






import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
							from ...tokenization_utils_base import TextInput
from ...utils import logging


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			{'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE__				     =			{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
SCREAMING_SNAKE_CASE__				     =			{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

SCREAMING_SNAKE_CASE__				     =			'''▁'''






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       int							=							PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       int							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       Tuple							=							['''input_ids''', '''attention_mask''']

		def __init__(					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Dict="</s>"    ,       SCREAMING_SNAKE_CASE__		:    List[Any]="<unk>"    ,       SCREAMING_SNAKE_CASE__		:    List[Any]="<pad>"    ,       SCREAMING_SNAKE_CASE__		:    List[str]=1_0_0    ,       SCREAMING_SNAKE_CASE__		:    Tuple=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Dict[str, Any]] = None    ,       SCREAMING_SNAKE_CASE__		:    List[str]=True    ,       **SCREAMING_SNAKE_CASE__		:    str    ,       ):



					'''simple docstring'''



					if extra_ids > 0 and additional_special_tokens is None:
								__a						:  int									=     [f'''<extra_id_{i}>''' for i in range(SCREAMING_SNAKE_CASE__    )]
					elif extra_ids > 0 and additional_special_tokens is not None:
								# Check that we have the right number of extra_id special tokens
								__a						:  List[Any]									=     len(set(filter(lambda SCREAMING_SNAKE_CASE__    : bool('extra_id' in str(SCREAMING_SNAKE_CASE__    )    )    ,       SCREAMING_SNAKE_CASE__    )    )    )
								if extra_tokens != extra_ids:
											raise ValueError(
											    f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
											    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
											    ' tokens'    )
					if legacy:
								logger.warning_once(
								    f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
								    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'    )

					__a						:  Union[str, Any]									=     legacy
					__a						:  List[Any]									=     {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    eos_token=SCREAMING_SNAKE_CASE__    ,       unk_token=SCREAMING_SNAKE_CASE__    ,       pad_token=SCREAMING_SNAKE_CASE__    ,       extra_ids=SCREAMING_SNAKE_CASE__    ,       additional_special_tokens=SCREAMING_SNAKE_CASE__    ,       sp_model_kwargs=self.sp_model_kwargs    ,       legacy=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    ,       )

					__a						:  Optional[int]									=     vocab_file
					__a						:  List[Any]									=     extra_ids

					__a						:  Union[str, Any]									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(SCREAMING_SNAKE_CASE__    )

		@staticmethod
		def    __lowerCAmelCase  (					SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    int    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ):



					'''simple docstring'''



					if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
								__a						:  Union[str, Any]									=     TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
								if init_max_model_length is not None and init_max_model_length != max_model_length:
											return init_max_model_length
								elif init_max_model_length is None:
											warnings.warn(
											    'This tokenizer was incorrectly instantiated with a model max length of'
											    f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
											    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
											    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
											    f''' {pretrained_model_name_or_path} automatically truncating your input to'''
											    f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
											    f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
											    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
											    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'    ,       SCREAMING_SNAKE_CASE__    ,       )

					return max_model_length

		@property
		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					return self.sp_model.get_piece_size() + self._extra_ids

		def    __lowerCAmelCase  (					self		:    Any    ):



					'''simple docstring'''



					__a						:  List[Any]									=     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    ): i for i in range(self.vocab_size    )}
					vocab.update(self.added_tokens_encoder    )
					return vocab

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ,       SCREAMING_SNAKE_CASE__		:    bool = False    ):



					'''simple docstring'''



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

					# normal case: some special tokens
					if token_ids_a is None:
								return ([0] * len(SCREAMING_SNAKE_CASE__    )) + [1]
					return ([0] * len(SCREAMING_SNAKE_CASE__    )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__    )) + [1]

		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					return list(
					    set(filter(lambda SCREAMING_SNAKE_CASE__    : bool(re.search(r'<extra_id_\d+>'    ,       SCREAMING_SNAKE_CASE__    )    ) is not None    ,       self.additional_special_tokens    )    )    )

		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					return [self._convert_token_to_id(SCREAMING_SNAKE_CASE__    ) for token in self.get_sentinel_tokens()]

		def    __lowerCAmelCase  (					self		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ):



					'''simple docstring'''



					if len(SCREAMING_SNAKE_CASE__    ) > 0 and token_ids[-1] == self.eos_token_id:
								warnings.warn(
								    f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
								    ' eos tokens being added.'    )
								return token_ids
					else:
								return token_ids + [self.eos_token_id]

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ):



					'''simple docstring'''



					__a						:  str									=     [self.eos_token_id]

					if token_ids_a is None:
								return len(token_ids_a + eos    ) * [0]
					return len(token_ids_a + eos + token_ids_a + eos    ) * [0]

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ):



					'''simple docstring'''



					__a						:  str									=     self._add_eos_if_not_present(SCREAMING_SNAKE_CASE__    )
					if token_ids_a is None:
								return token_ids_a
					else:
								__a						:  int									=     self._add_eos_if_not_present(SCREAMING_SNAKE_CASE__    )
								return token_ids_a + token_ids_a

		def __getstate__(					self		:    str    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     self.__dict__.copy()
					__a						:  Union[str, Any]									=     None
					return state

		def __setstate__(					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  Optional[int]									=     d

					# for backward compatibility
					if not hasattr(self    ,       'sp_model_kwargs'    ):
								__a						:  Dict									=     {}

					__a						:  Any									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.vocab_file    )

		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    "TextInput"    ,       **SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					if not self.legacy:
								__a						:  Tuple									=     SPIECE_UNDERLINE + text.replace(SCREAMING_SNAKE_CASE__    ,       ' '    )
					return super().tokenize(SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       **SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



					if not self.legacy:
								__a						:  Optional[int]									=     text.startswith(SCREAMING_SNAKE_CASE__    )
								if is_first:
											__a						:  List[Any]									=     text[1:]

					__a						:  List[Any]									=     self.sp_model.encode(SCREAMING_SNAKE_CASE__    ,       out_type=SCREAMING_SNAKE_CASE__    )

					if not self.legacy and not is_first and not text.startswith(' '    ) and tokens[0].startswith(SCREAMING_SNAKE_CASE__    ):
								__a						:  Union[str, Any]									=     ([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
					return tokens

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					if token.startswith('<extra_id_'    ):
								__a						:  Dict									=     re.match(r'<extra_id_(\d+)>'    ,       SCREAMING_SNAKE_CASE__    )
								__a						:  List[Any]									=     int(match.group(1    )    )
								return self.vocab_size - num - 1
					return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					if index < self.sp_model.get_piece_size():
								__a						:  Union[str, Any]									=     self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__    )
					else:
								__a						:  List[str]									=     f'''<extra_id_{self.vocab_size - 1 - index}>'''
					return token

		def    __lowerCAmelCase  (					self		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  Tuple									=     []
					__a						:  List[Any]									=     ''
					__a						:  Optional[int]									=     False
					for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
											if not prev_is_special:
														out_string += " "
											out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__    ) + token
											__a						:  str									=     True
											__a						:  str									=     []
								else:
											current_sub_tokens.append(SCREAMING_SNAKE_CASE__    )
											__a						:  Dict									=     False
					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__    )
					return out_string.strip()



		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ):



					'''simple docstring'''



					if not os.path.isdir(SCREAMING_SNAKE_CASE__    ):
								logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
								return
					__a						:  Optional[int]									=     os.path.join(
					    SCREAMING_SNAKE_CASE__    ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

					if os.path.abspath(self.vocab_file    ) != os.path.abspath(SCREAMING_SNAKE_CASE__    ) and os.path.isfile(self.vocab_file    ):
								copyfile(self.vocab_file    ,       SCREAMING_SNAKE_CASE__    )
					elif not os.path.isfile(self.vocab_file    ):
								with open(SCREAMING_SNAKE_CASE__    ,       'wb'    ) as fi:
											__a						:  List[Any]									=     self.sp_model.serialized_model_proto()
											fi.write(SCREAMING_SNAKE_CASE__    )

					return (out_vocab_file,)













"""simple docstring"""



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase	:	Dict				=      {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """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	:	str				=      {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """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	:	Dict				=      {
    """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	:	int				=      {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str				)->		str:






     '''simple docstring'''



     if isinstance(snake_case			,					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 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Optional[Any]			,					snake_case       : List[str]			,					snake_case       : Any			,					snake_case       : str=False				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.in_layers.0.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.in_layers.0.bias']
     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.in_layers.2.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.in_layers.2.bias']
     UpperCAmelCase__						:    Optional[int]							=							checkpoint[f'{old_prefix}.emb_layers.1.weight']
     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.emb_layers.1.bias']
     UpperCAmelCase__						:    Any							=							checkpoint[f'{old_prefix}.out_layers.0.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.out_layers.0.bias']
     UpperCAmelCase__						:    int							=							checkpoint[f'{old_prefix}.out_layers.3.weight']
     UpperCAmelCase__						:    int							=							checkpoint[f'{old_prefix}.out_layers.3.bias']

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

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Union[str, Any]			,					snake_case       : Any			,					snake_case       : Tuple			,					snake_case       : Dict=None				)->		Tuple:






     '''simple docstring'''



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

     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.norm.weight']
     UpperCAmelCase__						:    Tuple							=							checkpoint[f'{old_prefix}.norm.bias']

     UpperCAmelCase__						:    Tuple							=							weight_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    int							=							bias_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							weight_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[int]							=							bias_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Tuple							=							weight_v.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							bias_v.squeeze(-1				).squeeze(-1				)

     UpperCAmelCase__						:    Union[str, Any]							=							(
         checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1				).squeeze(-1				)
     )
     UpperCAmelCase__						:    List[str]							=							checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1				).squeeze(-1				)

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Union[str, Any]				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Any							=							{}

     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.0.weight"]
     UpperCAmelCase__						:    Optional[int]							=							checkpoint["time_embed.0.bias"]
     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.2.weight"]
     UpperCAmelCase__						:    Optional[Any]							=							checkpoint["time_embed.2.bias"]

     if unet_config["num_class_embeds"] is not None:
          UpperCAmelCase__						:    List[Any]							=							checkpoint["label_emb.weight"]

     UpperCAmelCase__						:    Dict							=							checkpoint["input_blocks.0.0.weight"]
     UpperCAmelCase__						:    List[Any]							=							checkpoint["input_blocks.0.0.bias"]

     UpperCAmelCase__						:    Any							=							unet_config["down_block_types"]
     UpperCAmelCase__						:    Tuple							=							unet_config["layers_per_block"]
     UpperCAmelCase__						:    str							=							unet_config["attention_head_dim"]
     UpperCAmelCase__						:    str							=							unet_config["block_out_channels"]
     UpperCAmelCase__						:    Optional[int]							=							1
     UpperCAmelCase__						:    Tuple							=							channels_list[0]

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

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

          if i != len(snake_case				) - 1:
               UpperCAmelCase__						:    Dict							=							f'down_blocks.{i}.downsamplers.0'
               UpperCAmelCase__						:    Optional[int]							=							f'input_blocks.{current_layer}.0'
               UpperCAmelCase__						:    Tuple							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
               current_layer += 1

          UpperCAmelCase__						:    Dict							=							current_channels

     # hardcoded the mid-block for now
     UpperCAmelCase__						:    int							=							"mid_block.resnets.0"
     UpperCAmelCase__						:    Optional[int]							=							"middle_block.0"
     UpperCAmelCase__						:    Any							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							"mid_block.attentions.0"
     UpperCAmelCase__						:    Tuple							=							"middle_block.1"
     UpperCAmelCase__						:    Any							=							convert_attention(snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							"mid_block.resnets.1"
     UpperCAmelCase__						:    Union[str, Any]							=							"middle_block.2"
     UpperCAmelCase__						:    Optional[int]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)

     UpperCAmelCase__						:    Optional[Any]							=							0
     UpperCAmelCase__						:    int							=							unet_config["up_block_types"]

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

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

               if i != len(snake_case				) - 1:
                    UpperCAmelCase__						:    Dict							=							f'up_blocks.{i}.upsamplers.0'
                    UpperCAmelCase__						:    Any							=							f'output_blocks.{current_layer-1}.2'
                    UpperCAmelCase__						:    int							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							checkpoint["out.0.weight"]
     UpperCAmelCase__						:    Any							=							checkpoint["out.0.bias"]
     UpperCAmelCase__						:    int							=							checkpoint["out.2.weight"]
     UpperCAmelCase__						:    str							=							checkpoint["out.2.bias"]

     return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase	:	str				=      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	:	int				=      parser.parse_args()
 _lowerCAmelCase	:	Optional[int]				=      strabool(args.class_cond)

 _lowerCAmelCase	:	Optional[Any]				=      os.path.basename(args.unet_path)
 print(F"""Checkpoint: {ckpt_name}""")

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

 if not args.class_cond:
  _lowerCAmelCase	:	int				=      None

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

 _lowerCAmelCase	:	Optional[Any]				=      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	:	Optional[int]				=      CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  _lowerCAmelCase	:	str				=      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	:	Any				=      CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 _lowerCAmelCase	:	Dict				=      CMStochasticIterativeScheduler(**scheduler_config)

 _lowerCAmelCase	:	Tuple				=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)


