
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__  (					__A   , __A	):







    '''simple docstring'''
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        _lowerCamelCase     :		Optional[int] 						=						flax_key_tuple[:-1] + ("""weight""",)
        _lowerCamelCase     :		List[str] 						=						torch.permute(__A   , (0, 2, 1)	)
    elif flax_key_tuple[-1] == "kernel" and ".".join(__A	):
        # linear layer
        _lowerCamelCase     :		Optional[Any] 						=						flax_key_tuple[:-1] + ("""weight""",)
        _lowerCamelCase     :		Union[str, Any] 						=						flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        _lowerCamelCase     :		Dict 						=						flax_key_tuple[:-1] + ("""weight""",)

    return flax_key_tuple, flax_tensor







def 						A__  (					__A   , __A   , __A	):







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

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

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

    return curr_real_layer_name, split_layer, content







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						rename_keys(__A	)
    _lowerCamelCase     :		Optional[Any] 						=						{}
    for k, v in current_block.items():
        _lowerCamelCase     :		Optional[Any] 						=						v
    _lowerCamelCase     :		Dict 						=						new_current_block
    torch.save(__A   , __A	)







def 						A__  (					__A   , __A   , __A   , __A   , __A = WEIGHTS_NAME	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						convert_file_size_to_int(__A	)
    _lowerCamelCase     :		List[str] 						=						[]
    _lowerCamelCase     :		List[str] 						=						{}
    _lowerCamelCase     :		Any 						=						0
    _lowerCamelCase     :		Tuple 						=						0

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

    _lowerCamelCase     :		int 						=						{}
    for layer in checkpoint_info.keys():
        _lowerCamelCase     :		Union[str, Any] 						=						get_key_and_tensorstore_dict(
            __A   , __A   , __A	)
        if curr_real_layer_name in all_layers:
            _lowerCamelCase     :		Tuple 						=						content
        else:
            _lowerCamelCase     :		Tuple 						=						{split_layer[-1]: content}

    for key in all_layers.keys():
        # open tensorstore file
        _lowerCamelCase     :		Dict 						=						ts.open(unflatten_dict(all_layers[key]	)	).result().read().result()
        _lowerCamelCase     :		Dict 						=						torch.tensor(__A	)
        _lowerCamelCase     :		Optional[Any] 						=						raw_weights.numel() * dtype_byte_size(raw_weights.dtype	)

        # use the renaming pattern from the small conversion scripts
        _lowerCamelCase     :		Optional[int] 						=						rename_base_flax_keys(tuple(key.split("""/"""	)	)   , __A	)
        _lowerCamelCase     :		List[Any] 						=						"""/""".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:
            _lowerCamelCase     :		List[str] 						=						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
            _lowerCamelCase     :		Union[str, Any] 						=						{}
            _lowerCamelCase     :		Dict 						=						0

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

    # Add the last block
    _lowerCamelCase     :		Any 						=						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
    _lowerCamelCase     :		int 						=						{}
    _lowerCamelCase     :		List[str] 						=						{}
    for idx, shard in enumerate(__A	):
        _lowerCamelCase     :		Optional[Any] 						=						weights_name.replace(
            """.bin"""   , F"""-{idx+1:05d}-of-{len(__A	):05d}.bin"""	)  # len(sharded_state_dicts):05d}
        _lowerCamelCase     :		Tuple 						=						os.path.join(__A   , weights_name.replace(""".bin"""   , F"""-{idx+1:05d}-of-???.bin"""	)	)
        os.rename(__A   , os.path.join(__A   , __A	)	)
        _lowerCamelCase     :		Optional[Any] 						=						shard
        for key in shard:
            _lowerCamelCase     :		List[str] 						=						shard_file

    # Add the metadata
    _lowerCamelCase     :		List[str] 						=						{"""total_size""": total_size}
    _lowerCamelCase     :		Optional[Any] 						=						{"""metadata""": metadata, """weight_map""": weight_map}

    with open(os.path.join(__A   , __A	)   , """w"""   , encoding="""utf-8"""	) as f:
        _lowerCamelCase     :		Optional[int] 						=						json.dumps(__A   , indent=2   , sort_keys=__A	) + """\n"""
        f.write(__A	)

    return metadata, index


if __name__ == "__main__":
    lowerCAmelCase			:							Dict     =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			:							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__  (					):







    '''simple docstring'''
    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

    _lowerCamelCase     :		Optional[int] 						=						SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""	)
    config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""	)
    _lowerCamelCase     :		Union[str, Any] 						=						SwitchTransformersForConditionalGeneration.from_pretrained(
        """/home/arthur_huggingface_co/transformers/switch_converted"""   , device_map="""auto"""	)

    _lowerCamelCase     :		List[str] 						=						TaTokenizer.from_pretrained("""t5-small"""	)
    _lowerCamelCase     :		int 						=						"""A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

    _lowerCamelCase     :		Tuple 						=						tokenizer(__A   , return_tensors="""pt"""	).input_ids
    _lowerCamelCase     :		str 						=						model.generate(__A   , decoder_start_token_id=0	)
    print(tokenizer.decode(out[0]	)	)





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowerCAmelCase			:							int     ={
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()







def 						A__  (					__A   , __A   , __A   , __A=None	):







    '''simple docstring'''
    # Initialise PyTorch model
    _lowerCamelCase     :		Tuple 						=						XLNetConfig.from_json_file(__A	)

    _lowerCamelCase     :		List[Any] 						=						finetuning_task.lower() if finetuning_task is not None else """"""
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
        _lowerCamelCase     :		int 						=						finetuning_task
        _lowerCamelCase     :		Union[str, Any] 						=						GLUE_TASKS_NUM_LABELS[finetuning_task]
        _lowerCamelCase     :		int 						=						XLNetForSequenceClassification(__A	)
    elif "squad" in finetuning_task:
        _lowerCamelCase     :		Dict 						=						finetuning_task
        _lowerCamelCase     :		Optional[Any] 						=						XLNetForQuestionAnswering(__A	)
    else:
        _lowerCamelCase     :		Any 						=						XLNetLMHeadModel(__A	)

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(__A   , __A   , __A	)

    # Save pytorch-model
    _lowerCamelCase     :		Optional[Any] 						=						os.path.join(__A   , __A	)
    _lowerCamelCase     :		Any 						=						os.path.join(__A   , __A	)
    print(F"""Save PyTorch model to {os.path.abspath(__A	)}"""	)
    torch.save(model.state_dict()   , __A	)
    print(F"""Save configuration file to {os.path.abspath(__A	)}"""	)
    with open(__A   , """w"""   , encoding="""utf-8"""	) as f:
        f.write(config.to_json_string()	)


if __name__ == "__main__":
    lowerCAmelCase			:							Dict     =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--xlnet_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained XLNet model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--finetuning_task",
        default=None,
        type=str,
        help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
    )
    lowerCAmelCase			:							Union[str, Any]     =parser.parse_args()
    print(args)

    convert_xlnet_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
    )




