




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	,      _A				):







	'''simple docstring'''





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

	return flax_key_tuple, flax_tensor


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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

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

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

	return curr_real_layer_name, split_layer, content


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





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


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A	,      _A = WEIGHTS_NAME				):







	'''simple docstring'''





	snake_case_						       =	convert_file_size_to_int(_A				)
	snake_case_						       =	[]
	snake_case_						       =	{}
	snake_case_						       =	0
	snake_case_						       =	0

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

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

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

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

		snake_case_						       =	raw_weights.to(getattr(_A	,      _A				)				)
		current_block_size += weight_size
		total_size += weight_size

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

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

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

	return metadata, index


if __name__ == "__main__":
		lowercase__							:	Optional[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__							:	Union[str, Any]											=		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

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

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

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






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (DPMSolverSinglestepScheduler,)
						lowerCAmelCase_							=  (('''num_inference_steps''', 25),)






						def 			snake_case__		(		self				:	str			,      **__lowercase				:	Any		):






							"""simple docstring"""






							snake_case_						       =	{
							    "num_train_timesteps": 10_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							    "prediction_type": "epsilon",
							    "thresholding": False,
							    "sample_max_value": 1.0,
							    "algorithm_type": "dpmsolver++",
							    "solver_type": "midpoint",
							    "lambda_min_clipped": -float("inf"		),
							    "variance_type": None,
							}

							config.update(**__lowercase		)
							return config






						def 			snake_case__		(		self				:	str			,      __lowercase				:	int=0			,      **__lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	dict(self.forward_default_kwargs		)
							snake_case_						       =	kwargs.pop("num_inference_steps"			,      __lowercase		)
							snake_case_						       =	self.dummy_sample
							snake_case_						       =	0.1 * sample
							snake_case_						       =	[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								snake_case_						       =	self.get_scheduler_config(**__lowercase		)
								snake_case_						       =	scheduler_class(**__lowercase		)
								scheduler.set_timesteps(__lowercase		)
								# copy over dummy past residuals
								snake_case_						       =	dummy_past_residuals[: scheduler.config.solver_order]

								with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(__lowercase		)
									snake_case_						       =	scheduler_class.from_pretrained(__lowercase		)
									new_scheduler.set_timesteps(__lowercase		)
									# copy over dummy past residuals
									snake_case_						       =	dummy_past_residuals[: new_scheduler.config.solver_order]

								snake_case_       , snake_case_						       =	sample, sample
								for t in range(__lowercase			,      time_step + scheduler.config.solver_order + 1		):
									snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample
									snake_case_						       =	new_scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							pass






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	Optional[int]=0			,      **__lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	dict(self.forward_default_kwargs		)
							snake_case_						       =	kwargs.pop("num_inference_steps"			,      __lowercase		)
							snake_case_						       =	self.dummy_sample
							snake_case_						       =	0.1 * sample
							snake_case_						       =	[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								snake_case_						       =	self.get_scheduler_config()
								snake_case_						       =	scheduler_class(**__lowercase		)
								scheduler.set_timesteps(__lowercase		)

								# copy over dummy past residuals (must be after setting timesteps)
								snake_case_						       =	dummy_past_residuals[: scheduler.config.solver_order]

								with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(__lowercase		)
									snake_case_						       =	scheduler_class.from_pretrained(__lowercase		)
									# copy over dummy past residuals
									new_scheduler.set_timesteps(__lowercase		)

									# copy over dummy past residual (must be after setting timesteps)
									snake_case_						       =	dummy_past_residuals[: new_scheduler.config.solver_order]

								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample
								snake_case_						       =	new_scheduler.step(__lowercase			,      __lowercase			,      __lowercase			,      **__lowercase		).prev_sample

								assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Any=None			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






							if scheduler is None:
								snake_case_						       =	self.scheduler_classes[0]
								snake_case_						       =	self.get_scheduler_config(**__lowercase		)
								snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	self.scheduler_classes[0]
							snake_case_						       =	self.get_scheduler_config(**__lowercase		)
							snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	10
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowercase		)

							for i, t in enumerate(scheduler.timesteps		):
								snake_case_						       =	model(__lowercase			,      __lowercase		)
								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase		).prev_sample

							return sample






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
							snake_case_						       =	50
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowercase		)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]		):
								snake_case_						       =	model(__lowercase			,      __lowercase		)
								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase		).prev_sample

							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2574		) < 1E-3






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase		)






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
							snake_case_						       =	self.full_loop(scheduler=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3

							snake_case_						       =	DEISMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	DPMSolverMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	UniPCMultistepScheduler.from_config(scheduler.config		)
							snake_case_						       =	DPMSolverSinglestepScheduler.from_config(scheduler.config		)

							snake_case_						       =	self.full_loop(scheduler=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							self.check_over_configs(thresholding=__lowercase		)
							for order in [1, 2, 3]:
								for solver_type in ["midpoint", "heun"]:
									for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    thresholding=__lowercase			,      prediction_type=__lowercase			,      sample_max_value=__lowercase			,      algorithm_type="dpmsolver++"			,      solver_order=__lowercase			,      solver_type=__lowercase			,      )






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
								for solver_type in ["midpoint", "heun"]:
									for order in [1, 2, 3]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    solver_order=__lowercase			,      solver_type=__lowercase			,      prediction_type=__lowercase			,      algorithm_type=__lowercase			,      )
											snake_case_						       =	self.full_loop(
											    solver_order=__lowercase			,      solver_type=__lowercase			,      prediction_type=__lowercase			,      algorithm_type=__lowercase			,      )
											assert not torch.isnan(__lowercase		).any(), "Samples have nan numbers"






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.check_over_configs(lower_order_final=__lowercase		)
							self.check_over_configs(lower_order_final=__lowercase		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
							self.check_over_configs(lambda_min_clipped=-5.1		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.check_over_configs(variance_type=__lowercase		)
							self.check_over_configs(variance_type="learned_range"		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
								self.check_over_forward(num_inference_steps=__lowercase			,      time_step=0		)






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop()
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2791		) < 1E-3






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(use_karras_sigmas=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.2248		) < 1E-3






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(prediction_type="v_prediction"		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.1453		) < 1E-3






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.full_loop(prediction_type="v_prediction"			,      use_karras_sigmas=__lowercase		)
							snake_case_						       =	torch.mean(torch.abs(__lowercase		)		)

							assert abs(result_mean.item() - 0.0649		) < 1E-3






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.scheduler_classes[0]
							snake_case_						       =	self.get_scheduler_config(thresholding=__lowercase			,      dynamic_thresholding_ratio=0		)
							snake_case_						       =	scheduler_class(**__lowercase		)

							snake_case_						       =	10
							snake_case_						       =	self.dummy_model()
							snake_case_						       =	self.dummy_sample_deter.half()
							scheduler.set_timesteps(__lowercase		)

							for i, t in enumerate(scheduler.timesteps		):
								snake_case_						       =	model(__lowercase			,      __lowercase		)
								snake_case_						       =	scheduler.step(__lowercase			,      __lowercase			,      __lowercase		).prev_sample

							assert sample.dtype == torch.floataa
