






"""simple docstring"""



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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_snake_case       :      int       =					logging.get_logger(__name__)

_snake_case       :      Optional[Any]       =					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_snake_case       :      Tuple       =					{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
}

_snake_case       :      Optional[int]       =					{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}




@lru_cache()
def 						lowerCamelCase_				(  ):
					lowerCamelCase__				=(
					    list(range(ord('!'			)			,	ord('~'			) + 1			)			) + list(range(ord('¡'			)			,	ord('¬'			) + 1			)			) + list(range(ord('®'			)			,	ord('ÿ'			) + 1			)			)
					)
					lowerCamelCase__				=bs[:]
					lowerCamelCase__				=0
					for b in range(2**8			):
										if b not in bs:
															bs.append(__A			)
															cs.append(2**8 + n			)
															n += 1
					lowerCamelCase__				=[chr(__A			) for n in cs]
					return dict(zip(__A			,	__A			)			)




def 						lowerCamelCase_				(  __A  :		str			):
					lowerCamelCase__				=set()
					lowerCamelCase__				=word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)			)
										lowerCamelCase__				=char
					return pairs




class        __lowercase(       _UpperCamelCase  ):
						lowerCAmelCase_		:							Optional[int]=       VOCAB_FILES_NAMES
						lowerCAmelCase_		:							Any=       PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_		:							int=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_		:							Optional[Any]=       ["""input_ids""", """attention_mask"""]






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE="replace"    ,						_SCREAMING_SNAKE_CASE="<s>"    ,						_SCREAMING_SNAKE_CASE="</s>"    ,						_SCREAMING_SNAKE_CASE="</s>"    ,						_SCREAMING_SNAKE_CASE="<s>"    ,						_SCREAMING_SNAKE_CASE="<unk>"    ,						_SCREAMING_SNAKE_CASE="<pad>"    ,						_SCREAMING_SNAKE_CASE="<mask>"    ,						_SCREAMING_SNAKE_CASE=False    ,						**_SCREAMING_SNAKE_CASE    ,						)			->  Any:




											"""simple docstring"""





											lowerCamelCase__				=AddedToken(_SCREAMING_SNAKE_CASE    ,						lstrip=_SCREAMING_SNAKE_CASE    ,						rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE) else bos_token
											lowerCamelCase__				=AddedToken(_SCREAMING_SNAKE_CASE    ,						lstrip=_SCREAMING_SNAKE_CASE    ,						rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE) else eos_token
											lowerCamelCase__				=AddedToken(_SCREAMING_SNAKE_CASE    ,						lstrip=_SCREAMING_SNAKE_CASE    ,						rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE) else sep_token
											lowerCamelCase__				=AddedToken(_SCREAMING_SNAKE_CASE    ,						lstrip=_SCREAMING_SNAKE_CASE    ,						rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE) else cls_token
											lowerCamelCase__				=AddedToken(_SCREAMING_SNAKE_CASE    ,						lstrip=_SCREAMING_SNAKE_CASE    ,						rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE) else unk_token
											lowerCamelCase__				=AddedToken(_SCREAMING_SNAKE_CASE    ,						lstrip=_SCREAMING_SNAKE_CASE    ,						rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE) else pad_token

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

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

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

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






						@property
						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





											return len(self.encoder)






						def 		lowerCAmelCase   (					self)			->  List[str]:




											"""simple docstring"""





											return dict(self.encoder    ,						**self.added_tokens_encoder)






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[int]:




											"""simple docstring"""





											if token in self.cache:
																return self.cache[token]
											lowerCamelCase__				=tuple(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=get_pairs(_SCREAMING_SNAKE_CASE)

											if not pairs:
																return token

											while True:
																lowerCamelCase__				=min(_SCREAMING_SNAKE_CASE    ,						key=lambda _SCREAMING_SNAKE_CASE: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE    ,						float('inf')))
																if bigram not in self.bpe_ranks:
																					break
																lowerCamelCase__				,     lowerCamelCase__				=bigram
																lowerCamelCase__				=[]
																lowerCamelCase__				=0
																while i < len(_SCREAMING_SNAKE_CASE):
																					try:
																										lowerCamelCase__				=word.index(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
																					except ValueError:
																										new_word.extend(word[i:])
																										break
																					else:
																										new_word.extend(word[i:j])
																										lowerCamelCase__				=j

																					if word[i] == first and i < len(_SCREAMING_SNAKE_CASE) - 1 and word[i + 1] == second:
																										new_word.append(first + second)
																										i += 2
																					else:
																										new_word.append(word[i])
																										i += 1
																lowerCamelCase__				=tuple(_SCREAMING_SNAKE_CASE)
																lowerCamelCase__				=new_word
																if len(_SCREAMING_SNAKE_CASE) == 1:
																					break
																else:
																					lowerCamelCase__				=get_pairs(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=' '.join(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=word
											return word






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  int:




											"""simple docstring"""





											lowerCamelCase__				=[]
											for token in re.findall(self.pat    ,						_SCREAMING_SNAKE_CASE):
																lowerCamelCase__				=''.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(_SCREAMING_SNAKE_CASE).split(' '))
											return bpe_tokens






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  int:




											"""simple docstring"""





											return self.encoder.get(_SCREAMING_SNAKE_CASE    ,						self.encoder.get(self.unk_token))






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  Any:




											"""simple docstring"""





											return self.decoder.get(_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  List[Any]:




											"""simple docstring"""





											lowerCamelCase__				=''.join(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'    ,						errors=self.errors)
											return text






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE = None)			->  Tuple[str]:




											"""simple docstring"""





											if not os.path.isdir(_SCREAMING_SNAKE_CASE):
																logger.error(F"Vocabulary path ({save_directory}) should be a directory")
																return
											lowerCamelCase__				=os.path.join(
											    _SCREAMING_SNAKE_CASE    ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
											lowerCamelCase__				=os.path.join(
											    _SCREAMING_SNAKE_CASE    ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

											lowerCamelCase__				=0
											with open(_SCREAMING_SNAKE_CASE    ,						'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 _SCREAMING_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!')
																										lowerCamelCase__				=token_index
																					writer.write(' '.join(_SCREAMING_SNAKE_CASE) + '\n')
																					index += 1

											return vocab_file, merge_file






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE = None)			->  List[int]:




											"""simple docstring"""





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






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE = None    ,						_SCREAMING_SNAKE_CASE = False)			->  List[int]:




											"""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)

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






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE = None)			->  List[int]:




											"""simple docstring"""





											lowerCamelCase__				=[self.sep_token_id]
											lowerCamelCase__				=[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 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=False    ,						**_SCREAMING_SNAKE_CASE)			->  Any:




											"""simple docstring"""





											lowerCamelCase__				=kwargs.pop('add_prefix_space'    ,						self.add_prefix_space)
											if (is_split_into_words or add_prefix_space) and (len(_SCREAMING_SNAKE_CASE) > 0 and not text[0].isspace()):
																lowerCamelCase__				=' ' + text
											return (text, kwargs)







"""simple docstring"""



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_snake_case       :      Optional[Any]       =					"pcuenq/k-upscaler"




def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		Optional[int]			,	*, __A  :		int			,	__A  :		Union[str, Any]			):
					lowerCamelCase__				={
					    # norm1
					    f"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[f"{resnet_prefix}.main.0.mapper.weight"],
					    f"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[f"{resnet_prefix}.main.0.mapper.bias"],
					    # conv1
					    f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.main.2.weight"],
					    f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.main.2.bias"],
					    # norm2
					    f"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[f"{resnet_prefix}.main.4.mapper.weight"],
					    f"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[f"{resnet_prefix}.main.4.mapper.bias"],
					    # conv2
					    f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.main.6.weight"],
					    f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.main.6.bias"],
					}

					if resnet.conv_shortcut is not None:
										rv.update(
										    {
										        f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.skip.weight"],
										    }			)

					return rv




def 						lowerCamelCase_				(  __A  :		Dict			,	*, __A  :		Union[str, Any]			,	__A  :		Union[str, Any]			):
					lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3			,	dim=0			)
					lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3			,	dim=0			)
					lowerCamelCase__				={
					    # norm
					    f"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[f"{attention_prefix}.norm_in.mapper.weight"],
					    f"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[f"{attention_prefix}.norm_in.mapper.bias"],
					    # to_q
					    f"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q,
					    # to_k
					    f"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k,
					    # to_v
					    f"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v,
					    # to_out
					    f"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[f"{attention_prefix}.out_proj.weight"]
					    .squeeze(-1			)
					    .squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[f"{attention_prefix}.out_proj.bias"],
					}

					return rv




def 						lowerCamelCase_				(  __A  :		List[str]			,	*, __A  :		Optional[Any]			,	__A  :		List[str]			,	__A  :		Optional[int]			):
					lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2			,	dim=0			)
					lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2			,	dim=0			)

					lowerCamelCase__				={
					    # norm2 (ada groupnorm)
					    f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[
					        f"{attention_prefix}.norm_dec.mapper.weight"
					    ],
					    f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[
					        f"{attention_prefix}.norm_dec.mapper.bias"
					    ],
					    # layernorm on encoder_hidden_state
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[
					        f"{attention_prefix}.norm_enc.weight"
					    ],
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[
					        f"{attention_prefix}.norm_enc.bias"
					    ],
					    # to_q
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[
					        f"{attention_prefix}.q_proj.weight"
					    ]
					    .squeeze(-1			)
					    .squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[
					        f"{attention_prefix}.q_proj.bias"
					    ],
					    # to_k
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k,
					    # to_v
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v,
					    # to_out
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[
					        f"{attention_prefix}.out_proj.weight"
					    ]
					    .squeeze(-1			)
					    .squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[
					        f"{attention_prefix}.out_proj.bias"
					    ],
					}

					return rv




def 						lowerCamelCase_				(  __A  :		Tuple			,	__A  :		Union[str, Any]			,	__A  :		List[str]			,	__A  :		List[str]			):
					lowerCamelCase__				='inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
					lowerCamelCase__				=f"{block_prefix}.{block_idx}"

					lowerCamelCase__				={}

					if not hasattr(__A			,	'attentions'			):
										lowerCamelCase__				=1  # resnet only
					elif not block.attentions[0].add_self_attention:
										lowerCamelCase__				=2  # resnet -> cross-attention
					else:
										lowerCamelCase__				=3  # resnet -> self-attention -> cross-attention)

					for resnet_idx, resnet in enumerate(block.resnets			):
										# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
										lowerCamelCase__				=f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
										lowerCamelCase__				=n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
										lowerCamelCase__				=f"{block_prefix}.{idx}" if block_type == 'up' else f"{block_prefix}.{idx}"

										diffusers_checkpoint.update(
										    resnet_to_diffusers_checkpoint(
										        __A			,	__A			,	diffusers_resnet_prefix=__A			,	resnet_prefix=__A			)			)

					if hasattr(__A			,	'attentions'			):
										for attention_idx, attention in enumerate(block.attentions			):
															lowerCamelCase__				=f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
															lowerCamelCase__				=n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
															lowerCamelCase__				=f"{block_prefix}.{idx}"
															lowerCamelCase__				=f"{block_prefix}.{idx }"
															lowerCamelCase__				=1 if not attention.add_self_attention else 2
															lowerCamelCase__				=(
															    n * attention_idx + cross_attention_index
															    if block_type == 'up'
															    else n * attention_idx + cross_attention_index + 1
															)
															lowerCamelCase__				=f"{block_prefix}.{idx }"

															diffusers_checkpoint.update(
															    cross_attn_to_diffusers_checkpoint(
															        __A			,	diffusers_attention_prefix=__A			,	diffusers_attention_index=2			,	attention_prefix=__A			,	)			)

															if attention.add_self_attention is True:
																				diffusers_checkpoint.update(
																				    self_attn_to_diffusers_checkpoint(
																				        __A			,	diffusers_attention_prefix=__A			,	attention_prefix=__A			,	)			)

					return diffusers_checkpoint




def 						lowerCamelCase_				(  __A  :		str			,	__A  :		Dict			):
					lowerCamelCase__				={}

					# pre-processing
					diffusers_checkpoint.update(
					    {
					        'conv_in.weight': checkpoint['inner_model.proj_in.weight'],
					        'conv_in.bias': checkpoint['inner_model.proj_in.bias'],
					    }			)

					# timestep and class embedding
					diffusers_checkpoint.update(
					    {
					        'time_proj.weight': checkpoint['inner_model.timestep_embed.weight'].squeeze(-1			),
					        'time_embedding.linear_1.weight': checkpoint['inner_model.mapping.0.weight'],
					        'time_embedding.linear_1.bias': checkpoint['inner_model.mapping.0.bias'],
					        'time_embedding.linear_2.weight': checkpoint['inner_model.mapping.2.weight'],
					        'time_embedding.linear_2.bias': checkpoint['inner_model.mapping.2.bias'],
					        'time_embedding.cond_proj.weight': checkpoint['inner_model.mapping_cond.weight'],
					    }			)

					# down_blocks
					for down_block_idx, down_block in enumerate(model.down_blocks			):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(__A			,	__A			,	__A			,	'down'			)			)

					# up_blocks
					for up_block_idx, up_block in enumerate(model.up_blocks			):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(__A			,	__A			,	__A			,	'up'			)			)

					# post-processing
					diffusers_checkpoint.update(
					    {
					        'conv_out.weight': checkpoint['inner_model.proj_out.weight'],
					        'conv_out.bias': checkpoint['inner_model.proj_out.bias'],
					    }			)

					return diffusers_checkpoint




def 						lowerCamelCase_				(  __A  :		Optional[int]			):
					lowerCamelCase__				=original_config['input_channels'] + original_config['unet_cond_dim']
					lowerCamelCase__				=original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

					lowerCamelCase__				=original_config['channels']

					assert (
					    len(set(original_config['depths']			)			) == 1
					), "UNet2DConditionModel currently do not support blocks with different number of layers"
					lowerCamelCase__				=original_config['depths'][0]

					lowerCamelCase__				=original_config['mapping_cond_dim']
					lowerCamelCase__				=original_config['cross_cond_dim']

					lowerCamelCase__				=[]
					lowerCamelCase__				=[]
					for s, c in zip(original_config['self_attn_depths']			,	original_config['cross_attn_depths']			):
										if s:
															lowerCamelCase__				='self'
															lowerCamelCase__				='cross' if c else None
										elif c:
															lowerCamelCase__				='cross'
															lowerCamelCase__				=None
										else:
															lowerCamelCase__				=None
															lowerCamelCase__				=None
										attna_types.append(__A			)
										attna_types.append(__A			)

					lowerCamelCase__				=UNetaDConditionModel(
					    in_channels=__A			,	out_channels=__A			,	down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')			,	mid_block_type=__A			,	up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')			,	block_out_channels=__A			,	layers_per_block=__A			,	act_fn='gelu'			,	norm_num_groups=__A			,	cross_attention_dim=__A			,	attention_head_dim=64			,	time_cond_proj_dim=__A			,	resnet_time_scale_shift='scale_shift'			,	time_embedding_type='fourier'			,	timestep_post_act='gelu'			,	conv_in_kernel=1			,	conv_out_kernel=1			,	)

					return unet




def 						lowerCamelCase_				(  __A  :		int			):
					lowerCamelCase__				=torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

					lowerCamelCase__				=huggingface_hub.hf_hub_download(__A			,	'config_laion_text_cond_latent_upscaler_2.json'			)
					lowerCamelCase__				=huggingface_hub.hf_hub_download(
					    __A			,	'laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'			)
					print(f"loading original model configuration from {orig_config_path}"			)
					print(f"loading original model checkpoint from {orig_weights_path}"			)

					print('converting to diffusers unet'			)
					lowerCamelCase__				=K.config.load_config(open(__A			)			)['model']
					lowerCamelCase__				=unet_model_from_original_config(__A			)

					lowerCamelCase__				=torch.load(__A			,	map_location=__A			)['model_ema']
					lowerCamelCase__				=unet_to_diffusers_checkpoint(__A			,	__A			)

					model.load_state_dict(__A			,	strict=__A			)
					model.save_pretrained(args.dump_path			)
					print(f"saving converted unet model in {args.dump_path}"			)


if __name__ == "__main__":
						_snake_case       :      Optional[int]       =					argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
						_snake_case       :      List[Any]       =					parser.parse_args()

						main(args)