
import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def 							A__       (    lowerCamelCase    )	->						Optional[int]:
				UpperCamelCase_:				Optional[int]						=    image.size
				UpperCamelCase_:				List[Any]						=    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				UpperCamelCase_:				Any						=    image.resize((w, h)  ,     resample=PIL_INTERPOLATION["""lanczos"""]    )
				UpperCamelCase_:				Dict						=    np.array(lowerCamelCase    ).astype(np.floataa    ) / 255.0
				UpperCamelCase_:				Tuple						=    image[None].transpose(0  ,     3  ,     1  ,     2    )
				UpperCamelCase_:				Union[str, Any]						=    torch.from_numpy(lowerCamelCase    )
				return 2.0 * image - 1.0





class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							VQModel    ,						snake_case_			:							UNetaDModel    ,						snake_case_			:							Union[
			        DDIMScheduler,
			        PNDMScheduler,
			        LMSDiscreteScheduler,
			        EulerDiscreteScheduler,
			        EulerAncestralDiscreteScheduler,
			        DPMSolverMultistepScheduler,
			    ]    ,						):
							super().__init__()
							self.register_modules(vqvae=snake_case_    ,						unet=snake_case_    ,						scheduler=snake_case_   )




			@torch.no_grad()
			def __call__(							self			:							Optional[Any]    ,						snake_case_			:							Union[torch.Tensor, PIL.Image.Image] = None    ,						snake_case_			:							Optional[int] = 1    ,						snake_case_			:							Optional[int] = 100    ,						snake_case_			:							Optional[float] = 0.0    ,						snake_case_			:							Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						snake_case_			:							Optional[str] = "pil"    ,						snake_case_			:							bool = True    ,						):
							if isinstance(snake_case_    ,						PIL.Image.Image   ):
											UpperCamelCase_:				int						=    1
							elif isinstance(snake_case_    ,						torch.Tensor   ):
											UpperCamelCase_:				Dict						=    image.shape[0]
							else:
											raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_   )}'''   )

							if isinstance(snake_case_    ,						PIL.Image.Image   ):
											UpperCamelCase_:				str						=    preprocess(snake_case_   )

							UpperCamelCase_:				List[Any]						=    image.shape[-2:]

							# in_channels should be 6: 3 for latents, 3 for low resolution image
							UpperCamelCase_:				int						=    (batch_size, self.unet.config.in_channels // 2, height, width)
							UpperCamelCase_:				Optional[int]						=    next(self.unet.parameters()   ).dtype

							UpperCamelCase_:				List[Any]						=    randn_tensor(snake_case_    ,						generator=snake_case_    ,						device=self.device    ,						dtype=snake_case_   )

							UpperCamelCase_:				Any						=    image.to(device=self.device    ,						dtype=snake_case_   )

							# set timesteps and move to the correct device
							self.scheduler.set_timesteps(snake_case_    ,						device=self.device   )
							UpperCamelCase_:				Tuple						=    self.scheduler.timesteps

							# scale the initial noise by the standard deviation required by the scheduler
							UpperCamelCase_:				str						=    latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							UpperCamelCase_:				Tuple						=    "eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
							UpperCamelCase_:				Optional[Any]						=    {}
							if accepts_eta:
											UpperCamelCase_:				Dict						=    eta

							for t in self.progress_bar(snake_case_   ):
											# concat latents and low resolution image in the channel dimension.
											UpperCamelCase_:				str						=    torch.cat([latents, image]    ,						dim=1   )
											UpperCamelCase_:				str						=    self.scheduler.scale_model_input(snake_case_    ,						snake_case_   )
											# predict the noise residual
											UpperCamelCase_:				Any						=    self.unet(snake_case_    ,						snake_case_   ).sample
											# compute the previous noisy sample x_t -> x_t-1
											UpperCamelCase_:				Union[str, Any]						=    self.scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						**snake_case_   ).prev_sample

							# decode the image latents with the VQVAE
							UpperCamelCase_:				Any						=    self.vqvae.decode(snake_case_   ).sample
							UpperCamelCase_:				Union[str, Any]						=    torch.clamp(snake_case_    ,						-1.0    ,						1.0   )
							UpperCamelCase_:				Optional[Any]						=    image / 2 + 0.5
							UpperCamelCase_:				Any						=    image.cpu().permute(0    ,						2    ,						3    ,						1   ).numpy()

							if output_type == "pil":
											UpperCamelCase_:				List[str]						=    self.numpy_to_pil(snake_case_   )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=snake_case_   )










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     lowercase	(    a     , a ):



		'''simple docstring'''




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

		return flax_key_tuple, flax_tensor


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




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

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

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

		return curr_real_layer_name, split_layer, content


def     lowercase	(    a     , a ):



		'''simple docstring'''




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


def     lowercase	(    a     , a     , a     , a     , a = WEIGHTS_NAME ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						convert_file_size_to_int(a )
		SCREAMING_SNAKE_CASE_				:int				     =						[]
		SCREAMING_SNAKE_CASE_				:str				     =						{}
		SCREAMING_SNAKE_CASE_				:List[str]				     =						0
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						0

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

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

		for key in all_layers.keys():
				# open tensorstore file
				SCREAMING_SNAKE_CASE_				:Any				     =						ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.tensor(a )
				SCREAMING_SNAKE_CASE_				:str				     =						raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

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

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						raw_weights.to(getattr(a     , a ) )
				current_block_size += weight_size
				total_size += weight_size

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

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

		with open(os.path.join(a     , a )     , "w"     , encoding="utf-8" ) as f:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						json.dumps(a     , indent=2     , sort_keys=a ) + "\n"
				f.write(a )

		return metadata, index


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


def     lowercase	(    ):



		'''simple docstring'''




		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						TaTokenizer.from_pretrained("t5-small" )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						tokenizer(a     , return_tensors="pt" ).input_ids
		SCREAMING_SNAKE_CASE_				:List[str]				     =						model.generate(a     , decoder_start_token_id=0 )
		print(tokenizer.decode(out[0] ) )






