
import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
	from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
	raise ImportError("""You need to install the version of fairseq from the Mega repo!""")

class        UpperCamelCase__(    nn.Module	):

	"""simple docstring"""

	def __init__(      self				:			List[str]							,			snake_case__				:			List[str]							,			snake_case__				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			super().__init__()
			A				=mega_args
			A				=nn.Embedding(snake_case__							,			self.mega_args.encoder_embed_dim )
			A				=nn.ModuleList([MegaEncoderLayer(self.mega_args ) for _ in range(snake_case__ )] )
			A				=depth







	def        _a					(      self				:			str							,			snake_case__				:			Optional[int]							,			snake_case__				:			str							,			snake_case__				:			Optional[int]=True							,			snake_case__				:			int=0 ):
			"""simple docstring"""





			if batch_first:
					A				=input_ids.T

			# to make things more confusing, Mega expects the attention mask to
			# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
			# which is the opposite of what HF returns
			if ignore_mask_value == 0:
					A				=1 - attention_mask

			# get token embeddings from IDs
			A				=self.embedding_layer(snake_case__ )

			# pass through the Mega layers
			# input is (time, batch, encoder dim) and output is the same
			for encoder in self.encoders:
					A				=encoder(snake_case__							,			snake_case__ )

			# return according to the shape specified
			if batch_first:
					# (T, B, H) --> (B, T, H)
					return torch.transpose(snake_case__							,			0							,			1 )
			else:
					return embeds


class        UpperCamelCase__(    nn.Module	):

	"""simple docstring"""

	def __init__(      self				:			Union[str, Any]							,			snake_case__				:			List[str]							,			snake_case__				:			Optional[Any]							,			snake_case__				:			Optional[int] ):
			"""simple docstring"""





			super().__init__()
			A				=MegaLM(snake_case__							,			snake_case__							,			snake_case__ )
			A				=nn.Linear(mega_args.encoder_embed_dim							,			snake_case__ )
			A				=nn.Dropout(p=0.1 )







	def        _a					(      self				:			List[str]							,			snake_case__				:			Optional[int]							,			snake_case__				:			Optional[int]							,			snake_case__				:			Tuple=True							,			snake_case__				:			Tuple=0 ):
			"""simple docstring"""





			A				=self.mega(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ )
			return self.mlm_head(self.dropout(snake_case__ ) )


def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->List[Any]:
		with open(os.path.join(a_  ,				"model_args.pkl"		)  ,				"rb"		) as f:
				A				=pkl.load(a_		)

		# load the original encoder
		A				=OriginalMegaForMaskedLM(**a_		).eval()

		# load its weights
		print(
		    "Original Mega encoder:"  ,				original_mlm.mega.load_state_dict(
		        torch.load(os.path.join(a_  ,				"encoder_weights.pt"		)  ,				map_location="cpu"		)		)  ,				)
		print(
		    "Original Mega MLM layer:"  ,				original_mlm.mlm_head.load_state_dict(
		        torch.load(os.path.join(a_  ,				"mlm_head_weights.pt"		)  ,				map_location="cpu"		)		)  ,				)

		# create a new config from the old one
		A				=MegaConfig(
		    num_hidden_layers=mega_original_args["depth"]  ,				vocab_size=mega_original_args["vocab_size"]  ,				hidden_size=mega_original_args["mega_args"].encoder_embed_dim  ,				shared_representation_size=mega_original_args["mega_args"].encoder_z_dim  ,				intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim  ,				ema_projection_size=mega_original_args["mega_args"].encoder_n_dim  ,				dropout_prob=mega_original_args["mega_args"].dropout  ,				attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout  ,				hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout  ,				activation=mega_original_args["mega_args"].activation_fn  ,				attention_activation=mega_original_args["mega_args"].attention_activation_fn  ,				bidirectional=mega_original_args["mega_args"].bidirectional  ,				use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0  ,				chunk_size=mega_original_args["mega_args"].encoder_chunk_size  ,				truncation=mega_original_args["mega_args"].truncation_length  ,				normalization_type=mega_original_args["mega_args"].normalization_type  ,				normalize_before_mega=a_  ,				norm_affine=a_  ,				use_feature_dropout=mega_original_args["mega_args"].feature_dropout  ,				relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias  ,				max_positions=mega_original_args["mega_args"].max_source_positions  ,				nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim  ,				normalize_before_ffn=mega_original_args["mega_args"].normalize_before  ,				nffn_activation_dropout_prob=0.0  ,				add_token_type_embeddings=a_  ,				add_lm_hidden_dense_layer=a_  ,				)

		A				=MegaForMaskedLM(a_		).eval()

		# the originl checkpoint just uses nn.Embedding for the word embeddings
		# we use a wrapper module for embeddings to add support for positional embeddings
		A				=original_mlm.mega.embedding_layer.weight

		# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
		# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
		# also renaming previously confusing parameter names
		A				=original_mlm.mega.encoders.state_dict()
		A				={}
		for module_name in original_state_dict.keys():
				A				=None
				# have to handle gamma, beta, and alpha differently due to their use
				# in multiple modules within the original repository;
				# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
				# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
				if "beta" in module_name:
						# EMA sub-layers were always called "move" in the original repo
						if "move.beta" in module_name:
								A				=module_name.replace("move.beta"  ,				"ema_gate.ema_expansion_matrix"		)
						elif "mega_layer.beta" in module_name:
								A				=module_name.replace("beta"  ,				"qk_bias"		)
						else:
								A				=module_name.replace("beta"  ,				"b_param"		)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
				elif "gamma" in module_name:
						if "move.gamma" in module_name:
								A				=module_name.replace("move.gamma"  ,				"ema_gate.kernel_projection_matrix"		)
						elif "mega_layer.gamma" in module_name:
								A				=module_name.replace("gamma"  ,				"qk_weight"		)
						else:
								A				=module_name.replace("gamma"  ,				"g_param"		)
        # alpha is used in EMA and positional bias; renaming to improve readability
				elif "move.alpha" in module_name:
						A				=module_name.replace("move.alpha"  ,				"ema_gate.decay_factor"		)
				# delta is only used in EMA; renaming to improve readability
				elif "move.delta" in module_name:
						A				=module_name.replace("move.delta"  ,				"ema_gate.damping_factor"		)
				# omega is only used in EMA; renaming to improve readability
				elif "omega" in module_name:
						A				=module_name.replace("move.omega"  ,				"ema_gate.residual_weight"		)

				if new_module_name:
						A				=new_module_name

		if len(a_		) != 0:
				print(f'''Renaming these keys: {updated_keys.keys()}'''		)
		else:
				print("No need to rename state dict entries"		)
		for old, new in updated_keys.items():
				A				=original_state_dict.pop(a_		)

		# now attempt to load the state dictionary with updated names
		# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
		print("HF Mega encoder:"  ,				hf_mlm.mega.layers.load_state_dict(a_		)		)

		# load the MLM head weights directly
		print(
		    "HF Mega MLM layer:"  ,				hf_mlm.mlm_head.load_state_dict(
		        torch.load(os.path.join(a_  ,				"mlm_head_weights.pt"		)  ,				map_location="cpu"		)		)  ,				)

		# test on a randomly generated input sequence
		A				=torch.randint(0  ,				hf_config.vocab_size  ,				size=(4, 256)		)
		A				=torch.ones_like(a_		)
		# mask a few tokens to make sure masking is applied appropriately :)
		A				=0

		# run forward passes
		A				=original_mlm(a_  ,				a_  ,				batch_first=a_  ,				ignore_mask_value=0		)
		A				=hf_mlm(a_  ,				a_		)[0]

		# print shapes and diff
		print(f'''original output {original_output.shape}'''		)
		print(f'''hf output {hf_output.shape}'''		)
		print(f'''max diff: {(original_output - hf_output).max()}'''		)  # 0.0
		A				=torch.allclose(a_  ,				a_  ,				atol=1E-3		)

		if success:
				print("Yay!"		)
				hf_mlm.save_pretrained(a_		)
		else:
				raise RuntimeError(f'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''		)

		if includes_tokenizer:
				print("Transferring tokenizer"		)
				A				=AutoTokenizer.from_pretrained(a_		)
				tokenizer.save_pretrained(a_		)


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

	parser.add_argument(
	    """--pretrained_checkpoint_path""",
	    default=None,
	    type=str,
	    required=True,
	    help="""Point to the directory containing your model weights using the official Mega repo""",
	)

	parser.add_argument(
	    """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
	)

	parser.add_argument(
	    """--includes_tokenizer""",
	    action="""store_true""",
	    help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
	)

	__a							  =						parser.parse_args()

	convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)






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

		return orig_key

def    UpperCamelCase_ (	a_		)     ->Dict:
		# transformer decoder self-attention layers
		for i in range(6		):
				# read in weights + bias of input projection layer of self-attention
				A				=state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''		)
				A				=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
				A				=in_proj_weight[:256, :]
				A				=in_proj_bias[:256]
				A				=in_proj_weight[256:512, :]
				A				=in_proj_bias[256:512]
				A				=in_proj_weight[-256:, :]
				A				=in_proj_bias[-256:]

def    UpperCamelCase_ (	)     ->Any:
		A				="http://images.cocodataset.org/val2017/000000039769.jpg"
		A				=Image.open(requests.get(a_  ,				stream=a_		).raw		)

		return im

@torch.no_grad()
def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_  ,				a_  ,				a_  ,				a_  ,				)     ->Optional[Any]:
		A				=DeformableDetrConfig()
		# set config attributes
		if single_scale:
				A				=1
		A				=dilation
		A				=with_box_refine
		A				=two_stage
		# set labels
		A				=91
		A				="huggingface/label-files"
		A				="coco-detection-id2label.json"
		A				=json.load(open(cached_download(hf_hub_url(a_  ,				a_  ,				repo_type="dataset"		)		)  ,				"r"		)		)
		A				={int(a_		): v for k, v in idalabel.items()}
		A				=idalabel
		A				={v: k for k, v in idalabel.items()}

		# load image processor
		A				=DeformableDetrImageProcessor(format="coco_detection"		)

		# prepare image
		A				=prepare_img()
		A				=image_processor(images=a_  ,				return_tensors="pt"		)
		A				=encoding["pixel_values"]

		logger.info("Converting model..."		)

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

		A				="cuda" if torch.cuda.is_available() else "cpu"
		model.to(a_		)
		# verify our conversion
		A				=model(pixel_values.to(a_		)		)

		A				=torch.tensor(
		    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]		)
		A				=torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]		)

		if single_scale:
				A				=torch.tensor(
				    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]		)
				A				=torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]		)

		if single_scale and dilation:
				A				=torch.tensor(
				    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]		)
				A				=torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]		)

		if with_box_refine:
				A				=torch.tensor(
				    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]		)
				A				=torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]		)

		if with_box_refine and two_stage:
				A				=torch.tensor(
				    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]		)
				A				=torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]		)

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

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

		# Push to hub
		if push_to_hub:
				A				="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=a_  ,				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,
	)




