
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], [-1_0.5_6_3_4, -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], [-1_0.0_6_6_5, -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], [-1_0.1_1_7_8, -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 unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.electra.modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						)
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_3	,							SCREAMING_SNAKE_CASE_  :      int=7	,							SCREAMING_SNAKE_CASE_  :      Tuple=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      Dict=True	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=9_9	,							SCREAMING_SNAKE_CASE_  :      Any=2_4	,							SCREAMING_SNAKE_CASE_  :      int=3_2	,							SCREAMING_SNAKE_CASE_  :      Any=5	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_7	,							SCREAMING_SNAKE_CASE_  :      Optional[int]="gelu"	,							SCREAMING_SNAKE_CASE_  :      Any=0.1	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      Dict=5_1_2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_6	,							SCREAMING_SNAKE_CASE_  :      List[Any]=2	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.02	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							)	->					Optional[int]:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_attention_mask
					lowercase_					  = use_token_type_ids
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = embedding_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = num_attention_heads
					lowercase_					  = intermediate_size
					lowercase_					  = hidden_act
					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = max_position_embeddings
					lowercase_					  = type_vocab_size
					lowercase_					  = type_sequence_label_size
					lowercase_					  = initializer_range
					lowercase_					  = num_choices
	def 						_lowercase  (	self  :      Optional[int]	)	->					Tuple:
					lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size	)

					lowercase_					  = None
					if self.use_attention_mask:
									lowercase_					  = random_attention_mask([self.batch_size, self.seq_length]	)

					lowercase_					  = None
					if self.use_token_type_ids:
									lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size	)

					lowercase_					  = ElectraConfig(
					    vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							embedding_size=self.embedding_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							initializer_range=self.initializer_range	,							)

					return config, input_ids, token_type_ids, attention_mask




	def 						_lowercase  (	self  :      Any	)	->					str:
					lowercase_					  = self.prepare_config_and_inputs()
					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = config_and_inputs
					lowercase_					  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

@require_flax
class 			lowercase__(    UpperCAmelCase			,						unittest.TestCase							):



	"""simple docstring"""




	a      :List[str]   					=     True

	a      :List[Any]   					=     (
	    (
	        FlaxElectraModel,
	        FlaxElectraForCausalLM,
	        FlaxElectraForMaskedLM,
	        FlaxElectraForPreTraining,
	        FlaxElectraForTokenClassification,
	        FlaxElectraForQuestionAnswering,
	        FlaxElectraForMultipleChoice,
	        FlaxElectraForSequenceClassification,
	    )
	    if is_flax_available()
	    else ()
	)
	def 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = FlaxElectraModelTester(self	)




	@slow
	def 						_lowercase  (	self  :      List[str]	)	->					int:
					for model_class_name in self.all_model_classes:
									if model_class_name == FlaxElectraForMaskedLM:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-generator'''	)
									else:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-discriminator'''	)
									lowercase_					  = model(np.ones((1, 1)	)	)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




