






import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Tuple:
       if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
              # expert layer
              _UpperCAmelCase							=    flax_key_tuple[:-1] + ("""weight""",)
              _UpperCAmelCase							=    torch.permute(__snake_case   ,      (0, 2, 1) )
       elif flax_key_tuple[-1] == "kernel" and ".".join(__snake_case ):
              # linear layer
              _UpperCAmelCase							=    flax_key_tuple[:-1] + ("""weight""",)
              _UpperCAmelCase							=    flax_tensor.T
       elif flax_key_tuple[-1] in ["scale", "embedding"]:
              _UpperCAmelCase							=    flax_key_tuple[:-1] + ("""weight""",)

       return flax_key_tuple, flax_tensor




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       if "metadata" in layer:
              _UpperCAmelCase							=    layer.split("""metadata""" )
              _UpperCAmelCase							=    """""".join(split_layer[0] )[:-1]
              _UpperCAmelCase							=    [tuple(("""metadata""" + split_layer[1]).split("""/""" ) )]
       elif "kvstore" in layer:
              _UpperCAmelCase							=    layer.split("""kvstore""" )
              _UpperCAmelCase							=    """""".join(split_layer[0] )[:-1]
              _UpperCAmelCase							=    [tuple(("""kvstore""" + split_layer[1]).split("""/""" ) )]

       else:
              _UpperCAmelCase							=    layer.split("""/""" )
              _UpperCAmelCase							=    """/""".join(split_layer[:-1] )
              _UpperCAmelCase							=    (split_layer[-1],)

       if "kvstore/path" in layer:
              _UpperCAmelCase							=    f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
       elif "kvstore/driver" in layer:
              _UpperCAmelCase							=    """file"""
       else:
              _UpperCAmelCase							=    checkpoint_info[layer]

       return curr_real_layer_name, split_layer, content




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[Any]:
       _UpperCAmelCase							=    rename_keys(__snake_case )
       _UpperCAmelCase							=    {}
       for k, v in current_block.items():
              _UpperCAmelCase							=    v
       _UpperCAmelCase							=    new_current_block
       torch.save(__snake_case   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = WEIGHTS_NAME )				->						Any:
       _UpperCAmelCase							=    convert_file_size_to_int(__snake_case )
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    0
       _UpperCAmelCase							=    0

       os.makedirs(__snake_case   ,      exist_ok=__snake_case )
       with gfile.GFile(switch_checkpoint_path + """/checkpoint"""   ,      """rb""" ) as fp:
              _UpperCAmelCase							=    serialization.msgpack_restore(fp.read() )["""optimizer"""]["""target"""]
              _UpperCAmelCase							=    flatten_dict(__snake_case   ,      sep="""/""" )

       _UpperCAmelCase							=    {}
       for layer in checkpoint_info.keys():
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_key_and_tensorstore_dict(
                  __snake_case   ,      __snake_case   ,      __snake_case )
              if curr_real_layer_name in all_layers:
                     _UpperCAmelCase							=    content
              else:
                     _UpperCAmelCase							=    {split_layer[-1]: content}

       for key in all_layers.keys():
              # open tensorstore file
              _UpperCAmelCase							=    ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
              _UpperCAmelCase							=    torch.tensor(__snake_case )
              _UpperCAmelCase							=    raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

              # use the renaming pattern from the small conversion scripts
              _UpperCAmelCase	,			_UpperCAmelCase							=    rename_base_flax_keys(tuple(key.split("""/""" ) )   ,      __snake_case )
              _UpperCAmelCase							=    """/""".join(__snake_case )

              # If this weight is going to tip up over the maximal size, we split.
              if current_block_size + weight_size > max_shard_size:
                     _UpperCAmelCase							=    os.path.join(
                         __snake_case   ,      weights_name.replace(""".bin"""   ,      f"""-{len(__snake_case )+1:05d}-of-???.bin""" ) )
                     rename_and_save_block(__snake_case   ,      __snake_case )
                     sharded_state_dicts.append(current_block.keys() )
                     del current_block
                     _UpperCAmelCase							=    {}
                     _UpperCAmelCase							=    0

              _UpperCAmelCase							=    raw_weights.to(getattr(__snake_case   ,      __snake_case ) )
              current_block_size += weight_size
              total_size += weight_size

       # Add the last block
       _UpperCAmelCase							=    os.path.join(__snake_case   ,      weights_name.replace(""".bin"""   ,      f"""-{len(__snake_case )+1:05d}-of-???.bin""" ) )
       rename_and_save_block(__snake_case   ,      __snake_case )
       sharded_state_dicts.append(current_block.keys() )

       # If we only have one shard, we return it
       if len(__snake_case ) == 1:
              return {weights_name: sharded_state_dicts[0]}, None

       # Otherwise, let's build the index
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    {}
       for idx, shard in enumerate(__snake_case ):
              _UpperCAmelCase							=    weights_name.replace(
                  """.bin"""   ,      f"""-{idx+1:05d}-of-{len(__snake_case ):05d}.bin""" )  # len(sharded_state_dicts):05d}
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      weights_name.replace(""".bin"""   ,      f"""-{idx+1:05d}-of-???.bin""" ) )
              os.rename(__snake_case   ,      os.path.join(__snake_case   ,      __snake_case ) )
              _UpperCAmelCase							=    shard
              for key in shard:
                     _UpperCAmelCase							=    shard_file

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

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

       return metadata, index


if __name__ == "__main__":
  __a:     Optional[int]   					=					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--switch_t5x_checkpoint_path''',
      default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
      type=str,
      required=False,
      help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
  )
  parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
  parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
      type=str,
      required=False,
      help='''Path to the output pytorch model.''',
  )
  __a:     int   					=					parser.parse_args()
  shard_on_the_fly(
      args.switch_tax_checkpoint_path,
      args.pytorch_dump_folder_path,
      args.max_shard_size,
      args.dtype,
  )





def       _SCREAMING_SNAKE_CASE (							)				->						Optional[int]:
       from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

       _UpperCAmelCase							=    SwitchTransformersConfig.from_pretrained("""google/switch-base-8""" )
       config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""" )
       _UpperCAmelCase							=    SwitchTransformersForConditionalGeneration.from_pretrained(
           """/home/arthur_huggingface_co/transformers/switch_converted"""   ,      device_map="""auto""" )

       _UpperCAmelCase							=    TaTokenizer.from_pretrained("""t5-small""" )
       _UpperCAmelCase							=    """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

       _UpperCAmelCase							=    tokenizer(__snake_case   ,      return_tensors="""pt""" ).input_ids
       _UpperCAmelCase							=    model.generate(__snake_case   ,      decoder_start_token_id=0 )
       print(tokenizer.decode(out[0] ) )






"""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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any			):
 lowerCAmelCase    				= SwinaSRConfig()

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

 return config

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

 if name == "norm.weight":
  lowerCAmelCase    				= 'layernorm.weight'
 if name == "norm.bias":
  lowerCAmelCase    				= 'layernorm.bias'

 if "conv_first" in name:
  lowerCAmelCase    				= 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:
   lowerCAmelCase    				= name.replace('conv_last'  ,     'final_convolution'			)
  if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
   if "conv_before_upsample.0" in name:
    lowerCAmelCase    				= name.replace('conv_before_upsample.0'  ,     'conv_before_upsample'			)
   if "upsample.0" in name:
    lowerCAmelCase    				= name.replace('upsample.0'  ,     'upsample.convolution_0'			)
   if "upsample.2" in name:
    lowerCAmelCase    				= name.replace('upsample.2'  ,     'upsample.convolution_1'			)
   lowerCAmelCase    				= 'upsample.' + name
  elif config.upsampler == "pixelshuffledirect":
   lowerCAmelCase    				= name.replace('upsample.0.weight'  ,     'upsample.conv.weight'			)
   lowerCAmelCase    				= name.replace('upsample.0.bias'  ,     'upsample.conv.bias'			)
  else:
   pass
 else:
  lowerCAmelCase    				= 'swin2sr.' + name

 return name

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Dict			):
 for key in orig_state_dict.copy().keys():
  lowerCAmelCase    				= orig_state_dict.pop(_UpperCAmelCase			)

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

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

 return orig_state_dict

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Tuple			):
 lowerCAmelCase    				= get_config(_UpperCAmelCase			)
 lowerCAmelCase    				= SwinaSRForImageSuperResolution(_UpperCAmelCase			)
 model.eval()

 lowerCAmelCase    				= torch.hub.load_state_dict_from_url(_UpperCAmelCase  ,     map_location='cpu'			)
 lowerCAmelCase    				= convert_state_dict(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase				,lowerCAmelCase    				= 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
 lowerCAmelCase    				= 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
 lowerCAmelCase    				= Image.open(requests.get(_UpperCAmelCase  ,     stream=_UpperCAmelCase			).raw			).convert('RGB'			)
 lowerCAmelCase    				= SwinaSRImageProcessor()
 # pixel_values = processor(image, return_tensors="pt").pixel_values

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

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

 lowerCAmelCase    				= model(_UpperCAmelCase			)

 # assert values
 if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 512, 512]			)
  lowerCAmelCase    				= 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:
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= 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
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= 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:
  lowerCAmelCase    				= torch.Size([1, 3, 512, 512]			)
  lowerCAmelCase    				= 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:
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= 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!'			)

 lowerCAmelCase    				= {
     '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'
     ),
 }
 lowerCAmelCase    				= 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     :						Any						       =  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     :						Optional[int]						       =  parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





