



"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def  UpperCamelCase						(       _A			:				Tuple    )->       int:





						"""simple docstring"""





						A__							=       SwinaSRConfig()

						if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												A__							=       4
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												A__							=       4
												A__							=       48
												A__							=       "pixelshuffle_aux"
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												A__							=       [6, 6, 6, 6]
												A__							=       60
												A__							=       [6, 6, 6, 6]
												A__							=       "pixelshuffledirect"
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												A__							=       4
												A__							=       "nearest+conv"
						elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
												A__							=       1
												A__							=       1
												A__							=       126
												A__							=       7
												A__							=       255.0
												A__							=       ""

						return config
def  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				Dict    )->       Optional[int]:





						"""simple docstring"""





						if "patch_embed.proj" in name and "layers" not in name:
												A__							=       name.replace("patch_embed.proj"			,							"embeddings.patch_embeddings.projection"    )
						if "patch_embed.norm" in name:
												A__							=       name.replace("patch_embed.norm"			,							"embeddings.patch_embeddings.layernorm"    )
						if "layers" in name:
												A__							=       name.replace("layers"			,							"encoder.stages"    )
						if "residual_group.blocks" in name:
												A__							=       name.replace("residual_group.blocks"			,							"layers"    )
						if "attn.proj" in name:
												A__							=       name.replace("attn.proj"			,							"attention.output.dense"    )
						if "attn" in name:
												A__							=       name.replace("attn"			,							"attention.self"    )
						if "norm1" in name:
												A__							=       name.replace("norm1"			,							"layernorm_before"    )
						if "norm2" in name:
												A__							=       name.replace("norm2"			,							"layernorm_after"    )
						if "mlp.fc1" in name:
												A__							=       name.replace("mlp.fc1"			,							"intermediate.dense"    )
						if "mlp.fc2" in name:
												A__							=       name.replace("mlp.fc2"			,							"output.dense"    )
						if "q_bias" in name:
												A__							=       name.replace("q_bias"			,							"query.bias"    )
						if "k_bias" in name:
												A__							=       name.replace("k_bias"			,							"key.bias"    )
						if "v_bias" in name:
												A__							=       name.replace("v_bias"			,							"value.bias"    )
						if "cpb_mlp" in name:
												A__							=       name.replace("cpb_mlp"			,							"continuous_position_bias_mlp"    )
						if "patch_embed.proj" in name:
												A__							=       name.replace("patch_embed.proj"			,							"patch_embed.projection"    )

						if name == "norm.weight":
												A__							=       "layernorm.weight"
						if name == "norm.bias":
												A__							=       "layernorm.bias"

						if "conv_first" in name:
												A__							=       name.replace("conv_first"			,							"first_convolution"    )

						if (
						    "upsample" in name
						    or "conv_before_upsample" in name
						    or "conv_bicubic" in name
						    or "conv_up" in name
						    or "conv_hr" in name
						    or "conv_last" in name
						    or "aux" in name
						):
												# heads
												if "conv_last" in name:
																		A__							=       name.replace("conv_last"			,							"final_convolution"    )
												if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
																		if "conv_before_upsample.0" in name:
																								A__							=       name.replace("conv_before_upsample.0"			,							"conv_before_upsample"    )
																		if "upsample.0" in name:
																								A__							=       name.replace("upsample.0"			,							"upsample.convolution_0"    )
																		if "upsample.2" in name:
																								A__							=       name.replace("upsample.2"			,							"upsample.convolution_1"    )
																		A__							=       "upsample." + name
												elif config.upsampler == "pixelshuffledirect":
																		A__							=       name.replace("upsample.0.weight"			,							"upsample.conv.weight"    )
																		A__							=       name.replace("upsample.0.bias"			,							"upsample.conv.bias"    )
												else:
																		pass
						else:
												A__							=       "swin2sr." + name

						return name
def  UpperCamelCase						(       _A			:				List[str]			,							_A			:				List[Any]    )->       str:





						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												A__							=       orig_state_dict.pop(UpperCamelCase__    )

												if "qkv" in key:
																		A__							=       key.split("."    )
																		A__							=       int(key_split[1]    )
																		A__							=       int(key_split[4]    )
																		A__							=       config.embed_dim

																		if "weight" in key:
																								A__							=       val[:dim, :]
																								A__							=       val[dim : dim * 2, :]
																								A__							=       val[-dim:, :]
																		else:
																								A__							=       val[:dim]
																								A__							=       val[dim : dim * 2]
																								A__							=       val[-dim:]
																		pass
												else:
																		A__							=       val

						return orig_state_dict
def  UpperCamelCase						(       _A			:				Dict			,							_A			:				int			,							_A			:				Union[str, Any]    )->       List[str]:





						"""simple docstring"""





						A__							=       get_config(UpperCamelCase__    )
						A__							=       SwinaSRForImageSuperResolution(UpperCamelCase__    )
						model.eval()

						A__							=       torch.hub.load_state_dict_from_url(UpperCamelCase__			,							map_location="cpu"    )
						A__							=       convert_state_dict(UpperCamelCase__			,							UpperCamelCase__    )
						A__						,       A__							=       model.load_state_dict(UpperCamelCase__			,							strict=UpperCamelCase__    )

						if len(UpperCamelCase__    ) > 0:
												raise ValueError("Missing keys when converting: {}".format(UpperCamelCase__    )    )
						for key in unexpected_keys:
												if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
																		raise ValueError(f"""Unexpected key {key} in state_dict"""    )

    # verify values
						A__							=       "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
						A__							=       Image.open(requests.get(UpperCamelCase__			,							stream=UpperCamelCase__    ).raw    ).convert("RGB"    )
						A__							=       SwinaSRImageProcessor()
						# pixel_values = processor(image, return_tensors="pt").pixel_values

						A__							=       126 if "Jpeg" in checkpoint_url else 256
						A__							=       Compose(
						    [
						        Resize((image_size, image_size)    ),
						        ToTensor(),
						        Normalize(mean=[0.485, 0.456, 0.406]			,							std=[0.229, 0.224, 0.225]    ),
						    ]    )
						A__							=       transforms(UpperCamelCase__    ).unsqueeze(0    )

						if config.num_channels == 1:
												A__							=       pixel_values[:, 0, :, :].unsqueeze(1    )

						A__							=       model(UpperCamelCase__    )

						# assert values
						if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
												A__							=       torch.Size([1, 3, 512, 512]    )
												A__							=       torch.tensor(
												    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]    )
						elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												A__							=       torch.Size([1, 3, 1024, 1024]    )
												A__							=       torch.tensor(
												    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]    )
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												# TODO values didn't match exactly here
												A__							=       torch.Size([1, 3, 1024, 1024]    )
												A__							=       torch.tensor(
												    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]    )
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												A__							=       torch.Size([1, 3, 512, 512]    )
												A__							=       torch.tensor(
												    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]    )
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												A__							=       torch.Size([1, 3, 1024, 1024]    )
												A__							=       torch.tensor(
												    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]    )

						assert (
						    outputs.reconstruction.shape == expected_shape
						), f"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
						assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]			,							UpperCamelCase__			,							atol=1E-3    )
						print("Looks ok!"    )

						A__							=       {
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
						        "swin2SR-classical-sr-x2-64"
						    ),
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
						        "swin2SR-classical-sr-x4-64"
						    ),
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
						        "swin2SR-compressed-sr-x4-48"
						    ),
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
						        "swin2SR-lightweight-x2-64"
						    ),
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
						        "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
						    ),
						}
						A__							=       url_to_name[checkpoint_url]

						if pytorch_dump_folder_path is not None:
												print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""    )
												model.save_pretrained(UpperCamelCase__    )
												print(f"""Saving image processor to {pytorch_dump_folder_path}"""    )
												processor.save_pretrained(UpperCamelCase__    )

						if push_to_hub:
												model.push_to_hub(f"""caidas/{model_name}"""    )
												processor.push_to_hub(f"""caidas/{model_name}"""    )


if __name__ == "__main__":
						UpperCAmelCase_							:      Tuple      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
						    type=str,
						    help="URL of the original Swin2SR checkpoint you'd like to convert.",
						)
						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 to push the converted model to the hub.")

						UpperCAmelCase_							:      Union[str, Any]      =  parser.parse_args()
						convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def  UpperCamelCase						(       _A			:				Tuple    )->       Dict:





						"""simple docstring"""





						A__							=       [
						    "encoder.version",
						    "decoder.version",
						    "model.encoder.version",
						    "model.decoder.version",
						    "decoder.output_projection.weight",
						    "_float_tensor",
						    "encoder.embed_positions._float_tensor",
						    "decoder.embed_positions._float_tensor",
						]
						for k in ignore_keys:
												state_dict.pop(_A			,							_A    )
def  UpperCamelCase						(       _A			:				int    )->       Optional[Any]:





						"""simple docstring"""





						A__						,       A__							=       emb.weight.shape
						A__							=       nn.Linear(_A			,							_A			,							bias=_A    )
						A__							=       emb.weight.data
						return lin_layer
def  UpperCamelCase						(       _A			:				str			,							_A			:				Optional[Any]=None    )->       str:





						"""simple docstring"""





						A__							=       {}
						for old_key in state_dict.keys():
												A__							=       old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								A__							=       key.replace("moe_layer.experts.0"			,							f"""ffn.experts.expert_{expert_idx}"""    )
																		else:
																								A__							=       key.replace("moe_layer.experts."			,							"ffn.experts.expert_"    )
												if "gate" in key:
																		A__							=       key.replace(".moe_layer.gate.wg"			,							".ffn.router.classifier"    )
												if "fc2" and "experts" not in key:
																		A__							=       key.replace(".fc2."			,							".ffn.fc2."    )
												if "fc1" and "experts" not in key:
																		A__							=       key.replace(".fc1."			,							".ffn.fc1."    )
												if ".encoder_attn." in key:
																		A__							=       key.replace(".encoder_attn."			,							".cross_attention."    )
												if "encoder_attn_layer_norm" in key:
																		A__							=       key.replace("encoder_attn_layer_norm"			,							"cross_attention_layer_norm"    )
												if "final_layer_norm" in key:
																		A__							=       key.replace("final_layer_norm"			,							"ff_layer_norm"    )
												A__							=       state_dict[old_key]
						return new_dict
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				Tuple			,							_A			:				int			,							_A			:				str			,							_A			:				str = WEIGHTS_NAME    )->       List[str]:





						"""simple docstring"""





						A__							=       []
						A__							=       0
						os.makedirs(_A			,							exist_ok=_A    )

						for expert in range(_A    ):
												A__							=       switch_checkpoint_path + f"""-rank-{expert}.pt"""
												if os.path.isfile(_A    ):
																		A__							=       torch.load(_A    )["model"]
																		remove_ignore_keys_(_A    )
																		A__							=       rename_fairseq_keys(_A			,							_A    )
																		A__							=       os.path.join(
																		    _A			,							weights_name.replace(".bin"			,							f"""-{len(_A    )+1:05d}-of-???.bin"""    )    )
																		torch.save(_A			,							_A    )
																		sharded_state_dicts.append(expert_state.keys()    )
																		total_size += sum([value.numel() for key, value in expert_state.items()]    ) * dtype_byte_size(
																		    expert_state[list(_A    )[0]].dtype    )

    # Add the last block
						A__							=       os.path.join(_A			,							weights_name.replace(".bin"			,							f"""-{len(_A    )+1:05d}-of-???.bin"""    )    )
						A__							=       torch.load(switch_checkpoint_path + "-shared.pt"    )["model"]
						remove_ignore_keys_(_A    )
						A__							=       rename_fairseq_keys(_A			,							_A    )
						A__							=       shared_weights["decoder.embed_tokens.weight"]
						sharded_state_dicts.append(shared_weights.keys()    )

						# If we only have the shared weights (dummy model/experts saved on the same file)
						if len(_A    ) == 1:
												A__							=       os.path.join(_A			,							_A    )
												torch.save(_A			,							_A    )
												return {weights_name: sharded_state_dicts[0]}, None
						else:
												torch.save(_A			,							_A    )
						# Otherwise, let's build the index
						A__							=       {}
						for idx, shard in enumerate(_A    ):
												A__							=       weights_name.replace(".bin"			,							f"""-{idx+1:05d}-of-{len(_A    ):05d}.bin"""    )
												A__							=       os.path.join(_A			,							weights_name.replace(".bin"			,							f"""-{idx+1:05d}-of-???.bin"""    )    )
												os.rename(_A			,							os.path.join(_A			,							_A    )    )
												for key in shard:
																		A__							=       shard_file

    # Add the metadata
						A__							=       {"total_size": total_size}
						A__							=       {"metadata": metadata, "weight_map": weight_map}

						with open(os.path.join(_A			,							_A    )			,							"w"			,							encoding="utf-8"    ) as f:
												A__							=       json.dumps(_A			,							indent=2			,							sort_keys=_A    ) + "\n"
												f.write(_A    )

						return metadata, index


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--nllb_moe_checkpoint_path",
						    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
						    type=str,
						    required=False,
						    help="Path to a directory containing a folder per layer. Follows the original Google format.",
						)
						parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
						    type=str,
						    required=False,
						    help="Path to the output pytorch model.",
						)
						UpperCAmelCase_							:      Union[str, Any]      =  parser.parse_args()
						UpperCAmelCase_   ,	UpperCAmelCase_							:      Optional[int]      =  shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						UpperCAmelCase_							:      Any      =  NllbMoeConfig.from_pretrained(
						    "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						UpperCAmelCase_							:      Tuple      =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)





