





'''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 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



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

  return flax_key_tuple, flax_tensor




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



  if "metadata" in layer:
    _snake_case  		=					layer.split("metadata"							)
    _snake_case  		=					"".join(split_layer[0]							)[:-1]
    _snake_case  		=					[tuple(("metadata" + split_layer[1]).split("/"							)							)]
  elif "kvstore" in layer:
    _snake_case  		=					layer.split("kvstore"							)
    _snake_case  		=					"".join(split_layer[0]							)[:-1]
    _snake_case  		=					[tuple(("kvstore" + split_layer[1]).split("/"							)							)]

  else:
    _snake_case  		=					layer.split("/"							)
    _snake_case  		=					"/".join(split_layer[:-1]							)
    _snake_case  		=					(split_layer[-1],)

  if "kvstore/path" in layer:
    _snake_case  		=					f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
  elif "kvstore/driver" in layer:
    _snake_case  		=					"file"
  else:
    _snake_case  		=					checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



  _snake_case  		=					rename_keys(__a							)
  _snake_case  		=					{}
  for k, v in current_block.items():
    _snake_case  		=					v
  _snake_case  		=					new_current_block
  torch.save(__a       ,						__a							)




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__ = WEIGHTS_NAME							):







  '''simple docstring'''



  _snake_case  		=					convert_file_size_to_int(__a							)
  _snake_case  		=					[]
  _snake_case  		=					{}
  _snake_case  		=					0
  _snake_case  		=					0

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

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

  for key in all_layers.keys():
    # open tensorstore file
    _snake_case  		=					ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
    _snake_case  		=					torch.tensor(__a							)
    _snake_case  		=					raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

    # use the renaming pattern from the small conversion scripts
    _snake_case		,		_snake_case  		=					rename_base_flax_keys(tuple(key.split("/"							)							)       ,						__a							)
    _snake_case  		=					"/".join(__a							)

    # If this weight is going to tip up over the maximal size, we split.
    if current_block_size + weight_size > max_shard_size:
      _snake_case  		=					os.path.join(
          __a       ,						weights_name.replace(".bin"       ,						f'''-{len(__a							)+1:05d}-of-???.bin'''							)							)
      rename_and_save_block(__a       ,						__a							)
      sharded_state_dicts.append(current_block.keys()							)
      del current_block
      _snake_case  		=					{}
      _snake_case  		=					0

    _snake_case  		=					raw_weights.to(getattr(__a       ,						__a							)							)
    current_block_size += weight_size
    total_size += weight_size

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

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

  # Otherwise, let's build the index
  _snake_case  		=					{}
  _snake_case  		=					{}
  for idx, shard in enumerate(__a							):
    _snake_case  		=					weights_name.replace(
        ".bin"       ,						f'''-{idx+1:05d}-of-{len(__a							):05d}.bin'''							)  # len(sharded_state_dicts):05d}
    _snake_case  		=					os.path.join(__a       ,						weights_name.replace(".bin"       ,						f'''-{idx+1:05d}-of-???.bin'''							)							)
    os.rename(__a       ,						os.path.join(__a       ,						__a							)							)
    _snake_case  		=					shard
    for key in shard:
      _snake_case  		=					shard_file

    # Add the metadata
  _snake_case  		=					{"total_size": total_size}
  _snake_case  		=					{"metadata": metadata, "weight_map": weight_map}

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

  return metadata, index


if __name__ == "__main__":
 __magic_name__ :    Optional[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.""",
 )
 __magic_name__ :    Union[str, 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 					snake_case_   (		):







  '''simple docstring'''



  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

  _snake_case  		=					SwitchTransformersConfig.from_pretrained("google/switch-base-8"							)
  config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"							)
  _snake_case  		=					SwitchTransformersForConditionalGeneration.from_pretrained(
      "/home/arthur_huggingface_co/transformers/switch_converted"       ,						device_map="auto"							)

  _snake_case  		=					TaTokenizer.from_pretrained("t5-small"							)
  _snake_case  		=					"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

  _snake_case  		=					tokenizer(__a       ,						return_tensors="pt"							).input_ids
  _snake_case  		=					model.generate(__a       ,						decoder_start_token_id=0							)
  print(tokenizer.decode(out[0]							)							)






from __future__ import annotations

from typing import Any




class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> None:
											a__      =num_of_nodes
											a__      =[]
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:
											self.m_edges.append([u_node, v_node, weight])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:

											if self.m_component[u_node] == u_node:
																		return u_node
											return self.find_component(self.m_component[u_node])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:

											if self.m_component[u_node] != u_node:
																		for k in self.m_component:
																									a__      =self.find_component(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:

											if component_size[u_node] <= component_size[v_node]:
																		a__      =v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(lowercase_)

											elif component_size[u_node] >= component_size[v_node]:
																		a__      =self.find_component(lowercase_)
																		component_size[u_node] += component_size[v_node]
																		self.set_component(lowercase_)





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =[]
											a__      =0

											a__      =[-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes):
																		self.m_component.update({node: node})
																		component_size.append(1)

											a__      =self.m_num_of_nodes

											while num_of_components > 1:
																		for edge in self.m_edges:
																									a__    ,     a__    ,     a__      =edge

																									a__      =self.m_component[u]
																									a__      =self.m_component[v]

																									if u_component != v_component:

																																for component in (u_component, v_component):
																																							if (
																																							    minimum_weight_edge[component] == -1
																																							    or minimum_weight_edge[component][2] > w
																																							):
																																														a__      =[u, v, w]

																		for edge in minimum_weight_edge:
																									if isinstance(lowercase_       ,  lowercase_):
																																a__    ,     a__    ,     a__      =edge

																																a__      =self.m_component[u]
																																a__      =self.m_component[v]

																																if u_component != v_component:
																																							mst_weight += w
																																							self.union(lowercase_       ,  lowercase_       ,  lowercase_)
																																							print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
																																							num_of_components -= 1

																		a__      =[-1] * self.m_num_of_nodes
											print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")







def   _lowercase(					):
							pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()

