




'''simple docstring'''




import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__lowerCamelCase   :					Optional[Any]   							=					logging.getLogger()

def 	__UpperCAmelCase       ( )->    Tuple:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       argparse.ArgumentParser()
			parser.add_argument("-f"						)
			snake_case_				:					int   =       parser.parse_args()
			return args.f

def 	__UpperCAmelCase       ( __magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



			snake_case_				:					Dict   =       {}
			snake_case_				:					Union[str, Any]   =       os.path.join(_lowercase     ,"all_results.json"						)
			if os.path.exists(_lowercase						):
						with open(_lowercase     ,"r"						) as f:
									snake_case_				:					Tuple   =       json.load(_lowercase						)
			else:
						raise ValueError(F'''can\'t find {path}'''						)
			return results

def 	__UpperCAmelCase       ( )->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					str   =       torch.cuda.is_available() and torch_device == "cuda"
			return is_using_cuda and is_apex_available()


__lowerCamelCase   :					int   							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class    A_	(a_							):
							"""simple docstring"""




							@classmethod
							def 							_A	(		cls			:List[Any]				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       tempfile.mkdtemp()
										snake_case_				:					Dict   =       os.path.join(cls.tmpdir       ,							"default_config.yml"				)
										write_basic_config(save_location=cls.configPath				)
										snake_case_				:					Tuple   =       ["accelerate", "launch", "--config_file", cls.configPath]

							@classmethod
							def 							_A	(		cls			:List[str]				)				->  Optional[Any]:


										'''simple docstring'''




										shutil.rmtree(cls.tmpdir				)

							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:Optional[int]				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					Union[str, Any]   =       F'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

										if is_cuda_and_apex_available():
													testargs.append("--fp16"				)

										run_command(self._launch_args + testargs				)
										snake_case_				:					Any   =       get_results(__A				)
										self.assertGreaterEqual(result["eval_accuracy"]       ,							0.7_5				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"epoch_0"				)				)				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"glue_no_trainer"				)				)				)

							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:int				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					Tuple   =       F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

										if torch.cuda.device_count() > 1:
													# Skipping because there are not enough batches to train the model + would need a drop_last to work.
													return

										run_command(self._launch_args + testargs				)
										snake_case_				:					List[str]   =       get_results(__A				)
										self.assertLess(result["perplexity"]       ,							100				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"epoch_0"				)				)				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"clm_no_trainer"				)				)				)

							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:int				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					Any   =       F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

										run_command(self._launch_args + testargs				)
										snake_case_				:					Any   =       get_results(__A				)
										self.assertLess(result["perplexity"]       ,							42				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"epoch_0"				)				)				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"mlm_no_trainer"				)				)				)

							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:Optional[int]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       7 if get_gpu_count() > 1 else 2

										snake_case_				:					Tuple   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					Union[str, Any]   =       F'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

										run_command(self._launch_args + testargs				)
										snake_case_				:					Dict   =       get_results(__A				)
										self.assertGreaterEqual(result["eval_accuracy"]       ,							0.7_5				)
										self.assertLess(result["train_loss"]       ,							0.5				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"epoch_0"				)				)				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"ner_no_trainer"				)				)				)

							@unittest.skip(reason="Fix me @muellerzr"				)
							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:Tuple				)				->  int:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					str   =       F'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

										run_command(self._launch_args + testargs				)
										snake_case_				:					Tuple   =       get_results(__A				)
										# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
										self.assertGreaterEqual(result["eval_f1"]       ,							28				)
										self.assertGreaterEqual(result["eval_exact"]       ,							28				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"epoch_0"				)				)				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"qa_no_trainer"				)				)				)

							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:List[str]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					List[Any]   =       F'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

										run_command(self._launch_args + testargs				)
										snake_case_				:					int   =       get_results(__A				)
										self.assertGreaterEqual(result["eval_accuracy"]       ,							0.8				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"swag_no_trainer"				)				)				)

							@slow
							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:List[Any]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					str   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					Dict   =       F'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

										run_command(self._launch_args + testargs				)
										snake_case_				:					Optional[int]   =       get_results(__A				)
										self.assertGreaterEqual(result["eval_rouge1"]       ,							10				)
										self.assertGreaterEqual(result["eval_rouge2"]       ,							2				)
										self.assertGreaterEqual(result["eval_rougeL"]       ,							7				)
										self.assertGreaterEqual(result["eval_rougeLsum"]       ,							7				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"epoch_0"				)				)				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"summarization_no_trainer"				)				)				)

							@slow
							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:Any				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					int   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					Union[str, Any]   =       F'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

										run_command(self._launch_args + testargs				)
										snake_case_				:					Union[str, Any]   =       get_results(__A				)
										self.assertGreaterEqual(result["eval_bleu"]       ,							30				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"epoch_0"				)				)				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"translation_no_trainer"				)				)				)

							@slow
							def 							_A	(		self			:Union[str, Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       logging.StreamHandler(sys.stdout				)
										logger.addHandler(__A				)

										snake_case_				:					Any   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					Any   =       F'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

										run_command(self._launch_args + testargs				)
										snake_case_				:					Union[str, Any]   =       get_results(__A				)
										self.assertGreaterEqual(result["eval_overall_accuracy"]       ,							0.1_0				)






							@mock.patch.dict(os.environ       ,							{"WANDB_MODE": "offline"}				)
							def 							_A	(		self			:str				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       self.get_auto_remove_tmp_dir()
										snake_case_				:					List[str]   =       F'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

										if is_cuda_and_apex_available():
													testargs.append("--fp16"				)

										run_command(self._launch_args + testargs				)
										snake_case_				:					Optional[int]   =       get_results(__A				)
										# The base model scores a 25%
										self.assertGreaterEqual(result["eval_accuracy"]       ,							0.6				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"step_1"				)				)				)
										self.assertTrue(os.path.exists(os.path.join(__A       ,							"image_classification_no_trainer"				)				)				)












'''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()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase   :					Optional[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 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       state_dict.pop(__magic_name__						)
			snake_case_				:					Any   =       val

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Any   =       OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									snake_case_				:					Optional[Any]   =       key.replace("backbone.0.body"     ,"backbone.conv_encoder.model"						)
									snake_case_				:					int   =       value
						else:
									snake_case_				:					int   =       value

			return new_state_dict

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=False						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					str   =       ""
			if is_panoptic:
						snake_case_				:					Dict   =       "conditional_detr."

			# first: transformer encoder
			for i in range(6						):
						# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
						snake_case_				:					Any   =       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
						snake_case_				:					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
						snake_case_				:					Tuple   =       in_proj_weight[:256, :]
						snake_case_				:					List[Any]   =       in_proj_bias[:256]
						snake_case_				:					Optional[Any]   =       in_proj_weight[256:512, :]
						snake_case_				:					Optional[int]   =       in_proj_bias[256:512]
						snake_case_				:					Optional[int]   =       in_proj_weight[-256:, :]
						snake_case_				:					str   =       in_proj_bias[-256:]

def 	__UpperCAmelCase       ( )->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
			snake_case_				:					Optional[Any]   =       Image.open(requests.get(__magic_name__     ,stream=__magic_name__						).raw						)

			return im

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



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

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

			# prepare image
			snake_case_				:					str   =       prepare_img()
			snake_case_				:					int   =       image_processor(images=__magic_name__     ,return_tensors="pt"						)
			snake_case_				:					Union[str, Any]   =       encoding["pixel_values"]

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

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


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




