


"""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						(						lowercase	,   lowercase		):







    """simple docstring"""
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        a					=flax_key_tuple[:-1] + ('''weight''',)
        a					=torch.permute(lowercase	,   (0, 2, 1)		)
    elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase		):
        # linear layer
        a					=flax_key_tuple[:-1] + ('''weight''',)
        a					=flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        a					=flax_key_tuple[:-1] + ('''weight''',)

    return flax_key_tuple, flax_tensor
def _A						(						lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    if "metadata" in layer:
        a					=layer.split('''metadata'''		)
        a					=''''''.join(split_layer[0]		)[:-1]
        a					=[tuple(('''metadata''' + split_layer[1]).split('''/'''		)		)]
    elif "kvstore" in layer:
        a					=layer.split('''kvstore'''		)
        a					=''''''.join(split_layer[0]		)[:-1]
        a					=[tuple(('''kvstore''' + split_layer[1]).split('''/'''		)		)]

    else:
        a					=layer.split('''/'''		)
        a					='''/'''.join(split_layer[:-1]		)
        a					=(split_layer[-1],)

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

    return curr_real_layer_name, split_layer, content
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					=rename_keys(lowercase		)
    a					={}
    for k, v in current_block.items():
        a					=v
    a					=new_current_block
    torch.save(lowercase	,   lowercase		)
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase	,   lowercase = WEIGHTS_NAME		):







    """simple docstring"""
    a					=convert_file_size_to_int(lowercase		)
    a					=[]
    a					={}
    a					=0
    a					=0

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

    a					={}
    for layer in checkpoint_info.keys():
        a		,				a		,				a					=get_key_and_tensorstore_dict(
            lowercase	,   lowercase	,   lowercase		)
        if curr_real_layer_name in all_layers:
            a					=content
        else:
            a					={split_layer[-1]: content}

    for key in all_layers.keys():
        # open tensorstore file
        a					=ts.open(unflatten_dict(all_layers[key]		)		).result().read().result()
        a					=torch.tensor(lowercase		)
        a					=raw_weights.numel() * dtype_byte_size(raw_weights.dtype		)

        # use the renaming pattern from the small conversion scripts
        a		,				a					=rename_base_flax_keys(tuple(key.split('''/'''		)		)	,   lowercase		)
        a					='''/'''.join(lowercase		)

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

        a					=raw_weights.to(getattr(lowercase	,   lowercase		)		)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    a					=os.path.join(lowercase	,   weights_name.replace('''.bin'''	,   f'''-{len(lowercase		)+1:05d}-of-???.bin'''		)		)
    rename_and_save_block(lowercase	,   lowercase		)
    sharded_state_dicts.append(current_block.keys()		)

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

    # Otherwise, let's build the index
    a					={}
    a					={}
    for idx, shard in enumerate(lowercase		):
        a					=weights_name.replace(
            '''.bin'''	,   f'''-{idx+1:05d}-of-{len(lowercase		):05d}.bin'''		)  # len(sharded_state_dicts):05d}
        a					=os.path.join(lowercase	,   weights_name.replace('''.bin'''	,   f'''-{idx+1:05d}-of-???.bin'''		)		)
        os.rename(lowercase	,   os.path.join(lowercase	,   lowercase		)		)
        a					=shard
        for key in shard:
            a					=shard_file

    # Add the metadata
    a					={'''total_size''': total_size}
    a					={'''metadata''': metadata, '''weight_map''': weight_map}

    with open(os.path.join(lowercase	,   lowercase		)	,   '''w'''	,   encoding='''utf-8'''		) as f:
        a					=json.dumps(lowercase	,   indent=2	,   sort_keys=lowercase		) + '''\n'''
        f.write(lowercase		)

    return metadata, index


if __name__ == "__main__":
       lowerCamelCase_       :       Optional[Any]      						=  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_       :       Dict      						=  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

    a					=SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''		)
    config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''		)
    a					=SwitchTransformersForConditionalGeneration.from_pretrained(
        '''/home/arthur_huggingface_co/transformers/switch_converted'''	,   device_map='''auto'''		)

    a					=TaTokenizer.from_pretrained('''t5-small'''		)
    a					='''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

    a					=tokenizer(lowercase	,   return_tensors='''pt'''		).input_ids
    a					=model.generate(lowercase	,   decoder_start_token_id=0		)
    print(tokenizer.decode(out[0]		)		)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_       :       int      						=  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""MBartTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  ["""MBartTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[str]      						=  [
           """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MBartForCausalLM""",
           """MBartForConditionalGeneration""",
           """MBartForQuestionAnswering""",
           """MBartForSequenceClassification""",
           """MBartModel""",
           """MBartPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  [
           """TFMBartForConditionalGeneration""",
           """TFMBartModel""",
           """TFMBartPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  [
           """FlaxMBartForConditionalGeneration""",
           """FlaxMBartForQuestionAnswering""",
           """FlaxMBartForSequenceClassification""",
           """FlaxMBartModel""",
           """FlaxMBartPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_       :       List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)