


'''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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				_UpperCAmelCase						:				Dict	    =					flax_key_tuple[:-1] + ('weight',)
				_UpperCAmelCase						:				Tuple	    =					torch.permute(_lowerCAmelCase					,       (0, 2, 1)      )
		elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase      ):
				# linear layer
				_UpperCAmelCase						:				Optional[Any]	    =					flax_key_tuple[:-1] + ('weight',)
				_UpperCAmelCase						:				List[str]	    =					flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				_UpperCAmelCase						:				List[Any]	    =					flax_key_tuple[:-1] + ('weight',)

		return flax_key_tuple, flax_tensor






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

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

		if "kvstore/path" in layer:
				_UpperCAmelCase						:				Dict	    =					F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
		elif "kvstore/driver" in layer:
				_UpperCAmelCase						:				Any	    =					'file'
		else:
				_UpperCAmelCase						:				Any	    =					checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Optional[int]	    =					rename_keys(_lowerCAmelCase      )
		_UpperCAmelCase						:				str	    =					{}
		for k, v in current_block.items():
				_UpperCAmelCase						:				Union[str, Any]	    =					v
		_UpperCAmelCase						:				Union[str, Any]	    =					new_current_block
		torch.save(_lowerCAmelCase					,       _lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase = WEIGHTS_NAME      ):
		_UpperCAmelCase						:				List[Any]	    =					convert_file_size_to_int(_lowerCAmelCase      )
		_UpperCAmelCase						:				Dict	    =					[]
		_UpperCAmelCase						:				List[Any]	    =					{}
		_UpperCAmelCase						:				Union[str, Any]	    =					0
		_UpperCAmelCase						:				Optional[int]	    =					0

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

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

		for key in all_layers.keys():
				# open tensorstore file
				_UpperCAmelCase						:				int	    =					ts.open(unflatten_dict(all_layers[key]      )      ).result().read().result()
				_UpperCAmelCase						:				int	    =					torch.tensor(_lowerCAmelCase      )
				_UpperCAmelCase						:				Union[str, Any]	    =					raw_weights.numel() * dtype_byte_size(raw_weights.dtype      )

				# use the renaming pattern from the small conversion scripts
				_UpperCAmelCase						:				Union[str, Any]	    =					rename_base_flax_keys(tuple(key.split("/"      )      )					,       _lowerCAmelCase      )
				_UpperCAmelCase						:				int	    =					'/'.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:
						_UpperCAmelCase						:				Optional[int]	    =					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
						_UpperCAmelCase						:				int	    =					{}
						_UpperCAmelCase						:				int	    =					0

				_UpperCAmelCase						:				Optional[Any]	    =					raw_weights.to(getattr(_lowerCAmelCase					,       _lowerCAmelCase      )      )
				current_block_size += weight_size
				total_size += weight_size

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

				# Add the metadata
		_UpperCAmelCase						:				List[Any]	    =					{'total_size': total_size}
		_UpperCAmelCase						:				Union[str, Any]	    =					{'metadata': metadata, 'weight_map': weight_map}

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

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

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

		_UpperCAmelCase						:				List[str]	    =					TaTokenizer.from_pretrained("t5-small"      )
		_UpperCAmelCase						:				Dict	    =					'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[int]	    =					tokenizer(_lowerCAmelCase					,       return_tensors="pt"      ).input_ids
		_UpperCAmelCase						:				str	    =					model.generate(_lowerCAmelCase					,       decoder_start_token_id=0      )
		print(tokenizer.decode(out[0]      )      )








'''simple docstring'''





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_UpperCamelCase											=						8

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Any:
  __lowerCamelCase       :       List[str]           = x.device

  __lowerCamelCase       :       int           = (x * 255).int().clamp(0       ,255       )

  __lowerCamelCase       :       Any           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       )
  __lowerCamelCase       :       List[str]           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Optional[Any]           = rearrange(_lowerCAmelCase       ,'b c h w -> b c 1 h w'       )

  __lowerCamelCase       :       Optional[Any]           = ((x & mask) != 0).float()
  __lowerCamelCase       :       Tuple           = rearrange(_lowerCAmelCase       ,'b c d h w -> b (c d) h w'       )
  __lowerCamelCase       :       Union[str, Any]           = bits * 2 - 1
  return bits

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Optional[Any]:
  __lowerCamelCase       :       int           = x.device

  __lowerCamelCase       :       int           = (x > 0).int()
  __lowerCamelCase       :       List[str]           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       ,dtype=torch.intaa       )

  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'b (c d) h w -> b c d h w'       ,d=8       )
  __lowerCamelCase       :       List[str]           = reduce(x * mask       ,'b c d h w -> b c h w'       ,'sum'       )
  return (dec / 255).clamp(0.0       ,1.0       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 0.0       ,_lowerCAmelCase = True       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDIMSchedulerOutput, Tuple]:
  if self.num_inference_steps is None:
    raise ValueError(
        'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'       )

  # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  # Ideally, read DDIM paper in-detail understanding

  # Notation (<variable name> -> <name in paper>
  # - pred_noise_t -> e_theta(x_t, t)
  # - pred_original_sample -> f_theta(x_t, t) or x_0
  # - std_dev_t -> sigma_t
  # - eta -> η
  # - pred_sample_direction -> "direction pointing to x_t"
  # - pred_prev_sample -> "x_t-1"

  # 1. get previous step value (=t-1)
  __lowerCamelCase       :       Optional[Any]           = timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  __lowerCamelCase       :       str           = self.alphas_cumprod[timestep]
  __lowerCamelCase       :       Optional[Any]           = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  __lowerCamelCase       :       Union[str, Any]           = 1 - alpha_prod_t

  # 3. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Optional[int]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  __lowerCamelCase       :       Dict           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Optional[Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 5. compute variance: "sigma_t(η)" -> see formula (16)
  # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  __lowerCamelCase       :       int           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Tuple           = eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    __lowerCamelCase       :       List[str]           = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

  # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Union[str, Any]           = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

  # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Any           = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

  if eta > 0:
    # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
    __lowerCamelCase       :       Dict           = model_output.device if torch.is_tensor(_lowerCAmelCase       ) else 'cpu'
    __lowerCamelCase       :       Dict           = torch.randn(model_output.shape       ,dtype=model_output.dtype       ,generator=_lowerCAmelCase       ).to(_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       ) ** 0.5 * eta * noise

    __lowerCamelCase       :       int           = prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase="epsilon"       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDPMSchedulerOutput, Tuple]:
  __lowerCamelCase       :       Dict           = timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = torch.split(_lowerCAmelCase       ,sample.shape[1]       ,dim=1       )
  else:
    __lowerCamelCase       :       Optional[Any]           = None

  # 1. compute alphas, betas
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t]
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t - 1] if t > 0 else self.one
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t_prev

  # 2. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  if prediction_type == "epsilon":
    __lowerCamelCase       :       List[Any]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    __lowerCamelCase       :       Any           = model_output
  else:
    raise ValueError(F'Unsupported prediction_type {prediction_type}.'       )

  # 3. Clip "predicted x_0"
  __lowerCamelCase       :       Optional[Any]           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __lowerCamelCase       :       Union[str, Any]           = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  __lowerCamelCase       :       Dict           = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

  # 5. Compute predicted previous sample µ_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __lowerCamelCase       :       Dict           = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  __lowerCamelCase       :       Optional[Any]           = 0
  if t > 0:
    __lowerCamelCase       :       Any           = torch.randn(
        model_output.size()       ,dtype=model_output.dtype       ,layout=model_output.layout       ,generator=_lowerCAmelCase       ).to(model_output.device       )
    __lowerCamelCase       :       Optional[Any]           = (self._get_variance(_lowerCAmelCase       ,predicted_variance=_lowerCAmelCase       ) ** 0.5) * noise

  __lowerCamelCase       :       str           = pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	List[str]			,	_a      :	UNetaDConditionModel			,	_a      :	Union[DDIMScheduler, DDPMScheduler]			,	_a      :	Optional[float] = 1.0			,	)					->					Union[str, Any]:
    super().__init__()
    __lowerCamelCase       :       Dict           = bit_scale
    __lowerCamelCase       :       List[str]           = (
        ddim_bit_scheduler_step if isinstance(_a			,	_a   ) else ddpm_bit_scheduler_step
    )

    self.register_modules(unet=_a			,	scheduler=_a   )




  @torch.no_grad()
  def __call__( self      :	Tuple			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 50			,	_a      :	Optional[torch.Generator] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	**_a      :	Dict			,	)					->					Union[Tuple, ImagePipelineOutput]:
    __lowerCamelCase       :       str           = torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)			,	generator=_a			,	)
    __lowerCamelCase       :       int           = decimal_to_bits(_a   ) * self.bit_scale
    __lowerCamelCase       :       Dict           = latents.to(self.device   )

    self.scheduler.set_timesteps(_a   )

    for t in self.progress_bar(self.scheduler.timesteps   ):
      # predict the noise residual
      __lowerCamelCase       :       int           = self.unet(_a			,	_a   ).sample

      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a   ).prev_sample

    __lowerCamelCase       :       Dict           = bits_to_decimal(_a   )

    if output_type == "pil":
      __lowerCamelCase       :       int           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






