





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

   return flax_key_tuple, flax_tensor






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   if "metadata" in layer:
      a__	:						int      		=  layer.split("metadata"   )
      a__	:						Any      		=  "".join(split_layer[0]   )[:-1]
      a__	:						Tuple      		=  [tuple(("metadata" + split_layer[1]).split("/"   )   )]
   elif "kvstore" in layer:
      a__	:						Union[str, Any]      		=  layer.split("kvstore"   )
      a__	:						Optional[Any]      		=  "".join(split_layer[0]   )[:-1]
      a__	:						Any      		=  [tuple(("kvstore" + split_layer[1]).split("/"   )   )]

   else:
      a__	:						Optional[int]      		=  layer.split("/"   )
      a__	:						List[Any]      		=  "/".join(split_layer[:-1]   )
      a__	:						Optional[Any]      		=  (split_layer[-1],)

   if "kvstore/path" in layer:
      a__	:						Optional[Any]      		=  F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
   elif "kvstore/driver" in layer:
      a__	:						Dict      		=  "file"
   else:
      a__	:						Optional[Any]      		=  checkpoint_info[layer]

   return curr_real_layer_name, split_layer, content






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Union[str, Any]      		=  rename_keys(lowerCamelCase   )
   a__	:						Union[str, Any]      		=  {}
   for k, v in current_block.items():
      a__	:						List[str]      		=  v
   a__	:						str      		=  new_current_block
   torch.save(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase = WEIGHTS_NAME   ):
   a__	:						Dict      		=  convert_file_size_to_int(lowerCamelCase   )
   a__	:						Tuple      		=  []
   a__	:						str      		=  {}
   a__	:						Tuple      		=  0
   a__	:						Union[str, Any]      		=  0

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

   a__	:						List[str]      		=  {}
   for layer in checkpoint_info.keys():
      a__		,     a__		,     a__	:						Union[str, Any]      		=  get_key_and_tensorstore_dict(
          lowerCamelCase							, lowerCamelCase							, lowerCamelCase   )
      if curr_real_layer_name in all_layers:
         a__	:						List[Any]      		=  content
      else:
         a__	:						List[Any]      		=  {split_layer[-1]: content}

   for key in all_layers.keys():
      # open tensorstore file
      a__	:						int      		=  ts.open(unflatten_dict(all_layers[key]   )   ).result().read().result()
      a__	:						Tuple      		=  torch.tensor(lowerCamelCase   )
      a__	:						Dict      		=  raw_weights.numel() * dtype_byte_size(raw_weights.dtype   )

      # use the renaming pattern from the small conversion scripts
      a__		,     a__	:						Tuple      		=  rename_base_flax_keys(tuple(key.split("/"   )   )							, lowerCamelCase   )
      a__	:						str      		=  "/".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__	:						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()   )
         del current_block
         a__	:						Union[str, Any]      		=  {}
         a__	:						Dict      		=  0

      a__	:						Any      		=  raw_weights.to(getattr(lowerCamelCase							, lowerCamelCase   )   )
      current_block_size += weight_size
      total_size += weight_size

   # Add the last block
   a__	:						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()   )

   # 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__	:						str      		=  {}
   a__	:						Dict      		=  {}
   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__	:						List[Any]      		=  os.path.join(lowerCamelCase							, weights_name.replace(".bin"							, F"""-{idx+1:05d}-of-???.bin"""   )   )
      os.rename(lowerCamelCase							, os.path.join(lowerCamelCase							, lowerCamelCase   )   )
      a__	:						Tuple      		=  shard
      for key in shard:
         a__	:						List[str]      		=  shard_file

    # Add the metadata
   a__	:						Union[str, Any]      		=  {"total_size": total_size}
   a__	:						List[str]      		=  {"metadata": metadata, "weight_map": weight_map}

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

   return metadata, index


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			Any			      =						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.""",
  )
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						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

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

   a__	:						int      		=  tokenizer(lowerCamelCase							, return_tensors="pt"   ).input_ids
   a__	:						Union[str, Any]      		=  model.generate(lowerCamelCase							, decoder_start_token_id=0   )
   print(tokenizer.decode(out[0]   )   )








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__       :			Tuple			      =						"""patrickvonplaten/t5-tiny-random"""







@require_torch
class   __lowerCAmelCase			(					unittest.TestCase  ):





   @cached_property
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      return AutoConfig.from_pretrained(snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.num_hidden_layers					,			1      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__		,     *a__	:						Any      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			self.teacher_config.encoder_layers      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__		,     *a__	:						List[str]      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			1      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      with self.assertRaises(snake_case      ):
         create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=snake_case					,			d=snake_case      )

