



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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = WEIGHTS_NAME				):
    __a						       =				convert_file_size_to_int(_UpperCAmelCase				)
    __a						       =				[]
    __a						       =				{}
    __a						       =				0
    __a						       =				0

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

    __a						       =				{}
    for layer in checkpoint_info.keys():
        __a       ,   __a       ,   __a						       =				get_key_and_tensorstore_dict(
            _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)
        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(_UpperCAmelCase				)
        __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('''/'''				)				)   , _UpperCAmelCase				)
        __a						       =				'''/'''.join(_UpperCAmelCase				)

        # 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(
                _UpperCAmelCase   , weights_name.replace('''.bin'''   , f'-{len(_UpperCAmelCase				)+1:05d}-of-???.bin'				)				)
            rename_and_save_block(_UpperCAmelCase   , _UpperCAmelCase				)
            sharded_state_dicts.append(current_block.keys()				)
            del current_block
            __a						       =				{}
            __a						       =				0

        __a						       =				raw_weights.to(getattr(_UpperCAmelCase   , _UpperCAmelCase				)				)
        current_block_size += weight_size
        total_size += weight_size

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

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

    # Otherwise, let's build the index
    __a						       =				{}
    __a						       =				{}
    for idx, shard in enumerate(_UpperCAmelCase				):
        __a						       =				weights_name.replace(
            '''.bin'''   , f'-{idx+1:05d}-of-{len(_UpperCAmelCase				):05d}.bin'				)  # len(sharded_state_dicts):05d}
        __a						       =				os.path.join(_UpperCAmelCase   , weights_name.replace('''.bin'''   , f'-{idx+1:05d}-of-???.bin'				)				)
        os.rename(_UpperCAmelCase   , os.path.join(_UpperCAmelCase   , _UpperCAmelCase				)				)
        __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(_UpperCAmelCase   , _UpperCAmelCase				)   , '''w'''   , encoding='''utf-8'''				) as f:
        __a						       =				json.dumps(_UpperCAmelCase   , indent=2   , sort_keys=_UpperCAmelCase				) + '''\n'''
        f.write(_UpperCAmelCase				)

    return metadata, index


if __name__ == "__main__":
 __snake_case						:List[str]      		=     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.''',
 )
 __snake_case						:int      		=     parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )





def      __snake_case	(			):
    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(_UpperCAmelCase   , return_tensors='''pt'''				).input_ids
    __a						       =				model.generate(_UpperCAmelCase   , decoder_start_token_id=0				)
    print(tokenizer.decode(out[0]				)				)










import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				BigBirdConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)

    if is_trivia_qa:
        __a						       =				BigBirdForQuestionAnswering(_UpperCAmelCase				)
    else:
        __a						       =				BigBirdForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_big_bird(_UpperCAmelCase   , _UpperCAmelCase   , is_trivia_qa=_UpperCAmelCase				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:Tuple      		=     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(
     '''--big_bird_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained BERT model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
 )
 __snake_case						:Any      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
 )





