


'''simple docstring'''

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 						__SCREAMING_SNAKE_CASE					(      snake_case__ ):
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				None







def __UpperCamelCase   (			__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Any=0.9_99      ,  __lowerCamelCase		: Union[str, Any]="cosine"      ,  )      ->     Optional[int]:







     '''simple docstring'''






     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__lowerCamelCase		: List[Any]				):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__lowerCamelCase		: List[Any]				):
               return math.exp(t * -12.0				)

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

     _a						=		[]
     for i in range(_SCREAMING_SNAKE_CASE				):
          _a						=		i / num_diffusion_timesteps
          _a						=		(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE				) / alpha_bar_fn(_SCREAMING_SNAKE_CASE				)      ,  _SCREAMING_SNAKE_CASE				)				)
     return torch.tensor(_SCREAMING_SNAKE_CASE      ,  dtype=torch.floataa				)






class 						__SCREAMING_SNAKE_CASE					(      snake_case__ ,			snake_case__ ):






     @register_to_config
     def __init__(			self     ,							__UpperCamelCase = 1_000     ,							__UpperCamelCase = "fixed_small_log"     ,							__UpperCamelCase = True     ,							__UpperCamelCase = 1.0     ,							__UpperCamelCase = "epsilon"     ,							__UpperCamelCase = "squaredcos_cap_v2"     ,							)       ->    Optional[int]:



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

          _a						=		betas_for_alpha_bar(UpperCAmelCase_		)

          _a						=		1.0 - self.betas
          _a						=		torch.cumprod(self.alphas     ,							dim=0		)
          _a						=		torch.tensor(1.0		)

          # standard deviation of the initial noise distribution
          _a						=		1.0

          # setable values
          _a						=		None
          _a						=		torch.from_numpy(np.arange(0     ,							UpperCAmelCase_		)[::-1].copy()		)

          _a						=		variance_type






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None		)       ->    torch.FloatTensor:
          return sample






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None		)       ->    Tuple:
          _a						=		num_inference_steps
          _a						=		(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
          _a						=		(np.arange(0     ,							UpperCAmelCase_		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
          _a						=		torch.from_numpy(UpperCAmelCase_		).to(UpperCAmelCase_		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None		)       ->    str:



          if prev_timestep is None:
               _a						=		t - 1

          _a						=		self.alphas_cumprod[t]
          _a						=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          _a						=		1 - alpha_prod_t
          _a						=		1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
               _a						=		self.betas[t]
          else:
               _a						=		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
          _a						=		beta_prod_t_prev / beta_prod_t * beta

          if variance_type is None:
               _a						=		self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small_log":
               _a						=		torch.log(torch.clamp(UpperCAmelCase_     ,							min=1e-20		)		)
               _a						=		torch.exp(0.5 * variance		)
          elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               _a						=		variance.log()
               _a						=		beta.log()

               _a						=		(predicted_variance + 1) / 2
               _a						=		frac * max_log + (1 - frac) * min_log

          return variance






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase=None     ,							__UpperCamelCase = True     ,							)       ->    Union[UnCLIPSchedulerOutput, Tuple]:
          _a						=		timestep

          if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
               _a			,			_a						=		torch.split(UpperCAmelCase_     ,							sample.shape[1]     ,							dim=1		)
          else:
               _a						=		None

          # 1. compute alphas, betas
          if prev_timestep is None:
               _a						=		t - 1

          _a						=		self.alphas_cumprod[t]
          _a						=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          _a						=		1 - alpha_prod_t
          _a						=		1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
               _a						=		self.betas[t]
               _a						=		self.alphas[t]
          else:
               _a						=		1 - alpha_prod_t / alpha_prod_t_prev
               _a						=		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":
               _a						=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
               _a						=		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:
               _a						=		torch.clamp(
                   UpperCAmelCase_     ,							-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
          _a						=		(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
          _a						=		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
          _a						=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          _a						=		0
          if t > 0:
               _a						=		randn_tensor(
                   model_output.shape     ,							dtype=model_output.dtype     ,							generator=UpperCAmelCase_     ,							device=model_output.device		)

               _a						=		self._get_variance(
                   UpperCAmelCase_     ,							predicted_variance=UpperCAmelCase_     ,							prev_timestep=UpperCAmelCase_     ,							)

               if self.variance_type == "fixed_small_log":
                    _a						=		variance
               elif self.variance_type == "learned_range":
                    _a						=		(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."		)

               _a						=		variance * variance_noise

          _a						=		pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample,)

          return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase_     ,							pred_original_sample=UpperCAmelCase_		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							)       ->    torch.FloatTensor:
          _a						=		self.alphas_cumprod.to(device=original_samples.device     ,							dtype=original_samples.dtype		)
          _a						=		timesteps.to(original_samples.device		)

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

          _a						=		(1 - alphas_cumprod[timesteps]) ** 0.5
          _a						=		sqrt_one_minus_alpha_prod.flatten()
          while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
               _a						=		sqrt_one_minus_alpha_prod.unsqueeze(-1		)

          _a						=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
          return noisy_samples





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def __UpperCamelCase   (			__lowerCamelCase		: BertModel      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     List[str]:







     '''simple docstring'''






     _a						=		("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _a						=		(
         ("layer.", "layer_"),
         ("word_embeddings.weight", "word_embeddings"),
         ("position_embeddings.weight", "position_embeddings"),
         ("token_type_embeddings.weight", "token_type_embeddings"),
         (".", "/"),
         ("LayerNorm/weight", "LayerNorm/gamma"),
         ("LayerNorm/bias", "LayerNorm/beta"),
         ("weight", "kernel"),
     )

     if not os.path.isdir(__lowerCamelCase				):
          os.makedirs(__lowerCamelCase				)

     _a						=		model.state_dict()

     def to_tf_var_name(__lowerCamelCase		: str				):
          for patt, repl in iter(__lowerCamelCase				):
               _a						=		name.replace(__lowerCamelCase      ,  __lowerCamelCase				)
          return F"bert/{name}"

     def create_tf_var(__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: tf.Session				):
          _a						=		tf.dtypes.as_dtype(tensor.dtype				)
          _a						=		tf.get_variable(dtype=__lowerCamelCase      ,  shape=tensor.shape      ,  name=__lowerCamelCase      ,  initializer=tf.zeros_initializer()				)
          session.run(tf.variables_initializer([tf_var]				)				)
          session.run(__lowerCamelCase				)
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               _a						=		to_tf_var_name(__lowerCamelCase				)
               _a						=		state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose				):
                    _a						=		torch_tensor.T
               _a						=		create_tf_var(tensor=__lowerCamelCase      ,  name=__lowerCamelCase      ,  session=__lowerCamelCase				)
               tf.keras.backend.set_value(__lowerCamelCase      ,  __lowerCamelCase				)
               _a						=		session.run(__lowerCamelCase				)
               print(F"Successfully created {tf_name}: {np.allclose(__lowerCamelCase      ,  __lowerCamelCase				)}"				)

          _a						=		tf.train.Saver(tf.trainable_variables()				)
          saver.save(__lowerCamelCase      ,  os.path.join(__lowerCamelCase      ,  model_name.replace("-"      ,  "_"				) + ".ckpt"				)				)






def __UpperCamelCase   (			__lowerCamelCase		: str=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--model_name"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="model name e.g. bert-base-uncased"				)
     parser.add_argument(
         "--cache_dir"      ,  type=__lowerCamelCase      ,  default=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory containing pytorch model"				)
     parser.add_argument("--pytorch_model_path"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="/path/to/<pytorch-model-name>.bin"				)
     parser.add_argument("--tf_cache_dir"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory in which to save tensorflow model"				)
     _a						=		parser.parse_args(__lowerCamelCase				)

     _a						=		BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name      ,  state_dict=torch.load(args.pytorch_model_path				)      ,  cache_dir=args.cache_dir      ,  )

     convert_pytorch_checkpoint_to_tf(model=__lowerCamelCase      ,  ckpt_dir=args.tf_cache_dir      ,  model_name=args.model_name				)


if __name__ == "__main__":
   main()

