import argparse
import json
from pathlib import Path

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

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a        = logging.get_logger(__name__)




def     a						(							snake_case__: Any ):


				'''simple docstring'''
				if "backbone.0.body" in orig_key:
								lowercase_					  = orig_key.replace('''backbone.0.body'''						,       '''backbone.conv_encoder.model''' )
				if "transformer" in orig_key:
								lowercase_					  = orig_key.replace('''transformer.'''						,       '''''' )
				if "norm1" in orig_key:
								if "encoder" in orig_key:
												lowercase_					  = orig_key.replace('''norm1'''						,       '''self_attn_layer_norm''' )
								else:
												lowercase_					  = orig_key.replace('''norm1'''						,       '''encoder_attn_layer_norm''' )
				if "norm2" in orig_key:
								if "encoder" in orig_key:
												lowercase_					  = orig_key.replace('''norm2'''						,       '''final_layer_norm''' )
								else:
												lowercase_					  = orig_key.replace('''norm2'''						,       '''self_attn_layer_norm''' )
				if "norm3" in orig_key:
								lowercase_					  = orig_key.replace('''norm3'''						,       '''final_layer_norm''' )
				if "linear1" in orig_key:
								lowercase_					  = orig_key.replace('''linear1'''						,       '''fc1''' )
				if "linear2" in orig_key:
								lowercase_					  = orig_key.replace('''linear2'''						,       '''fc2''' )
				if "query_embed" in orig_key:
								lowercase_					  = orig_key.replace('''query_embed'''						,       '''query_position_embeddings''' )
				if "cross_attn" in orig_key:
								lowercase_					  = orig_key.replace('''cross_attn'''						,       '''encoder_attn''' )

				return orig_key




def     a						(							snake_case__: Any ):


				'''simple docstring'''
				# transformer decoder self-attention layers
				for i in range(6 ):
								# read in weights + bias of input projection layer of self-attention
								lowercase_					  = state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight''' )
								lowercase_					  = state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias''' )
								# next, add query, keys and values (in that order) to the state dict
								lowercase_					  = in_proj_weight[:256, :]
								lowercase_					  = in_proj_bias[:256]
								lowercase_					  = in_proj_weight[256:512, :]
								lowercase_					  = in_proj_bias[256:512]
								lowercase_					  = in_proj_weight[-256:, :]
								lowercase_					  = in_proj_bias[-256:]




def     a						(							):


				'''simple docstring'''
				lowercase_					  = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase_					  = Image.open(requests.get(snake_case__						,       stream=snake_case__ ).raw )

				return im




@torch.no_grad()
def     a						(							snake_case__: str						,       snake_case__: Tuple						,       snake_case__: List[Any]						,       snake_case__: Tuple						,       snake_case__: Dict						,       snake_case__: str						,       snake_case__: List[Any]						,       ):


				'''simple docstring'''
				lowercase_					  = DeformableDetrConfig()
				# set config attributes
				if single_scale:
								lowercase_					  = 1
				lowercase_					  = dilation
				lowercase_					  = with_box_refine
				lowercase_					  = two_stage
				# set labels
				lowercase_					  = 91
				lowercase_					  = '''huggingface/label-files'''
				lowercase_					  = '''coco-detection-id2label.json'''
				lowercase_					  = json.load(open(cached_download(hf_hub_url(snake_case__						,       snake_case__						,       repo_type='''dataset''' ) )						,       '''r''' ) )
				lowercase_					  = {int(snake_case__ ): v for k, v in idalabel.items()}
				lowercase_					  = idalabel
				lowercase_					  = {v: k for k, v in idalabel.items()}

				# load image processor
				lowercase_					  = DeformableDetrImageProcessor(format='''coco_detection''' )

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

				logger.info('''Converting model...''' )

				# load original state dict
				lowercase_					  = torch.load(snake_case__						,       map_location='''cpu''' )['''model''']
				# rename keys
				for key in state_dict.copy().keys():
								lowercase_					  = state_dict.pop(snake_case__ )
								lowercase_					  = val
				# query, key and value matrices need special treatment
				read_in_q_k_v(snake_case__ )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowercase_					  = '''model.'''
				for key in state_dict.copy().keys():
								if not key.startswith('''class_embed''' ) and not key.startswith('''bbox_embed''' ):
												lowercase_					  = state_dict.pop(snake_case__ )
												lowercase_					  = val
    # finally, create HuggingFace model and load state dict
				lowercase_					  = DeformableDetrForObjectDetection(snake_case__ )
				model.load_state_dict(snake_case__ )
				model.eval()

				lowercase_					  = '''cuda''' if torch.cuda.is_available() else '''cpu'''
				model.to(snake_case__ )
				# verify our conversion
				lowercase_					  = model(pixel_values.to(snake_case__ ) )

				lowercase_					  = torch.tensor(
				    [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-10.5_634, -5.3_3_7_9, -7.5_1_1_6]] )
				lowercase_					  = torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]] )

				if single_scale:
								lowercase_					  = torch.tensor(
								    [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-10.0_665, -5.8_4_7_0, -7.7_0_0_3]] )
								lowercase_					  = torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]] )

				if single_scale and dilation:
								lowercase_					  = torch.tensor(
								    [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-10.1_178, -4.5_2_7_5, -6.2_6_7_1]] )
								lowercase_					  = torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]] )

				if with_box_refine:
								lowercase_					  = torch.tensor(
								    [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]] )
								lowercase_					  = torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]] )

				if with_box_refine and two_stage:
								lowercase_					  = torch.tensor(
								    [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]] )
								lowercase_					  = torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]] )

				print('''Logits:'''						,       outputs.logits[0, :3, :3] )

				assert torch.allclose(outputs.logits[0, :3, :3]						,       expected_logits.to(snake_case__ )						,       atol=1e-4 )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,       expected_boxes.to(snake_case__ )						,       atol=1e-4 )

				print('''Everything ok!''' )

				# Save model and image processor
				logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
				Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
				model.save_pretrained(snake_case__ )
				image_processor.save_pretrained(snake_case__ )

				# Push to hub
				if push_to_hub:
								lowercase_					  = '''deformable-detr'''
								model_name += "-single-scale" if single_scale else ""
								model_name += "-dc5" if dilation else ""
								model_name += "-with-box-refine" if with_box_refine else ""
								model_name += "-two-stage" if two_stage else ""
								print('''Pushing model to hub...''' )
								model.push_to_hub(repo_path_or_name=snake_case__						,       organization='''nielsr'''						,       commit_message='''Add model''' )


if __name__ == "__main__":
						__a        = argparse.ArgumentParser()

						parser.add_argument(
						    '--checkpoint_path',
						    type=str,
						    default='/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth',
						    help='Path to Pytorch checkpoint (.pth file) you\'d like to convert.',
						)
						parser.add_argument('--single_scale', action='store_true', help='Whether to set config.num_features_levels = 1.')
						parser.add_argument('--dilation', action='store_true', help='Whether to set config.dilation=True.')
						parser.add_argument('--with_box_refine', action='store_true', help='Whether to set config.with_box_refine=True.')
						parser.add_argument('--two_stage', action='store_true', help='Whether to set config.two_stage=True.')
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=str,
						    required=True,
						    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.'
						)
						__a        = parser.parse_args()
						convert_deformable_detr_checkpoint(
						    args.checkpoint_path,
						    args.single_scale,
						    args.dilation,
						    args.with_box_refine,
						    args.two_stage,
						    args.pytorch_dump_folder_path,
						    args.push_to_hub,
						)






import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__a        = [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
						import run_clm
						import run_image_classification
						import run_mlm
						import run_ner
						import run_qa as run_squad
						import run_summarization
						import run_swag
						import run_text_classification
						import run_translation


logging.basicConfig(level=logging.DEBUG)

__a        = logging.getLogger()




def     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				lowercase_					  = parser.parse_args()
				return args.f




def     a						(							snake_case__: List[Any] ):


				'''simple docstring'''
				lowercase_					  = {}
				lowercase_					  = os.path.join(snake_case__						,       '''all_results.json''' )
				if os.path.exists(snake_case__ ):
								with open(snake_case__						,       '''r''' ) as f:
												lowercase_					  = json.load(snake_case__ )
				else:
								raise ValueError(F'''can\'t find {path}''' )
				return results




def     a						(							):


				'''simple docstring'''
				return bool(tf.config.list_physical_devices('''GPU''' ) )


__a        = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''	)
	def 						_lowercase  (	self  :      Tuple	)	->					List[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

					if is_cuda_available():
									testargs.append('''--fp16'''	)

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_text_classification.main()
									# Reset the mixed precision policy so we don't break other tests
									tf.keras.mixed_precision.set_global_policy('''float32'''	)
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''eval_accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

					if len(tf.config.list_physical_devices('''GPU'''	)	) > 1:
									# Skipping because there are not enough batches to train the model + would need a drop_last to work.
									return

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_clm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							1_0_0	)
	def 						_lowercase  (	self  :      Any	)	->					Union[str, Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_mlm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							4_2	)
	def 						_lowercase  (	self  :      Any	)	->					str:
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					lowercase_					  = 7 if get_gpu_count() > 1 else 2

					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_ner.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}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_ner.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Any	)	->					Optional[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_qa.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}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_squad.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''f1''']	,							3_0	)
									self.assertGreaterEqual(result['''exact''']	,							3_0	)
	def 						_lowercase  (	self  :      str	)	->					Dict:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_swag.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}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_swag.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''val_accuracy''']	,							0.8	)
	@slow
	def 						_lowercase  (	self  :      int	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_summarization.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}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_summarization.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''rouge1''']	,							1_0	)
									self.assertGreaterEqual(result['''rouge2''']	,							2	)
									self.assertGreaterEqual(result['''rougeL''']	,							7	)
									self.assertGreaterEqual(result['''rougeLsum''']	,							7	)
	@slow
	def 						_lowercase  (	self  :      Dict	)	->					Any:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_translation.py
            --model_name_or_path Rocketknight1/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}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_translation.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''bleu''']	,							3_0	)




	def 						_lowercase  (	self  :      int	)	->					List[str]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_image_classification.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.7	)




