






"""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 					_A			(__a    ,				__a							) ->       List[Any]:
    """simple docstring"""






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

    return flax_key_tuple, flax_tensor



def 					_A			(__a    ,				__a    ,				__a							) ->       Union[str, Any]:
    """simple docstring"""






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

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

    return curr_real_layer_name, split_layer, content



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		rename_keys(__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		{}
    for k, v in current_block.items():
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		v
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		new_current_block
    torch.save(__a    ,				__a							)



def 					_A			(__a    ,				__a    ,				__a    ,				__a    ,				__a = WEIGHTS_NAME							) ->       Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		convert_file_size_to_int(__a							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[]
    SCREAMING_SNAKE_CASE_      :  Any    						=		{}
    SCREAMING_SNAKE_CASE_      :  Any    						=		0
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0

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

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

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

        # use the renaming pattern from the small conversion scripts
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		rename_base_flax_keys(tuple(key.split('''/'''							)							)    ,				__a							)
        SCREAMING_SNAKE_CASE_      :  Optional[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:
            SCREAMING_SNAKE_CASE_      :  List[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()							)
            del current_block
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{}
            SCREAMING_SNAKE_CASE_      :  str    						=		0

        SCREAMING_SNAKE_CASE_      :  str    						=		raw_weights.to(getattr(__a    ,				__a							)							)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    SCREAMING_SNAKE_CASE_      :  Dict    						=		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
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{}
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{}
    for idx, shard in enumerate(__a							):
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		weights_name.replace(
            '''.bin'''    ,				f'-{idx+1:05d}-of-{len(__a							):05d}.bin'							)  # len(sharded_state_dicts):05d}
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		os.path.join(__a    ,				weights_name.replace('''.bin'''    ,				f'-{idx+1:05d}-of-???.bin'							)							)
        os.rename(__a    ,				os.path.join(__a    ,				__a							)							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		shard
        for key in shard:
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		shard_file

    # Add the metadata
    SCREAMING_SNAKE_CASE_      :  Dict    						=		{'''total_size''': total_size}
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{'''metadata''': metadata, '''weight_map''': weight_map}

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

    return metadata, index


if __name__ == "__main__":
  UpperCAmelCase_   : Tuple   			= 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_   : 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 					_A			() ->       Tuple:
    """simple docstring"""






    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_      :  Optional[int]    						=		SwitchTransformersForConditionalGeneration.from_pretrained(
        '''/home/arthur_huggingface_co/transformers/switch_converted'''    ,				device_map='''auto'''							)

    SCREAMING_SNAKE_CASE_      :  str    						=		TaTokenizer.from_pretrained('''t5-small'''							)
    SCREAMING_SNAKE_CASE_      :  str    						=		'''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[str]    						=		tokenizer(__a    ,				return_tensors='''pt'''							).input_ids
    SCREAMING_SNAKE_CASE_      :  int    						=		model.generate(__a    ,				decoder_start_token_id=0							)
    print(tokenizer.decode(out[0]							)							)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      "xmod"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=False,		lowerCamelCase__=2,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=("en_XX",),		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       int          =   vocab_size
        A :       int          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[str]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Dict          =   intermediate_size
        A :       Optional[int]          =   hidden_dropout_prob
        A :       Union[str, Any]          =   attention_probs_dropout_prob
        A :       int          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Union[str, Any]          =   position_embedding_type
        A :       Any          =   use_cache
        A :       int          =   classifier_dropout
        A :       int          =   pre_norm
        A :       List[str]          =   adapter_reduction_factor
        A :       Any          =   adapter_layer_norm
        A :       Any          =   adapter_reuse_layer_norm
        A :       str          =   ln_before_adapter
        A :       Dict          =   list(lowerCamelCase__		)
        A :       Union[str, Any]          =   default_language






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       Any          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       int          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


