





"""simple docstring"""





import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
    from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
    _lowerCamelCase       =	None

if is_ray_available():
    import ray  # noqa: E402 # isort:skip
    from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
    _lowerCamelCase       =	None
    _lowerCamelCase       =	None
    _lowerCamelCase       =	None




def 		lowerCAmelCase_	(  lowercase_   :							List[Any]		):

 '''simple docstring'''






 if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
  __SCREAMING_SNAKE_CASE :			str												=       unittest.skip('''test requires Datasets, Faiss, psutil'''		)(lowercase_		)
 return test_case






@require_distributed_retrieval
class 			snake_case		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			str												=       tempfile.mkdtemp()
       __SCREAMING_SNAKE_CASE :			List[Any]												=       8

       # DPR tok
       __SCREAMING_SNAKE_CASE :			Tuple												=       [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       __SCREAMING_SNAKE_CASE :			List[Any]												=       os.path.join(self.tmpdirname						,							'''dpr_tokenizer'''						)
       os.makedirs(_lowerCamelCase						,							exist_ok=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       os.path.join(_lowerCamelCase						,							DPR_VOCAB_FILES_NAMES['''vocab_file''']						)
       with open(self.vocab_file						,							'''w'''						,							encoding='''utf-8'''						) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]						)						)

       # BART tok
       __SCREAMING_SNAKE_CASE :			Tuple												=       [
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       __SCREAMING_SNAKE_CASE :			int												=       dict(zip(_lowerCamelCase						,							range(len(_lowerCamelCase						)						)						)						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       {'''unk_token''': '''<unk>'''}

       __SCREAMING_SNAKE_CASE :			int												=       os.path.join(self.tmpdirname						,							'''bart_tokenizer'''						)
       os.makedirs(_lowerCamelCase						,							exist_ok=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Any												=       os.path.join(_lowerCamelCase						,							BART_VOCAB_FILES_NAMES['''vocab_file''']						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       os.path.join(_lowerCamelCase						,							BART_VOCAB_FILES_NAMES['''merges_file''']						)
       with open(self.vocab_file						,							'''w'''						,							encoding='''utf-8'''						) as fp:
        fp.write(json.dumps(_lowerCamelCase						) + '''\n'''						)
       with open(self.merges_file						,							'''w'''						,							encoding='''utf-8'''						) as fp:
        fp.write('''\n'''.join(_lowerCamelCase						)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,							'''dpr_tokenizer'''						)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,							'''bart_tokenizer'''						)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       shutil.rmtree(self.tmpdirname						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       Dataset.from_dict(
           {
               '''id''': ['''0''', '''1'''],
               '''text''': ['''foo''', '''bar'''],
               '''title''': ['''Foo''', '''Bar'''],
               '''embeddings''': [np.ones(self.retrieval_vector_size						), 2 * np.ones(self.retrieval_vector_size						)],
           }						)
       dataset.add_faiss_index('''embeddings'''						,							string_factory='''Flat'''						,							metric_type=faiss.METRIC_INNER_PRODUCT						)
       return dataset

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						,							_lowerCamelCase   :bool						,							_lowerCamelCase   :Union[str, Any]=1_2_3_4_5						):
       __SCREAMING_SNAKE_CASE :			Any												=       self.get_dummy_dataset()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       RagConfig(
           retrieval_vector_size=self.retrieval_vector_size						,							question_encoder=DPRConfig().to_dict()						,							generator=BartConfig().to_dict()						,							)
       with patch('''transformers.models.rag.retrieval_rag.load_dataset'''						) as mock_load_dataset:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       dataset
        __SCREAMING_SNAKE_CASE :			List[str]												=       RagPyTorchDistributedRetriever(
            _lowerCamelCase						,							question_encoder_tokenizer=self.get_dpr_tokenizer()						,							generator_tokenizer=self.get_bart_tokenizer()						,							)
        if init_retrieval:
         retriever.init_retrieval(_lowerCamelCase						)
       return retriever

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :bool						):
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       RagConfig(
           retrieval_vector_size=self.retrieval_vector_size						,							question_encoder=DPRConfig().to_dict()						,							generator=BartConfig().to_dict()						,							)
       __SCREAMING_SNAKE_CASE :			Any												=       ray.remote(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       [remote_cls.remote() for _ in range(1						)]
       with patch('''transformers.models.rag.retrieval_rag.load_dataset'''						) as mock_load_dataset:
        __SCREAMING_SNAKE_CASE :			Tuple												=       self.get_dummy_dataset()
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       RagRayDistributedRetriever(
            _lowerCamelCase						,							question_encoder_tokenizer=self.get_dpr_tokenizer()						,							generator_tokenizer=self.get_bart_tokenizer()						,							retrieval_workers=_lowerCamelCase						,							)
        if init_retrieval:
         retriever.init_retrieval()
       return retriever

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :bool						,							_lowerCamelCase   :bool						,							_lowerCamelCase   :List[Any]=1_2_3_4_5						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.get_dummy_dataset()
       __SCREAMING_SNAKE_CASE :			Any												=       RagConfig(
           retrieval_vector_size=self.retrieval_vector_size						,							question_encoder=DPRConfig().to_dict()						,							generator=BartConfig().to_dict()						,							index_name='''custom'''						,							)
       if from_disk:
        __SCREAMING_SNAKE_CASE :			str												=       os.path.join(self.tmpdirname						,							'''dataset'''						)
        __SCREAMING_SNAKE_CASE :			str												=       os.path.join(self.tmpdirname						,							'''index.faiss'''						)
        dataset.get_index('''embeddings'''						).save(os.path.join(self.tmpdirname						,							'''index.faiss'''						)						)
        dataset.drop_index('''embeddings'''						)
        dataset.save_to_disk(os.path.join(self.tmpdirname						,							'''dataset'''						)						)
        del dataset
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       RagPyTorchDistributedRetriever(
            _lowerCamelCase						,							question_encoder_tokenizer=self.get_dpr_tokenizer()						,							generator_tokenizer=self.get_bart_tokenizer()						,							)
       else:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       RagPyTorchDistributedRetriever(
            _lowerCamelCase						,							question_encoder_tokenizer=self.get_dpr_tokenizer()						,							generator_tokenizer=self.get_bart_tokenizer()						,							index=CustomHFIndex(config.retrieval_vector_size						,							_lowerCamelCase						)						,							)
       if init_retrieval:
        retriever.init_retrieval(_lowerCamelCase						)
       return retriever

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :bool						,							_lowerCamelCase   :bool						):
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       self.get_dummy_dataset()
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       RagConfig(
           retrieval_vector_size=self.retrieval_vector_size						,							question_encoder=DPRConfig().to_dict()						,							generator=BartConfig().to_dict()						,							index_name='''custom'''						,							)
       __SCREAMING_SNAKE_CASE :			List[str]												=       ray.remote(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       [remote_cls.remote() for _ in range(1						)]
       if from_disk:
        __SCREAMING_SNAKE_CASE :			List[Any]												=       os.path.join(self.tmpdirname						,							'''dataset'''						)
        __SCREAMING_SNAKE_CASE :			Any												=       os.path.join(self.tmpdirname						,							'''index.faiss'''						)
        dataset.get_index('''embeddings'''						).save(os.path.join(self.tmpdirname						,							'''index.faiss'''						)						)
        dataset.drop_index('''embeddings'''						)
        dataset.save_to_disk(os.path.join(self.tmpdirname						,							'''dataset'''						)						)
        del dataset
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       RagRayDistributedRetriever(
            _lowerCamelCase						,							question_encoder_tokenizer=self.get_dpr_tokenizer()						,							generator_tokenizer=self.get_bart_tokenizer()						,							retrieval_workers=_lowerCamelCase						,							index=CustomHFIndex.load_from_disk(
                vector_size=config.retrieval_vector_size						,							dataset_path=config.passages_path						,							index_path=config.index_path						,							)						,							)
       else:
        __SCREAMING_SNAKE_CASE :			int												=       RagRayDistributedRetriever(
            _lowerCamelCase						,							question_encoder_tokenizer=self.get_dpr_tokenizer()						,							generator_tokenizer=self.get_bart_tokenizer()						,							retrieval_workers=_lowerCamelCase						,							index=CustomHFIndex(config.retrieval_vector_size						,							_lowerCamelCase						)						,							)
       if init_retrieval:
        retriever.init_retrieval()
       return retriever

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :RagRetriever						,							_lowerCamelCase   :np.array						,							_lowerCamelCase   :int						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       retriever.retrieve(_lowerCamelCase						,							n_docs=_lowerCamelCase						)
       self.assertEqual(retrieved_doc_embeds.shape						,							(2, n_docs, self.retrieval_vector_size)						)
       self.assertEqual(len(_lowerCamelCase						)						,							2						)
       self.assertEqual(sorted(doc_dicts[0]						)						,							['''embeddings''', '''id''', '''text''', '''title''']						)
       self.assertEqual(len(doc_dicts[0]['''id''']						)						,							_lowerCamelCase						)
       self.assertEqual(doc_dicts[0]['''id'''][0]						,							'''1'''						)  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]['''id'''][0]						,							'''0'''						)  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()						,							[[1], [0]]						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       1
       __SCREAMING_SNAKE_CASE :			Dict												=       np.array(
           [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,							dtype=np.floataa						)

       self.distributed_retriever_check(
           self.get_dummy_pytorch_distributed_retriever(init_retrieval=_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			str												=       1
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       np.array(
           [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,							dtype=np.floataa						)

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowerCamelCase						,							from_disk=_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						,							)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       1
       __SCREAMING_SNAKE_CASE :			str												=       np.array(
           [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,							dtype=np.floataa						)

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowerCamelCase						,							from_disk=_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						,							)

      @require_ray
      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       1
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       np.array(
           [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,							dtype=np.floataa						)

       self.distributed_retriever_check(
           self.get_dummy_ray_distributed_retriever(init_retrieval=_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						)
       ray.shutdown()

      @require_ray
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			str												=       1
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       np.array(
           [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,							dtype=np.floataa						)
       with self.assertRaises(_lowerCamelCase						):
        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowerCamelCase						,							from_disk=_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						,							)
       ray.shutdown()



      @require_ray
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			str												=       1
       __SCREAMING_SNAKE_CASE :			str												=       np.array(
           [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,							dtype=np.floataa						)

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowerCamelCase						,							from_disk=_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						)
       ray.shutdown()


"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a							='pcuenq/k-upscaler'





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						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_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					int:




						'''simple docstring'''



						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_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						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_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						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(__lowerCAmelCase   ,    "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(
												        __lowerCAmelCase   ,    __lowerCAmelCase   ,    diffusers_resnet_prefix=__lowerCAmelCase   ,    resnet_prefix=__lowerCAmelCase				)				)

						if hasattr(__lowerCAmelCase   ,    "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(
																		        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    diffusers_attention_index=2   ,    attention_prefix=__lowerCAmelCase   ,    )				)

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

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						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(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "down"				)				)

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "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_ (					__lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						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(__lowerCAmelCase				)
												attna_types.append(__lowerCAmelCase				)

						lowerCamelCase__					=UNetaDConditionModel(
						    in_channels=__lowerCAmelCase   ,    out_channels=__lowerCAmelCase   ,    down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")   ,    mid_block_type=__lowerCAmelCase   ,    up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")   ,    block_out_channels=__lowerCAmelCase   ,    layers_per_block=__lowerCAmelCase   ,    act_fn="gelu"   ,    norm_num_groups=__lowerCAmelCase   ,    cross_attention_dim=__lowerCAmelCase   ,    attention_head_dim=64   ,    time_cond_proj_dim=__lowerCAmelCase   ,    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_ (					__lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					=torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

						lowerCamelCase__					=huggingface_hub.hf_hub_download(__lowerCAmelCase   ,    "config_laion_text_cond_latent_upscaler_2.json"				)
						lowerCamelCase__					=huggingface_hub.hf_hub_download(
						    __lowerCAmelCase   ,    "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(__lowerCAmelCase				)				)["model"]
						lowerCamelCase__					=unet_model_from_original_config(__lowerCAmelCase				)

						lowerCamelCase__					=torch.load(__lowerCAmelCase   ,    map_location=__lowerCAmelCase				)["model_ema"]
						lowerCamelCase__					=unet_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase				)

						model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase				)
						model.save_pretrained(args.dump_path				)
						print(F'''saving converted unet model in {args.dump_path}'''				)


if __name__ == "__main__":
	a							=argparse.ArgumentParser()

	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	a							=parser.parse_args()

	main(args)


