


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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   Dict:





	"""simple docstring"""







	if hor == 1_28:
		A								=				("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
		A								=				(32, 1_28, 2_56)
		A								=				("""UpResnetBlock1D""", """UpResnetBlock1D""")

	elif hor == 32:
		A								=				("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
		A								=				(32, 64, 1_28, 2_56)
		A								=				("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
	A								=				torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'						)
	A								=				model.state_dict()
	A								=				{
	    """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_55_36,
	    """mid_block_type""": """MidResTemporalBlock1D""",
	    """act_fn""": """mish""",
	}
	A								=				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()						)}'						)
	A								=				dict(zip(model.state_dict().keys()							,					hf_value_function.state_dict().keys()						)						)
	for k, v in mapping.items():
		A								=				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   _lowerCAmelCase				(   ) ->   Optional[int]:





	"""simple docstring"""







	A								=				{
	    """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, 1_28, 2_56),
	    """layers_per_block""": 1,
	    """downsample_each_block""": True,
	    """sample_size""": 6_55_36,
	    """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""",
	}

	A								=				torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
	A								=				model
	A								=				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()						)}'						)

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







import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
				from transformers import SpeechaTextFeatureExtractor

_lowercase       :      Tuple	=	random.Random()





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]							,					UpperCamelCase__:     str=1.0							,					UpperCamelCase__:     int=None							,					UpperCamelCase__:     Optional[Any]=None						) ->   str:





	"""simple docstring"""







	if rng is None:
		A								=				global_rng

	A								=				[]
	for batch_idx in range(shape[0]						):
		values.append([]						)
		for _ in range(shape[1]						):
			values[-1].append(rng.random() * scale						)

	return values






@require_torch
@require_torchaudio
class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__=7				,   a__=400				,   a__=2000				,   a__=24				,   a__=24				,   a__=0.0				,   a__=1_6000				,   a__=True				,   a__=True				,   )	->				Any:
					A								=				parent
					A								=				batch_size
					A								=				min_seq_length
					A								=				max_seq_length
					A								=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					A								=				feature_size
					A								=				num_mel_bins
					A								=				padding_value
					A								=				sampling_rate
					A								=				return_attention_mask
					A								=				do_normalize

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					return {
					    "feature_size": self.feature_size,
					    "num_mel_bins": self.num_mel_bins,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}

				def      _UpperCAmelCase					(						self				,   a__=False				,   a__=False   )	->				Tuple:
					def _flatten(a__   ):
						return list(itertools.chain(*a__   )   )

					if equal_length:
						A								=				[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
					else:
						# make sure that inputs increase in size
						A								=				[
						    floats_list((x, self.feature_size)   )
						    for x in range(self.min_seq_length				,   self.max_seq_length				,   self.seq_length_diff   )
						]
					if numpify:
						A								=				[np.asarray(a__   ) for x in speech_inputs]
					return speech_inputs






@require_torch
@require_torchaudio
class       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= SpeechaTextFeatureExtractor if is_speech_available() else None

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				SpeechaTextFeatureExtractionTester(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Any:
					self.assertTrue(np.all(np.mean(a__				,   axis=0   ) < 1e-3   )   )
					self.assertTrue(np.all(np.abs(np.var(a__				,   axis=0   ) - 1   ) < 1e-3   )   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					# create three inputs of length 800, 1000, and 1200
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				[np.asarray(a__   ) for speech_input in speech_inputs]

					# Test feature size
					A								=				feature_extractor(a__				,   padding=a__				,   return_tensors="""np"""   ).input_features
					self.assertTrue(input_features.ndim == 3   )
					self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size   )

					# Test not batched input
					A								=				feature_extractor(speech_inputs[0]				,   return_tensors="""np"""   ).input_features
					A								=				feature_extractor(np_speech_inputs[0]				,   return_tensors="""np"""   ).input_features
					self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

					# Test batched
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					for enc_seq_a, enc_seq_a in zip(a__				,   a__   ):
						self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

					# Test 2-D numpy arrays are batched.
					A								=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
					A								=				np.asarray(a__   )
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					for enc_seq_a, enc_seq_a in zip(a__				,   a__   ):
						self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]

					A								=				["""longest""", """max_length""", """do_not_pad"""]
					A								=				[None, 16, None]
					for max_length, padding in zip(a__				,   a__   ):
						A								=				feature_extractor(
						    a__				,   padding=a__				,   max_length=a__				,   return_attention_mask=a__   )
						A								=				inputs.input_features
						A								=				inputs.attention_mask
						A								=				[np.sum(a__   ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]

					A								=				["""longest""", """max_length""", """do_not_pad"""]
					A								=				[None, 16, None]
					for max_length, padding in zip(a__				,   a__   ):
						A								=				feature_extractor(
						    a__				,   max_length=a__				,   padding=a__				,   return_tensors="""np"""				,   return_attention_mask=a__   )
						A								=				inputs.input_features
						A								=				inputs.attention_mask
						A								=				[np.sum(a__   ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
						self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6   )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
						self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6   )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""max_length"""				,   max_length=4				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""longest"""				,   max_length=4				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape				,   (3, 4, 24)   )

					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""longest"""				,   max_length=16				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape				,   (3, 6, 24)   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					import torch

					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				np.random.rand(100				,   32   ).astype(np.floataa   )
					A								=				np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
						A								=				feature_extractor.pad([{"""input_features""": inputs}]				,   return_tensors="""np"""   )
						self.assertTrue(np_processed.input_features.dtype == np.floataa   )
						A								=				feature_extractor.pad([{"""input_features""": inputs}]				,   return_tensors="""pt"""   )
						self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					from datasets import load_dataset

					A								=				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,   """clean"""				,   split="""validation"""   )
					# automatic decoding with librispeech
					A								=				ds.sort("""id"""   ).select(range(a__   )   )[:num_samples]["""audio"""]

					return [x["array"] for x in speech_samples]

				def      _UpperCAmelCase					(						self   )	->				Any:
					# fmt: off
					A								=				np.array([
					    -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
					    -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
					    -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
					]   )
					# fmt: on

					A								=				self._load_datasamples(1   )
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				feature_extractor(a__				,   return_tensors="""pt"""   ).input_features
					self.assertEquals(input_features.shape				,   (1, 584, 24)   )
					self.assertTrue(np.allclose(input_features[0, 0, :30]				,   a__				,   atol=1e-4   )   )



