



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

import numpy as np
import torch

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


snake_case     :    List[Any]						=    logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 				lowerCAmelCase__	(						UpperCamelCase    ):
					__A  :     torch.FloatTensor
					__A  :     Optional[torch.FloatTensor]     =    None




def 						snake_case__							(       __lowercase				,  __lowercase=0.999				,  __lowercase="cosine"				,  )		->		Optional[int]:







						"""simple docstring"""

						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

						else:
												raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'							)

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




class 				lowerCAmelCase__	(						UpperCamelCase       ,				UpperCamelCase    ):
					__A  :     Optional[Any]     =    [e.name for e in KarrasDiffusionSchedulers]
					__A  :     Tuple     =    1







					@register_to_config
					def __init__(							self   :  Optional[int]      ,		_A   :  int = 1000      ,		_A   :  float = 0.00_01      ,		_A   :  float = 0.02      ,		_A   :  str = "linear"      ,		_A   :  Optional[Union[np.ndarray, List[float]]] = None      ,		_A   :  str = "epsilon"      ,		_A   :  str = "linspace"      ,		_A   :  int = 0      ,		):
											if trained_betas is not None:
																	A__						:				Any         =			torch.tensor(_A      ,		dtype=torch.floataa)
											elif beta_schedule == "linear":
																	A__						:				Union[str, Any]         =			torch.linspace(_A      ,		_A      ,		_A      ,		dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	A__						:				Optional[int]         =			(
																	    torch.linspace(beta_start**0.5      ,		beta_end**0.5      ,		_A      ,		dtype=torch.floataa) ** 2
																	)
											elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	A__						:				List[str]         =			betas_for_alpha_bar(_A)
											else:
																	raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

											A__						:				int         =			1.0 - self.betas
											A__						:				Any         =			torch.cumprod(self.alphas      ,		dim=0)

											A__						:				Any         =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
											A__						:				Dict         =			np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
											A__						:				Tuple         =			torch.from_numpy(_A)

											# setable values
											A__						:				Union[str, Any]         =			None
											A__						:				Any         =			np.linspace(0      ,		num_train_timesteps - 1      ,		_A      ,		dtype=_A)[::-1].copy()
											A__						:				List[Any]         =			torch.from_numpy(_A)
											A__						:				Union[str, Any]         =			False







					@property
					def 			_lowercase      (							self   :  Union[str, Any]):
											# standard deviation of the initial noise distribution
											if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5







					def 			_lowercase      (							self   :  List[str]      ,		_A   :  torch.FloatTensor      ,		_A   :  Union[float, torch.FloatTensor]):
											if isinstance(_A      ,		torch.Tensor):
																	A__						:				Dict         =			timestep.to(self.timesteps.device)
											A__						:				Tuple         =			(self.timesteps == timestep).nonzero().item()
											A__						:				int         =			self.sigmas[step_index]
											A__						:				str         =			sample / ((sigma**2 + 1) ** 0.5)
											A__						:				str         =			True
											return sample







					def 			_lowercase      (							self   :  int      ,		_A   :  int      ,		_A   :  Union[str, torch.device] = None):
											A__						:				str         =			num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																	A__						:				List[str]         =			np.linspace(0      ,		self.config.num_train_timesteps - 1      ,		_A      ,		dtype=_A)[
																	    ::-1
																	].copy()
											elif self.config.timestep_spacing == "leading":
																	A__						:				str         =			self.config.num_train_timesteps // self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	A__						:				List[str]         =			(np.arange(0      ,		_A) * step_ratio).round()[::-1].copy().astype(_A)
																	timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																	A__						:				Optional[Any]         =			self.config.num_train_timesteps / self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	A__						:				Any         =			(np.arange(self.config.num_train_timesteps      ,		0      ,		-step_ratio)).round().copy().astype(_A)
																	timesteps -= 1
											else:
																	raise ValueError(
																	    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.')

											A__						:				int         =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
											A__						:				Optional[Any]         =			np.interp(_A      ,		np.arange(0      ,		len(_A))      ,		_A)
											A__						:				Optional[Any]         =			np.concatenate([sigmas, [0.0]]).astype(np.floataa)
											A__						:				Dict         =			torch.from_numpy(_A).to(device=_A)
											if str(_A).startswith("mps"):
																	# mps does not support float64
																	A__						:				List[str]         =			torch.from_numpy(_A).to(_A      ,		dtype=torch.floataa)
											else:
																	A__						:				str         =			torch.from_numpy(_A).to(device=_A)







					def 			_lowercase      (							self   :  Any      ,		_A   :  torch.FloatTensor      ,		_A   :  Union[float, torch.FloatTensor]      ,		_A   :  torch.FloatTensor      ,		_A   :  Optional[torch.Generator] = None      ,		_A   :  bool = True      ,		):

											if (
											    isinstance(_A      ,		_A)
											    or isinstance(_A      ,		torch.IntTensor)
											    or isinstance(_A      ,		torch.LongTensor)
											):
																	raise ValueError(
																	    (
																	        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
																	        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
																	        " one of the `scheduler.timesteps` as a timestep."
																	    )      ,		)

											if not self.is_scale_input_called:
																	logger.warning(
																	    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
																	    "See `StableDiffusionPipeline` for a usage example.")

											if isinstance(_A      ,		torch.Tensor):
																	A__						:				Union[str, Any]         =			timestep.to(self.timesteps.device)

											A__						:				List[Any]         =			(self.timesteps == timestep).nonzero().item()
											A__						:				Union[str, Any]         =			self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																	A__						:				Optional[Any]         =			sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
																	# * c_out + input * c_skip
																	A__						:				Optional[Any]         =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											elif self.config.prediction_type == "sample":
																	raise NotImplementedError("prediction_type not implemented yet: sample")
											else:
																	raise ValueError(
																	    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`')

											A__						:				Union[str, Any]         =			self.sigmas[step_index]
											A__						:				str         =			self.sigmas[step_index + 1]
											A__						:				Optional[Any]         =			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
											A__						:				Any         =			(sigma_to**2 - sigma_up**2) ** 0.5

											# 2. Convert to an ODE derivative
											A__						:				Optional[Any]         =			(sample - pred_original_sample) / sigma

											A__						:				Tuple         =			sigma_down - sigma

											A__						:				Optional[int]         =			sample + derivative * dt

											A__						:				Optional[Any]         =			model_output.device
											A__						:				Union[str, Any]         =			randn_tensor(model_output.shape      ,		dtype=model_output.dtype      ,		device=_A      ,		generator=_A)

											A__						:				List[Any]         =			prev_sample + noise * sigma_up

											if not return_dict:
																	return (prev_sample,)

											return EulerAncestralDiscreteSchedulerOutput(
											    prev_sample=_A      ,		pred_original_sample=_A)







					def 			_lowercase      (							self   :  str      ,		_A   :  torch.FloatTensor      ,		_A   :  torch.FloatTensor      ,		_A   :  torch.FloatTensor      ,		):
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											A__						:				str         =			self.sigmas.to(device=original_samples.device      ,		dtype=original_samples.dtype)
											if original_samples.device.type == "mps" and torch.is_floating_point(_A):
																	# mps does not support float64
																	A__						:				Dict         =			self.timesteps.to(original_samples.device      ,		dtype=torch.floataa)
																	A__						:				Tuple         =			timesteps.to(original_samples.device      ,		dtype=torch.floataa)
											else:
																	A__						:				Any         =			self.timesteps.to(original_samples.device)
																	A__						:				Dict         =			timesteps.to(original_samples.device)

											A__						:				List[Any]         =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

											A__						:				int         =			sigmas[step_indices].flatten()
											while len(sigma.shape) < len(original_samples.shape):
																	A__						:				Tuple         =			sigma.unsqueeze(-1)

											A__						:				int         =			original_samples + noise * sigma
											return noisy_samples







					def __len__(							self   :  Any):
											return self.config.num_train_timesteps




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)