


'''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     :    Tuple   ,     A     :    int  ):




						'''simple docstring'''






						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												UpperCAmelCase         =			flax_key_tuple[:-1] + ('''weight''',)
												UpperCAmelCase         =			torch.permute(__lowerCAmelCase   ,     (0, 2, 1)  )
						elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase  ):
												# 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     :    Tuple   ,     A     :    int   ,     A     :    Union[str, 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     :    str   ,     A     :    Tuple  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    Dict   ,     A     :    Optional[Any]   ,     A     :    Union[str, Any]   ,     A     :    List[str]   ,     A     :    str = WEIGHTS_NAME  ):




						'''simple docstring'''






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

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

						UpperCAmelCase         =			{}
						for layer in checkpoint_info.keys():
												UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			get_key_and_tensorstore_dict(
												    __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase  )
												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(__lowerCAmelCase  )
												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('''/'''  )  )   ,     __lowerCAmelCase  )
												UpperCAmelCase         =			'''/'''.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:
																		UpperCAmelCase         =			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
																		UpperCAmelCase         =			{}
																		UpperCAmelCase         =			0

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

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

						return metadata, index


if __name__ == "__main__":
	_lowercase    :    List[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    :    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    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(__lowerCAmelCase   ,     return_tensors='''pt'''  ).input_ids
						UpperCAmelCase         =			model.generate(__lowerCAmelCase   ,     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    :    Any			     =  {
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  ["""MaskFormerFeatureExtractor"""]
	_lowercase    :    Dict			     =  ["""MaskFormerImageProcessor"""]


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  [
	    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MaskFormerForInstanceSegmentation""",
	    """MaskFormerModel""",
	    """MaskFormerPreTrainedModel""",
	]
	_lowercase    :    List[Any]			     =  [
	    """MaskFormerSwinBackbone""",
	    """MaskFormerSwinModel""",
	    """MaskFormerSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
	from .configuration_maskformer_swin import MaskFormerSwinConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_maskformer import MaskFormerFeatureExtractor
		from .image_processing_maskformer import MaskFormerImageProcessor
	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_maskformer import (
		    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MaskFormerForInstanceSegmentation,
		    MaskFormerModel,
		    MaskFormerPreTrainedModel,
		)
		from .modeling_maskformer_swin import (
		    MaskFormerSwinBackbone,
		    MaskFormerSwinModel,
		    MaskFormerSwinPreTrainedModel,
		)


else:
	import sys

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





