
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 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Optional[Any]  ):






	"""simple docstring"""


	if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
		# expert layer
		__lowerCAmelCase         =							flax_key_tuple[:-1] + ("weight",)
		__lowerCAmelCase         =							torch.permute(UpperCamelCase			,							(0, 2, 1)  )
	elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase  ):
		# linear layer
		__lowerCAmelCase         =							flax_key_tuple[:-1] + ("weight",)
		__lowerCAmelCase         =							flax_tensor.T
	elif flax_key_tuple[-1] in ["scale", "embedding"]:
		__lowerCAmelCase         =							flax_key_tuple[:-1] + ("weight",)

	return flax_key_tuple, flax_tensor






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


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

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

	if "kvstore/path" in layer:
		__lowerCAmelCase         =							F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
	elif "kvstore/driver" in layer:
		__lowerCAmelCase         =							"file"
	else:
		__lowerCAmelCase         =							checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Union[str, Any]  ):






	"""simple docstring"""


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






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     int			,							UpperCamelCase:     Any			,							UpperCamelCase:     str = WEIGHTS_NAME  ):






	"""simple docstring"""


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

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

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

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

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

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

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

    # Add the metadata
	__lowerCAmelCase         =							{"total_size": total_size}
	__lowerCAmelCase         =							{"metadata": metadata, "weight_map": weight_map}

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

	return metadata, index


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







def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

	__lowerCAmelCase         =							tokenizer(UpperCamelCase			,							return_tensors="pt"  ).input_ids
	__lowerCAmelCase         =							model.generate(UpperCamelCase			,							decoder_start_token_id=0  )
	print(tokenizer.decode(out[0]  )  )








# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)






