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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
      import faiss

@require_faiss
class    snake_case       (   UpperCamelCase_    ):




  def 						__lowercase(   self  :						List[str]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		tempfile.mkdtemp()
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		8

       # DPR tok
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       SCREAMING_SNAKE_CASE__					:					Dict      =		os.path.join(self.tmpdirname  ,			'dpr_tokenizer'					)
       os.makedirs(a_  ,			exist_ok=a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		os.path.join(a_  ,			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__					:					List[str]      =		[
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '<unk>',
       ]
       SCREAMING_SNAKE_CASE__					:					Any      =		dict(zip(a_  ,			range(len(a_					)					)					)					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		{'unk_token': '<unk>'}

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




  def 						__lowercase(   self  :						Union[str, Any]					)->							DPRQuestionEncoderTokenizer:




       """simple docstring"""
       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,			'dpr_tokenizer'					)					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							DPRContextEncoderTokenizer:




       """simple docstring"""
       return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,			'dpr_tokenizer'					)					)




  def 						__lowercase(   self  :						List[Any]					)->							BartTokenizer:




       """simple docstring"""
       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,			'bart_tokenizer'					)					)




  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       shutil.rmtree(self.tmpdirname					)




  def 						__lowercase(   self  :						str					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		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 						__lowercase(   self  :						List[str]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, 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__					:					Any      =		dataset
            SCREAMING_SNAKE_CASE__					:					int      =		RagRetriever(
                a_  ,			question_encoder_tokenizer=self.get_dpr_tokenizer()  ,			generator_tokenizer=self.get_bart_tokenizer()  ,			)
       return retriever




  def 						__lowercase(   self  :						Tuple  ,			a_  :						bool					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.get_dummy_dataset()
       SCREAMING_SNAKE_CASE__					:					Union[str, 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__					:					Optional[Any]      =		os.path.join(self.tmpdirname  ,			'dataset'					)
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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]      =		RagRetriever(
                a_  ,			question_encoder_tokenizer=self.get_dpr_tokenizer()  ,			generator_tokenizer=self.get_bart_tokenizer()  ,			)
       else:
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		RagRetriever(
                a_  ,			question_encoder_tokenizer=self.get_dpr_tokenizer()  ,			generator_tokenizer=self.get_bart_tokenizer()  ,			index=CustomHFIndex(config.retrieval_vector_size  ,			a_					)  ,			)
       return retriever




  def 						__lowercase(   self  :						Any					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		Dataset.from_dict(
           {
               'id': ['0', '1'],
               'text': ['foo', 'bar'],
               'title': ['Foo', 'Bar'],
               'embeddings': [np.ones(self.retrieval_vector_size + 1					), 2 * np.ones(self.retrieval_vector_size + 1					)],
           }					)
       dataset.add_faiss_index('embeddings'  ,			string_factory='Flat'  ,			metric_type=faiss.METRIC_INNER_PRODUCT					)

       SCREAMING_SNAKE_CASE__					:					str      =		os.path.join(self.tmpdirname  ,			'hf_bert_base.hnswSQ8_correct_phi_128.c_index'					)
       dataset.save_faiss_index('embeddings'  ,			index_file_name + '.index.dpr'					)
       pickle.dump(dataset['id']  ,			open(index_file_name + '.index_meta.dpr'  ,			'wb'					)					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		os.path.join(self.tmpdirname  ,			'psgs_w100.tsv.pkl'					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		{sample['id']: [sample['text'], sample['title']] for sample in dataset}
       pickle.dump(a_  ,			open(a_  ,			'wb'					)					)

       SCREAMING_SNAKE_CASE__					:					Any      =		RagConfig(
           retrieval_vector_size=self.retrieval_vector_size  ,			question_encoder=DPRConfig().to_dict()  ,			generator=BartConfig().to_dict()  ,			index_name='legacy'  ,			index_path=self.tmpdirname  ,			)
       SCREAMING_SNAKE_CASE__					:					int      =		RagRetriever(
           a_  ,			question_encoder_tokenizer=self.get_dpr_tokenizer()  ,			generator_tokenizer=self.get_bart_tokenizer()					)
       return retriever




  def 						__lowercase(   self  :						Tuple					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		1
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_dummy_canonical_hf_index_retriever()
       SCREAMING_SNAKE_CASE__					:					Any      =		np.array(
           [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		retriever.retrieve(a_  ,			n_docs=a_					)
       self.assertEqual(retrieved_doc_embeds.shape  ,			(2, n_docs, self.retrieval_vector_size)					)
       self.assertEqual(len(a_					)  ,			2					)
       self.assertEqual(sorted(doc_dicts[0]					)  ,			['embeddings', 'id', 'text', 'title']					)
       self.assertEqual(len(doc_dicts[0]['id']					)  ,			a_					)
       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 						__lowercase(   self  :						List[str]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.get_dummy_canonical_hf_index_retriever()
       with tempfile.TemporaryDirectory() as tmp_dirname:
            with patch('transformers.models.rag.retrieval_rag.load_dataset'					) as mock_load_dataset:
                 SCREAMING_SNAKE_CASE__					:					int      =		self.get_dummy_dataset()
                 retriever.save_pretrained(a_					)
                 SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		RagRetriever.from_pretrained(a_					)
                 self.assertIsInstance(a_  ,			a_					)
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		np.array(
                     [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		retriever.retrieve(a_  ,			n_docs=1					)
                 self.assertTrue(out is not None					)




  def 						__lowercase(   self  :						Optional[Any]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		1
       SCREAMING_SNAKE_CASE__					:					int      =		self.get_dummy_custom_hf_index_retriever(from_disk=a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		np.array(
           [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		retriever.retrieve(a_  ,			n_docs=a_					)
       self.assertEqual(retrieved_doc_embeds.shape  ,			(2, n_docs, self.retrieval_vector_size)					)
       self.assertEqual(len(a_					)  ,			2					)
       self.assertEqual(sorted(doc_dicts[0]					)  ,			['embeddings', 'id', 'text', 'title']					)
       self.assertEqual(len(doc_dicts[0]['id']					)  ,			a_					)
       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 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_dummy_custom_hf_index_retriever(from_disk=a_					)
       with tempfile.TemporaryDirectory() as tmp_dirname:
            retriever.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					List[str]      =		RagRetriever.from_pretrained(a_					)
            self.assertIsInstance(a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		np.array(
                [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		retriever.retrieve(a_  ,			n_docs=1					)
            self.assertTrue(out is not None					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		1
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.get_dummy_custom_hf_index_retriever(from_disk=a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		np.array(
           [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Dict      =		retriever.retrieve(a_  ,			n_docs=a_					)
       self.assertEqual(retrieved_doc_embeds.shape  ,			(2, n_docs, self.retrieval_vector_size)					)
       self.assertEqual(len(a_					)  ,			2					)
       self.assertEqual(sorted(doc_dicts[0]					)  ,			['embeddings', 'id', 'text', 'title']					)
       self.assertEqual(len(doc_dicts[0]['id']					)  ,			a_					)
       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 						__lowercase(   self  :						Dict					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_dummy_custom_hf_index_retriever(from_disk=a_					)
       with tempfile.TemporaryDirectory() as tmp_dirname:
            retriever.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		RagRetriever.from_pretrained(a_					)
            self.assertIsInstance(a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		np.array(
                [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
            SCREAMING_SNAKE_CASE__					:					Any      =		retriever.retrieve(a_  ,			n_docs=1					)
            self.assertTrue(out is not None					)




  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		1
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_dummy_legacy_index_retriever()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		np.array(
           [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[int]      =		retriever.retrieve(a_  ,			n_docs=a_					)
       self.assertEqual(retrieved_doc_embeds.shape  ,			(2, n_docs, self.retrieval_vector_size)					)
       self.assertEqual(len(a_					)  ,			2					)
       self.assertEqual(sorted(doc_dicts[0]					)  ,			['text', 'title']					)
       self.assertEqual(len(doc_dicts[0]['text']					)  ,			a_					)
       self.assertEqual(doc_dicts[0]['text'][0]  ,			'bar'					)  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]['text'][0]  ,			'foo'					)  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()  ,			[[1], [0]]					)




  def 						__lowercase(   self  :						Dict					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.get_dummy_legacy_index_retriever()
       with tempfile.TemporaryDirectory() as tmp_dirname:
            retriever.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		RagRetriever.from_pretrained(a_					)
            self.assertIsInstance(a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Any      =		np.array(
                [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		retriever.retrieve(a_  ,			n_docs=1					)
            self.assertTrue(out is not None					)




  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def 						__lowercase(   self  :						int					)->							Tuple:




       """simple docstring"""
       import torch

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		1
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_dummy_canonical_hf_index_retriever()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[[5, 7], [10, 11]]
       SCREAMING_SNAKE_CASE__					:					str      =		np.array(
           [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		retriever(a_  ,			a_  ,			prefix=retriever.config.generator.prefix  ,			n_docs=a_					)
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		(
           out['context_input_ids'],
           out['context_attention_mask'],
           out['retrieved_doc_embeds'],
       )
       self.assertEqual(retrieved_doc_embeds.shape  ,			(2, n_docs, self.retrieval_vector_size)					)
       self.assertIsInstance(a_  ,			a_					)
       self.assertIsInstance(a_  ,			a_					)
       self.assertIsInstance(a_  ,			np.ndarray					)

       SCREAMING_SNAKE_CASE__					:					int      =		retriever(
           a_  ,			a_  ,			prefix=retriever.config.generator.prefix  ,			n_docs=a_  ,			return_tensors='pt'  ,			)
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		(  # noqa: F841
           out['context_input_ids'],
           out['context_attention_mask'],
           out['retrieved_doc_embeds'],
           out['doc_ids'],
       )
       self.assertEqual(retrieved_doc_embeds.shape  ,			(2, n_docs, self.retrieval_vector_size)					)
       self.assertIsInstance(a_  ,			torch.Tensor					)
       self.assertIsInstance(a_  ,			torch.Tensor					)
       self.assertIsInstance(a_  ,			torch.Tensor					)




  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def 						__lowercase(   self  :						List[Any]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_dpr_ctx_encoder_tokenizer()
       SCREAMING_SNAKE_CASE__					:					Dict      =		1
       SCREAMING_SNAKE_CASE__					:					int      =		self.get_dummy_custom_hf_index_retriever(from_disk=a_					)
       retriever.set_ctx_encoder_tokenizer(a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[[5, 7], [10, 11]]
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		np.array(
           [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,			dtype=np.floataa					)
       SCREAMING_SNAKE_CASE__					:					int      =		retriever(a_  ,			a_  ,			prefix=retriever.config.generator.prefix  ,			n_docs=a_					)

       self.assertEqual(
           len(a_					)  ,			6					)  # check whether the retriever output consist of 6 attributes including tokenized docs
       self.assertEqual(
           all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')					)  ,			a_					)  # check for doc token related keys in dictionary.





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case      :							Tuple     	=    False
_snake_case      :							Optional[int]     	=    True
_snake_case      :							Any     	=    False


if __name__ == "__main__":
						_snake_case      :							Any     	=    argparse.ArgumentParser()

						parser.add_argument(
						    "--repo_path",
						    default=None,
						    type=str,
						    required=True,
						    help="The config json file corresponding to the architecture.",
						)

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

						_snake_case      :							Tuple     	=    parser.parse_args()

						_snake_case      :							Optional[Any]     	=    {
						    "image_size": "sample_size",
						    "num_res_blocks": "layers_per_block",
						    "block_channels": "block_out_channels",
						    "down_blocks": "down_block_types",
						    "up_blocks": "up_block_types",
						    "downscale_freq_shift": "freq_shift",
						    "resnet_num_groups": "norm_num_groups",
						    "resnet_act_fn": "act_fn",
						    "resnet_eps": "norm_eps",
						    "num_head_channels": "attention_head_dim",
						}

						_snake_case      :							Tuple     	=    {
						    "time_steps": "time_proj",
						    "mid": "mid_block",
						    "downsample_blocks": "down_blocks",
						    "upsample_blocks": "up_blocks",
						}

						_snake_case      :							Any     	=    "" if has_file(args.repo_path, "config.json") else "unet"

						with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
												_snake_case      :							List[Any]     	=    reader.read()
												_snake_case      :							List[Any]     	=    json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, "config.json"):
												_snake_case      :							List[Any]     	=    UNetaDModel(**config)
						else:
												_snake_case      :							int     	=    UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
												_snake_case      :							Optional[Any]     	=    class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						_snake_case      :							Any     	=    dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								_snake_case      :							Any     	=    config[key]
																								del config[key]

												_snake_case      :							List[str]     	=    [k.replace("UNetRes", "") for k in config["down_block_types"]]
												_snake_case      :							Any     	=    [k.replace("UNetRes", "") for k in config["up_block_types"]]

						if do_only_weights:
												_snake_case      :							Optional[int]     	=    torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

												_snake_case      :							Any     	=    {}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																								continue
																		_snake_case      :							List[Any]     	=    False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split(".")[0] == key:
																														_snake_case      :							Dict     	=    param_value
																														_snake_case      :							Any     	=    True
																		if not has_changed:
																								_snake_case      :							int     	=    param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))
