
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					(   __lowerCAmelCase			)->						str:





						"""simple docstring"""



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

						return orig_key


def __A					(   __lowerCAmelCase			)->						Dict:





						"""simple docstring"""



						for i in range(6			):
												# read in weights + bias of input projection layer of self-attention
												_UpperCAmelCase             =    state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
												_UpperCAmelCase             =    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
												_UpperCAmelCase             =    in_proj_weight[:256, :]
												_UpperCAmelCase             =    in_proj_bias[:256]
												_UpperCAmelCase             =    in_proj_weight[256:512, :]
												_UpperCAmelCase             =    in_proj_bias[256:512]
												_UpperCAmelCase             =    in_proj_weight[-256:, :]
												_UpperCAmelCase             =    in_proj_bias[-256:]


def __A					(   )->						List[str]:





						"""simple docstring"""



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

						return im


@torch.no_grad()
def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	)->						List[str]:





						"""simple docstring"""



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

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

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

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

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

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

						_UpperCAmelCase             =    torch.tensor(
						    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]			)
						_UpperCAmelCase             =    torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]			)

						if single_scale:
												_UpperCAmelCase             =    torch.tensor(
												    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]			)
												_UpperCAmelCase             =    torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]			)

						if single_scale and dilation:
												_UpperCAmelCase             =    torch.tensor(
												    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]			)
												_UpperCAmelCase             =    torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]			)

						if with_box_refine:
												_UpperCAmelCase             =    torch.tensor(
												    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]			)
												_UpperCAmelCase             =    torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]			)

						if with_box_refine and two_stage:
												_UpperCAmelCase             =    torch.tensor(
												    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]			)
												_UpperCAmelCase             =    torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]			)

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

						assert torch.allclose(outputs.logits[0, :3, :3]				,	expected_logits.to(__lowerCAmelCase			)				,	atol=1E-4			)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,	expected_boxes.to(__lowerCAmelCase			)				,	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(__lowerCAmelCase			).mkdir(exist_ok=__lowerCAmelCase			)
						model.save_pretrained(__lowerCAmelCase			)
						image_processor.save_pretrained(__lowerCAmelCase			)

						# Push to hub
						if push_to_hub:
												_UpperCAmelCase             =    '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=__lowerCAmelCase				,	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,
		)








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_a   			=						logging.get_logger(__name__)

_a   			=						{
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   "m2m_100"
	UpperCamelCase__			=   ["past_key_values"]
	UpperCamelCase__			=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

	def __init__(							self ,					UpperCAmelCase=12_8112 ,					UpperCAmelCase=1024 ,					UpperCAmelCase=12 ,					UpperCAmelCase=4096 ,					UpperCAmelCase=16 ,					UpperCAmelCase=12 ,					UpperCAmelCase=4096 ,					UpperCAmelCase=16 ,					UpperCAmelCase=0.05 ,					UpperCAmelCase=0.05 ,					UpperCAmelCase=True ,					UpperCAmelCase=True ,					UpperCAmelCase="relu" ,					UpperCAmelCase=1024 ,					UpperCAmelCase=0.1 ,					UpperCAmelCase=0.1 ,					UpperCAmelCase=0.0 ,					UpperCAmelCase=0.02 ,					UpperCAmelCase=2 ,					UpperCAmelCase=True ,					UpperCAmelCase=1 ,					UpperCAmelCase=0 ,					UpperCAmelCase=2 ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    vocab_size
							_UpperCAmelCase             =    max_position_embeddings
							_UpperCAmelCase             =    d_model
							_UpperCAmelCase             =    encoder_ffn_dim
							_UpperCAmelCase             =    encoder_layers
							_UpperCAmelCase             =    encoder_attention_heads
							_UpperCAmelCase             =    decoder_ffn_dim
							_UpperCAmelCase             =    decoder_layers
							_UpperCAmelCase             =    decoder_attention_heads
							_UpperCAmelCase             =    dropout
							_UpperCAmelCase             =    attention_dropout
							_UpperCAmelCase             =    activation_dropout
							_UpperCAmelCase             =    activation_function
							_UpperCAmelCase             =    init_std
							_UpperCAmelCase             =    encoder_layerdrop
							_UpperCAmelCase             =    decoder_layerdrop
							_UpperCAmelCase             =    use_cache
							_UpperCAmelCase             =    encoder_layers
							_UpperCAmelCase             =    scale_embedding  # scale factor will be sqrt(d_model) if True

							super().__init__(
							    pad_token_id=UpperCAmelCase ,					bos_token_id=UpperCAmelCase ,					eos_token_id=UpperCAmelCase ,					is_encoder_decoder=UpperCAmelCase ,					decoder_start_token_id=UpperCAmelCase ,					**UpperCAmelCase ,					)





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	@property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
							        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
							    ]					)

							if self.use_past:
													_UpperCAmelCase             =    {0: 'batch'}
													_UpperCAmelCase             =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
													_UpperCAmelCase             =    {0: 'batch', 1: 'decoder_sequence'}
													_UpperCAmelCase             =    {0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
													self.fill_with_past_key_values_(UpperCAmelCase ,					direction='inputs'					)
							return common_inputs

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase = -1 ,					UpperCAmelCase = -1 ,					UpperCAmelCase = False ,					UpperCAmelCase = None ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    compute_effective_axis_dimension(
							    UpperCAmelCase ,					fixed_dimension=OnnxConfig.default_fixed_batch ,					num_token_to_add=0					)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							_UpperCAmelCase             =    tokenizer.num_special_tokens_to_add(UpperCAmelCase					)
							_UpperCAmelCase             =    compute_effective_axis_dimension(
							    UpperCAmelCase ,					fixed_dimension=OnnxConfig.default_fixed_sequence ,					num_token_to_add=UpperCAmelCase					)

							# Generate dummy inputs according to compute batch and sequence
							_UpperCAmelCase             =    [' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
							_UpperCAmelCase             =    dict(tokenizer(UpperCAmelCase ,					return_tensors=UpperCAmelCase					)					)
							return common_inputs

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase = -1 ,					UpperCAmelCase = -1 ,					UpperCAmelCase = False ,					UpperCAmelCase = None ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					)

							# Generate decoder inputs
							_UpperCAmelCase             =    seq_length if not self.use_past else 1
							_UpperCAmelCase             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					)
							_UpperCAmelCase             =    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
							_UpperCAmelCase             =    dict(**UpperCAmelCase ,					**UpperCAmelCase					)

							if self.use_past:
													if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
													else:
																			import torch
													_UpperCAmelCase      ,				_UpperCAmelCase             =    common_inputs['input_ids'].shape
													_UpperCAmelCase             =    common_inputs['decoder_input_ids'].shape[1]
													_UpperCAmelCase      ,				_UpperCAmelCase             =    self.num_attention_heads
													_UpperCAmelCase             =    (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													_UpperCAmelCase             =    decoder_seq_length + 3
													_UpperCAmelCase             =    (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													_UpperCAmelCase             =    torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase ,					UpperCAmelCase					)] ,					dim=1					)

													_UpperCAmelCase             =    []
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													_UpperCAmelCase      ,				_UpperCAmelCase             =    self.num_layers
													_UpperCAmelCase             =    min(UpperCAmelCase ,					UpperCAmelCase					)
													_UpperCAmelCase             =    max(UpperCAmelCase ,					UpperCAmelCase					) - min_num_layers
													_UpperCAmelCase             =    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

													for _ in range(UpperCAmelCase					):
																			common_inputs["past_key_values"].append(
																			    (
																			        torch.zeros(UpperCAmelCase					),
																			        torch.zeros(UpperCAmelCase					),
																			        torch.zeros(UpperCAmelCase					),
																			        torch.zeros(UpperCAmelCase					),
																			    )					)
													# TODO: test this.
													_UpperCAmelCase             =    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
													for _ in range(UpperCAmelCase ,					UpperCAmelCase					):
																			common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase					), torch.zeros(UpperCAmelCase					))					)
							return common_inputs

	UpperCamelCase__			=   _generate_dummy_inputs_for_default_and_seqaseq_lm
