
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
 import transformers







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      random.seed(_lowerCAmelCase							)
      np.random.seed(_lowerCAmelCase							)
      torch.manual_seed(_lowerCAmelCase							)
      torch.cuda.manual_seed_all(_lowerCAmelCase							)
      # ^^ safe to call this function even if cuda is not available



class  UpperCAmelCase__						:



    def __init__(	self      :       List[str],__A      :       Iterable[torch.nn.Parameter],__A      :       float = 0.9999,__A      :       float = 0.0,__A      :       int = 0,__A      :       bool = False,__A      :       Union[float, int] = 1.0,__A      :       Union[float, int] = 2 / 3,__A      :       Optional[Any] = None,__A      :       Dict[str, Any] = None,**__A      :       Optional[Any],):

          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   Any								=   (
                    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
                    "Please pass the parameters of the module instead."
                )
                deprecate(
                    "passing a `torch.nn.Module` to `ExponentialMovingAverage`","1.0.0",__A,standard_warn=__A,)
                _lowerCamelCase				:   Dict								=   parameters.parameters()

                # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                _lowerCamelCase				:   Optional[Any]								=   True

          if kwargs.get("max_value",__A     ) is not None:
                _lowerCamelCase				:   Optional[int]								=   "The `max_value` argument is deprecated. Please use `decay` instead."
                deprecate("max_value","1.0.0",__A,standard_warn=__A     )
                _lowerCamelCase				:   Optional[Any]								=   kwargs["max_value"]

          if kwargs.get("min_value",__A     ) is not None:
                _lowerCamelCase				:   Tuple								=   "The `min_value` argument is deprecated. Please use `min_decay` instead."
                deprecate("min_value","1.0.0",__A,standard_warn=__A     )
                _lowerCamelCase				:   Any								=   kwargs["min_value"]

          _lowerCamelCase				:   Optional[int]								=   list(__A     )
          _lowerCamelCase				:   List[Any]								=   [p.clone().detach() for p in parameters]

          if kwargs.get("device",__A     ) is not None:
                _lowerCamelCase				:   Optional[int]								=   "The `device` argument is deprecated. Please use `to` instead."
                deprecate("device","1.0.0",__A,standard_warn=__A     )
                self.to(device=kwargs["device"]     )

          _lowerCamelCase				:   Tuple								=   None

          _lowerCamelCase				:   Dict								=   decay
          _lowerCamelCase				:   List[Any]								=   min_decay
          _lowerCamelCase				:   Optional[Any]								=   update_after_step
          _lowerCamelCase				:   Any								=   use_ema_warmup
          _lowerCamelCase				:   Union[str, Any]								=   inv_gamma
          _lowerCamelCase				:   str								=   power
          _lowerCamelCase				:   Union[str, Any]								=   0
          _lowerCamelCase				:   Union[str, Any]								=   None  # set in `step()`

          _lowerCamelCase				:   List[str]								=   model_cls
          _lowerCamelCase				:   Dict								=   model_config



    @classmethod
    def   lowerCamelCase_  (	cls      :       Union[str, Any],__A      :       List[str],__A      :       Optional[int]     ):
          _lowerCamelCase				:   Optional[int]								=   model_cls.load_config(__A,return_unused_kwargs=__A     )
          _lowerCamelCase				:   Optional[Any]								=   model_cls.from_pretrained(__A     )

          _lowerCamelCase				:   Union[str, Any]								=   cls(model.parameters(),model_cls=__A,model_config=model.config     )

          ema_model.load_state_dict(__A     )
          return ema_model



    def   lowerCamelCase_  (	self      :       str,__A      :       int     ):
          if self.model_cls is None:
                raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."     )

          if self.model_config is None:
                raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."     )

          _lowerCamelCase				:   Tuple								=   self.model_cls.from_config(self.model_config     )
          _lowerCamelCase				:   List[str]								=   self.state_dict()
          state_dict.pop("shadow_params",__A     )

          model.register_to_config(**__A     )
          self.copy_to(model.parameters()     )
          model.save_pretrained(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       int     ):
          _lowerCamelCase				:   str								=   max(0,optimization_step - self.update_after_step - 1     )

          if step <= 0:
                return 0.0

          if self.use_ema_warmup:
                _lowerCamelCase				:   Tuple								=   1 - (1 + step / self.inv_gamma) ** -self.power
          else:
                _lowerCamelCase				:   List[str]								=   (1 + step) / (1_0 + step)

          _lowerCamelCase				:   Union[str, Any]								=   min(__A,self.decay     )
          # make sure decay is not smaller than min_decay
          _lowerCamelCase				:   Union[str, Any]								=   max(__A,self.min_decay     )
          return cur_decay_value



    @torch.no_grad()
    def   lowerCamelCase_  (	self      :       Any,__A      :       Iterable[torch.nn.Parameter]     ):
          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   Dict								=   (
                    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
                    "Please pass the parameters of the module instead."
                )
                deprecate(
                    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`","1.0.0",__A,standard_warn=__A,)
                _lowerCamelCase				:   Any								=   parameters.parameters()

          _lowerCamelCase				:   str								=   list(__A     )

          self.optimization_step += 1

          # Compute the decay factor for the exponential moving average.
          _lowerCamelCase				:   Dict								=   self.get_decay(self.optimization_step     )
          _lowerCamelCase				:   Optional[Any]								=   decay
          _lowerCamelCase				:   List[Any]								=   1 - decay

          _lowerCamelCase				:   List[Any]								=   contextlib.nullcontext
          if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                import deepspeed

          for s_param, param in zip(self.shadow_params,__A     ):
                if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                      _lowerCamelCase				:   List[Any]								=   deepspeed.zero.GatheredParameters(__A,modifier_rank=__A     )

                with context_manager():
                      if param.requires_grad:
                            s_param.sub_(one_minus_decay * (s_param - param)     )
                      else:
                            s_param.copy_(__A     )



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   list(__A     )
          for s_param, param in zip(self.shadow_params,__A     ):
                param.data.copy_(s_param.to(param.device     ).data     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Dict=None,__A      :       Any=None     ):
          _lowerCamelCase				:   int								=   [
              p.to(device=__A,dtype=__A     ) if p.is_floating_point() else p.to(device=__A     )
              for p in self.shadow_params
          ]



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          return {
              "decay": self.decay,
              "min_decay": self.min_decay,
              "optimization_step": self.optimization_step,
              "update_after_step": self.update_after_step,
              "use_ema_warmup": self.use_ema_warmup,
              "inv_gamma": self.inv_gamma,
              "power": self.power,
              "shadow_params": self.shadow_params,
          }



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   [param.detach().cpu().clone() for param in parameters]



    def   lowerCamelCase_  (	self      :       int,__A      :       Iterable[torch.nn.Parameter]     ):
          if self.temp_stored_params is None:
                raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"     )
          for c_param, param in zip(self.temp_stored_params,__A     ):
                param.data.copy_(c_param.data     )

          # Better memory-wise.
          _lowerCamelCase				:   List[str]								=   None



    def   lowerCamelCase_  (	self      :       Dict,__A      :       dict     ):
          _lowerCamelCase				:   List[str]								=   copy.deepcopy(__A     )

          _lowerCamelCase				:   Optional[Any]								=   state_dict.get("decay",self.decay     )
          if self.decay < 0.0 or self.decay > 1.0:
                raise ValueError("Decay must be between 0 and 1"     )

          _lowerCamelCase				:   Dict								=   state_dict.get("min_decay",self.min_decay     )
          if not isinstance(self.min_decay,__A     ):
                raise ValueError("Invalid min_decay"     )

          _lowerCamelCase				:   Tuple								=   state_dict.get("optimization_step",self.optimization_step     )
          if not isinstance(self.optimization_step,__A     ):
                raise ValueError("Invalid optimization_step"     )

          _lowerCamelCase				:   Any								=   state_dict.get("update_after_step",self.update_after_step     )
          if not isinstance(self.update_after_step,__A     ):
                raise ValueError("Invalid update_after_step"     )

          _lowerCamelCase				:   List[Any]								=   state_dict.get("use_ema_warmup",self.use_ema_warmup     )
          if not isinstance(self.use_ema_warmup,__A     ):
                raise ValueError("Invalid use_ema_warmup"     )

          _lowerCamelCase				:   Tuple								=   state_dict.get("inv_gamma",self.inv_gamma     )
          if not isinstance(self.inv_gamma,(float, int)     ):
                raise ValueError("Invalid inv_gamma"     )

          _lowerCamelCase				:   Union[str, Any]								=   state_dict.get("power",self.power     )
          if not isinstance(self.power,(float, int)     ):
                raise ValueError("Invalid power"     )

          _lowerCamelCase				:   Optional[Any]								=   state_dict.get("shadow_params",__A     )
          if shadow_params is not None:
                _lowerCamelCase				:   str								=   shadow_params
                if not isinstance(self.shadow_params,__A     ):
                      raise ValueError("shadow_params must be a list"     )
                if not all(isinstance(__A,torch.Tensor     ) for p in self.shadow_params     ):
                      raise ValueError("shadow_params must all be Tensors"     )

'''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 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Tuple							):



      """simple docstring"""




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

      return flax_key_tuple, flax_tensor







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Any							):



      """simple docstring"""




      if "metadata" in layer:
            _lowerCamelCase				:   Optional[int]								=   layer.split("metadata"							)
            _lowerCamelCase				:   Union[str, Any]								=   "".join(split_layer[0]							)[:-1]
            _lowerCamelCase				:   Dict								=   [tuple(("metadata" + split_layer[1]).split("/"							)							)]
      elif "kvstore" in layer:
            _lowerCamelCase				:   List[str]								=   layer.split("kvstore"							)
            _lowerCamelCase				:   Optional[int]								=   "".join(split_layer[0]							)[:-1]
            _lowerCamelCase				:   List[Any]								=   [tuple(("kvstore" + split_layer[1]).split("/"							)							)]

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

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

      return curr_real_layer_name, split_layer, content







def 		A_       (	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   rename_keys(_lowerCAmelCase							)
      _lowerCamelCase				:   Dict								=   {}
      for k, v in current_block.items():
            _lowerCamelCase				:   Union[str, Any]								=   v
      _lowerCamelCase				:   str								=   new_current_block
      torch.save(_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    str = WEIGHTS_NAME							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   convert_file_size_to_int(_lowerCAmelCase							)
      _lowerCamelCase				:   Union[str, Any]								=   []
      _lowerCamelCase				:   Dict								=   {}
      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   List[str]								=   0

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

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

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

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

            _lowerCamelCase				:   Optional[int]								=   raw_weights.to(getattr(_lowerCAmelCase				,	_lowerCAmelCase							)							)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      _lowerCamelCase				:   List[str]								=   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
      _lowerCamelCase				:   Union[str, Any]								=   {}
      _lowerCamelCase				:   str								=   {}
      for idx, shard in enumerate(_lowerCAmelCase							):
            _lowerCamelCase				:   Dict								=   weights_name.replace(
                ".bin"				,	F'-{idx+1:05d}-of-{len(_lowerCAmelCase							):05d}.bin'							)  # len(sharded_state_dicts):05d}
            _lowerCamelCase				:   Union[str, Any]								=   os.path.join(_lowerCAmelCase				,	weights_name.replace(".bin"				,	F'-{idx+1:05d}-of-???.bin'							)							)
            os.rename(_lowerCAmelCase				,	os.path.join(_lowerCAmelCase				,	_lowerCAmelCase							)							)
            _lowerCamelCase				:   Tuple								=   shard
            for key in shard:
                  _lowerCamelCase				:   str								=   shard_file

    # Add the metadata
      _lowerCamelCase				:   Optional[Any]								=   {"total_size": total_size}
      _lowerCamelCase				:   Dict								=   {"metadata": metadata, "weight_map": weight_map}

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

      return metadata, index


if __name__ == "__main__":
 UpperCAmelCase_       :   List[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.',
 )
 UpperCAmelCase_       :   Dict   =       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_       (	):



      """simple docstring"""




      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

      _lowerCamelCase				:   Optional[int]								=   tokenizer(_lowerCAmelCase				,	return_tensors="pt"							).input_ids
      _lowerCamelCase				:   List[Any]								=   model.generate(_lowerCAmelCase				,	decoder_start_token_id=0							)
      print(tokenizer.decode(out[0]							)							)