


'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def 	_A  (       A							)  ->				Dict:
	lowercase						:		Tuple   					=		SwinConfig(image_size=1_9_2							)

	if "base" in model_name:
		lowercase						:		Optional[Any]   					=		6
		lowercase						:		int   					=		1_2_8
		lowercase						:		Tuple   					=		(2, 2, 1_8, 2)
		lowercase						:		Tuple   					=		(4, 8, 1_6, 3_2)
	elif "large" in model_name:
		lowercase						:		Tuple   					=		1_2
		lowercase						:		Any   					=		1_9_2
		lowercase						:		Union[str, Any]   					=		(2, 2, 1_8, 2)
		lowercase						:		List[str]   					=		(6, 1_2, 2_4, 4_8)
	else:
		raise ValueError("Model not supported, only supports base and large variants"							)

	lowercase						:		Any   					=		window_size
	lowercase						:		Dict   					=		embed_dim
	lowercase						:		Union[str, Any]   					=		depths
	lowercase						:		int   					=		num_heads

	return config




def 	_A  (       A							)  ->				int:
	if "encoder.mask_token" in name:
		lowercase						:		List[Any]   					=		name.replace("encoder.mask_token"							,"embeddings.mask_token"							)
	if "encoder.patch_embed.proj" in name:
		lowercase						:		int   					=		name.replace("encoder.patch_embed.proj"							,"embeddings.patch_embeddings.projection"							)
	if "encoder.patch_embed.norm" in name:
		lowercase						:		Tuple   					=		name.replace("encoder.patch_embed.norm"							,"embeddings.norm"							)
	if "attn.proj" in name:
		lowercase						:		List[str]   					=		name.replace("attn.proj"							,"attention.output.dense"							)
	if "attn" in name:
		lowercase						:		List[Any]   					=		name.replace("attn"							,"attention.self"							)
	if "norm1" in name:
		lowercase						:		Any   					=		name.replace("norm1"							,"layernorm_before"							)
	if "norm2" in name:
		lowercase						:		Dict   					=		name.replace("norm2"							,"layernorm_after"							)
	if "mlp.fc1" in name:
		lowercase						:		Tuple   					=		name.replace("mlp.fc1"							,"intermediate.dense"							)
	if "mlp.fc2" in name:
		lowercase						:		str   					=		name.replace("mlp.fc2"							,"output.dense"							)

	if name == "encoder.norm.weight":
		lowercase						:		Optional[int]   					=		"layernorm.weight"
	if name == "encoder.norm.bias":
		lowercase						:		Optional[Any]   					=		"layernorm.bias"

	if "decoder" in name:
		pass
	else:
		lowercase						:		Optional[Any]   					=		"swin." + name

	return name




def 	_A  (       A							,A							)  ->				List[Any]:
	for key in orig_state_dict.copy().keys():
		lowercase						:		Tuple   					=		orig_state_dict.pop(A							)

		if "attn_mask" in key:
			pass
		elif "qkv" in key:
			lowercase						:		Union[str, Any]   					=		key.split("."							)
			lowercase						:		Union[str, Any]   					=		int(key_split[2]							)
			lowercase						:		Dict   					=		int(key_split[4]							)
			lowercase						:		Optional[Any]   					=		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				lowercase						:		Dict   					=		val[:dim, :]
				lowercase						:		Any   					=		val[
				    dim : dim * 2, :
				]
				lowercase						:		int   					=		val[-dim:, :]
			else:
				lowercase						:		Tuple   					=		val[
				    :dim
				]
				lowercase						:		int   					=		val[
				    dim : dim * 2
				]
				lowercase						:		Dict   					=		val[
				    -dim:
				]
		else:
			lowercase						:		int   					=		val

	return orig_state_dict




def 	_A  (       A							,A							,A							,A							)  ->				Optional[int]:
	lowercase						:		Union[str, Any]   					=		torch.load(A							,map_location="cpu"							)["model"]

	lowercase						:		List[Any]   					=		get_swin_config(A							)
	lowercase						:		List[str]   					=		SwinForMaskedImageModeling(A							)
	model.eval()

	lowercase						:		str   					=		convert_state_dict(A							,A							)
	model.load_state_dict(A							)

	lowercase						:		Union[str, Any]   					=		"http://images.cocodataset.org/val2017/000000039769.jpg"

	lowercase						:		List[str]   					=		ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}							)
	lowercase						:		List[str]   					=		Image.open(requests.get(A							,stream=A							).raw							)
	lowercase						:		Any   					=		image_processor(images=A							,return_tensors="pt"							)

	with torch.no_grad():
		lowercase						:		List[Any]   					=		model(**A							).logits

	print(outputs.keys()							)
	print("Looks ok!"							)

	if pytorch_dump_folder_path is not None:
		print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
		model.save_pretrained(A							)

		print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
		image_processor.save_pretrained(A							)

	if push_to_hub:
		print(F'''Pushing model and image processor for {model_name} to hub'''							)
		model.push_to_hub(F'''microsoft/{model_name}'''							)
		image_processor.push_to_hub(F'''microsoft/{model_name}'''							)


if __name__ == "__main__":
					lowerCAmelCase   : List[Any]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""swin-base-simmim-window6-192""",
					    type=str,
					    choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
					    help="""Name of the Swin SimMIM model you'd like to convert.""",
					)
					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
					    type=str,
					    help="""Path to the original PyTorch checkpoint (.pth file).""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					lowerCAmelCase   : List[str]             =				parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
					raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
lowerCAmelCase   : Optional[int]             =				logging.get_logger(__name__)

lowerCAmelCase   : Tuple             =				"""Hello world! cécé herlolip"""




def 	_A  (       A							,A							,A							)  ->				str:
	lowercase						:		Optional[Any]   					=		FairseqRobertaModel.from_pretrained(A							)
	roberta.eval()  # disable dropout
	lowercase						:		int   					=		roberta.model.encoder.sentence_encoder
	lowercase						:		Tuple   					=		XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings							,hidden_size=roberta.cfg.model.encoder_embed_dim							,num_hidden_layers=roberta.cfg.model.encoder_layers							,num_attention_heads=roberta.cfg.model.encoder_attention_heads							,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim							,max_position_embeddings=5_1_4							,type_vocab_size=1							,layer_norm_eps=1e-5							,)
	if classification_head:
		lowercase						:		Tuple   					=		roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"							,A							)

	lowercase						:		Union[str, Any]   					=		XLMRobertaXLForSequenceClassification(A							) if classification_head else XLMRobertaXLForMaskedLM(A							)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowercase						:		Optional[Any]   					=		roberta_sent_encoder.embed_tokens.weight
	lowercase						:		Dict   					=		roberta_sent_encoder.embed_positions.weight
	lowercase						:		str   					=		torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.

	lowercase						:		Dict   					=		roberta_sent_encoder.layer_norm.weight
	lowercase						:		str   					=		roberta_sent_encoder.layer_norm.bias

	for i in range(config.num_hidden_layers							):
		# Encoder: start of layer
		lowercase						:		BertLayer   					=		model.roberta.encoder.layer[i]
		lowercase						:		TransformerSentenceEncoderLayer   					=		roberta_sent_encoder.layers[i]

		lowercase						:		RobertaAttention   					=		layer.attention
		lowercase						:		str   					=		roberta_layer.self_attn_layer_norm.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowercase						:		BertSelfAttention   					=		layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)							)
		)

		lowercase						:		str   					=		roberta_layer.self_attn.q_proj.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn.q_proj.bias
		lowercase						:		Union[str, Any]   					=		roberta_layer.self_attn.k_proj.weight
		lowercase						:		List[Any]   					=		roberta_layer.self_attn.k_proj.bias
		lowercase						:		Dict   					=		roberta_layer.self_attn.v_proj.weight
		lowercase						:		Tuple   					=		roberta_layer.self_attn.v_proj.bias

		# self-attention output
		lowercase						:		BertSelfOutput   					=		layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowercase						:		Tuple   					=		roberta_layer.self_attn.out_proj.weight
		lowercase						:		str   					=		roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		lowercase						:		List[Any]   					=		roberta_layer.final_layer_norm.weight
		lowercase						:		Optional[Any]   					=		roberta_layer.final_layer_norm.bias

		# intermediate
		lowercase						:		BertIntermediate   					=		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		Dict   					=		roberta_layer.fca.bias

		# output
		lowercase						:		BertOutput   					=		layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		str   					=		roberta_layer.fca.bias
		# end of layer

	if classification_head:
		lowercase						:		int   					=		roberta.model.classification_heads["mnli"].dense.weight
		lowercase						:		List[Any]   					=		roberta.model.classification_heads["mnli"].dense.bias
		lowercase						:		Union[str, Any]   					=		roberta.model.classification_heads["mnli"].out_proj.weight
		lowercase						:		Any   					=		roberta.model.classification_heads["mnli"].out_proj.bias
	else:
		# LM Head
		lowercase						:		str   					=		roberta.model.encoder.lm_head.dense.weight
		lowercase						:		List[Any]   					=		roberta.model.encoder.lm_head.dense.bias
		lowercase						:		str   					=		roberta.model.encoder.lm_head.layer_norm.weight
		lowercase						:		Optional[Any]   					=		roberta.model.encoder.lm_head.layer_norm.bias
		lowercase						:		int   					=		roberta.model.encoder.lm_head.weight
		lowercase						:		Optional[int]   					=		roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowercase						:		torch.Tensor   					=		roberta.encode(A							).unsqueeze(0							)  # batch of size 1

	lowercase						:		Tuple   					=		model(A							)[0]
	if classification_head:
		lowercase						:		Union[str, Any]   					=		roberta.model.classification_heads["mnli"](roberta.extract_features(A							)							)
	else:
		lowercase						:		Optional[Any]   					=		roberta.model(A							)[0]
	print(our_output.shape							,their_output.shape							)
	lowercase						:		Optional[Any]   					=		torch.max(torch.abs(our_output - their_output							)							).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
	lowercase						:		Dict   					=		torch.allclose(A							,A							,atol=1e-3							)
	print("Do both models output the same tensors?"							,"🔥" if success else "💩"							)
	if not success:
		raise Exception("Something went wRoNg"							)

	pathlib.Path(A							).mkdir(parents=A							,exist_ok=A							)
	print(F'''Saving model to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
					)
					lowerCAmelCase   : Optional[Any]             =				parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)


