
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__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        snake_case__						=				flax_key_tuple[:-1] + ("weight",)
        snake_case__						=				torch.permute(__lowerCAmelCase    ,		(0, 2, 1)							)
    elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase							):
        # 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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				rename_keys(__lowerCAmelCase							)
    snake_case__						=				{}
    for k, v in current_block.items():
        snake_case__						=				v
    snake_case__						=				new_current_block
    torch.save(__lowerCAmelCase    ,		__lowerCAmelCase							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = WEIGHTS_NAME							):
    snake_case__						=				convert_file_size_to_int(__lowerCAmelCase							)
    snake_case__						=				[]
    snake_case__						=				{}
    snake_case__						=				0
    snake_case__						=				0

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

    snake_case__						=				{}
    for layer in checkpoint_info.keys():
        snake_case__						,					snake_case__						,					snake_case__						=				get_key_and_tensorstore_dict(
            __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)
        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(__lowerCAmelCase							)
        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("/"							)							)    ,		__lowerCAmelCase							)
        snake_case__						=				"/".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:
            snake_case__						=				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
            snake_case__						=				{}
            snake_case__						=				0

        snake_case__						=				raw_weights.to(getattr(__lowerCAmelCase    ,		__lowerCAmelCase							)							)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    snake_case__						=				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
    snake_case__						=				{}
    snake_case__						=				{}
    for idx, shard in enumerate(__lowerCAmelCase							):
        snake_case__						=				weights_name.replace(
            ".bin"    ,		F"""-{idx+1:05d}-of-{len(__lowerCAmelCase							):05d}.bin"""							)  # len(sharded_state_dicts):05d}
        snake_case__						=				os.path.join(__lowerCAmelCase    ,		weights_name.replace(".bin"    ,		F"""-{idx+1:05d}-of-???.bin"""							)							)
        os.rename(__lowerCAmelCase    ,		os.path.join(__lowerCAmelCase    ,		__lowerCAmelCase							)							)
        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(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		"w"    ,		encoding="utf-8"							) as f:
        snake_case__						=				json.dumps(__lowerCAmelCase    ,		indent=2    ,		sort_keys=__lowerCAmelCase							) + "\n"
        f.write(__lowerCAmelCase							)

    return metadata, index


if __name__ == "__main__":
    __magic_name__            =  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__            =  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__    (      ):
    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(__lowerCAmelCase    ,		return_tensors="pt"							).input_ids
    snake_case__						=				model.generate(__lowerCAmelCase    ,		decoder_start_token_id=0							)
    print(tokenizer.decode(out[0]							)							)





from __future__ import annotations

from fractions import Fraction





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return (
        num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
    )





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[]
    snake_case__						=				11
    snake_case__						=				int("1" + "0" * digit_len							)
    for num in range(__lowerCAmelCase    ,		__lowerCAmelCase							):
        while den <= 99:
            if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                if is_digit_cancelling(__lowerCAmelCase    ,		__lowerCAmelCase							):
                    solutions.append(F"""{num}/{den}"""							)
            den += 1
        num += 1
        snake_case__						=				10
    return solutions





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = 2							):
    snake_case__						=				1.0
    for fraction in fraction_list(__lowerCAmelCase							):
        snake_case__						=				Fraction(__lowerCAmelCase							)
        result *= frac.denominator / frac.numerator
    return int(__lowerCAmelCase							)


if __name__ == "__main__":
     print(solution())



