): v for k, v in idalabel.items()}
												__lowerCAmelCase							       =				idalabel
												__lowerCAmelCase							       =				{v: k for k, v in idalabel.items()}

						# load image processor
						__lowerCAmelCase							       =				'''coco_panoptic''' if is_panoptic else '''coco_detection'''
						__lowerCAmelCase							       =				ConditionalDetrImageProcessor(format=lowerCamelCase)

						# prepare image
						__lowerCAmelCase							       =				prepare_img()
						__lowerCAmelCase							       =				image_processor(images=lowerCamelCase,				return_tensors='''pt''')
						__lowerCAmelCase							       =				encoding['''pixel_values''']

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

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


if __name__ == "__main__":
	_UpperCAmelCase				: str							    =				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."""
	)
	_UpperCAmelCase				: Any							    =				parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

