
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 						lowercase_      (       A__			,		A__		)							->							int:
    """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 						lowercase_      (       A__			,		A__			,		A__		)							->							List[Any]:
    """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 						lowercase_      (       A__			,		A__		)							->							Tuple:
    """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 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__ = WEIGHTS_NAME		)							->							Optional[int]:
    """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__":
   _A   =			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.",
   )
   _A   =			parser.parse_args()
   shard_on_the_fly(
       args.switch_tax_checkpoint_path,
       args.pytorch_dump_folder_path,
       args.max_shard_size,
       args.dtype,
   )





def 						lowercase_      (       )							->							Dict:
    """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 collections import defaultdict







class 					lowerCamelCase		:





     def __init__(self				:   Tuple    ,   _A				:   Optional[int]    ,   _A				:   List[str]       )						->						Union[str, Any]:
         snake_case								= total  # total no of tasks (N)

         # DP table will have a dimension of (2^M)*N
         # initially all values are set to -1
         snake_case								= [
             [-1 for i in range(total + 1       )] for j in range(2 ** len(_A       )       )
         ]

         snake_case								= defaultdict(_A       )  # stores the list of persons for each task

         # final_mask is used to check if all persons are included by setting all bits
         # to 1
         snake_case								= (1 << len(_A       )) - 1





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Optional[Any]    ,   _A				:   List[Any]       )						->						str:
         # if mask == self.finalmask all persons are distributed tasks, return 1
         if mask == self.final_mask:
             return 1

         # if not everyone gets the task and no more tasks are available, return 0
         if task_no > self.total_tasks:
             return 0

         # if case already considered
         if self.dp[mask][task_no] != -1:
             return self.dp[mask][task_no]

         # Number of ways when we don't this task in the arrangement
         snake_case								= self.count_ways_until(_A    ,   task_no + 1       )

         # now assign the tasks one by one to all possible persons and recursively
         # assign for the remaining tasks.
         if task_no in self.task:
             for p in self.task[task_no]:
                 # if p is already given a task
                 if mask & (1 << p):
                     continue

                 # assign this task to p and change the mask value. And recursively
                 # assign tasks with the new mask value.
                 total_ways_util += self.count_ways_until(mask | (1 << p)    ,   task_no + 1       )

        # save the value.
         snake_case								= total_ways_util

         return self.dp[mask][task_no]





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Dict       )						->						Optional[Any]:
         # Store the list of persons for each task
         for i in range(len(_A       )       ):
             for j in task_performed[i]:
                 self.task[j].append(_A       )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
         return self.count_ways_until(0    ,   1       )


if __name__ == "__main__":
   _A   =			5  # total no of tasks (the value of N)

   # the list of tasks that can be done by M persons.
   _A   =			[[1, 3, 4], [1, 2, 5], [3, 4]]
   print(
       AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
           task_performed
       )
   )

