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      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            SCREAMING_SNAKE_CASE_          =  flax_key_tuple[:-1] + ("weight",)
            SCREAMING_SNAKE_CASE_          =  torch.permute(__UpperCamelCase     ,      (0, 2, 1)				)
      elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase				):
            # linear layer
            SCREAMING_SNAKE_CASE_          =  flax_key_tuple[:-1] + ("weight",)
            SCREAMING_SNAKE_CASE_          =  flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            SCREAMING_SNAKE_CASE_          =  flax_key_tuple[:-1] + ("weight",)

      return flax_key_tuple, flax_tensor


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      if "metadata" in layer:
            SCREAMING_SNAKE_CASE_          =  layer.split("metadata"				)
            SCREAMING_SNAKE_CASE_          =  "".join(split_layer[0]				)[:-1]
            SCREAMING_SNAKE_CASE_          =  [tuple(("metadata" + split_layer[1]).split("/"				)				)]
      elif "kvstore" in layer:
            SCREAMING_SNAKE_CASE_          =  layer.split("kvstore"				)
            SCREAMING_SNAKE_CASE_          =  "".join(split_layer[0]				)[:-1]
            SCREAMING_SNAKE_CASE_          =  [tuple(("kvstore" + split_layer[1]).split("/"				)				)]

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

      if "kvstore/path" in layer:
            SCREAMING_SNAKE_CASE_          =  F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
      elif "kvstore/driver" in layer:
            SCREAMING_SNAKE_CASE_          =  "file"
      else:
            SCREAMING_SNAKE_CASE_          =  checkpoint_info[layer]

      return curr_real_layer_name, split_layer, content


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  rename_keys(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  {}
      for k, v in current_block.items():
            SCREAMING_SNAKE_CASE_          =  v
      SCREAMING_SNAKE_CASE_          =  new_current_block
      torch.save(__UpperCamelCase     ,      __UpperCamelCase				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = WEIGHTS_NAME				):
      SCREAMING_SNAKE_CASE_          =  convert_file_size_to_int(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  []
      SCREAMING_SNAKE_CASE_          =  {}
      SCREAMING_SNAKE_CASE_          =  0
      SCREAMING_SNAKE_CASE_          =  0

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

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

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

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

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

            SCREAMING_SNAKE_CASE_          =  raw_weights.to(getattr(__UpperCamelCase     ,      __UpperCamelCase				)				)
            current_block_size += weight_size
            total_size += weight_size

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

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

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

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

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

      return metadata, index


if __name__ == "__main__":
   A     :  Dict					     =       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     :  Any					     =       parser.parse_args()
   shard_on_the_fly(
       args.switch_tax_checkpoint_path,
       args.pytorch_dump_folder_path,
       args.max_shard_size,
       args.dtype,
   )





def      a__ (		):
      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A     :  Optional[Any]					     =       logging.get_logger(__name__)

A     :  List[Any]					     =       torch.device("cpu")


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_          =  Image.open(requests.get(__UpperCamelCase     ,      stream=__UpperCamelCase				).raw				)
      return im


def      a__ (		__UpperCamelCase				):
      if swiftformer_name == "swiftformer_xs":
            return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]				)

      elif swiftformer_name == "swiftformer_s":
            return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]				)

      elif swiftformer_name == "swiftformer_l1":
            return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]				)

      elif swiftformer_name == "swiftformer_l3":
            return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  dct.pop(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  val


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  []
      for k in state_dict.keys():
            SCREAMING_SNAKE_CASE_          =  k
            if ".pwconv" in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".pwconv"     ,      ".point_wise_conv"				)
            if ".dwconv" in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".dwconv"     ,      ".depth_wise_conv"				)
            if ".Proj." in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".Proj."     ,      ".proj."				)
            if "patch_embed" in k_new:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace("patch_embed"     ,      "swiftformer.patch_embed.patch_embedding"				)
            if "network" in k_new:
                  SCREAMING_SNAKE_CASE_          =  k_new.split("."				)
                  if ls[2].isdigit():
                        SCREAMING_SNAKE_CASE_          =  "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]				)
                  else:
                        SCREAMING_SNAKE_CASE_          =  k_new.replace("network"     ,      "swiftformer.encoder.network"				)
            rename_keys.append((k, k_new)				)
      return rename_keys


@torch.no_grad()
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_          =  1_0_0_0
      SCREAMING_SNAKE_CASE_          =  "huggingface/label-files"
      SCREAMING_SNAKE_CASE_          =  "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_          =  json.load(open(hf_hub_download(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"				)     ,      "r"				)				)
      SCREAMING_SNAKE_CASE_          =  {int(__UpperCamelCase				): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_          =  idalabel
      SCREAMING_SNAKE_CASE_          =  {v: k for k, v in idalabel.items()}

      # size of the architecture
      if swiftformer_name == "swiftformer_xs":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 6, 4]
            SCREAMING_SNAKE_CASE_          =  [4_8, 5_6, 1_1_2, 2_2_0]

      elif swiftformer_name == "swiftformer_s":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 9, 6]
            SCREAMING_SNAKE_CASE_          =  [4_8, 6_4, 1_6_8, 2_2_4]

      elif swiftformer_name == "swiftformer_l1":
            SCREAMING_SNAKE_CASE_          =  [4, 3, 1_0, 5]
            SCREAMING_SNAKE_CASE_          =  [4_8, 9_6, 1_9_2, 3_8_4]

      elif swiftformer_name == "swiftformer_l3":
            SCREAMING_SNAKE_CASE_          =  [4, 4, 1_2, 6]
            SCREAMING_SNAKE_CASE_          =  [6_4, 1_2_8, 3_2_0, 5_1_2]

      # load state_dict of original model, remove and rename some keys
      if original_ckpt:
            if original_ckpt.startswith("https"				):
                  SCREAMING_SNAKE_CASE_          =  torch.hub.load_state_dict_from_url(__UpperCamelCase     ,      map_location="cpu"     ,      check_hash=__UpperCamelCase				)
            else:
                  SCREAMING_SNAKE_CASE_          =  torch.load(__UpperCamelCase     ,      map_location="cpu"				)
      SCREAMING_SNAKE_CASE_          =  checkpoint

      SCREAMING_SNAKE_CASE_          =  create_rename_keys(__UpperCamelCase				)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_          =  SwiftFormerForImageClassification(__UpperCamelCase				).eval()
      hf_model.load_state_dict(__UpperCamelCase				)

      # prepare test inputs
      SCREAMING_SNAKE_CASE_          =  prepare_img()
      SCREAMING_SNAKE_CASE_          =  ViTImageProcessor.from_pretrained("preprocessor_config"				)
      SCREAMING_SNAKE_CASE_          =  processor(images=__UpperCamelCase     ,      return_tensors="pt"				)

      # compare outputs from both models
      SCREAMING_SNAKE_CASE_          =  get_expected_output(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  hf_model(inputs["pixel_values"]				).logits

      assert hf_logits.shape == torch.Size([1, 1_0_0_0]				)
      assert torch.allclose(hf_logits[0, 0:5]     ,      __UpperCamelCase     ,      atol=1E-3				)

      Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
      print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''				)
      hf_model.save_pretrained(__UpperCamelCase				)


if __name__ == "__main__":
      A     :  Optional[Any]					     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--swiftformer_name",
          default="swiftformer_xs",
          choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
          type=str,
          help="Name of the SwiftFormer model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default="./converted_outputs/",
          type=str,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

      A     :  List[Any]					     =       parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


