
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =None

def  lowerCamelCase__ (					_a							,			_a=0.999							,			_a="cosine"							,			):




	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_a):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(_a):
			return math.exp(t * -12.0)

	else:
		raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")

	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	for i in range(_lowerCAmelCase):
		SCREAMING_SNAKE_CASE   :      int				     =       i / num_diffusion_timesteps
		SCREAMING_SNAKE_CASE   :      str				     =       (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase) / alpha_bar_fn(_lowerCAmelCase)							,			_lowerCAmelCase))
	return torch.tensor(_lowerCAmelCase							,			dtype=torch.floataa)






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: Any      ,      a							: int = 1000      ,      a							: str = "fixed_small_log"      ,      a							: bool = True      ,      a							: Optional[float] = 1.0      ,      a							: str = "epsilon"      ,      a							: str = "squaredcos_cap_v2"      ,      )							->       Union[str, Any]:

				"""simple docstring"""

				if beta_schedule != "squaredcos_cap_v2":
					raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       betas_for_alpha_bar(__A	)

				SCREAMING_SNAKE_CASE   :      str				     =       1.0 - self.betas
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.cumprod(self.alphas      ,      dim=0	)
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor(1.0	)

				# standard deviation of the initial noise distribution
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       1.0

				# setable values
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       None
				SCREAMING_SNAKE_CASE   :      str				     =       torch.from_numpy(np.arange(0      ,      __A	)[::-1].copy()	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       variance_type
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: torch.FloatTensor      ,      a							: Optional[int] = None	)							->       Tuple:

				"""simple docstring"""

				return sample
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: int      ,      a							: Union[str, torch.device] = None	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       num_inference_steps
				SCREAMING_SNAKE_CASE   :      List[str]				     =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (np.arange(0      ,      __A	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.from_numpy(__A	).to(__A	)
			def 							__UpperCamelCase (			self							: str      ,      a							: Optional[Any]      ,      a							: str=None      ,      a							: str=None      ,      a							: Union[str, Any]=None	)							->       Dict:

				"""simple docstring"""

				if prev_timestep is None:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       t - 1

				SCREAMING_SNAKE_CASE   :      str				     =       self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				SCREAMING_SNAKE_CASE   :      int				     =       1 - alpha_prod_t
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					SCREAMING_SNAKE_CASE   :      Any				     =       self.betas[t]
				else:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       1 - alpha_prod_t / alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
				# and sample from it to get previous sample
				# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
				SCREAMING_SNAKE_CASE   :      str				     =       beta_prod_t_prev / beta_prod_t * beta

				if variance_type is None:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self.config.variance_type

				# hacks - were probably added for training stability
				if variance_type == "fixed_small_log":
					SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.log(torch.clamp(__A      ,      min=1e-20	)	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.exp(0.5 * variance	)
				elif variance_type == "learned_range":
					# NOTE difference with DDPM scheduler
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       variance.log()
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       beta.log()

					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (predicted_variance + 1) / 2
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       frac * max_log + (1 - frac) * min_log

				return variance
			def 							__UpperCamelCase (			self							: int      ,      a							: torch.FloatTensor      ,      a							: int      ,      a							: torch.FloatTensor      ,      a							: Optional[int] = None      ,      a							: Optional[Any]=None      ,      a							: bool = True      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.split(__A      ,      sample.shape[1]      ,      dim=1	)
				else:
					SCREAMING_SNAKE_CASE   :      int				     =       None

				# 1. compute alphas, betas
				if prev_timestep is None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       t - 1

				SCREAMING_SNAKE_CASE   :      str				     =       self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				SCREAMING_SNAKE_CASE   :      Any				     =       1 - alpha_prod_t
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.betas[t]
					SCREAMING_SNAKE_CASE   :      str				     =       self.alphas[t]
				else:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       1 - alpha_prod_t / alpha_prod_t_prev
					SCREAMING_SNAKE_CASE   :      Dict				     =       1 - beta

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if self.config.prediction_type == "epsilon":
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
					SCREAMING_SNAKE_CASE   :      str				     =       model_output
				else:
					raise ValueError(
					    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
					    " for the UnCLIPScheduler."	)

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
					SCREAMING_SNAKE_CASE   :      int				     =       torch.clamp(
					    __A      ,      -self.config.clip_sample_range      ,      self.config.clip_sample_range	)

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE   :      Tuple				     =       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       0
				if t > 0:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       randn_tensor(
					    model_output.shape      ,      dtype=model_output.dtype      ,      generator=__A      ,      device=model_output.device	)

					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self._get_variance(
					    __A      ,      predicted_variance=__A      ,      prev_timestep=__A      ,      )

					if self.variance_type == "fixed_small_log":
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       variance
					elif self.variance_type == "learned_range":
						SCREAMING_SNAKE_CASE   :      Any				     =       (0.5 * variance).exp()
					else:
						raise ValueError(
						    F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
						    " for the UnCLIPScheduler."	)

					SCREAMING_SNAKE_CASE   :      Tuple				     =       variance * variance_noise

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pred_prev_sample + variance

				if not return_dict:
					return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=__A      ,      pred_original_sample=__A	)






			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: torch.FloatTensor      ,      a							: torch.FloatTensor      ,      a							: torch.IntTensor      ,      )							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.alphas_cumprod.to(device=original_samples.device      ,      dtype=original_samples.dtype	)
				SCREAMING_SNAKE_CASE   :      str				     =       timesteps.to(original_samples.device	)

				SCREAMING_SNAKE_CASE   :      str				     =       alphas_cumprod[timesteps] ** 0.5
				SCREAMING_SNAKE_CASE   :      Dict				     =       sqrt_alpha_prod.flatten()
				while len(sqrt_alpha_prod.shape	) < len(original_samples.shape	):
					SCREAMING_SNAKE_CASE   :      str				     =       sqrt_alpha_prod.unsqueeze(-1	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (1 - alphas_cumprod[timesteps]) ** 0.5
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       sqrt_one_minus_alpha_prod.flatten()
				while len(sqrt_one_minus_alpha_prod.shape	) < len(original_samples.shape	):
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       sqrt_one_minus_alpha_prod.unsqueeze(-1	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples


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__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "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": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       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   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       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  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "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": 65536,
	    "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   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       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   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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(32)
			# unet(128)
			value_function()