


"""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        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Dict			) ->      Any:


		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				__a           =			flax_key_tuple[:-1] + ("""weight""",)
				__a           =			torch.permute(lowerCAmelCase_    ,					(0, 2, 1)			)
		elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase_			):
				# 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        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     Any			) ->      Union[str, Any]:


		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        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     List[Any]			) ->      int:
		__a           =			rename_keys(lowerCAmelCase_			)
		__a           =			{}
		for k, v in current_block.items():
				__a           =			v
		__a           =			new_current_block
		torch.save(lowerCAmelCase_    ,					lowerCAmelCase_			)

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Dict = WEIGHTS_NAME			) ->      int:
		__a           =			convert_file_size_to_int(lowerCAmelCase_			)
		__a           =			[]
		__a           =			{}
		__a           =			0
		__a           =			0

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

		__a           =			{}
		for layer in checkpoint_info.keys():
				__a           =			get_key_and_tensorstore_dict(
				    lowerCAmelCase_    ,					lowerCAmelCase_    ,					lowerCAmelCase_			)
				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(lowerCAmelCase_			)
				__a           =			raw_weights.numel() * dtype_byte_size(raw_weights.dtype			)

				# use the renaming pattern from the small conversion scripts
				__a           =			rename_base_flax_keys(tuple(key.split('''/'''			)			)    ,					lowerCAmelCase_			)
				__a           =			"""/""".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:
						__a           =			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
						__a           =			{}
						__a           =			0

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

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

		return metadata, index


if __name__ == "__main__":
				lowercase_			     =  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.",
				)
				lowercase_			     =  parser.parse_args()
				shard_on_the_fly(
				    args.switch_tax_checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.max_shard_size,
				    args.dtype,
				)




def        lowercase							(		) ->      List[str]:
		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(lowerCAmelCase_    ,					return_tensors='''pt'''			).input_ids
		__a           =			model.generate(lowerCAmelCase_    ,					decoder_start_token_id=0			)
		print(tokenizer.decode(out[0]			)			)











'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_							: Any						 =	logging.get_logger(__name__)

A_							: Any						 =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """markuplm"""







 def __init__(							self  ,a_=30_522  ,a_=768  ,a_=12  ,a_=12  ,a_=3_072  ,a_="gelu"  ,a_=0.1  ,a_=0.1  ,a_=512  ,a_=2  ,a_=0.02  ,a_=1E-1_2  ,a_=0  ,a_=0  ,a_=2  ,a_=256  ,a_=1_024  ,a_=216  ,a_=1_001  ,a_=32  ,a_=50  ,a_="absolute"  ,a_=True  ,a_=None  ,**a_  ,)		->   Union[str, Any]:
        super().__init__(
            pad_token_id=a_  ,bos_token_id=a_  ,eos_token_id=a_  ,**a_  ,)
        _UpperCAmelCase  :						Optional[int]			     = vocab_size
        _UpperCAmelCase  :						Tuple			     = hidden_size
        _UpperCAmelCase  :						str			     = num_hidden_layers
        _UpperCAmelCase  :						Dict			     = num_attention_heads
        _UpperCAmelCase  :						int			     = hidden_act
        _UpperCAmelCase  :						Optional[Any]			     = intermediate_size
        _UpperCAmelCase  :						Tuple			     = hidden_dropout_prob
        _UpperCAmelCase  :						List[str]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						Optional[int]			     = max_position_embeddings
        _UpperCAmelCase  :						Tuple			     = type_vocab_size
        _UpperCAmelCase  :						Dict			     = initializer_range
        _UpperCAmelCase  :						List[Any]			     = layer_norm_eps
        _UpperCAmelCase  :						Optional[Any]			     = position_embedding_type
        _UpperCAmelCase  :						Any			     = use_cache
        _UpperCAmelCase  :						List[Any]			     = classifier_dropout
        # additional properties
        _UpperCAmelCase  :						Dict			     = max_depth
        _UpperCAmelCase  :						Union[str, Any]			     = max_xpath_tag_unit_embeddings
        _UpperCAmelCase  :						Optional[int]			     = max_xpath_subs_unit_embeddings
        _UpperCAmelCase  :						List[Any]			     = tag_pad_id
        _UpperCAmelCase  :						Tuple			     = subs_pad_id
        _UpperCAmelCase  :						List[str]			     = xpath_unit_hidden_size



