






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

			return flax_key_tuple, flax_tensor

def 				snake_case      (   A__	,A__	,A__						):
			if "metadata" in layer:
						UpperCAmelCase_     : List[str]			     =					layer.split("metadata"						)
						UpperCAmelCase_     : Any			     =					"".join(split_layer[0]						)[:-1]
						UpperCAmelCase_     : Any			     =					[tuple(("metadata" + split_layer[1]).split("/"						)						)]
			elif "kvstore" in layer:
						UpperCAmelCase_     : int			     =					layer.split("kvstore"						)
						UpperCAmelCase_     : Any			     =					"".join(split_layer[0]						)[:-1]
						UpperCAmelCase_     : Tuple			     =					[tuple(("kvstore" + split_layer[1]).split("/"						)						)]

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

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

			return curr_real_layer_name, split_layer, content

def 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : str			     =					rename_keys(A__						)
			UpperCAmelCase_     : Any			     =					{}
			for k, v in current_block.items():
						UpperCAmelCase_     : Any			     =					v
			UpperCAmelCase_     : List[str]			     =					new_current_block
			torch.save(A__	,A__						)

def 				snake_case      (   A__	,A__	,A__	,A__	,A__ = WEIGHTS_NAME						):
			UpperCAmelCase_     : Dict			     =					convert_file_size_to_int(A__						)
			UpperCAmelCase_     : Tuple			     =					[]
			UpperCAmelCase_     : List[str]			     =					{}
			UpperCAmelCase_     : Dict			     =					0
			UpperCAmelCase_     : Any			     =					0

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

			UpperCAmelCase_     : Optional[int]			     =					{}
			for layer in checkpoint_info.keys():
						UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[int]			     =					get_key_and_tensorstore_dict(
						    A__	,A__	,A__						)
						if curr_real_layer_name in all_layers:
									UpperCAmelCase_     : Union[str, Any]			     =					content
						else:
									UpperCAmelCase_     : int			     =					{split_layer[-1]: content}

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

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

						UpperCAmelCase_     : Tuple			     =					raw_weights.to(getattr(A__	,A__						)						)
						current_block_size += weight_size
						total_size += weight_size

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

    # Add the metadata
			UpperCAmelCase_     : Dict			     =					{"total_size": total_size}
			UpperCAmelCase_     : Any			     =					{"metadata": metadata, "weight_map": weight_map}

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

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

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

			UpperCAmelCase_     : Dict			     =					TaTokenizer.from_pretrained("t5-small"						)
			UpperCAmelCase_     : 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>."

			UpperCAmelCase_     : Optional[Any]			     =					tokenizer(A__	,return_tensors="pt"						).input_ids
			UpperCAmelCase_     : Tuple			     =					model.generate(A__	,decoder_start_token_id=0						)
			print(tokenizer.decode(out[0]						)						)











"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



