



"""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 _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






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

      return flax_key_tuple, flax_tensor

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

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

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

      return curr_real_layer_name, split_layer, content

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case = WEIGHTS_NAME							):







      """simple docstring"""






      UpperCAmelCase          =	convert_file_size_to_int(_snake_case							)
      UpperCAmelCase          =	[]
      UpperCAmelCase          =	{}
      UpperCAmelCase          =	0
      UpperCAmelCase          =	0

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

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

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

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

            UpperCAmelCase          =	raw_weights.to(getattr(_snake_case				,	_snake_case							)							)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      UpperCAmelCase          =	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
      UpperCAmelCase          =	{}
      UpperCAmelCase          =	{}
      for idx, shard in enumerate(_snake_case							):
            UpperCAmelCase          =	weights_name.replace(
                """.bin"""				,	F'''-{idx+1:05d}-of-{len(_snake_case							):05d}.bin'''							)  # len(sharded_state_dicts):05d}
            UpperCAmelCase          =	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							)							)
            UpperCAmelCase          =	shard
            for key in shard:
                  UpperCAmelCase          =	shard_file

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

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

      return metadata, index


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






def _a			(		):







      """simple docstring"""






      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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











"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



