





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   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: Any         =tempfile.mkdtemp()
   lowerCamelCase__: Any         =8

   # DPR tok
   lowerCamelCase__: Tuple         =[
       "[UNK]",
       "[CLS]",
       "[SEP]",
       "[PAD]",
       "[MASK]",
       "want",
       "##want",
       "##ed",
       "wa",
       "un",
       "runn",
       "##ing",
       ",",
       "low",
       "lowest",
   ]
   lowerCamelCase__: int         =os.path.join(self.tmpdirname			,					"dpr_tokenizer")
   os.makedirs(UpperCAmelCase_			,					exist_ok=UpperCAmelCase_)
   lowerCamelCase__: Any         =os.path.join(UpperCAmelCase_			,					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
   lowerCamelCase__: str         =[
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "\u0120",
       "\u0120l",
       "\u0120n",
       "\u0120lo",
       "\u0120low",
       "er",
       "\u0120lowest",
       "\u0120newer",
       "\u0120wider",
       "<unk>",
   ]
   lowerCamelCase__: Tuple         =dict(zip(UpperCAmelCase_			,					range(len(UpperCAmelCase_))))
   lowerCamelCase__: Tuple         =["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
   lowerCamelCase__: Any         ={"unk_token": "<unk>"}

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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->DPRQuestionEncoderTokenizer:



   '''simple docstring'''



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

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



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->BartTokenizer:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->str:



   '''simple docstring'''



   shutil.rmtree(self.tmpdirname)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =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							:  Union[str, Any])			->int:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.get_dummy_dataset()
   lowerCamelCase__: 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:
     lowerCamelCase__: int         =dataset
     lowerCamelCase__: str         =RagRetriever(
         UpperCAmelCase_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					)
   return retriever

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  bool)			->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: int         =self.get_dummy_dataset()
   lowerCamelCase__: int         =RagConfig(
       retrieval_vector_size=self.retrieval_vector_size			,					question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict()			,					index_name="custom"			,					)
   if from_disk:
     lowerCamelCase__: Optional[Any]         =os.path.join(self.tmpdirname			,					"dataset")
     lowerCamelCase__: Tuple         =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
     lowerCamelCase__: Dict         =RagRetriever(
         UpperCAmelCase_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					)
   else:
     lowerCamelCase__: Tuple         =RagRetriever(
         UpperCAmelCase_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					index=CustomHFIndex(config.retrieval_vector_size			,					UpperCAmelCase_)			,					)
   return retriever

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



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =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)

   lowerCamelCase__: Dict         =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"))

   lowerCamelCase__: Dict         =os.path.join(self.tmpdirname			,					"psgs_w100.tsv.pkl")
   lowerCamelCase__: str         ={sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
   pickle.dump(UpperCAmelCase_			,					open(UpperCAmelCase_			,					"wb"))

   lowerCamelCase__: Dict         =RagConfig(
       retrieval_vector_size=self.retrieval_vector_size			,					question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict()			,					index_name="legacy"			,					index_path=self.tmpdirname			,					)
   lowerCamelCase__: Any         =RagRetriever(
       UpperCAmelCase_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer())
   return retriever

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->List[str]:



   '''simple docstring'''



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



   '''simple docstring'''



   lowerCamelCase__: str         =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:
       lowerCamelCase__: List[Any]         =self.get_dummy_dataset()
       retriever.save_pretrained(UpperCAmelCase_)
       lowerCamelCase__: List[str]         =RagRetriever.from_pretrained(UpperCAmelCase_)
       self.assertIsInstance(UpperCAmelCase_			,					UpperCAmelCase_)
       lowerCamelCase__: Optional[Any]         =np.array(
           [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]			,					dtype=np.floataa)
       lowerCamelCase__: Any         =retriever.retrieve(UpperCAmelCase_			,					n_docs=1)
       self.assertTrue(out is not None)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->str:



   '''simple docstring'''



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



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Any:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =1
   lowerCamelCase__: Dict         =self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_)
   lowerCamelCase__: str         =np.array(
       [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]			,					dtype=np.floataa)
   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__: Optional[Any]         =retriever.retrieve(UpperCAmelCase_			,					n_docs=UpperCAmelCase_)
   self.assertEqual(retrieved_doc_embeds.shape			,					(2, n_docs, self.retrieval_vector_size))
   self.assertEqual(len(UpperCAmelCase_)			,					2)
   self.assertEqual(sorted(doc_dicts[0])			,					["embeddings", "id", "text", "title"])
   self.assertEqual(len(doc_dicts[0]["id"])			,					UpperCAmelCase_)
   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							:  Optional[int])			->Optional[int]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Any         =1
   lowerCamelCase__: Optional[int]         =self.get_dummy_legacy_index_retriever()
   lowerCamelCase__: Any         =np.array(
       [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]			,					dtype=np.floataa)
   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__: Optional[int]         =retriever.retrieve(UpperCAmelCase_			,					n_docs=UpperCAmelCase_)
   self.assertEqual(retrieved_doc_embeds.shape			,					(2, n_docs, self.retrieval_vector_size))
   self.assertEqual(len(UpperCAmelCase_)			,					2)
   self.assertEqual(sorted(doc_dicts[0])			,					["text", "title"])
   self.assertEqual(len(doc_dicts[0]["text"])			,					UpperCAmelCase_)
   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 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: str         =self.get_dummy_legacy_index_retriever()
   with tempfile.TemporaryDirectory() as tmp_dirname:
     retriever.save_pretrained(UpperCAmelCase_)
     lowerCamelCase__: Optional[int]         =RagRetriever.from_pretrained(UpperCAmelCase_)
     self.assertIsInstance(UpperCAmelCase_			,					UpperCAmelCase_)
     lowerCamelCase__: int         =np.array(
         [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]			,					dtype=np.floataa)
     lowerCamelCase__: Tuple         =retriever.retrieve(UpperCAmelCase_			,					n_docs=1)
     self.assertTrue(out is not None)

 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Union[str, Any]:



   '''simple docstring'''



   import torch

   lowerCamelCase__: Union[str, Any]         =1
   lowerCamelCase__: Optional[Any]         =self.get_dummy_canonical_hf_index_retriever()
   lowerCamelCase__: str         =[[5, 7], [10, 11]]
   lowerCamelCase__: List[str]         =np.array(
       [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]			,					dtype=np.floataa)
   lowerCamelCase__: Optional[int]         =retriever(UpperCAmelCase_			,					UpperCAmelCase_			,					prefix=retriever.config.generator.prefix			,					n_docs=UpperCAmelCase_)
   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__: Any         =(
       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(UpperCAmelCase_			,					UpperCAmelCase_)
   self.assertIsInstance(UpperCAmelCase_			,					UpperCAmelCase_)
   self.assertIsInstance(UpperCAmelCase_			,					np.ndarray)

   lowerCamelCase__: Optional[Any]         =retriever(
       UpperCAmelCase_			,					UpperCAmelCase_			,					prefix=retriever.config.generator.prefix			,					n_docs=UpperCAmelCase_			,					return_tensors="pt"			,					)
   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__: Optional[Any]         =(  # 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(UpperCAmelCase_			,					torch.Tensor)
   self.assertIsInstance(UpperCAmelCase_			,					torch.Tensor)
   self.assertIsInstance(UpperCAmelCase_			,					torch.Tensor)







 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->Any:



   '''simple docstring'''



   lowerCamelCase__: Any         =self.get_dpr_ctx_encoder_tokenizer()
   lowerCamelCase__: Dict         =1
   lowerCamelCase__: Tuple         =self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_)
   retriever.set_ctx_encoder_tokenizer(UpperCAmelCase_)

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

   self.assertEqual(
       len(UpperCAmelCase_)			,					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"))			,					UpperCAmelCase_)  # check for doc token related keys in dictionary.



import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)


def    a      ( a	)     ->List[Any]:




			'''simple docstring'''


			if hor == 128:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 32:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 64, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			SCREAMING_SNAKE_CASE     						= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
			SCREAMING_SNAKE_CASE     						= model.state_dict()
			SCREAMING_SNAKE_CASE     						= {
			    '''down_block_types''': down_block_types,
			    '''block_out_channels''': block_out_channels,
			    '''up_block_types''': up_block_types,
			    '''layers_per_block''': 1,
			    '''use_timestep_embedding''': True,
			    '''out_block_type''': '''OutConv1DBlock''',
			    '''norm_num_groups''': 8,
			    '''downsample_each_block''': False,
			    '''in_channels''': 14,
			    '''out_channels''': 14,
			    '''extra_in_channels''': 0,
			    '''time_embedding_type''': '''positional''',
			    '''flip_sin_to_cos''': False,
			    '''freq_shift''': 1,
			    '''sample_size''': 6_5536,
			    '''mid_block_type''': '''MidResTemporalBlock1D''',
			    '''act_fn''': '''mish''',
			}
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			print(F"""length of state dict: {len(state_dict.keys()	)}"""	)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()	)}"""	)
			SCREAMING_SNAKE_CASE     						= dict(zip(model.state_dict().keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)
			hf_value_function.load_state_dict(a	)

			torch.save(hf_value_function.state_dict()		,						F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""	)
			with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""		,						'''w'''	) as f:
						json.dump(a		,						a	)


def    a      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= {
			    '''in_channels''': 14,
			    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
			    '''up_block_types''': (),
			    '''out_block_type''': '''ValueFunction''',
			    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
			    '''block_out_channels''': (32, 64, 128, 256),
			    '''layers_per_block''': 1,
			    '''downsample_each_block''': True,
			    '''sample_size''': 6_5536,
			    '''out_channels''': 14,
			    '''extra_in_channels''': 0,
			    '''time_embedding_type''': '''positional''',
			    '''use_timestep_embedding''': True,
			    '''flip_sin_to_cos''': False,
			    '''freq_shift''': 1,
			    '''norm_num_groups''': 8,
			    '''act_fn''': '''mish''',
			}

			SCREAMING_SNAKE_CASE     						= torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''	)
			SCREAMING_SNAKE_CASE     						= model
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			print(F"""length of state dict: {len(state_dict.keys()	)}"""	)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()	)}"""	)

			SCREAMING_SNAKE_CASE     						= dict(zip(state_dict.keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)

			hf_value_function.load_state_dict(a	)

			torch.save(hf_value_function.state_dict()		,						'''hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'''	)
			with open('''hub/hopper-medium-v2/value_function/config.json'''		,						'''w'''	) as f:
						json.dump(a		,						a	)


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()