




'''simple docstring'''


from abc import ABC, abstractmethod
from typing import List, Optional







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def __init__( self   :  Optional[Any]       ):


           '''simple docstring'''

           self.test()






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  0
           a_     :							Optional[Any]     						=  False
           while not completed:
                if counter == 1:
                     self.reset()
                a_     :							Any     						=  self.advance()
                if not self.does_advance(lowercase__       ):
                     raise Exception(
                         """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."""       )

                a_						,     a_						,     a_     :							Optional[int]     						=  self.update(lowercase__       )
                counter += 1

                if counter > 1_0000:
                     raise Exception("""update() does not fulfill the constraint."""       )

           if self.remaining() != 0:
                raise Exception("""Custom Constraint is not defined correctly."""       )






      @abstractmethod
      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           raise NotImplementedError(
               F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )






      @abstractmethod
      def 							lowercase_						( self   :  Dict						,						lowercase__   :  int       ):


           '''simple docstring'''

           raise NotImplementedError(
               F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )






      @abstractmethod
      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  int       ):


           '''simple docstring'''

           raise NotImplementedError(
               F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )






      @abstractmethod
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           raise NotImplementedError(
               F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )






      @abstractmethod
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           raise NotImplementedError(
               F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )






      @abstractmethod
      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[str, Any]=False       ):


           '''simple docstring'''

           raise NotImplementedError(
               F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."       )







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def __init__( self   :  List[str]						,						lowercase__   :  List[int]       ):


           '''simple docstring'''

           super(lowercase__						,						self       ).__init__()

           if not isinstance(lowercase__						,						lowercase__       ) or len(lowercase__       ) == 0:
                raise ValueError(F"`token_ids` has to be a non-empty list, but is {token_ids}."       )
           if any((not isinstance(lowercase__						,						lowercase__       ) or token_id < 0) for token_id in token_ids       ):
                raise ValueError(F"Each list in `token_ids` has to be a list of positive integers, but is {token_ids}."       )

           a_     :							Optional[int]     						=  token_ids

           a_     :							Optional[Any]     						=  len(self.token_ids       )
           a_     :							List[str]     						=  -1  # the index of the currently fulfilled step
           a_     :							str     						=  False






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           if self.completed:
                return None
           return self.token_ids[self.fulfilled_idx + 1]






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  int       ):


           '''simple docstring'''

           if not isinstance(lowercase__						,						lowercase__       ):
                raise ValueError(F"`token_id` has to be an `int`, but is {token_id} of type {type(lowercase__       )}"       )

           if self.completed:
                return False

           return token_id == self.token_ids[self.fulfilled_idx + 1]






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  int       ):


           '''simple docstring'''

           if not isinstance(lowercase__						,						lowercase__       ):
                raise ValueError(F"`token_id` has to be an `int`, but is {token_id} of type {type(lowercase__       )}"       )

           a_     :							Dict     						=  False
           a_     :							Optional[Any]     						=  False
           a_     :							Dict     						=  False

           if self.does_advance(lowercase__       ):
                self.fulfilled_idx += 1
                a_     :							Union[str, Any]     						=  True
                if self.fulfilled_idx == (self.seqlen - 1):
                     a_     :							str     						=  True
                a_     :							Optional[int]     						=  completed
           else:
                # failed to make progress.
                a_     :							Optional[int]     						=  True
                self.reset()
           return stepped, completed, reset






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  False
           a_     :							Tuple     						=  0






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           return self.seqlen - (self.fulfilled_idx + 1)






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Union[str, Any]=False       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  PhrasalConstraint(self.token_ids       )

           if stateful:
                a_     :							Union[str, Any]     						=  self.seqlen
                a_     :							Optional[int]     						=  self.fulfilled_idx
                a_     :							Dict     						=  self.completed

           return new_constraint







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  Union[str, Any]						,						lowercase__   :  List[List[int]]						,						lowercase__   :  int=True       ):


           '''simple docstring'''

           a_     :							int     						=  max([len(lowercase__       ) for one in nested_token_ids]       )

           a_     :							Optional[Any]     						=  {}
           for token_ids in nested_token_ids:
                a_     :							List[Any]     						=  root
                for tidx, token_id in enumerate(lowercase__       ):
                     if token_id not in level:
                          a_     :							Union[str, Any]     						=  {}

                     a_     :							Tuple     						=  level[token_id]

           if no_subsets and self.has_subsets(lowercase__						,						lowercase__       ):
                raise ValueError(
                    """Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
                    F" {nested_token_ids}."       )

           a_     :							str     						=  root






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.trie

           for current_token in current_seq:
                a_     :							Optional[Any]     						=  start[current_token]

           a_     :							Dict     						=  list(start.keys()       )

           return next_tokens






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.next_tokens(lowercase__       )

           return len(lowercase__       ) == 0






      def 							lowercase_						( self   :  Any						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							List[str]     						=  list(root.values()       )
           if len(lowercase__       ) == 0:
                return 1
           else:
                return sum([self.count_leaves(lowercase__       ) for nn in next_nodes]       )






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.count_leaves(lowercase__       )
           return len(lowercase__       ) != leaf_count







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def __init__( self   :  List[Any]						,						lowercase__   :  List[List[int]]       ):


           '''simple docstring'''

           super(lowercase__						,						self       ).__init__()

           if not isinstance(lowercase__						,						lowercase__       ) or len(lowercase__       ) == 0:
                raise ValueError(F"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."       )
           if any(not isinstance(lowercase__						,						lowercase__       ) for token_ids in nested_token_ids       ):
                raise ValueError(F"`nested_token_ids` has to be a list of lists, but is {nested_token_ids}."       )
           if any(
               any((not isinstance(lowercase__						,						lowercase__       ) or token_id < 0) for token_id in token_ids       )
               for token_ids in nested_token_ids       ):
                raise ValueError(
                    F"Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."       )

           a_     :							str     						=  DisjunctiveTrie(lowercase__       )
           a_     :							List[str]     						=  nested_token_ids

           a_     :							Optional[Any]     						=  self.trie.max_height
           a_     :							List[str]     						=  []
           a_     :							str     						=  False






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Any     						=  self.trie.next_tokens(self.current_seq       )

           if len(lowercase__       ) == 0:
                return None
           else:
                return token_list






      def 							lowercase_						( self   :  int						,						lowercase__   :  int       ):


           '''simple docstring'''

           if not isinstance(lowercase__						,						lowercase__       ):
                raise ValueError(F"`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowercase__       )}"       )

           a_     :							Optional[int]     						=  self.trie.next_tokens(self.current_seq       )

           return token_id in next_tokens






      def 							lowercase_						( self   :  Any						,						lowercase__   :  int       ):


           '''simple docstring'''

           if not isinstance(lowercase__						,						lowercase__       ):
                raise ValueError(F"`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowercase__       )}"       )

           a_     :							Tuple     						=  False
           a_     :							List[Any]     						=  False
           a_     :							List[str]     						=  False

           if self.does_advance(lowercase__       ):
                self.current_seq.append(lowercase__       )
                a_     :							Any     						=  True
           else:
                a_     :							Optional[Any]     						=  True
                self.reset()

           a_     :							Any     						=  self.trie.reached_leaf(self.current_seq       )
           a_     :							Optional[Any]     						=  completed

           return stepped, completed, reset






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  False
           a_     :							List[Any]     						=  []






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           if self.completed:
                # since this can be completed without reaching max height
                return 0
           else:
                return self.seqlen - len(self.current_seq       )






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Union[str, Any]=False       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  DisjunctiveConstraint(self.token_ids       )

           if stateful:
                a_     :							Any     						=  self.seqlen
                a_     :							int     						=  self.current_seq
                a_     :							Optional[Any]     						=  self.completed

           return new_constraint







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  int						,						lowercase__   :  List[Constraint]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  constraints

           # max # of steps required to fulfill a given constraint
           a_     :							Dict     						=  max([c.seqlen for c in constraints]       )
           a_     :							Tuple     						=  len(lowercase__       )
           a_     :							Tuple     						=  False

           self.init_state()






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							str     						=  []
           a_     :							int     						=  None
           a_     :							Optional[int]     						=  [constraint.copy(stateful=lowercase__       ) for constraint in self.constraints]






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  0
           if self.inprogress_constraint:
                # extra points for having a constraint mid-fulfilled
                add += self.max_seqlen - self.inprogress_constraint.remaining()

           return (len(self.complete_constraints       ) * self.max_seqlen) + add






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Dict     						=  []
           if self.inprogress_constraint is None:
                for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                     a_     :							Any     						=  constraint.advance()
                     if isinstance(lowercase__						,						lowercase__       ):
                          token_list.append(lowercase__       )
                     elif isinstance(lowercase__						,						lowercase__       ):
                          token_list.extend(lowercase__       )
           else:
                a_     :							List[Any]     						=  self.inprogress_constraint.advance()
                if isinstance(lowercase__						,						lowercase__       ):
                     token_list.append(lowercase__       )
                elif isinstance(lowercase__						,						lowercase__       ):
                     token_list.extend(lowercase__       )

           if len(lowercase__       ) == 0:
                return None
           else:
                return token_list






      def 							lowercase_						( self   :  str						,						lowercase__   :  Optional[List[int]]       ):


           '''simple docstring'''

           self.init_state()

           if token_ids is not None:
                for token in token_ids:
                     # completes or steps **one** constraint
                     a_						,     a_     :							Optional[Any]     						=  self.add(lowercase__       )

                     # the entire list of constraints are fulfilled
                     if self.completed:
                          break






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  int       ):


           '''simple docstring'''

           if not isinstance(lowercase__						,						lowercase__       ):
                raise ValueError(F"`token_id` should be an `int`, but is `{token_id}`."       )

           a_						,     a_     :							Dict     						=  False, False

           if self.completed:
                a_     :							Union[str, Any]     						=  True
                a_     :							List[str]     						=  False
                return complete, stepped

           if self.inprogress_constraint is not None:
                # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
                # job, simply update the state

                a_						,     a_						,     a_     :							List[str]     						=  self.inprogress_constraint.update(lowercase__       )
                if reset:
                     # 1. If the next token breaks the progress, then we must restart.
                     #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

                     #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
                     #     constraint, not the full list of constraints.

                     self.pending_constraints.append(self.inprogress_constraint.copy(stateful=lowercase__       )       )
                     a_     :							Any     						=  None

                if complete:
                     # 2. If the next token completes the constraint, move it to completed list, set
                     #     inprogress to None. If there are no pending constraints either, then this full list of constraints
                     #     is complete.

                     self.complete_constraints.append(self.inprogress_constraint       )
                     a_     :							Union[str, Any]     						=  None

                     if len(self.pending_constraints       ) == 0:
                          # we're done!
                          a_     :							Optional[int]     						=  True

           else:
                # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
                # of constraints?

                for cidx, pending_constraint in enumerate(self.pending_constraints       ):
                     if pending_constraint.does_advance(lowercase__       ):
                          a_						,     a_						,     a_     :							str     						=  pending_constraint.update(lowercase__       )

                          if not stepped:
                               raise Exception(
                                   """`constraint.update(token_id)` is not yielding incremental progress, """
                                   """even though `constraint.does_advance(token_id)` is true."""       )

                          if complete:
                               self.complete_constraints.append(lowercase__       )
                               a_     :							Tuple     						=  None

                          if not complete and stepped:
                               a_     :							int     						=  pending_constraint

                          if complete or stepped:
                               # If we made any progress at all, then it's at least not a "pending constraint".

                               a_     :							Tuple     						=  (
                                   self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                               )

                               if len(self.pending_constraints       ) == 0 and self.inprogress_constraint is None:
                                    # If there's no longer any pending after this and no inprogress either, then we must be
                                    # complete.

                                    a_     :							List[Any]     						=  True

                               break  # prevent accidentally stepping through multiple constraints with just one token.

           return complete, stepped






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Optional[Any]=True       ):


           '''simple docstring'''

           a_     :							int     						=  ConstraintListState(self.constraints       )  # we actually never though self.constraints objects
           # throughout this process. So it's at initialization state.

           if stateful:
                a_     :							Optional[int]     						=  [
                    constraint.copy(stateful=lowercase__       ) for constraint in self.complete_constraints
                ]
                if self.inprogress_constraint is not None:
                     a_     :							Dict     						=  self.inprogress_constraint.copy(stateful=lowercase__       )
                a_     :							List[str]     						=  [constraint.copy() for constraint in self.pending_constraints]

           return new_state






'''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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					List[str]				):




     """simple docstring"""


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

     return flax_key_tuple, flax_tensor



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


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

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

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

     return curr_real_layer_name, split_layer, content



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     a_     :							int     						=  rename_keys(UpperCamelCase__				)
     a_     :							Any     						=  {}
     for k, v in current_block.items():
          a_     :							Union[str, Any]     						=  v
     a_     :							List[str]     						=  new_current_block
     torch.save(UpperCamelCase__ ,    UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					str = WEIGHTS_NAME				):




     """simple docstring"""


     a_     :							str     						=  convert_file_size_to_int(UpperCamelCase__				)
     a_     :							str     						=  []
     a_     :							Optional[int]     						=  {}
     a_     :							Optional[int]     						=  0
     a_     :							Any     						=  0

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

     a_     :							Dict     						=  {}
     for layer in checkpoint_info.keys():
          a_						,     a_						,     a_     :							List[Any]     						=  get_key_and_tensorstore_dict(
              UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__				)
          if curr_real_layer_name in all_layers:
               a_     :							int     						=  content
          else:
               a_     :							Tuple     						=  {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_     :							List[Any]     						=  torch.tensor(UpperCamelCase__				)
          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("""/"""				)				) ,    UpperCamelCase__				)
          a_     :							Dict     						=  """/""".join(UpperCamelCase__				)

          # 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(
                   UpperCamelCase__ ,    weights_name.replace(""".bin""" ,    F"-{len(UpperCamelCase__				)+1:05d}-of-???.bin"				)				)
               rename_and_save_block(UpperCamelCase__ ,    UpperCamelCase__				)
               sharded_state_dicts.append(current_block.keys()				)
               del current_block
               a_     :							Optional[int]     						=  {}
               a_     :							Union[str, Any]     						=  0

          a_     :							List[str]     						=  raw_weights.to(getattr(UpperCamelCase__ ,    UpperCamelCase__				)				)
          current_block_size += weight_size
          total_size += weight_size

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

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

     # Otherwise, let's build the index
     a_     :							Tuple     						=  {}
     a_     :							List[Any]     						=  {}
     for idx, shard in enumerate(UpperCamelCase__				):
          a_     :							str     						=  weights_name.replace(
              """.bin""" ,    F"-{idx+1:05d}-of-{len(UpperCamelCase__				):05d}.bin"				)  # len(sharded_state_dicts):05d}
          a_     :							Union[str, Any]     						=  os.path.join(UpperCamelCase__ ,    weights_name.replace(""".bin""" ,    F"-{idx+1:05d}-of-???.bin"				)				)
          os.rename(UpperCamelCase__ ,    os.path.join(UpperCamelCase__ ,    UpperCamelCase__				)				)
          a_     :							str     						=  shard
          for key in shard:
               a_     :							Union[str, Any]     						=  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(UpperCamelCase__ ,    UpperCamelCase__				) ,    """w""" ,    encoding="""utf-8"""				) as f:
          a_     :							Any     						=  json.dumps(UpperCamelCase__ ,    indent=2 ,    sort_keys=UpperCamelCase__				) + """\n"""
          f.write(UpperCamelCase__				)

     return metadata, index


if __name__ == "__main__":
 lowerCAmelCase_      :  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.',
 )
 lowerCAmelCase_      :  List[Any]				     =  parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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