


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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Tuple:
   if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
      # expert layer
      UpperCAmelCase      					=  flax_key_tuple[:-1] + ("""weight""",)
      UpperCAmelCase      					=  torch.permute(lowerCAmelCase_	,    (0, 2, 1) )
   elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase_ ):
      # 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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  rename_keys(lowerCAmelCase_ )
   UpperCAmelCase      					=  {}
   for k, v in current_block.items():
      UpperCAmelCase      					=  v
   UpperCAmelCase      					=  new_current_block
   torch.save(lowerCAmelCase_	,    lowerCAmelCase_ )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ = WEIGHTS_NAME )    ->Optional[int]:
   UpperCAmelCase      					=  convert_file_size_to_int(lowerCAmelCase_ )
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  {}
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0

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

   UpperCAmelCase      					=  {}
   for layer in checkpoint_info.keys():
      UpperCAmelCase					,	UpperCAmelCase					,	UpperCAmelCase      					=  get_key_and_tensorstore_dict(
          lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )
      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(lowerCAmelCase_ )
      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("""/""" ) )	,    lowerCAmelCase_ )
      UpperCAmelCase      					=  """/""".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:
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  {}
         UpperCAmelCase      					=  0

      UpperCAmelCase      					=  raw_weights.to(getattr(lowerCAmelCase_	,    lowerCAmelCase_ ) )
      current_block_size += weight_size
      total_size += weight_size

   # Add the last block
   UpperCAmelCase      					=  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
   UpperCAmelCase      					=  {}
   UpperCAmelCase      					=  {}
   for idx, shard in enumerate(lowerCAmelCase_ ):
      UpperCAmelCase      					=  weights_name.replace(
          """.bin"""	,    F"""-{idx+1:05d}-of-{len(lowerCAmelCase_ ):05d}.bin""" )  # len(sharded_state_dicts):05d}
      UpperCAmelCase      					=  os.path.join(lowerCAmelCase_	,    weights_name.replace(""".bin"""	,    F"""-{idx+1:05d}-of-???.bin""" ) )
      os.rename(lowerCAmelCase_	,    os.path.join(lowerCAmelCase_	,    lowerCAmelCase_ ) )
      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(lowerCAmelCase_	,    lowerCAmelCase_ )	,    """w"""	,    encoding="""utf-8""" ) as f:
      UpperCAmelCase      					=  json.dumps(lowerCAmelCase_	,    indent=2	,    sort_keys=lowerCAmelCase_ ) + """\n"""
      f.write(lowerCAmelCase_ )

   return metadata, index


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







def 	_UpperCamelCase					(    )    ->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(lowerCAmelCase_	,    return_tensors="""pt""" ).input_ids
   UpperCAmelCase      					=  model.generate(lowerCAmelCase_	,    decoder_start_token_id=0 )
   print(tokenizer.decode(out[0] ) )







__a	 =      256
# Modulus to hash a string
__a	 =      100_0003
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   if p_len > t_len:
      return False

   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  1

   # Calculating the hash of pattern and substring of text
   for i in range(lowerCAmelCase_ ):
      UpperCAmelCase      					=  (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
      UpperCAmelCase      					=  (ord(text[i] ) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      UpperCAmelCase      					=  (modulus_power * alphabet_size) % modulus

   for i in range(0	,    t_len - p_len + 1 ):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      UpperCAmelCase      					=  (
          (text_hash - ord(text[i] ) * modulus_power) * alphabet_size
          + ord(text[i + p_len] )
      ) % modulus
   return False
def 	_UpperCamelCase					(    )    ->None:
   UpperCAmelCase      					=  """abc1abc12"""
   UpperCAmelCase      					=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
   UpperCAmelCase      					=  """alskfjaldsk23adsfabcabc"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ ) and not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 2)
   UpperCAmelCase      					=  """ABABX"""
   UpperCAmelCase      					=  """ABABZABABYABABX"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 3)
   UpperCAmelCase      					=  """AAAB"""
   UpperCAmelCase      					=  """ABAAAAAB"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 4)
   UpperCAmelCase      					=  """abcdabcy"""
   UpperCAmelCase      					=  """abcxabcdabxabcdabcdabcy"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 5)
   UpperCAmelCase      					=  """Lü"""
   UpperCAmelCase      					=  """Lüsai"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  """Lue"""
   assert not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   print("""Success.""" )


if __name__ == "__main__":
 test_rabin_karp()



