






"""simple docstring"""




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 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
    # expert layer
    _a  :       Any             =	flax_key_tuple[:-1] + ("weight",)
    _a  :       Optional[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  :       Any             =	flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
    _a  :       List[str]             =	flax_key_tuple[:-1] + ("weight",)

  return flax_key_tuple, flax_tensor

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  if "metadata" in layer:
    _a  :       List[Any]             =	layer.split("""metadata"""				)
    _a  :       List[str]             =	"".join(split_layer[0]				)[:-1]
    _a  :       Dict             =	[tuple(("""metadata""" + split_layer[1]).split("""/"""				)				)]
  elif "kvstore" in layer:
    _a  :       int             =	layer.split("""kvstore"""				)
    _a  :       str             =	"".join(split_layer[0]				)[:-1]
    _a  :       str             =	[tuple(("""kvstore""" + split_layer[1]).split("""/"""				)				)]

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

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

  return curr_real_layer_name, split_layer, content

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Union[str, Any]             =	rename_keys(__lowerCamelCase				)
  _a  :       Dict             =	{}
  for k, v in current_block.items():
    _a  :       Tuple             =	v
  _a  :       Any             =	new_current_block
  torch.save(__lowerCamelCase     ,   __lowerCamelCase				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = WEIGHTS_NAME				):



  '''simple docstring'''
  _a  :       Optional[Any]             =	convert_file_size_to_int(__lowerCamelCase				)
  _a  :       Tuple             =	[]
  _a  :       List[Any]             =	{}
  _a  :       List[Any]             =	0
  _a  :       List[Any]             =	0

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

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

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

    # use the renaming pattern from the small conversion scripts
    _a  :       List[Any]             =	rename_base_flax_keys(tuple(key.split("""/"""				)				)     ,   __lowerCamelCase				)
    _a  :       Optional[int]             =	"/".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  :       Tuple             =	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  :       List[Any]             =	{}
      _a  :       Dict             =	0

    _a  :       Union[str, Any]             =	raw_weights.to(getattr(__lowerCamelCase     ,   __lowerCamelCase				)				)
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  _a  :       Optional[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  :       List[str]             =	{}
  _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  :       Union[str, Any]             =	shard
    for key in shard:
      _a  :       int             =	shard_file

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

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

  return metadata, index


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



def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

  _a  :       Union[str, Any]             =	TaTokenizer.from_pretrained("""t5-small"""				)
  _a  :       Union[str, 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  :       Tuple             =	tokenizer(__lowerCamelCase     ,   return_tensors="""pt"""				).input_ids
  _a  :       List[Any]             =	model.generate(__lowerCamelCase     ,   decoder_start_token_id=0				)
  print(tokenizer.decode(out[0]				)				)



"""simple docstring"""





from __future__ import annotations

import math







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    u
   for i in range(1,		__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    temp * (u - i)
   return temp







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    int(input("enter the numbers of values: "  )  )
   UpperCAmelCase_					:					list[list[float]]			     =    []
   for _ in range(__lowerCamelCase  ):
      y.append([]  )
   for i in range(__lowerCamelCase  ):
      for j in range(__lowerCamelCase  ):
         y[i].append(__lowerCamelCase  )
         UpperCAmelCase_					:					Tuple			     =    0

   print("enter the values of parameters in a list: "  )
   UpperCAmelCase_					:					Union[str, Any]			     =    list(map(__lowerCamelCase,		input().split()  )  )

   print("enter the values of corresponding parameters: "  )
   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    float(input()  )

   UpperCAmelCase_					:					Tuple			     =    int(input("enter the value to interpolate: "  )  )
   UpperCAmelCase_					:					Tuple			     =    (value - x[0]) / (x[1] - x[0])

   # for calculating forward difference table

   for i in range(1,		__lowerCamelCase  ):
      for j in range(n - i  ):
         UpperCAmelCase_					:					Union[str, Any]			     =    y[j + 1][i - 1] - y[j][i - 1]

   UpperCAmelCase_					:					Optional[int]			     =    y[0][0]
   for i in range(1,		__lowerCamelCase  ):
      summ += (ucal(__lowerCamelCase,		__lowerCamelCase  ) * y[0][i]) / math.factorial(__lowerCamelCase  )

   print(f"""the value at {value} is {summ}"""  )


if __name__ == "__main__":
    main()


