

import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowerCAmelCase__								=		logging.get_logger(__name__)


lowerCAmelCase__								=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
lowerCAmelCase__								=		{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__								=		{
    'allenai/led-base-16384': 1_6_3_8_4,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 					lowerCamelCase_     (							)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						(
	    list(range(ord('!'	)							,       ord('~'	) + 1	)	) + list(range(ord('¡'	)							,       ord('¬'	) + 1	)	) + list(range(ord('®'	)							,       ord('ÿ'	) + 1	)	)
	)
	_UpperCamelCase   :		int			=						bs[:]
	_UpperCamelCase   :		Optional[Any]			=						0
	for b in range(2**8	):
		if b not in bs:
			bs.append(lowerCAmelCase_	)
			cs.append(2**8 + n	)
			n += 1
	_UpperCamelCase   :		int			=						[chr(lowerCAmelCase_	) for n in cs]
	return dict(zip(lowerCAmelCase_							,       lowerCAmelCase_	)	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						set()
	_UpperCamelCase   :		Tuple			=						word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)	)
		_UpperCamelCase   :		Tuple			=						char
	return pairs







class 	lowercase    (				a__							):


		"""simple docstring"""




		a__					       =  VOCAB_FILES_NAMES
		a__					       =  PRETRAINED_VOCAB_FILES_MAP
		a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__					       =  ["""input_ids""", """attention_mask"""]


		def __init__(				self	,       __snake_case	,       __snake_case	,       __snake_case="replace"	,       __snake_case="<s>"	,       __snake_case="</s>"	,       __snake_case="</s>"	,       __snake_case="<s>"	,       __snake_case="<unk>"	,       __snake_case="<pad>"	,       __snake_case="<mask>"	,       __snake_case=False	,       **__snake_case	,       ):
			_UpperCamelCase   :		Tuple			=						AddedToken(lowercase__	,       lstrip=lowercase__	,       rstrip=lowercase__) if isinstance(lowercase__	,       lowercase__) else bos_token
			_UpperCamelCase   :		Dict			=						AddedToken(lowercase__	,       lstrip=lowercase__	,       rstrip=lowercase__) if isinstance(lowercase__	,       lowercase__) else eos_token
			_UpperCamelCase   :		Union[str, Any]			=						AddedToken(lowercase__	,       lstrip=lowercase__	,       rstrip=lowercase__) if isinstance(lowercase__	,       lowercase__) else sep_token
			_UpperCamelCase   :		Optional[int]			=						AddedToken(lowercase__	,       lstrip=lowercase__	,       rstrip=lowercase__) if isinstance(lowercase__	,       lowercase__) else cls_token
			_UpperCamelCase   :		Tuple			=						AddedToken(lowercase__	,       lstrip=lowercase__	,       rstrip=lowercase__) if isinstance(lowercase__	,       lowercase__) else unk_token
			_UpperCamelCase   :		Any			=						AddedToken(lowercase__	,       lstrip=lowercase__	,       rstrip=lowercase__) if isinstance(lowercase__	,       lowercase__) else pad_token

			# Mask token behave like a normal word, i.e. include the space before it
			_UpperCamelCase   :		str			=						AddedToken(lowercase__	,       lstrip=lowercase__	,       rstrip=lowercase__) if isinstance(lowercase__	,       lowercase__) else mask_token

			super().__init__(
			    errors=lowercase__	,       bos_token=lowercase__	,       eos_token=lowercase__	,       unk_token=lowercase__	,       sep_token=lowercase__	,       cls_token=lowercase__	,       pad_token=lowercase__	,       mask_token=lowercase__	,       add_prefix_space=lowercase__	,       **lowercase__	,       )

			with open(lowercase__	,       encoding='utf-8') as vocab_handle:
				_UpperCamelCase   :		List[str]			=						json.load(lowercase__)
			_UpperCamelCase   :		Any			=						{v: k for k, v in self.encoder.items()}
			_UpperCamelCase   :		Dict			=						errors  # how to handle errors in decoding
			_UpperCamelCase   :		Optional[int]			=						bytes_to_unicode()
			_UpperCamelCase   :		Tuple			=						{v: k for k, v in self.byte_encoder.items()}
			with open(lowercase__	,       encoding='utf-8') as merges_handle:
				_UpperCamelCase   :		Tuple			=						merges_handle.read().split('\n')[1:-1]
			_UpperCamelCase   :		Tuple			=						[tuple(merge.split()) for merge in bpe_merges]
			_UpperCamelCase   :		int			=						dict(zip(lowercase__	,       range(len(lowercase__))))
			_UpperCamelCase   :		List[Any]			=						{}
			_UpperCamelCase   :		Tuple			=						add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			_UpperCamelCase   :		Optional[int]			=						re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')


		@property
		# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
		def 		A__					(				self):
			return len(self.encoder)


		def 		A__					(				self):
			return dict(self.encoder	,       **self.added_tokens_encoder)


		def 		A__					(				self	,       __snake_case):





			if token in self.cache:
				return self.cache[token]
			_UpperCamelCase   :		Optional[int]			=						tuple(lowercase__)
			_UpperCamelCase   :		List[str]			=						get_pairs(lowercase__)

			if not pairs:
				return token

			while True:
				_UpperCamelCase   :		Dict			=						min(lowercase__	,       key=lambda __snake_case: self.bpe_ranks.get(lowercase__	,       float('inf')))
				if bigram not in self.bpe_ranks:
					break
				_UpperCamelCase   :		Any			=						bigram
				_UpperCamelCase   :		Union[str, Any]			=						[]
				_UpperCamelCase   :		Tuple			=						0
				while i < len(lowercase__):
					try:
						_UpperCamelCase   :		Union[str, Any]			=						word.index(lowercase__	,       lowercase__)
					except ValueError:
						new_word.extend(word[i:])
						break
					else:
						new_word.extend(word[i:j])
						_UpperCamelCase   :		Union[str, Any]			=						j

					if word[i] == first and i < len(lowercase__) - 1 and word[i + 1] == second:
						new_word.append(first + second)
						i += 2
					else:
						new_word.append(word[i])
						i += 1
				_UpperCamelCase   :		str			=						tuple(lowercase__)
				_UpperCamelCase   :		Any			=						new_word
				if len(lowercase__) == 1:
					break
				else:
					_UpperCamelCase   :		str			=						get_pairs(lowercase__)
			_UpperCamelCase   :		Union[str, Any]			=						''' '''.join(lowercase__)
			_UpperCamelCase   :		Optional[int]			=						word
			return word


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		List[str]			=						[]
			for token in re.findall(self.pat	,       lowercase__):
				_UpperCamelCase   :		Optional[int]			=						''''''.join(
				    self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
				bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__).split(' '))
			return bpe_tokens


		def 		A__					(				self	,       __snake_case):
			return self.encoder.get(lowercase__	,       self.encoder.get(self.unk_token))


		def 		A__					(				self	,       __snake_case):
			return self.decoder.get(lowercase__)


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Any			=						''''''.join(lowercase__)
			_UpperCamelCase   :		List[Any]			=						bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'	,       errors=self.errors)
			return text


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):





			if not os.path.isdir(lowercase__):
				logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
				return
			_UpperCamelCase   :		Tuple			=						os.path.join(
			    lowercase__	,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
			_UpperCamelCase   :		List[str]			=						os.path.join(
			    lowercase__	,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

			with open(lowercase__	,       'w'	,       encoding='utf-8') as f:
				f.write(json.dumps(self.encoder	,       indent=2	,       sort_keys=lowercase__	,       ensure_ascii=lowercase__) + '\n')

			_UpperCamelCase   :		Dict			=						0
			with open(lowercase__	,       'w'	,       encoding='utf-8') as writer:
				writer.write('#version: 0.2\n')
				for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,       key=lambda __snake_case: kv[1]):
					if index != token_index:
						logger.warning(
						    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
						    ' Please check that the tokenizer is not corrupted!')
						_UpperCamelCase   :		Optional[Any]			=						token_index
					writer.write(' '.join(lowercase__) + '\n')
					index += 1

			return vocab_file, merge_file


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):





			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			_UpperCamelCase   :		str			=						[self.cls_token_id]
			_UpperCamelCase   :		Tuple			=						[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep


		def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = False):





			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=lowercase__	,       token_ids_a=lowercase__	,       already_has_special_tokens=lowercase__)

			if token_ids_a is None:
				return [1] + ([0] * len(lowercase__)) + [1]
			return [1] + ([0] * len(lowercase__)) + [1, 1] + ([0] * len(lowercase__)) + [1]


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Optional[Any]			=						[self.sep_token_id]
			_UpperCamelCase   :		int			=						[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 + sep + token_ids_a + sep) * [0]


		def 		A__					(				self	,       __snake_case	,       __snake_case=False	,       **__snake_case):
			_UpperCamelCase   :		Union[str, Any]			=						kwargs.pop('add_prefix_space'	,       self.add_prefix_space)
			if (is_split_into_words or add_prefix_space) and (len(lowercase__) > 0 and not text[0].isspace()):
				_UpperCamelCase   :		Dict			=						''' ''' + text
			return (text, kwargs)






		def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = PaddingStrategy.DO_NOT_PAD	,       __snake_case = None	,       __snake_case = None	,       ):
			_UpperCamelCase   :		int			=						super()._pad(
			    encoded_inputs=lowercase__	,       max_length=lowercase__	,       padding_strategy=lowercase__	,       pad_to_multiple_of=lowercase__	,       return_attention_mask=lowercase__	,       )

			# Load from model defaults
			if return_attention_mask is None:
				_UpperCamelCase   :		Optional[Any]			=						'''attention_mask''' in self.model_input_names

			if return_attention_mask and "global_attention_mask" in encoded_inputs:
				_UpperCamelCase   :		Optional[Any]			=						encoded_inputs[self.model_input_names[0]]
				# `global_attention_mask` need to have the same length as other (sequential) inputs.
				_UpperCamelCase   :		Dict			=						len(encoded_inputs['global_attention_mask']) != len(lowercase__)

				if needs_to_be_padded:
					_UpperCamelCase   :		Any			=						len(lowercase__) - len(encoded_inputs['global_attention_mask'])

					if self.padding_side == "right":
						# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
						_UpperCamelCase   :		Any			=						(
						    encoded_inputs['''global_attention_mask'''] + [-1] * difference
						)
					elif self.padding_side == "left":
						_UpperCamelCase   :		Optional[Any]			=						[-1] * difference + encoded_inputs[
						    '''global_attention_mask'''
						]
					else:
						raise ValueError('Invalid padding strategy:' + str(self.padding_side))

			return encoded_inputs









import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__								=		{
    """sample_size""": 3_2,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [3_2, 6_4],
    """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__								=		{
    """sample_size""": 6_4,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    """attention_head_dim""": 6_4,
    """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__								=		{
    """sample_size""": 2_5_6,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    """attention_head_dim""": 6_4,
    """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__								=		{
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

lowerCAmelCase__								=		{
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

lowerCAmelCase__								=		{
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				List[str]:




	'''simple docstring'''




	if isinstance(UpperCAmelCase_							,       UpperCAmelCase_	):
		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 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Union[str, Any]=False	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						checkpoint[F'''{old_prefix}.in_layers.0.weight''']
	_UpperCamelCase   :		str			=						checkpoint[F'''{old_prefix}.in_layers.0.bias''']
	_UpperCamelCase   :		str			=						checkpoint[F'''{old_prefix}.in_layers.2.weight''']
	_UpperCamelCase   :		Union[str, Any]			=						checkpoint[F'''{old_prefix}.in_layers.2.bias''']
	_UpperCamelCase   :		Dict			=						checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
	_UpperCamelCase   :		Optional[int]			=						checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
	_UpperCamelCase   :		Tuple			=						checkpoint[F'''{old_prefix}.out_layers.0.weight''']
	_UpperCamelCase   :		List[Any]			=						checkpoint[F'''{old_prefix}.out_layers.0.bias''']
	_UpperCamelCase   :		Optional[Any]			=						checkpoint[F'''{old_prefix}.out_layers.3.weight''']
	_UpperCamelCase   :		Union[str, Any]			=						checkpoint[F'''{old_prefix}.out_layers.3.bias''']

	if has_skip:
		_UpperCamelCase   :		Tuple			=						checkpoint[F'''{old_prefix}.skip_connection.weight''']
		_UpperCamelCase   :		Optional[int]			=						checkpoint[F'''{old_prefix}.skip_connection.bias''']

	return new_checkpoint






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Any=None	)						->				int:




	'''simple docstring'''




	_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3							,       dim=0	)
	_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3							,       dim=0	)

	_UpperCamelCase   :		Dict			=						checkpoint[F'''{old_prefix}.norm.weight''']
	_UpperCamelCase   :		Optional[int]			=						checkpoint[F'''{old_prefix}.norm.bias''']

	_UpperCamelCase   :		List[str]			=						weight_q.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		Dict			=						bias_q.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		Any			=						weight_k.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		List[Any]			=						bias_k.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		Dict			=						weight_v.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		Tuple			=						bias_v.squeeze(-1	).squeeze(-1	)

	_UpperCamelCase   :		Optional[Any]			=						(
	    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1	).squeeze(-1	)
	)
	_UpperCamelCase   :		Dict			=						checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1	).squeeze(-1	)

	return new_checkpoint






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Optional[int]	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		Union[str, Any]			=						{}

	_UpperCamelCase   :		Optional[int]			=						checkpoint['time_embed.0.weight']
	_UpperCamelCase   :		List[Any]			=						checkpoint['time_embed.0.bias']
	_UpperCamelCase   :		Dict			=						checkpoint['time_embed.2.weight']
	_UpperCamelCase   :		Optional[Any]			=						checkpoint['time_embed.2.bias']

	if unet_config["num_class_embeds"] is not None:
		_UpperCamelCase   :		List[str]			=						checkpoint['label_emb.weight']

	_UpperCamelCase   :		Optional[int]			=						checkpoint['input_blocks.0.0.weight']
	_UpperCamelCase   :		Union[str, Any]			=						checkpoint['input_blocks.0.0.bias']

	_UpperCamelCase   :		Optional[int]			=						unet_config['down_block_types']
	_UpperCamelCase   :		Optional[Any]			=						unet_config['layers_per_block']
	_UpperCamelCase   :		Dict			=						unet_config['attention_head_dim']
	_UpperCamelCase   :		List[str]			=						unet_config['block_out_channels']
	_UpperCamelCase   :		str			=						1
	_UpperCamelCase   :		Optional[int]			=						channels_list[0]

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

		elif layer_type == "AttnDownBlock2D":
			for j in range(UpperCAmelCase_	):
				_UpperCamelCase   :		List[str]			=						F'''down_blocks.{i}.resnets.{j}'''
				_UpperCamelCase   :		str			=						F'''input_blocks.{current_layer}.0'''
				_UpperCamelCase   :		int			=						True if j == 0 and downsample_block_has_skip else False
				_UpperCamelCase   :		Any			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       has_skip=UpperCAmelCase_	)
				_UpperCamelCase   :		Dict			=						F'''down_blocks.{i}.attentions.{j}'''
				_UpperCamelCase   :		Optional[int]			=						F'''input_blocks.{current_layer}.1'''
				_UpperCamelCase   :		Dict			=						convert_attention(
				    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
				current_layer += 1

		if i != len(UpperCAmelCase_	) - 1:
			_UpperCamelCase   :		int			=						F'''down_blocks.{i}.downsamplers.0'''
			_UpperCamelCase   :		Optional[int]			=						F'''input_blocks.{current_layer}.0'''
			_UpperCamelCase   :		List[Any]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
			current_layer += 1

		_UpperCamelCase   :		Tuple			=						current_channels

	# hardcoded the mid-block for now
	_UpperCamelCase   :		Any			=						'mid_block.resnets.0'
	_UpperCamelCase   :		Optional[Any]			=						'middle_block.0'
	_UpperCamelCase   :		Tuple			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[Any]			=						'mid_block.attentions.0'
	_UpperCamelCase   :		Tuple			=						'middle_block.1'
	_UpperCamelCase   :		Union[str, Any]			=						convert_attention(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	_UpperCamelCase   :		Tuple			=						'mid_block.resnets.1'
	_UpperCamelCase   :		str			=						'middle_block.2'
	_UpperCamelCase   :		List[str]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	_UpperCamelCase   :		List[Any]			=						0
	_UpperCamelCase   :		Optional[int]			=						unet_config['up_block_types']

	for i, layer_type in enumerate(UpperCAmelCase_	):
		if layer_type == "ResnetUpsampleBlock2D":
			for j in range(layers_per_block + 1	):
				_UpperCamelCase   :		Optional[Any]			=						F'''up_blocks.{i}.resnets.{j}'''
				_UpperCamelCase   :		Optional[int]			=						F'''output_blocks.{current_layer}.0'''
				_UpperCamelCase   :		str			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       has_skip=UpperCAmelCase_	)
				current_layer += 1

			if i != len(UpperCAmelCase_	) - 1:
				_UpperCamelCase   :		List[Any]			=						F'''up_blocks.{i}.upsamplers.0'''
				_UpperCamelCase   :		Dict			=						F'''output_blocks.{current_layer-1}.1'''
				_UpperCamelCase   :		Optional[int]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
		elif layer_type == "AttnUpBlock2D":
			for j in range(layers_per_block + 1	):
				_UpperCamelCase   :		str			=						F'''up_blocks.{i}.resnets.{j}'''
				_UpperCamelCase   :		Union[str, Any]			=						F'''output_blocks.{current_layer}.0'''
				_UpperCamelCase   :		Optional[int]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       has_skip=UpperCAmelCase_	)
				_UpperCamelCase   :		int			=						F'''up_blocks.{i}.attentions.{j}'''
				_UpperCamelCase   :		List[Any]			=						F'''output_blocks.{current_layer}.1'''
				_UpperCamelCase   :		Optional[int]			=						convert_attention(
				    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
				current_layer += 1

			if i != len(UpperCAmelCase_	) - 1:
				_UpperCamelCase   :		List[Any]			=						F'''up_blocks.{i}.upsamplers.0'''
				_UpperCamelCase   :		Union[str, Any]			=						F'''output_blocks.{current_layer-1}.2'''
				_UpperCamelCase   :		List[str]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	_UpperCamelCase   :		List[Any]			=						checkpoint['out.0.weight']
	_UpperCamelCase   :		str			=						checkpoint['out.0.bias']
	_UpperCamelCase   :		int			=						checkpoint['out.2.weight']
	_UpperCamelCase   :		List[Any]			=						checkpoint['out.2.bias']

	return new_checkpoint


if __name__ == "__main__":
							lowerCAmelCase__								=		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__								=		parser.parse_args()
							lowerCAmelCase__								=		strabool(args.class_cond)

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

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

							if not args.class_cond:
														lowerCAmelCase__								=		None

							lowerCAmelCase__								=		con_pt_to_diffuser(args.unet_path, unet_config)

							lowerCAmelCase__								=		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__								=		CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														lowerCAmelCase__								=		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__								=		CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

							lowerCAmelCase__								=		CMStochasticIterativeScheduler(**scheduler_config)

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




