





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   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			str						)			->    List[Any]:
   if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
      # expert layer
      A_       :					Optional[Any]        =      flax_key_tuple[:-1] + ("weight",)
      A_       :					Union[str, Any]        =      torch.permute(_lowerCAmelCase					,					(0, 2, 1)						)
   elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase						):
      # linear layer
      A_       :					List[Any]        =      flax_key_tuple[:-1] + ("weight",)
      A_       :					List[str]        =      flax_tensor.T
   elif flax_key_tuple[-1] in ["scale", "embedding"]:
      A_       :					Optional[Any]        =      flax_key_tuple[:-1] + ("weight",)

   return flax_key_tuple, flax_tensor

def   __snake_case    (       _lowerCAmelCase			:			str					,					_lowerCAmelCase			:			Optional[int]					,					_lowerCAmelCase			:			Optional[int]						)			->    int:
   if "metadata" in layer:
      A_       :					Optional[int]        =      layer.split("metadata"						)
      A_       :					Any        =      "".join(split_layer[0]						)[:-1]
      A_       :					Union[str, Any]        =      [tuple(("metadata" + split_layer[1]).split("/"						)						)]
   elif "kvstore" in layer:
      A_       :					List[Any]        =      layer.split("kvstore"						)
      A_       :					Tuple        =      "".join(split_layer[0]						)[:-1]
      A_       :					List[Any]        =      [tuple(("kvstore" + split_layer[1]).split("/"						)						)]

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

   if "kvstore/path" in layer:
      A_       :					Union[str, Any]        =      f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
   elif "kvstore/driver" in layer:
      A_       :					Union[str, Any]        =      "file"
   else:
      A_       :					Optional[int]        =      checkpoint_info[layer]

   return curr_real_layer_name, split_layer, content

def   __snake_case    (       _lowerCAmelCase			:			Tuple					,					_lowerCAmelCase			:			Tuple						)			->    str:
   A_       :					List[str]        =      rename_keys(_lowerCAmelCase						)
   A_       :					Optional[int]        =      {}
   for k, v in current_block.items():
      A_       :					Any        =      v
   A_       :					List[str]        =      new_current_block
   torch.save(_lowerCAmelCase					,					_lowerCAmelCase						)

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			Tuple					,					_lowerCAmelCase			:			Dict					,					_lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			str = WEIGHTS_NAME						)			->    Optional[int]:
   A_       :					List[Any]        =      convert_file_size_to_int(_lowerCAmelCase						)
   A_       :					Union[str, Any]        =      []
   A_       :					str        =      {}
   A_       :					int        =      0
   A_       :					Union[str, Any]        =      0

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

   A_       :					Optional[Any]        =      {}
   for layer in checkpoint_info.keys():
      A_   ,    A_   ,    A_       :					int        =      get_key_and_tensorstore_dict(
          _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
      if curr_real_layer_name in all_layers:
         A_       :					Union[str, Any]        =      content
      else:
         A_       :					Dict        =      {split_layer[-1]: content}

   for key in all_layers.keys():
      # open tensorstore file
      A_       :					List[str]        =      ts.open(unflatten_dict(all_layers[key]						)						).result().read().result()
      A_       :					Any        =      torch.tensor(_lowerCAmelCase						)
      A_       :					str        =      raw_weights.numel() * dtype_byte_size(raw_weights.dtype						)

      # use the renaming pattern from the small conversion scripts
      A_   ,    A_       :					int        =      rename_base_flax_keys(tuple(key.split("/"						)						)					,					_lowerCAmelCase						)
      A_       :					Any        =      "/".join(_lowerCAmelCase						)

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

      A_       :					Any        =      raw_weights.to(getattr(_lowerCAmelCase					,					_lowerCAmelCase						)						)
      current_block_size += weight_size
      total_size += weight_size

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

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

   # Otherwise, let's build the index
   A_       :					Union[str, Any]        =      {}
   A_       :					Any        =      {}
   for idx, shard in enumerate(_lowerCAmelCase						):
      A_       :					List[str]        =      weights_name.replace(
          ".bin"					,					f"-{idx+1:05d}-of-{len(_lowerCAmelCase						):05d}.bin"						)  # len(sharded_state_dicts):05d}
      A_       :					Optional[int]        =      os.path.join(_lowerCAmelCase					,					weights_name.replace(".bin"					,					f"-{idx+1:05d}-of-???.bin"						)						)
      os.rename(_lowerCAmelCase					,					os.path.join(_lowerCAmelCase					,					_lowerCAmelCase						)						)
      A_       :					List[Any]        =      shard
      for key in shard:
         A_       :					Tuple        =      shard_file

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

   with open(os.path.join(_lowerCAmelCase					,					_lowerCAmelCase						)					,					"w"					,					encoding="utf-8"						) as f:
      A_       :					List[str]        =      json.dumps(_lowerCAmelCase					,					indent=2					,					sort_keys=_lowerCAmelCase						) + "\n"
      f.write(_lowerCAmelCase						)

   return metadata, index


if __name__ == "__main__":
 _lowerCAmelCase				:   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.''',
 )
 _lowerCAmelCase				:   Optional[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   __snake_case    (       )			->    Optional[int]:
   from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

   A_       :					Any        =      SwitchTransformersConfig.from_pretrained("google/switch-base-8"						)
   config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"						)
   A_       :					List[Any]        =      SwitchTransformersForConditionalGeneration.from_pretrained(
       "/home/arthur_huggingface_co/transformers/switch_converted"					,					device_map="auto"						)

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

   A_       :					List[str]        =      tokenizer(_lowerCAmelCase					,					return_tensors="pt"						).input_ids
   A_       :					Dict        =      model.generate(_lowerCAmelCase					,					decoder_start_token_id=0						)
   print(tokenizer.decode(out[0]						)						)







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Optional[Any]        =      UNetaDModel(
            block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Any        =      VQModel(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,   latent_channels=3      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1_000      ,   )
        return CLIPTextModel(snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.dummy_uncond_unet
        A_       :					Any        =      DDIMScheduler()
        A_       :					List[str]        =      self.dummy_vq_model

        A_       :					Union[str, Any]        =      LDMPipeline(unet=snake_case      ,   vqvae=snake_case      ,   scheduler=snake_case	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					Any        =      torch.manual_seed(0	)
        A_       :					Optional[Any]        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"	).images

        A_       :					Dict        =      torch.manual_seed(0	)
        A_       :					Any        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

        A_       :					int        =      image[0, -3:, -3:, -1]
        A_       :					Any        =      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_       :					str        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
        A_       :					List[str]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




@slow
@require_torch
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					int        =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      torch.manual_seed(0	)
        A_       :					List[str]        =      ldm(generator=snake_case      ,   num_inference_steps=5      ,   output_type="numpy"	).images

        A_       :					Tuple        =      image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        A_       :					Any        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
        A_       :					List[Any]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
