



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE     				=						{
    "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",
}

SCREAMING_SNAKE_CASE     				=						{
    "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",
}

SCREAMING_SNAKE_CASE     				=						{
    "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",
}

SCREAMING_SNAKE_CASE     				=						{
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

SCREAMING_SNAKE_CASE     				=						{
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

SCREAMING_SNAKE_CASE     				=						{
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
				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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=False )		->							int:
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.in_layers.0.weight''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.in_layers.0.bias''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.in_layers.2.weight''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.in_layers.2.bias''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.out_layers.0.weight''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.out_layers.0.bias''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.out_layers.3.weight''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.out_layers.3.bias''']

		if has_skip:
				UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.skip_connection.weight''']
				UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.skip_connection.bias''']

		return new_checkpoint







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None )		->							Tuple:
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3			,     dim=0 )
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3			,     dim=0 )

		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.norm.weight''']
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.norm.bias''']

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

		UpperCAmelCase_						=				(
		    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
		)
		UpperCAmelCase_						=				checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )

		return new_checkpoint







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				torch.load(__SCREAMING_SNAKE_CASE			,     map_location="cpu" )
		UpperCAmelCase_						=				{}

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

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

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

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

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

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

				if i != len(__SCREAMING_SNAKE_CASE ) - 1:
						UpperCAmelCase_						=				f'''down_blocks.{i}.downsamplers.0'''
						UpperCAmelCase_						=				f'''input_blocks.{current_layer}.0'''
						UpperCAmelCase_						=				convert_resnet(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
						current_layer += 1

				UpperCAmelCase_						=				current_channels

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

		UpperCAmelCase_						=				0
		UpperCAmelCase_						=				unet_config["""up_block_types"""]

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

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

						if i != len(__SCREAMING_SNAKE_CASE ) - 1:
								UpperCAmelCase_						=				f'''up_blocks.{i}.upsamplers.0'''
								UpperCAmelCase_						=				f'''output_blocks.{current_layer-1}.2'''
								UpperCAmelCase_						=				convert_resnet(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

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

		return new_checkpoint


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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.")

			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			SCREAMING_SNAKE_CASE     				=						strabool(args.class_cond)

			SCREAMING_SNAKE_CASE     				=						os.path.basename(args.unet_path)
			print(f'''Checkpoint: {ckpt_name}''')

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

			if not args.class_cond:
						SCREAMING_SNAKE_CASE     				=						None

			SCREAMING_SNAKE_CASE     				=						con_pt_to_diffuser(args.unet_path, unet_config)

			SCREAMING_SNAKE_CASE     				=						UNetaDModel(**unet_config)
			image_unet.load_state_dict(converted_unet_ckpt)

			# Get scheduler config
			if "cd" in ckpt_name or "test" in ckpt_name:
						SCREAMING_SNAKE_CASE     				=						CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						SCREAMING_SNAKE_CASE     				=						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)):
						SCREAMING_SNAKE_CASE     				=						CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

			SCREAMING_SNAKE_CASE     				=						CMStochasticIterativeScheduler(**scheduler_config)

			SCREAMING_SNAKE_CASE     				=						ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE     				=						{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "distilbert-base-uncased": 512,
    "distilbert-base-uncased-distilled-squad": 512,
    "distilbert-base-cased": 512,
    "distilbert-base-cased-distilled-squad": 512,
    "distilbert-base-german-cased": 512,
    "distilbert-base-multilingual-cased": 512,
}


SCREAMING_SNAKE_CASE     				=						{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    List[str]									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    str									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    Union[str, Any]									=  PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_				:    int									=  ['input_ids', 'attention_mask']
					lowerCAmelCase_				:    str									=  DistilBertTokenizer



					def __init__(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True    ,	lowerCAmelCase="[UNK]"    ,	lowerCAmelCase="[SEP]"    ,	lowerCAmelCase="[PAD]"    ,	lowerCAmelCase="[CLS]"    ,	lowerCAmelCase="[MASK]"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(
							    lowerCAmelCase    ,	tokenizer_file=lowerCAmelCase    ,	do_lower_case=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	sep_token=lowerCAmelCase    ,	pad_token=lowerCAmelCase    ,	cls_token=lowerCAmelCase    ,	mask_token=lowerCAmelCase    ,	tokenize_chinese_chars=lowerCAmelCase    ,	strip_accents=lowerCAmelCase    ,	**lowerCAmelCase    ,	)

							UpperCAmelCase_						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
							if (
							    normalizer_state.get("lowercase"    ,	lowerCAmelCase     ) != do_lower_case
							    or normalizer_state.get("strip_accents"    ,	lowerCAmelCase     ) != strip_accents
							    or normalizer_state.get("handle_chinese_chars"    ,	lowerCAmelCase     ) != tokenize_chinese_chars
							):
									UpperCAmelCase_						=				getattr(lowerCAmelCase    ,	normalizer_state.pop("type"     )     )
									UpperCAmelCase_						=				do_lower_case
									UpperCAmelCase_						=				strip_accents
									UpperCAmelCase_						=				tokenize_chinese_chars
									UpperCAmelCase_						=				normalizer_class(**lowerCAmelCase     )

							UpperCAmelCase_						=				do_lower_case



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				[self.sep_token_id]
							UpperCAmelCase_						=				[self.cls_token_id]
							if token_ids_a is None:
									return len(cls + token_ids_a + sep     ) * [0]
							return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )

