





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase							:	Tuple			=logging.get_logger(__name__)

__lowerCAmelCase							:	str			={
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""switch_transformers"""
 __lowercase   		=				["""past_key_values"""]
 __lowercase   		=				{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
 def __init__(				self      :Tuple						,       lowercase_      :List[str]=3_21_28						,       lowercase_      :Optional[Any]=7_68						,       lowercase_      :int=64						,       lowercase_      :List[str]=20_48						,       lowercase_      :List[Any]=64						,       lowercase_      :Union[str, Any]=12						,       lowercase_      :List[str]=3						,       lowercase_      :Dict=12						,       lowercase_      :str=3						,       lowercase_      :Dict=12						,       lowercase_      :str=8						,       lowercase_      :Any=False						,       lowercase_      :Any=0.0_1						,       lowercase_      :str="float32"						,       lowercase_      :Dict=False						,       lowercase_      :Union[str, Any]=32						,       lowercase_      :Dict=1_28						,       lowercase_      :List[str]=0.1						,       lowercase_      :Any=1E-6						,       lowercase_      :str=0.0_0_1						,       lowercase_      :str=0.0_0_1						,       lowercase_      :Optional[int]=1.0						,       lowercase_      :int="relu"						,       lowercase_      :Union[str, Any]=True						,       lowercase_      :Any=False						,       lowercase_      :List[str]=True						,       lowercase_      :Any=0						,       lowercase_      :Dict=1						,       **lowercase_      :str						,       )->							int:
     A__				=			vocab_size
     A__				=			d_model
     A__				=			d_kv
     A__				=			d_ff

     A__				=			num_sparse_encoder_layers

     A__				=			num_layers
     A__				=			(
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     A__				=			num_sparse_decoder_layers

     # This tells us, each how many encoder layer we'll have to set a sparse layer.
     if self.num_sparse_encoder_layers > 0:
         A__				=			self.num_layers // self.num_sparse_encoder_layers
     else:
         A__				=			self.num_layers  # HACK: this will create 0 sparse layers

     # This tells us, each how many encoder layer we'll have to set a sparse layer.
     if self.num_sparse_decoder_layers > 0:
         A__				=			self.num_decoder_layers // self.num_sparse_decoder_layers
     else:
         A__				=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

     A__				=			num_heads
     A__				=			num_experts
     A__				=			expert_capacity
     A__				=			router_bias
     A__				=			router_jitter_noise
     if router_dtype not in ["float32", "float16", "bfloat16"]:
         raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"							)
     A__				=			router_dtype

     A__				=			router_ignore_padding_tokens
     A__				=			relative_attention_num_buckets
     A__				=			relative_attention_max_distance

     A__				=			dropout_rate
     A__				=			layer_norm_epsilon
     A__				=			initializer_factor
     A__				=			feed_forward_proj
     A__				=			use_cache
     A__				=			add_router_probs

     A__				=			router_z_loss_coef
     A__				=			router_aux_loss_coef

     A__				=			self.feed_forward_proj.split("-"							)
     A__				=			act_info[-1]
     A__				=			act_info[0] == "gated"

     if len(lowercase_							) > 1 and act_info[0] != "gated" or len(lowercase_							) > 2:
         raise ValueError(
             F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
             "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
             "'gated-gelu' or 'relu'"							)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
         A__				=			"gelu_new"

     super().__init__(
         pad_token_id=lowercase_						,       eos_token_id=lowercase_						,       is_encoder_decoder=lowercase_						,       **lowercase_						,       )










'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase							:	List[str]			={
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowerCAmelCase							:	Dict			={
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowerCAmelCase							:	Union[str, Any]			={
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowerCAmelCase							:	str			={
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Tuple			={
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Dict			={
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple       ):
    if isinstance(_lowerCamelCase						,       _lowerCamelCase       ):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("boolean value expected"       )





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Optional[int]=False       ):
    A__				=			checkpoint[F"{old_prefix}.in_layers.0.weight"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.0.bias"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.2.weight"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.2.bias"]
    A__				=			checkpoint[F"{old_prefix}.emb_layers.1.weight"]
    A__				=			checkpoint[F"{old_prefix}.emb_layers.1.bias"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.0.weight"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.0.bias"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.3.weight"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.3.bias"]

    if has_skip:
        A__				=			checkpoint[F"{old_prefix}.skip_connection.weight"]
        A__				=			checkpoint[F"{old_prefix}.skip_connection.bias"]

    return new_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					List[Any]=None       ):
    A__,	A__,	A__				=			checkpoint[F"{old_prefix}.qkv.weight"].chunk(3						,       dim=0       )
    A__,	A__,	A__				=			checkpoint[F"{old_prefix}.qkv.bias"].chunk(3						,       dim=0       )

    A__				=			checkpoint[F"{old_prefix}.norm.weight"]
    A__				=			checkpoint[F"{old_prefix}.norm.bias"]

    A__				=			weight_q.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_q.squeeze(-1       ).squeeze(-1       )
    A__				=			weight_k.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_k.squeeze(-1       ).squeeze(-1       )
    A__				=			weight_v.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_v.squeeze(-1       ).squeeze(-1       )

    A__				=			(
        checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1       ).squeeze(-1       )
    )
    A__				=			checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1       ).squeeze(-1       )

    return new_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					List[str]       ):
    A__				=			torch.load(_lowerCamelCase						,       map_location="cpu"       )
    A__				=			{}

    A__				=			checkpoint["time_embed.0.weight"]
    A__				=			checkpoint["time_embed.0.bias"]
    A__				=			checkpoint["time_embed.2.weight"]
    A__				=			checkpoint["time_embed.2.bias"]

    if unet_config["num_class_embeds"] is not None:
        A__				=			checkpoint["label_emb.weight"]

    A__				=			checkpoint["input_blocks.0.0.weight"]
    A__				=			checkpoint["input_blocks.0.0.bias"]

    A__				=			unet_config["down_block_types"]
    A__				=			unet_config["layers_per_block"]
    A__				=			unet_config["attention_head_dim"]
    A__				=			unet_config["block_out_channels"]
    A__				=			1
    A__				=			channels_list[0]

    for i, layer_type in enumerate(_lowerCamelCase       ):
        A__				=			channels_list[i]
        A__				=			current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(_lowerCamelCase       ):
                A__				=			F"down_blocks.{i}.resnets.{j}"
                A__				=			F"input_blocks.{current_layer}.0"
                A__				=			True if j == 0 and downsample_block_has_skip else False
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(_lowerCamelCase       ):
                A__				=			F"down_blocks.{i}.resnets.{j}"
                A__				=			F"input_blocks.{current_layer}.0"
                A__				=			True if j == 0 and downsample_block_has_skip else False
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                A__				=			F"down_blocks.{i}.attentions.{j}"
                A__				=			F"input_blocks.{current_layer}.1"
                A__				=			convert_attention(
                    _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                current_layer += 1

        if i != len(_lowerCamelCase       ) - 1:
            A__				=			F"down_blocks.{i}.downsamplers.0"
            A__				=			F"input_blocks.{current_layer}.0"
            A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
            current_layer += 1

        A__				=			current_channels

    # hardcoded the mid-block for now
    A__				=			"mid_block.resnets.0"
    A__				=			"middle_block.0"
    A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    A__				=			"mid_block.attentions.0"
    A__				=			"middle_block.1"
    A__				=			convert_attention(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    A__				=			"mid_block.resnets.1"
    A__				=			"middle_block.2"
    A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    A__				=			0
    A__				=			unet_config["up_block_types"]

    for i, layer_type in enumerate(_lowerCamelCase       ):
        if layer_type == "ResnetUpsampleBlock2D":
            for j in range(layers_per_block + 1       ):
                A__				=			F"up_blocks.{i}.resnets.{j}"
                A__				=			F"output_blocks.{current_layer}.0"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                current_layer += 1

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.1"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1       ):
                A__				=			F"up_blocks.{i}.resnets.{j}"
                A__				=			F"output_blocks.{current_layer}.0"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                A__				=			F"up_blocks.{i}.attentions.{j}"
                A__				=			F"output_blocks.{current_layer}.1"
                A__				=			convert_attention(
                    _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                current_layer += 1

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.2"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    A__				=			checkpoint["out.0.weight"]
    A__				=			checkpoint["out.0.bias"]
    A__				=			checkpoint["out.2.weight"]
    A__				=			checkpoint["out.2.bias"]

    return new_checkpoint


if __name__ == "__main__":
  __lowerCAmelCase							:	List[Any]			=argparse.ArgumentParser()

  parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
  parser.add_argument(
      "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
  )
  parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

  __lowerCAmelCase							:	Optional[Any]			=parser.parse_args()
  __lowerCAmelCase							:	List[Any]			=strabool(args.class_cond)

  __lowerCAmelCase							:	List[str]			=os.path.basename(args.unet_path)
  print(f"""Checkpoint: {ckpt_name}""")

  # Get U-Net config
  if "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	List[str]			=IMAGENET_64_UNET_CONFIG
  elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    __lowerCAmelCase							:	List[str]			=LSUN_256_UNET_CONFIG
  elif "test" in ckpt_name:
    __lowerCAmelCase							:	Any			=TEST_UNET_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  if not args.class_cond:
    __lowerCAmelCase							:	Dict			=None

  __lowerCAmelCase							:	Optional[int]			=con_pt_to_diffuser(args.unet_path, unet_config)

  __lowerCAmelCase							:	Dict			=UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    __lowerCAmelCase							:	List[str]			=CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	Dict			=CT_IMAGENET_64_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    __lowerCAmelCase							:	Dict			=CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  __lowerCAmelCase							:	Dict			=CMStochasticIterativeScheduler(**scheduler_config)

  __lowerCAmelCase							:	str			=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)



