


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      )	->   List[str]:
      a__	:  int     =   SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            a__	:  List[Any]     =   4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            a__	:  str     =   4
            a__	:  Optional[int]     =   48
            a__	:  int     =   "pixelshuffle_aux"
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            a__	:  int     =   [6, 6, 6, 6]
            a__	:  List[str]     =   60
            a__	:  Union[str, Any]     =   [6, 6, 6, 6]
            a__	:  List[Any]     =   "pixelshuffledirect"
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            a__	:  Optional[Any]     =   4
            a__	:  Dict     =   "nearest+conv"
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            a__	:  int     =   1
            a__	:  Tuple     =   1
            a__	:  Optional[Any]     =   126
            a__	:  List[Any]     =   7
            a__	:  List[str]     =   255.0
            a__	:  Union[str, Any]     =   ""

      return config



def UpperCamelCase_				(				__a			,				__a      )	->   List[Any]:
      if "patch_embed.proj" in name and "layers" not in name:
            a__	:  int     =   name.replace("patch_embed.proj"			,				"embeddings.patch_embeddings.projection"      )
      if "patch_embed.norm" in name:
            a__	:  Union[str, Any]     =   name.replace("patch_embed.norm"			,				"embeddings.patch_embeddings.layernorm"      )
      if "layers" in name:
            a__	:  Dict     =   name.replace("layers"			,				"encoder.stages"      )
      if "residual_group.blocks" in name:
            a__	:  str     =   name.replace("residual_group.blocks"			,				"layers"      )
      if "attn.proj" in name:
            a__	:  List[Any]     =   name.replace("attn.proj"			,				"attention.output.dense"      )
      if "attn" in name:
            a__	:  List[Any]     =   name.replace("attn"			,				"attention.self"      )
      if "norm1" in name:
            a__	:  List[str]     =   name.replace("norm1"			,				"layernorm_before"      )
      if "norm2" in name:
            a__	:  Optional[Any]     =   name.replace("norm2"			,				"layernorm_after"      )
      if "mlp.fc1" in name:
            a__	:  str     =   name.replace("mlp.fc1"			,				"intermediate.dense"      )
      if "mlp.fc2" in name:
            a__	:  Optional[int]     =   name.replace("mlp.fc2"			,				"output.dense"      )
      if "q_bias" in name:
            a__	:  List[str]     =   name.replace("q_bias"			,				"query.bias"      )
      if "k_bias" in name:
            a__	:  Tuple     =   name.replace("k_bias"			,				"key.bias"      )
      if "v_bias" in name:
            a__	:  Any     =   name.replace("v_bias"			,				"value.bias"      )
      if "cpb_mlp" in name:
            a__	:  Tuple     =   name.replace("cpb_mlp"			,				"continuous_position_bias_mlp"      )
      if "patch_embed.proj" in name:
            a__	:  int     =   name.replace("patch_embed.proj"			,				"patch_embed.projection"      )

      if name == "norm.weight":
            a__	:  Optional[Any]     =   "layernorm.weight"
      if name == "norm.bias":
            a__	:  str     =   "layernorm.bias"

      if "conv_first" in name:
            a__	:  Dict     =   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__	:  Optional[Any]     =   name.replace("conv_last"			,				"final_convolution"      )
            if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
                  if "conv_before_upsample.0" in name:
                        a__	:  Optional[int]     =   name.replace("conv_before_upsample.0"			,				"conv_before_upsample"      )
                  if "upsample.0" in name:
                        a__	:  Any     =   name.replace("upsample.0"			,				"upsample.convolution_0"      )
                  if "upsample.2" in name:
                        a__	:  Optional[Any]     =   name.replace("upsample.2"			,				"upsample.convolution_1"      )
                  a__	:  Tuple     =   "upsample." + name
            elif config.upsampler == "pixelshuffledirect":
                  a__	:  List[Any]     =   name.replace("upsample.0.weight"			,				"upsample.conv.weight"      )
                  a__	:  str     =   name.replace("upsample.0.bias"			,				"upsample.conv.bias"      )
            else:
                  pass
      else:
            a__	:  Dict     =   "swin2sr." + name

      return name



def UpperCamelCase_				(				__a			,				__a      )	->   Any:
      for key in orig_state_dict.copy().keys():
            a__	:  Optional[int]     =   orig_state_dict.pop(__a      )

            if "qkv" in key:
                  a__	:  Tuple     =   key.split("."      )
                  a__	:  Tuple     =   int(key_split[1]      )
                  a__	:  List[Any]     =   int(key_split[4]      )
                  a__	:  Union[str, Any]     =   config.embed_dim

                  if "weight" in key:
                        a__	:  Tuple     =   val[:dim, :]
                        a__	:  List[Any]     =   val[dim : dim * 2, :]
                        a__	:  Dict     =   val[-dim:, :]
                  else:
                        a__	:  Any     =   val[:dim]
                        a__	:  Union[str, Any]     =   val[dim : dim * 2]
                        a__	:  List[Any]     =   val[-dim:]
                  pass
            else:
                  a__	:  Optional[Any]     =   val

      return orig_state_dict



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   str:
      a__	:  Optional[int]     =   get_config(__a      )
      a__	:  Optional[Any]     =   SwinaSRForImageSuperResolution(__a      )
      model.eval()

      a__	:  Any     =   torch.hub.load_state_dict_from_url(__a			,				map_location="cpu"      )
      a__	:  int     =   convert_state_dict(__a			,				__a      )
      a__,	a__	:  int     =   model.load_state_dict(__a			,				strict=__a      )

      if len(__a      ) > 0:
            raise ValueError("Missing keys when converting: {}".format(__a      )      )
      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__	:  str     =   "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
      a__	:  str     =   Image.open(requests.get(__a			,				stream=__a      ).raw      ).convert("RGB"      )
      a__	:  List[str]     =   SwinaSRImageProcessor()
      # pixel_values = processor(image, return_tensors="pt").pixel_values

      a__	:  Dict     =   126 if "Jpeg" in checkpoint_url else 256
      a__	:  List[str]     =   Compose(
          [
              Resize((image_size, image_size)      ),
              ToTensor(),
              Normalize(mean=[0.485, 0.456, 0.406]			,				std=[0.229, 0.224, 0.225]      ),
          ]      )
      a__	:  List[str]     =   transforms(__a      ).unsqueeze(0      )

      if config.num_channels == 1:
            a__	:  List[str]     =   pixel_values[:, 0, :, :].unsqueeze(1      )

      a__	:  List[Any]     =   model(__a      )

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            a__	:  Dict     =   torch.Size([1, 3, 512, 512]      )
            a__	:  int     =   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__	:  str     =   torch.Size([1, 3, 1_024, 1_024]      )
            a__	:  Dict     =   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__	:  Optional[int]     =   torch.Size([1, 3, 1_024, 1_024]      )
            a__	:  Optional[Any]     =   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__	:  int     =   torch.Size([1, 3, 512, 512]      )
            a__	:  Any     =   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__	:  str     =   torch.Size([1, 3, 1_024, 1_024]      )
            a__	:  Optional[Any]     =   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]			,				__a			,				atol=1e-3      )
      print("Looks ok!"      )

      a__	:  Union[str, Any]     =   {
          "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__	:  Union[str, Any]     =   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(__a      )
            print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
            processor.save_pretrained(__a      )

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


if __name__ == "__main__":
    UpperCamelCase					:       Optional[int]			    =     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					:       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     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[Any]:
				a__	:							Any      						= [
				    "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(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Optional[Any]:
				a__						,   a__	:							Optional[int]      						= emb.weight.shape
				a__	:							Union[str, Any]      						= nn.Linear(__UpperCamelCase	,				__UpperCamelCase	,				bias=__UpperCamelCase )
				a__	:							str      						= emb.weight.data
				return lin_layer






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=None )	->						Optional[int]:
				a__	:							Union[str, Any]      						= {}
				for old_key in state_dict.keys():
								a__	:							List[Any]      						= old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																a__	:							List[str]      						= key.replace("moe_layer.experts.0"	,				F'ffn.experts.expert_{expert_idx}' )
												else:
																a__	:							Optional[Any]      						= key.replace("moe_layer.experts."	,				"ffn.experts.expert_" )
								if "gate" in key:
												a__	:							Optional[Any]      						= key.replace(".moe_layer.gate.wg"	,				".ffn.router.classifier" )
								if "fc2" and "experts" not in key:
												a__	:							int      						= key.replace(".fc2."	,				".ffn.fc2." )
								if "fc1" and "experts" not in key:
												a__	:							Any      						= key.replace(".fc1."	,				".ffn.fc1." )
								if ".encoder_attn." in key:
												a__	:							str      						= key.replace(".encoder_attn."	,				".cross_attention." )
								if "encoder_attn_layer_norm" in key:
												a__	:							Optional[int]      						= key.replace("encoder_attn_layer_norm"	,				"cross_attention_layer_norm" )
								if "final_layer_norm" in key:
												a__	:							int      						= key.replace("final_layer_norm"	,				"ff_layer_norm" )
								a__	:							Optional[int]      						= state_dict[old_key]
				return new_dict






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Dict:
				a__	:							Any      						= []
				a__	:							List[str]      						= 0
				os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase )

				for expert in range(__UpperCamelCase ):
								a__	:							Union[str, Any]      						= switch_checkpoint_path + F'-rank-{expert}.pt'
								if os.path.isfile(__UpperCamelCase ):
												a__	:							str      						= torch.load(__UpperCamelCase )["model"]
												remove_ignore_keys_(__UpperCamelCase )
												a__	:							Tuple      						= rename_fairseq_keys(__UpperCamelCase	,				__UpperCamelCase )
												a__	:							str      						= os.path.join(
												    __UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
												torch.save(__UpperCamelCase	,				__UpperCamelCase )
												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(__UpperCamelCase )[0]].dtype )

    # Add the last block
				a__	:							int      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
				a__	:							Any      						= torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
				remove_ignore_keys_(__UpperCamelCase )
				a__	:							Optional[Any]      						= rename_fairseq_keys(__UpperCamelCase	,				__UpperCamelCase )
				a__	:							List[Any]      						= 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(__UpperCamelCase ) == 1:
								a__	:							Union[str, Any]      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
								torch.save(__UpperCamelCase	,				__UpperCamelCase )
								return {weights_name: sharded_state_dicts[0]}, None
				else:
								torch.save(__UpperCamelCase	,				__UpperCamelCase )
				# Otherwise, let's build the index
				a__	:							Any      						= {}
				for idx, shard in enumerate(__UpperCamelCase ):
								a__	:							Any      						= weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin' )
								a__	:							Optional[Any]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-???.bin' ) )
								os.rename(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				__UpperCamelCase ) )
								for key in shard:
												a__	:							Any      						= shard_file

    # Add the metadata
				a__	:							Optional[int]      						= {"total_size": total_size}
				a__	:							Optional[Any]      						= {"metadata": metadata, "weight_map": weight_map}

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

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      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.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						lowerCamelCase       ,   lowerCamelCase		       =      shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    1_28,
						    args.dtype,
						)

						lowerCamelCase		       =      NllbMoeConfig.from_pretrained(
						    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						lowerCamelCase		       =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("""Done""")
						model.save_pretrained(args.pytorch_dump_folder_path)


