






'''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__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			int						):
      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            lowerCamelCase__         =  flax_key_tuple[:-1] + ("""weight""",)
            lowerCamelCase__         =  torch.permute(__lowerCAmelCase   ,    (0, 2, 1)						)
      elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase						):
            # linear layer
            lowerCamelCase__         =  flax_key_tuple[:-1] + ("""weight""",)
            lowerCamelCase__         =  flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            lowerCamelCase__         =  flax_key_tuple[:-1] + ("""weight""",)

      return flax_key_tuple, flax_tensor
def 						A__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Union[str, Any]						):
      if "metadata" in layer:
            lowerCamelCase__         =  layer.split("""metadata"""						)
            lowerCamelCase__         =  """""".join(split_layer[0]						)[:-1]
            lowerCamelCase__         =  [tuple(("""metadata""" + split_layer[1]).split("""/"""						)						)]
      elif "kvstore" in layer:
            lowerCamelCase__         =  layer.split("""kvstore"""						)
            lowerCamelCase__         =  """""".join(split_layer[0]						)[:-1]
            lowerCamelCase__         =  [tuple(("""kvstore""" + split_layer[1]).split("""/"""						)						)]

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

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

      return curr_real_layer_name, split_layer, content
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple						):
      lowerCamelCase__         =  rename_keys(__lowerCAmelCase						)
      lowerCamelCase__         =  {}
      for k, v in current_block.items():
            lowerCamelCase__         =  v
      lowerCamelCase__         =  new_current_block
      torch.save(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Dict   ,    __lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			str = WEIGHTS_NAME						):
      lowerCamelCase__         =  convert_file_size_to_int(__lowerCAmelCase						)
      lowerCamelCase__         =  []
      lowerCamelCase__         =  {}
      lowerCamelCase__         =  0
      lowerCamelCase__         =  0

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

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

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

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

            lowerCamelCase__         =  raw_weights.to(getattr(__lowerCAmelCase   ,    __lowerCAmelCase						)						)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      lowerCamelCase__         =  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
      lowerCamelCase__         =  {}
      lowerCamelCase__         =  {}
      for idx, shard in enumerate(__lowerCAmelCase						):
            lowerCamelCase__         =  weights_name.replace(
                """.bin"""   ,    F'''-{idx+1:05d}-of-{len(__lowerCAmelCase						):05d}.bin'''						)  # len(sharded_state_dicts):05d}
            lowerCamelCase__         =  os.path.join(__lowerCAmelCase   ,    weights_name.replace(""".bin"""   ,    F'''-{idx+1:05d}-of-???.bin'''						)						)
            os.rename(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    __lowerCAmelCase						)						)
            lowerCamelCase__         =  shard
            for key in shard:
                  lowerCamelCase__         =  shard_file

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

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

      return metadata, index


if __name__ == "__main__":
       UpperCamelCase    :      List[Any]  			=		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    :      Dict  			=		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__			(				):
      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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









'''simple docstring'''







import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						if hor == 128:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""")

						elif hor == 32:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 64, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
						lowerCamelCase__         =  torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''						)
						lowerCamelCase__         =  model.state_dict()
						lowerCamelCase__         =  {
						    """down_block_types""": down_block_types,
						    """block_out_channels""": block_out_channels,
						    """up_block_types""": up_block_types,
						    """layers_per_block""": 1,
						    """use_timestep_embedding""": True,
						    """out_block_type""": """OutConv1DBlock""",
						    """norm_num_groups""": 8,
						    """downsample_each_block""": False,
						    """in_channels""": 14,
						    """out_channels""": 14,
						    """extra_in_channels""": 0,
						    """time_embedding_type""": """positional""",
						    """flip_sin_to_cos""": False,
						    """freq_shift""": 1,
						    """sample_size""": 6_5536,
						    """mid_block_type""": """MidResTemporalBlock1D""",
						    """act_fn""": """mish""",
						}
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)
						lowerCamelCase__         =  dict(zip(model.state_dict().keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)
						hf_value_function.load_state_dict(__lowerCAmelCase						)

						torch.save(hf_value_function.state_dict()   ,    F'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''						)
						with open(F'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''   ,    """w"""						) as f:
												json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				):
						lowerCamelCase__         =  {
						    """in_channels""": 14,
						    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
						    """up_block_types""": (),
						    """out_block_type""": """ValueFunction""",
						    """mid_block_type""": """ValueFunctionMidBlock1D""",
						    """block_out_channels""": (32, 64, 128, 256),
						    """layers_per_block""": 1,
						    """downsample_each_block""": True,
						    """sample_size""": 6_5536,
						    """out_channels""": 14,
						    """extra_in_channels""": 0,
						    """time_embedding_type""": """positional""",
						    """use_timestep_embedding""": True,
						    """flip_sin_to_cos""": False,
						    """freq_shift""": 1,
						    """norm_num_groups""": 8,
						    """act_fn""": """mish""",
						}

						lowerCamelCase__         =  torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
						lowerCamelCase__         =  model
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)

						lowerCamelCase__         =  dict(zip(state_dict.keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)

						hf_value_function.load_state_dict(__lowerCAmelCase						)

						torch.save(hf_value_function.state_dict()   ,    """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""						)
						with open("""hub/hopper-medium-v2/value_function/config.json"""   ,    """w"""						) as f:
												json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()

