


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				(   UpperCamelCase__:     Any							,					UpperCamelCase__:     Tuple						) ->   Tuple:





	"""simple docstring"""







	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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   Union[str, Any]:





	"""simple docstring"""







	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   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Optional[Any]						) ->   Union[str, Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     str = WEIGHTS_NAME						) ->   Dict:





	"""simple docstring"""







	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       :      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				(   ) ->   Dict:





	"""simple docstring"""







	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]						)						)







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class       _UpperCamelCase    :

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.get_dummy_input()

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					if self.block_type == "down":
						return (4, 32, 16, 16)
					elif self.block_type == "mid":
						return (4, 32, 32, 32)
					elif self.block_type == "up":
						return (4, 32, 64, 64)

					raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'   )

				def      _UpperCAmelCase					(						self				,   a__=True				,   a__=False				,   a__=False				,   a__=False				,   )	->				Optional[Any]:
					A								=				4
					A								=				32
					A								=				(32, 32)

					A								=				torch.manual_seed(0   )
					A								=				torch.device(a__   )
					A								=				(batch_size, num_channels) + sizes
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__   )
					A								=				{"""hidden_states""": hidden_states}

					if include_temb:
						A								=				128
						A								=				randn_tensor((batch_size, temb_channels)				,   generator=a__				,   device=a__   )

					if include_res_hidden_states_tuple:
						A								=				torch.manual_seed(1   )
						A								=				(randn_tensor(a__				,   generator=a__				,   device=a__   ),)

					if include_encoder_hidden_states:
						A								=				floats_tensor((batch_size, 32, 32)   ).to(a__   )

					if include_skip_sample:
						A								=				randn_tensor(((batch_size, 3) + sizes)				,   generator=a__				,   device=a__   )

					return dummy_input

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				{
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 128,
					}
					if self.block_type == "up":
						A								=				32

					if self.block_type == "mid":
						init_dict.pop("""out_channels"""   )

					A								=				self.dummy_input
					return init_dict, inputs_dict

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					unet_block.to(a__   )
					unet_block.eval()

					with torch.no_grad():
						A								=				unet_block(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					self.assertEqual(output.shape				,   self.output_shape   )

					A								=				output[0, -1, -3:, -3:]
					A								=				torch.tensor(a__   ).to(a__   )
					assert torch_all_close(output_slice.flatten()				,   a__				,   atol=5e-3   )

				@unittest.skipIf(torch_device == """mps"""				,   """Training is not supported in mps"""   )
				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					model.to(a__   )
					model.train()
					A								=				model(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					A								=				torch.device(a__   )
					A								=				randn_tensor(output.shape				,   device=a__   )
					A								=				torch.nn.functional.mse_loss(a__				,   a__   )
					loss.backward()



