


'''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 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Union[str, Any]					):




						'''simple docstring'''






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

						return flax_key_tuple, flax_tensor


def 				lowerCamelCase__				(	A       :      str				,       A       :      Tuple				,       A       :      Any					):




						'''simple docstring'''






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

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

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

						return curr_real_layer_name, split_layer, content


def 				lowerCamelCase__				(	A       :      Any				,       A       :      Dict					):




						'''simple docstring'''






						UpperCAmelCase					    =     rename_keys(A					)
						UpperCAmelCase					    =     {}
						for k, v in current_block.items():
												UpperCAmelCase					    =     v
						UpperCAmelCase					    =     new_current_block
						torch.save(A				,       A					)


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[Any]				,       A       :      int				,       A       :      Optional[Any]				,       A       :      str = WEIGHTS_NAME					):




						'''simple docstring'''






						UpperCAmelCase					    =     convert_file_size_to_int(A					)
						UpperCAmelCase					    =     []
						UpperCAmelCase					    =     {}
						UpperCAmelCase					    =     0
						UpperCAmelCase					    =     0

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

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

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

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

												UpperCAmelCase					    =     raw_weights.to(getattr(A				,       A					)					)
												current_block_size += weight_size
												total_size += weight_size

						# Add the last block
						UpperCAmelCase					    =     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
						UpperCAmelCase					    =     {}
						UpperCAmelCase					    =     {}
						for idx, shard in enumerate(A					):
												UpperCAmelCase					    =     weights_name.replace(
												    '''.bin'''				,       f"""-{idx+1:05d}-of-{len(A					):05d}.bin"""					)  # len(sharded_state_dicts):05d}
												UpperCAmelCase					    =     os.path.join(A				,       weights_name.replace('''.bin'''				,       f"""-{idx+1:05d}-of-???.bin"""					)					)
												os.rename(A				,       os.path.join(A				,       A					)					)
												UpperCAmelCase					    =     shard
												for key in shard:
																		UpperCAmelCase					    =     shard_file

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

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

						return metadata, index


if __name__ == "__main__":
						_lowercase      :      Union[str, 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.""",
						)
						_lowercase      :      List[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 				lowerCamelCase__				(	):




						'''simple docstring'''






						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase      :      Union[str, Any]            =					{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[Any]            =					["""PoolFormerFeatureExtractor"""]
	_lowercase      :      Any            =					["""PoolFormerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Tuple            =					[
	    """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """PoolFormerForImageClassification""",
	    """PoolFormerModel""",
	    """PoolFormerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_poolformer import (
	    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    PoolFormerConfig,
	    PoolFormerOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_poolformer import PoolFormerFeatureExtractor
		from .image_processing_poolformer import PoolFormerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_poolformer import (
		    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PoolFormerForImageClassification,
		    PoolFormerModel,
		    PoolFormerPreTrainedModel,
		)


else:
	import sys

	_lowercase      :      Dict            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





