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	(    _lowercase		,  _lowercase						):



						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							flax_key_tuple[:-1] + ('''weight''',)
												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.permute(snake_case__		,  (0, 2, 1)						)
						elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__						):
												# linear layer
												SCREAMING_SNAKE_CASE							:    Any							=							flax_key_tuple[:-1] + ('''weight''',)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
												SCREAMING_SNAKE_CASE							:    int							=							flax_key_tuple[:-1] + ('''weight''',)

						return flax_key_tuple, flax_tensor
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):



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

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

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

						return curr_real_layer_name, split_layer, content
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							rename_keys(snake_case__						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							{}
						for k, v in current_block.items():
												SCREAMING_SNAKE_CASE							:    Tuple							=							v
						SCREAMING_SNAKE_CASE							:    List[str]							=							new_current_block
						torch.save(snake_case__		,  snake_case__						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = WEIGHTS_NAME						):
						SCREAMING_SNAKE_CASE							:    Dict							=							convert_file_size_to_int(snake_case__						)
						SCREAMING_SNAKE_CASE							:    str							=							[]
						SCREAMING_SNAKE_CASE							:    str							=							{}
						SCREAMING_SNAKE_CASE							:    Any							=							0
						SCREAMING_SNAKE_CASE							:    List[str]							=							0

						os.makedirs(snake_case__		,  exist_ok=snake_case__						)
						with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''		,  '''rb'''						) as fp:
												SCREAMING_SNAKE_CASE							:    Tuple							=							serialization.msgpack_restore(fp.read()						)['''optimizer''']['''target''']
												SCREAMING_SNAKE_CASE							:    List[Any]							=							flatten_dict(snake_case__		,  sep='''/'''						)

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

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

												# use the renaming pattern from the small conversion scripts
												SCREAMING_SNAKE_CASE							:    int							=							rename_base_flax_keys(tuple(key.split('''/'''						)						)		,  snake_case__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''/'''.join(snake_case__						)

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							raw_weights.to(getattr(snake_case__		,  snake_case__						)						)
												current_block_size += weight_size
												total_size += weight_size

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

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

						# Otherwise, let's build the index
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{}
						SCREAMING_SNAKE_CASE							:    List[str]							=							{}
						for idx, shard in enumerate(snake_case__						):
												SCREAMING_SNAKE_CASE							:    int							=							weights_name.replace(
												    '''.bin'''		,  f"""-{idx+1:05d}-of-{len(snake_case__						):05d}.bin"""						)  # len(sharded_state_dicts):05d}
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							os.path.join(snake_case__		,  weights_name.replace('''.bin'''		,  f"""-{idx+1:05d}-of-???.bin"""						)						)
												os.rename(snake_case__		,  os.path.join(snake_case__		,  snake_case__						)						)
												SCREAMING_SNAKE_CASE							:    str							=							shard
												for key in shard:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							shard_file

    # Add the metadata
						SCREAMING_SNAKE_CASE							:    Tuple							=							{'''total_size''': total_size}
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							{'''metadata''': metadata, '''weight_map''': weight_map}

						with open(os.path.join(snake_case__		,  snake_case__						)		,  '''w'''		,  encoding='''utf-8'''						) as f:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							json.dumps(snake_case__		,  indent=2		,  sort_keys=snake_case__						) + '''\n'''
												f.write(snake_case__						)

						return metadata, index


if __name__ == "__main__":
					__UpperCamelCase		:       Optional[int]									=     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		:       List[str]									=     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	(    ):
						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

						SCREAMING_SNAKE_CASE							:    Any							=							TaTokenizer.from_pretrained('''t5-small'''						)
						SCREAMING_SNAKE_CASE							:    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>.'''

						SCREAMING_SNAKE_CASE							:    List[Any]							=							tokenizer(snake_case__		,  return_tensors='''pt'''						).input_ids
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model.generate(snake_case__		,  decoder_start_token_id=0						)
						print(tokenizer.decode(out[0]						)						)







'''simple docstring'''




class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   Any					=						None
         A       :   Optional[Any]					=						None
         A       :   Tuple					=						graph

         self._normalize_graph(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						len(SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						None





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         if sources is int:
             A       :   Dict					=						[sources]
         if sinks is int:
             A       :   str					=						[sinks]

         if len(SCREAMING_SNAKE_CASE	) == 0 or len(SCREAMING_SNAKE_CASE	) == 0:
             return

         A       :   Optional[int]					=						sources[0]
         A       :   Union[str, Any]					=						sinks[0]

         # make fake vertex if there are more
         # than one source or sink
         if len(SCREAMING_SNAKE_CASE	) > 1 or len(SCREAMING_SNAKE_CASE	) > 1:
             A       :   Optional[int]					=						0
             for i in sources:
                 max_input_flow += sum(self.graph[i]	)

             A       :   Dict					=						len(self.graph	) + 1
             for room in self.graph:
                 room.insert(0						,			0	)
             self.graph.insert(0						,			[0] * size	)
             for i in sources:
                 A       :   Dict					=						max_input_flow
             A       :   Tuple					=						0

             A       :   Tuple					=						len(self.graph	) + 1
             for room in self.graph:
                 room.append(0	)
             self.graph.append([0] * size	)
             for i in sinks:
                 A       :   Optional[Any]					=						max_input_flow
             A       :   Optional[Any]					=						size - 1





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         if self.maximum_flow_algorithm is None:
             raise Exception('''You need to set maximum flow algorithm before.'''	)
         if self.source_index is None or self.sink_index is None:
             return 0

         self.maximum_flow_algorithm.execute()
         return self.maximum_flow_algorithm.getMaximumFlow()





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   List[Any]					=						algorithm(self	)





class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						flow_network
         A       :   Optional[Any]					=						flow_network.verticesCount
         A       :   Tuple					=						flow_network.sourceIndex
         A       :   Dict					=						flow_network.sinkIndex
         # it's just a reference, so you shouldn't change
         # it in your algorithms, use deep copy before doing that
         A       :   str					=						flow_network.graph
         A       :   Optional[Any]					=						False





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         if not self.executed:
             self._algorithm()
             A       :   Optional[int]					=						True





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         pass





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)
         # use this to save your result
         A       :   List[str]					=						-1





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         if not self.executed:
             raise Exception('''You should execute algorithm before using its result!'''	)

         return self.maximum_flow





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						[[0] * self.verticies_count for i in range(self.verticies_count	)]

         A       :   Union[str, Any]					=						[0] * self.verticies_count
         A       :   List[Any]					=						[0] * self.verticies_count





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   Tuple					=						self.verticies_count

         # push some substance to graph
         for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]	):
             self.preflow[self.source_index][nextvertex_index] += bandwidth
             self.preflow[nextvertex_index][self.source_index] -= bandwidth
             self.excesses[nextvertex_index] += bandwidth

         # Relabel-to-front selection rule
         A       :   Optional[Any]					=						[
             i
             for i in range(self.verticies_count	)
             if i != self.source_index and i != self.sink_index
         ]

         # move through list
         A       :   Union[str, Any]					=						0
         while i < len(SCREAMING_SNAKE_CASE	):
             A       :   str					=						vertices_list[i]
             A       :   List[str]					=						self.heights[vertex_index]
             self.process_vertex(SCREAMING_SNAKE_CASE	)
             if self.heights[vertex_index] > previous_height:
                 # if it was relabeled, swap elements
                 # and start from 0 index
                 vertices_list.insert(0						,			vertices_list.pop(SCREAMING_SNAKE_CASE	)	)
                 A       :   int					=						0
             else:
                 i += 1

         A       :   Optional[Any]					=						sum(self.preflow[self.source_index]	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         while self.excesses[vertex_index] > 0:
             for neighbour_index in range(self.verticies_count	):
                 # if it's neighbour and current vertex is higher
                 if (
                     self.graph[vertex_index][neighbour_index]
                     - self.preflow[vertex_index][neighbour_index]
                     > 0
                     and self.heights[vertex_index] > self.heights[neighbour_index]
                 ):
                     self.push(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

             self.relabel(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Dict					=						min(
             self.excesses[from_index]						,			self.graph[from_index][to_index] - self.preflow[from_index][to_index]						,			)
         self.preflow[from_index][to_index] += preflow_delta
         self.preflow[to_index][from_index] -= preflow_delta
         self.excesses[from_index] -= preflow_delta
         self.excesses[to_index] += preflow_delta





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   Dict					=						None
         for to_index in range(self.verticies_count	):
             if (
                 self.graph[vertex_index][to_index]
                 - self.preflow[vertex_index][to_index]
                 > 0
             ) and (min_height is None or self.heights[to_index] < min_height):
                 A       :   Dict					=						self.heights[to_index]

         if min_height is not None:
             A       :   Dict					=						min_height + 1


if __name__ == "__main__":
      lowercase			:				Optional[int]    		=			[0]
      lowercase			:				List[Any]    		=			[3]
      # graph = [
      #     [0, 0, 4, 6, 0, 0],
      #     [0, 0, 5, 2, 0, 0],
      #     [0, 0, 0, 0, 4, 4],
      #     [0, 0, 0, 0, 6, 6],
      #     [0, 0, 0, 0, 0, 0],
      #     [0, 0, 0, 0, 0, 0],
      # ]
      lowercase			:				int    		=			[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

      # prepare our network
      lowercase			:				List[str]    		=			FlowNetwork(graph, entrances, exits)
      # set algorithm
      flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
      # and calculate
      lowercase			:				List[str]    		=			flow_network.find_maximum_flow()

      print(f'''maximum flow is {maximum_flow}''')




