






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

	return flax_key_tuple, flax_tensor






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	if "metadata" in layer:
		A_		:						str	    =   layer.split("""metadata"""					)
		A_		:						Optional[int]	    =   """""".join(split_layer[0]					)[:-1]
		A_		:						List[str]	    =   [tuple(("""metadata""" + split_layer[1]).split("""/"""					)					)]
	elif "kvstore" in layer:
		A_		:						List[Any]	    =   layer.split("""kvstore"""					)
		A_		:						List[str]	    =   """""".join(split_layer[0]					)[:-1]
		A_		:						List[str]	    =   [tuple(("""kvstore""" + split_layer[1]).split("""/"""					)					)]

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

	if "kvstore/path" in layer:
		A_		:						Union[str, Any]	    =   F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
	elif "kvstore/driver" in layer:
		A_		:						List[str]	    =   """file"""
	else:
		A_		:						str	    =   checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   rename_keys(snake_case__					)
	A_		:						Dict	    =   {}
	for k, v in current_block.items():
		A_		:						Union[str, Any]	    =   v
	A_		:						List[Any]	    =   new_current_block
	torch.save(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__ = WEIGHTS_NAME					):
	A_		:						List[Any]	    =   convert_file_size_to_int(snake_case__					)
	A_		:						Tuple	    =   []
	A_		:						Union[str, Any]	    =   {}
	A_		:						Optional[Any]	    =   0
	A_		:						Optional[Any]	    =   0

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

	A_		:						List[str]	    =   {}
	for layer in checkpoint_info.keys():
		A_		:						List[str]	    =   get_key_and_tensorstore_dict(
		    snake_case__   ,   snake_case__   ,   snake_case__					)
		if curr_real_layer_name in all_layers:
			A_		:						Any	    =   content
		else:
			A_		:						Union[str, Any]	    =   {split_layer[-1]: content}

	for key in all_layers.keys():
		# open tensorstore file
		A_		:						List[str]	    =   ts.open(unflatten_dict(all_layers[key]					)					).result().read().result()
		A_		:						Dict	    =   torch.tensor(snake_case__					)
		A_		:						Optional[int]	    =   raw_weights.numel() * dtype_byte_size(raw_weights.dtype					)

		# use the renaming pattern from the small conversion scripts
		A_		:						Union[str, Any]	    =   rename_base_flax_keys(tuple(key.split("""/"""					)					)   ,   snake_case__					)
		A_		:						Union[str, Any]	    =   """/""".join(snake_case__					)

		# If this weight is going to tip up over the maximal size, we split.
		if current_block_size + weight_size > max_shard_size:
			A_		:						Union[str, Any]	    =   os.path.join(
			    snake_case__   ,   weights_name.replace(""".bin"""   ,   F"""-{len(snake_case__					)+1:05d}-of-???.bin"""					)					)
			rename_and_save_block(snake_case__   ,   snake_case__					)
			sharded_state_dicts.append(current_block.keys()					)
			del current_block
			A_		:						Optional[Any]	    =   {}
			A_		:						Dict	    =   0

		A_		:						Any	    =   raw_weights.to(getattr(snake_case__   ,   snake_case__					)					)
		current_block_size += weight_size
		total_size += weight_size

	# Add the last block
	A_		:						int	    =   os.path.join(snake_case__   ,   weights_name.replace(""".bin"""   ,   F"""-{len(snake_case__					)+1:05d}-of-???.bin"""					)					)
	rename_and_save_block(snake_case__   ,   snake_case__					)
	sharded_state_dicts.append(current_block.keys()					)

	# If we only have one shard, we return it
	if len(snake_case__					) == 1:
		return {weights_name: sharded_state_dicts[0]}, None

	# Otherwise, let's build the index
	A_		:						Optional[Any]	    =   {}
	A_		:						int	    =   {}
	for idx, shard in enumerate(snake_case__					):
		A_		:						int	    =   weights_name.replace(
		    """.bin"""   ,   F"""-{idx+1:05d}-of-{len(snake_case__					):05d}.bin"""					)  # len(sharded_state_dicts):05d}
		A_		:						Union[str, Any]	    =   os.path.join(snake_case__   ,   weights_name.replace(""".bin"""   ,   F"""-{idx+1:05d}-of-???.bin"""					)					)
		os.rename(snake_case__   ,   os.path.join(snake_case__   ,   snake_case__					)					)
		A_		:						str	    =   shard
		for key in shard:
			A_		:						str	    =   shard_file

    # Add the metadata
	A_		:						List[str]	    =   {"""total_size""": total_size}
	A_		:						int	    =   {"""metadata""": metadata, """weight_map""": weight_map}

	with open(os.path.join(snake_case__   ,   snake_case__					)   ,   """w"""   ,   encoding="""utf-8"""					) as f:
		A_		:						Union[str, Any]	    =   json.dumps(snake_case__   ,   indent=2   ,   sort_keys=snake_case__					) + """\n"""
		f.write(snake_case__					)

	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 			__UpperCamelCase	(		):
	from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

	A_		:						Tuple	    =   SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""					)
	config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""					)
	A_		:						Optional[Any]	    =   SwitchTransformersForConditionalGeneration.from_pretrained(
	    """/home/arthur_huggingface_co/transformers/switch_converted"""   ,   device_map="""auto"""					)

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

	A_		:						int	    =   tokenizer(snake_case__   ,   return_tensors="""pt"""					).input_ids
	A_		:						List[Any]	    =   model.generate(snake_case__   ,   decoder_start_token_id=0					)
	print(tokenizer.decode(out[0]					)					)









"""simple docstring"""
import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation



class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   get_activation("""swish"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.SiLU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   get_activation("""silu"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.SiLU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   get_activation("""mish"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.Mish							)

		self.assertEqual(act(torch.tensor(-200     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   get_activation("""gelu"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.GELU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)

