




import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class        _a(    unittest.TestCase    ):
		lowerCamelCase__     :Optional[int]   				=		MODEL_FOR_MASKED_LM_MAPPING
		lowerCamelCase__     :Any   				=		TF_MODEL_FOR_MASKED_LM_MAPPING







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


							'''simple docstring'''

							super().tearDown()
							# clean-up as much as possible GPU memory occupied by PyTorch
							gc.collect()
							if is_torch_available():
												import torch

												torch.cuda.empty_cache()







		@require_tf
		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   pipeline(task="fill-mask" ,						model="sshleifer/tiny-distilroberta-base" ,						top_k=2 ,						framework="tf"				)
							_snake_case       :					Dict				       =   unmasker("My name is <mask>"				)
							self.assertEqual(
							    nested_simplify(lowercase_ ,						decimals=6				) ,						[
							        {"sequence": "My name is grouped", "score": 2.1E-05, "token": 3_8_0_1_5, "token_str": " grouped"},
							        {"sequence": "My name is accuser", "score": 2.1E-05, "token": 2_5_5_0_6, "token_str": " accuser"},
							    ] ,						)

							_snake_case       :					Union[str, Any]				       =   unmasker("The largest city in France is <mask>"				)
							self.assertEqual(
							    nested_simplify(lowercase_ ,						decimals=6				) ,						[
							        {
							            "sequence": "The largest city in France is grouped",
							            "score": 2.1E-05,
							            "token": 3_8_0_1_5,
							            "token_str": " grouped",
							        },
							        {
							            "sequence": "The largest city in France is accuser",
							            "score": 2.1E-05,
							            "token": 2_5_5_0_6,
							            "token_str": " accuser",
							        },
							    ] ,						)

							_snake_case       :					Tuple				       =   unmasker("My name is <mask>" ,						targets=[" Patrick", " Clara", " Teven"] ,						top_k=3				)
							self.assertEqual(
							    nested_simplify(lowercase_ ,						decimals=6				) ,						[
							        {"sequence": "My name is Clara", "score": 2E-05, "token": 1_3_6_0_6, "token_str": " Clara"},
							        {"sequence": "My name is Patrick", "score": 2E-05, "token": 3_4_9_9, "token_str": " Patrick"},
							        {"sequence": "My name is Te", "score": 1.9E-05, "token": 2_9_4_1, "token_str": " Te"},
							    ] ,						)







		@require_torch
		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   pipeline(task="fill-mask" ,						model="sshleifer/tiny-distilroberta-base" ,						top_k=2 ,						framework="pt"				)

							_snake_case       :					int				       =   unmasker("My name is <mask>"				)
							self.assertEqual(
							    nested_simplify(lowercase_ ,						decimals=6				) ,						[
							        {"sequence": "My name is Maul", "score": 2.2E-05, "token": 3_5_6_7_6, "token_str": " Maul"},
							        {"sequence": "My name isELS", "score": 2.2E-05, "token": 1_6_4_1_6, "token_str": "ELS"},
							    ] ,						)

							_snake_case       :					Optional[Any]				       =   unmasker("The largest city in France is <mask>"				)
							self.assertEqual(
							    nested_simplify(lowercase_ ,						decimals=6				) ,						[
							        {
							            "sequence": "The largest city in France is Maul",
							            "score": 2.2E-05,
							            "token": 3_5_6_7_6,
							            "token_str": " Maul",
							        },
							        {"sequence": "The largest city in France isELS", "score": 2.2E-05, "token": 1_6_4_1_6, "token_str": "ELS"},
							    ] ,						)

							_snake_case       :					Union[str, Any]				       =   unmasker("My name is <mask>" ,						targets=[" Patrick", " Clara", " Teven"] ,						top_k=3				)
							self.assertEqual(
							    nested_simplify(lowercase_ ,						decimals=6				) ,						[
							        {"sequence": "My name is Patrick", "score": 2.1E-05, "token": 3_4_9_9, "token_str": " Patrick"},
							        {"sequence": "My name is Te", "score": 2E-05, "token": 2_9_4_1, "token_str": " Te"},
							        {"sequence": "My name is Clara", "score": 2E-05, "token": 1_3_6_0_6, "token_str": " Clara"},
							    ] ,						)

							_snake_case       :					Optional[Any]				       =   unmasker("My name is <mask> <mask>" ,						top_k=2				)

							self.assertEqual(
							    nested_simplify(lowercase_ ,						decimals=6				) ,						[
							        [
							            {
							                "score": 2.2E-05,
							                "token": 3_5_6_7_6,
							                "token_str": " Maul",
							                "sequence": "<s>My name is Maul<mask></s>",
							            },
							            {"score": 2.2E-05, "token": 1_6_4_1_6, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
							        ],
							        [
							            {
							                "score": 2.2E-05,
							                "token": 3_5_6_7_6,
							                "token_str": " Maul",
							                "sequence": "<s>My name is<mask> Maul</s>",
							            },
							            {"score": 2.2E-05, "token": 1_6_4_1_6, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
							        ],
							    ] ,						)







		@require_torch_gpu
		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   pipeline("fill-mask" ,						model="hf-internal-testing/tiny-random-distilbert" ,						device=0 ,						framework="pt"				)

							# convert model to fp16
							pipe.model.half()

							_snake_case       :					Optional[int]				       =   pipe("Paris is the [MASK] of France."				)
							# We actually don't care about the result, we just want to make sure
							# it works, meaning the float16 tensor got casted back to float32
							# for postprocessing.
							self.assertIsInstance(lowercase_ ,						lowercase_				)







		@slow
		@require_torch
		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Any				       =   pipeline(task="fill-mask" ,						model="distilroberta-base" ,						top_k=2 ,						framework="pt"				)
							self.run_large_test(lowercase_				)







		@slow
		@require_tf
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   pipeline(task="fill-mask" ,						model="distilroberta-base" ,						top_k=2 ,						framework="tf"				)
							self.run_large_test(lowercase_				)







		def        lowercase       (    self ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   unmasker("My name is <mask>"				)
							self.assertEqual(
							    nested_simplify(lowercase_				) ,						[
							        {"sequence": "My name is John", "score": 0.0_08, "token": 6_1_0, "token_str": " John"},
							        {"sequence": "My name is Chris", "score": 0.0_07, "token": 1_5_7_3, "token_str": " Chris"},
							    ] ,						)
							_snake_case       :					str				       =   unmasker("The largest city in France is <mask>"				)
							self.assertEqual(
							    nested_simplify(lowercase_				) ,						[
							        {
							            "sequence": "The largest city in France is Paris",
							            "score": 0.2_51,
							            "token": 2_2_0_1,
							            "token_str": " Paris",
							        },
							        {
							            "sequence": "The largest city in France is Lyon",
							            "score": 0.2_14,
							            "token": 1_2_7_9_0,
							            "token_str": " Lyon",
							        },
							    ] ,						)

							_snake_case       :					Any				       =   unmasker("My name is <mask>" ,						targets=[" Patrick", " Clara", " Teven"] ,						top_k=3				)
							self.assertEqual(
							    nested_simplify(lowercase_				) ,						[
							        {"sequence": "My name is Patrick", "score": 0.0_05, "token": 3_4_9_9, "token_str": " Patrick"},
							        {"sequence": "My name is Clara", "score": 0.0_00, "token": 1_3_6_0_6, "token_str": " Clara"},
							        {"sequence": "My name is Te", "score": 0.0_00, "token": 2_9_4_1, "token_str": " Te"},
							    ] ,						)







		@require_torch
		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   pipeline(task="fill-mask" ,						model="sshleifer/tiny-distilroberta-base" ,						framework="pt"				)
							_snake_case       :					Tuple				       =   None
							_snake_case       :					Optional[int]				       =   None
							self.run_pipeline_test(lowercase_ ,						[]				)







		@require_tf
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   pipeline(task="fill-mask" ,						model="sshleifer/tiny-distilroberta-base" ,						framework="tf"				)
							_snake_case       :					Tuple				       =   None
							_snake_case       :					Optional[int]				       =   None
							self.run_pipeline_test(lowercase_ ,						[]				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      int:


							'''simple docstring'''

							if tokenizer is None or tokenizer.mask_token_id is None:
												self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"				)

							_snake_case       :					Optional[int]				       =   FillMaskPipeline(model=lowercase_ ,						tokenizer=lowercase_				)
							_snake_case       :					Tuple				       =   [
							    f"""This is another {tokenizer.mask_token} test""",
							]
							return fill_masker, examples







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Dict				       =   fill_masker.tokenizer
							_snake_case       :					List[Any]				       =   fill_masker.model

							_snake_case       :					Any				       =   fill_masker(
							    f"""This is a {tokenizer.mask_token}""" ,						)
							self.assertEqual(
							    lowercase_ ,						[
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							    ] ,						)

							_snake_case       :					Optional[int]				       =   fill_masker([f"""This is a {tokenizer.mask_token}"""]				)
							self.assertEqual(
							    lowercase_ ,						[
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							    ] ,						)

							_snake_case       :					Optional[int]				       =   fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""]				)
							self.assertEqual(
							    lowercase_ ,						[
							        [
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        ],
							        [
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        ],
							    ] ,						)

							with self.assertRaises(lowercase_				):
												fill_masker([None]				)
							# No mask_token is not supported
							with self.assertRaises(lowercase_				):
												fill_masker("This is"				)

							self.run_test_top_k(lowercase_ ,						lowercase_				)
							self.run_test_targets(lowercase_ ,						lowercase_				)
							self.run_test_top_k_targets(lowercase_ ,						lowercase_				)
							self.fill_mask_with_duplicate_targets_and_top_k(lowercase_ ,						lowercase_				)
							self.fill_mask_with_multiple_masks(lowercase_ ,						lowercase_				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   tokenizer.get_vocab()
							_snake_case       :					Dict				       =   sorted(vocab.keys()				)[:2]
							# Pipeline argument
							_snake_case       :					List[str]				       =   FillMaskPipeline(model=lowercase_ ,						tokenizer=lowercase_ ,						targets=lowercase_				)
							_snake_case       :					int				       =   fill_masker(f"""This is a {tokenizer.mask_token}"""				)
							self.assertEqual(
							    lowercase_ ,						[
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							    ] ,						)
							_snake_case       :					Optional[Any]				       =   {vocab[el] for el in targets}
							self.assertEqual({el["token"] for el in outputs} ,						lowercase_				)
							_snake_case       :					Any				       =   [tokenizer.decode([x]				) for x in target_ids]
							self.assertEqual({el["token_str"] for el in outputs} ,						set(lowercase_				)				)

							# Call argument
							_snake_case       :					Union[str, Any]				       =   FillMaskPipeline(model=lowercase_ ,						tokenizer=lowercase_				)
							_snake_case       :					Dict				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						targets=lowercase_				)
							self.assertEqual(
							    lowercase_ ,						[
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							    ] ,						)
							_snake_case       :					List[str]				       =   {vocab[el] for el in targets}
							self.assertEqual({el["token"] for el in outputs} ,						lowercase_				)
							_snake_case       :					Optional[int]				       =   [tokenizer.decode([x]				) for x in target_ids]
							self.assertEqual({el["token_str"] for el in outputs} ,						set(lowercase_				)				)

							# Score equivalence
							_snake_case       :					Tuple				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						targets=lowercase_				)
							_snake_case       :					List[str]				       =   [top_mask["token_str"] for top_mask in outputs]
							_snake_case       :					Dict				       =   [top_mask["score"] for top_mask in outputs]

							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(lowercase_				) == set(lowercase_				):
												_snake_case       :					Dict				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						targets=lowercase_				)
												_snake_case       :					Tuple				       =   [top_mask["score"] for top_mask in unmasked_targets]
												self.assertEqual(nested_simplify(lowercase_				) ,						nested_simplify(lowercase_				)				)

							# Raises with invalid
							with self.assertRaises(lowercase_				):
												_snake_case       :					Tuple				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						targets=[]				)
							# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
							if "" not in tokenizer.get_vocab():
												with self.assertRaises(lowercase_				):
																	_snake_case       :					Union[str, Any]				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						targets=[""]				)
												with self.assertRaises(lowercase_				):
																	_snake_case       :					Optional[Any]				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						targets=""				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Dict				       =   FillMaskPipeline(model=lowercase_ ,						tokenizer=lowercase_ ,						top_k=2				)
							_snake_case       :					int				       =   fill_masker(f"""This is a {tokenizer.mask_token}"""				)
							self.assertEqual(
							    lowercase_ ,						[
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							    ] ,						)

							_snake_case       :					List[str]				       =   FillMaskPipeline(model=lowercase_ ,						tokenizer=lowercase_				)
							_snake_case       :					List[Any]				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						top_k=2				)
							self.assertEqual(
							    lowercase_ ,						[
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							    ] ,						)
							self.assertEqual(nested_simplify(lowercase_				) ,						nested_simplify(lowercase_				)				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   tokenizer.get_vocab()
							_snake_case       :					Union[str, Any]				       =   FillMaskPipeline(model=lowercase_ ,						tokenizer=lowercase_				)

							# top_k=2, ntargets=3
							_snake_case       :					Union[str, Any]				       =   sorted(vocab.keys()				)[:3]
							_snake_case       :					int				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						top_k=2 ,						targets=lowercase_				)

							# If we use the most probably targets, and filter differently, we should still
							# have the same results
							_snake_case       :					Union[str, Any]				       =   [el["token_str"] for el in sorted(lowercase_ ,						key=lambda __snake_case				: x["score"] ,						reverse=lowercase_				)]
							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(lowercase_				).issubset(lowercase_				):
												_snake_case       :					List[Any]				       =   fill_masker(f"""This is a {tokenizer.mask_token}""" ,						top_k=3 ,						targets=lowercase_				)
												# They should yield exactly the same result
												self.assertEqual(nested_simplify(lowercase_				) ,						nested_simplify(lowercase_				)				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   FillMaskPipeline(model=lowercase_ ,						tokenizer=lowercase_				)
							_snake_case       :					Tuple				       =   tokenizer.get_vocab()
							# String duplicates + id duplicates
							_snake_case       :					str				       =   sorted(vocab.keys()				)[:3]
							_snake_case       :					int				       =   [targets[0], targets[1], targets[0], targets[2], targets[1]]
							_snake_case       :					str				       =   fill_masker(f"""My name is {tokenizer.mask_token}""" ,						targets=lowercase_ ,						top_k=1_0				)

							# The target list contains duplicates, so we can't output more
							# than them
							self.assertEqual(len(lowercase_				) ,						3				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   FillMaskPipeline(model=lowercase_ ,						tokenizer=lowercase_				)

							_snake_case       :					Union[str, Any]				       =   fill_masker(
							    f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}""" ,						top_k=2				)
							self.assertEqual(
							    lowercase_ ,						[
							        [
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        ],
							        [
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        ],
							        [
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							            {"sequence": ANY(lowercase_				), "score": ANY(lowercase_				), "token": ANY(lowercase_				), "token_str": ANY(lowercase_				)},
							        ],
							    ] ,						)






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				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										_snake_case       :					List[Any]				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Optional[int]				       =   (32, 64, 128, 256)
										_snake_case       :					int				       =   ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					_snake_case       :					Dict				       =   torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
					_snake_case       :					Dict				       =   model.state_dict()
					_snake_case       :					Tuple				       =   {
					    "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": 65_536,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					_snake_case       :					Any				       =   UNetaDModel(**UpperCAmelCase				)
					print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)
					_snake_case       :					List[Any]				       =   dict(zip(model.state_dict().keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Optional[int]				       =   state_dict.pop(UpperCAmelCase				)
					hf_value_function.load_state_dict(UpperCAmelCase				)

					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(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					Any				       =   {
					    "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": 65_536,
					    "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",
					}

					_snake_case       :					Dict				       =   torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"				)
					_snake_case       :					Optional[int]				       =   model
					_snake_case       :					List[str]				       =   UNetaDModel(**UpperCAmelCase				)
					print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)

					_snake_case       :					Optional[int]				       =   dict(zip(state_dict.keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Any				       =   state_dict.pop(UpperCAmelCase				)

					hf_value_function.load_state_dict(UpperCAmelCase				)

					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(UpperCAmelCase  ,  UpperCAmelCase				)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()