




"""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			(    __snake_case  :					Any,     __snake_case  :					Union[str, Any]	):



				'''simple docstring'''





				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								UpperCAmelCase_       :Optional[Any]         =   flax_key_tuple[:-1] + ("weight",)
								UpperCAmelCase_       :List[Any]         =   torch.permute(_UpperCAmelCase,     (0, 2, 1)	)
				elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase	):
								# linear layer
								UpperCAmelCase_       :str         =   flax_key_tuple[:-1] + ("weight",)
								UpperCAmelCase_       :Tuple         =   flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								UpperCAmelCase_       :List[str]         =   flax_key_tuple[:-1] + ("weight",)

				return flax_key_tuple, flax_tensor



def 					a			(    __snake_case  :					Dict,     __snake_case  :					Tuple,     __snake_case  :					List[str]	):



				'''simple docstring'''





				if "metadata" in layer:
								UpperCAmelCase_       :Dict         =   layer.split('''metadata'''	)
								UpperCAmelCase_       :Optional[int]         =   "".join(split_layer[0]	)[:-1]
								UpperCAmelCase_       :List[Any]         =   [tuple(('''metadata''' + split_layer[1]).split('''/'''	)	)]
				elif "kvstore" in layer:
								UpperCAmelCase_       :str         =   layer.split('''kvstore'''	)
								UpperCAmelCase_       :List[str]         =   "".join(split_layer[0]	)[:-1]
								UpperCAmelCase_       :List[str]         =   [tuple(('''kvstore''' + split_layer[1]).split('''/'''	)	)]

				else:
								UpperCAmelCase_       :List[str]         =   layer.split('''/'''	)
								UpperCAmelCase_       :Optional[Any]         =   "/".join(split_layer[:-1]	)
								UpperCAmelCase_       :Union[str, Any]         =   (split_layer[-1],)

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

				return curr_real_layer_name, split_layer, content



def 					a			(    __snake_case  :					Optional[int],     __snake_case  :					Union[str, Any]	):



				'''simple docstring'''





				UpperCAmelCase_       :Union[str, Any]         =   rename_keys(_UpperCAmelCase	)
				UpperCAmelCase_       :Any         =   {}
				for k, v in current_block.items():
								UpperCAmelCase_       :Optional[int]         =   v
				UpperCAmelCase_       :List[str]         =   new_current_block
				torch.save(_UpperCAmelCase,     _UpperCAmelCase	)



def 					a			(    __snake_case  :					List[str],     __snake_case  :					List[str],     __snake_case  :					Optional[Any],     __snake_case  :					int,     __snake_case  :					str = WEIGHTS_NAME	):



				'''simple docstring'''





				UpperCAmelCase_       :Union[str, Any]         =   convert_file_size_to_int(_UpperCAmelCase	)
				UpperCAmelCase_       :str         =   []
				UpperCAmelCase_       :Optional[Any]         =   {}
				UpperCAmelCase_       :Any         =   0
				UpperCAmelCase_       :Any         =   0

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

				UpperCAmelCase_       :Optional[int]         =   {}
				for layer in checkpoint_info.keys():
								UpperCAmelCase_       :Optional[int]         =   get_key_and_tensorstore_dict(
								    _UpperCAmelCase,     _UpperCAmelCase,     _UpperCAmelCase	)
								if curr_real_layer_name in all_layers:
												UpperCAmelCase_       :List[str]         =   content
								else:
												UpperCAmelCase_       :str         =   {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								UpperCAmelCase_       :Tuple         =   ts.open(unflatten_dict(all_layers[key]	)	).result().read().result()
								UpperCAmelCase_       :Union[str, Any]         =   torch.tensor(_UpperCAmelCase	)
								UpperCAmelCase_       :Dict         =   raw_weights.numel() * dtype_byte_size(raw_weights.dtype	)

								# use the renaming pattern from the small conversion scripts
								UpperCAmelCase_       :Any         =   rename_base_flax_keys(tuple(key.split('''/'''	)	),     _UpperCAmelCase	)
								UpperCAmelCase_       :List[str]         =   "/".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:
												UpperCAmelCase_       :Optional[Any]         =   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
												UpperCAmelCase_       :Tuple         =   {}
												UpperCAmelCase_       :Tuple         =   0

								UpperCAmelCase_       :int         =   raw_weights.to(getattr(_UpperCAmelCase,     _UpperCAmelCase	)	)
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				UpperCAmelCase_       :Dict         =   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
				UpperCAmelCase_       :str         =   {}
				UpperCAmelCase_       :List[Any]         =   {}
				for idx, shard in enumerate(_UpperCAmelCase	):
								UpperCAmelCase_       :Tuple         =   weights_name.replace(
								    '''.bin''',     f'-{idx+1:05d}-of-{len(_UpperCAmelCase	):05d}.bin'	)  # len(sharded_state_dicts):05d}
								UpperCAmelCase_       :str         =   os.path.join(_UpperCAmelCase,     weights_name.replace('''.bin''',     f'-{idx+1:05d}-of-???.bin'	)	)
								os.rename(_UpperCAmelCase,     os.path.join(_UpperCAmelCase,     _UpperCAmelCase	)	)
								UpperCAmelCase_       :Optional[int]         =   shard
								for key in shard:
												UpperCAmelCase_       :str         =   shard_file

    # Add the metadata
				UpperCAmelCase_       :List[Any]         =   {"total_size": total_size}
				UpperCAmelCase_       :Tuple         =   {"metadata": metadata, "weight_map": weight_map}

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

				return metadata, index


if __name__ == "__main__":
						__lowerCamelCase										=    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										=    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

				UpperCAmelCase_       :int         =   SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''	)
				config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''	)
				UpperCAmelCase_       :Optional[int]         =   SwitchTransformersForConditionalGeneration.from_pretrained(
				    '''/home/arthur_huggingface_co/transformers/switch_converted''',     device_map='''auto'''	)

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

				UpperCAmelCase_       :int         =   tokenizer(_UpperCAmelCase,     return_tensors='''pt'''	).input_ids
				UpperCAmelCase_       :Dict         =   model.generate(_UpperCAmelCase,     decoder_start_token_id=0	)
				print(tokenizer.decode(out[0]	)	)




from typing import TYPE_CHECKING

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


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




