


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def    _snake_case (       __snake_case					,  __snake_case=0.999					,  __snake_case="cosine"					,  )     ->     int:






      '''simple docstring'''







      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      UpperCAmelCase_  :	Optional[Any]				      =		[]
      for i in range(lowerCamelCase__    ):
            UpperCAmelCase_  :	Optional[int]				      =		i / num_diffusion_timesteps
            UpperCAmelCase_  :	Any				      =		(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   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				[e.name for e in KarrasDiffusionSchedulers]
       _lowerCamelCase			    =				2



       @register_to_config
       def __init__(     self     ,lowercase = 1000     ,lowercase = 0.0_0085     ,lowercase = 0.012     ,lowercase = "linear"     ,lowercase = None     ,lowercase = "epsilon"     ,lowercase = "linspace"     ,lowercase = 0     ,):

             """simple docstring"""





             if trained_betas is not None:
                   UpperCAmelCase_  :	Tuple				      =		torch.tensor(lowercase     ,dtype=torch.floataa)
             elif beta_schedule == "linear":
                   UpperCAmelCase_  :	Union[str, Any]				      =		torch.linspace(lowercase     ,lowercase     ,lowercase     ,dtype=torch.floataa)
             elif beta_schedule == "scaled_linear":
                   # this schedule is very specific to the latent diffusion model.
                   UpperCAmelCase_  :	Optional[int]				      =		(
                       torch.linspace(beta_start**0.5     ,beta_end**0.5     ,lowercase     ,dtype=torch.floataa) ** 2
                   )
             elif beta_schedule == "squaredcos_cap_v2":
                   # Glide cosine schedule
                   UpperCAmelCase_  :	Union[str, Any]				      =		betas_for_alpha_bar(lowercase)
             else:
                   raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

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

             #  set all values
             self.set_timesteps(lowercase     ,lowercase     ,lowercase)



       def  A_   (     self     ,lowercase     ,lowercase=None):

             """simple docstring"""





             if schedule_timesteps is None:
                   UpperCAmelCase_  :	Optional[Any]				      =		self.timesteps

             UpperCAmelCase_  :	Tuple				      =		(schedule_timesteps == timestep).nonzero()

             # The sigma index that is taken for the **very** first `step`
             # is always the second index (or the last index if there is only 1)
             # This way we can ensure we don't accidentally skip a sigma in
             # case we start in the middle of the denoising schedule (e.g. for image-to-image)
             if len(self._index_counter) == 0:
                   UpperCAmelCase_  :	List[Any]				      =		1 if len(lowercase) > 1 else 0
             else:
                   UpperCAmelCase_  :	Any				      =		timestep.cpu().item() if torch.is_tensor(lowercase) else timestep
                   UpperCAmelCase_  :	Any				      =		self._index_counter[timestep_int]

             return indices[pos].item()



       @property
       def  A_   (     self):

             """simple docstring"""





             if self.config.timestep_spacing in ["linspace", "trailing"]:
                   return self.sigmas.max()

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



       def  A_   (     self     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.index_for_timestep(lowercase)

             if self.state_in_first_order:
                   UpperCAmelCase_  :	List[str]				      =		self.sigmas[step_index]
             else:
                   UpperCAmelCase_  :	Optional[Any]				      =		self.sigmas_interpol[step_index]

             UpperCAmelCase_  :	Tuple				      =		sample / ((sigma**2 + 1) ** 0.5)
             return sample



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		num_inference_steps

             UpperCAmelCase_  :	Any				      =		num_train_timesteps or self.config.num_train_timesteps

             # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
             if self.config.timestep_spacing == "linspace":
                   UpperCAmelCase_  :	Tuple				      =		np.linspace(0     ,num_train_timesteps - 1     ,lowercase     ,dtype=lowercase)[::-1].copy()
             elif self.config.timestep_spacing == "leading":
                   UpperCAmelCase_  :	Optional[Any]				      =		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
                   UpperCAmelCase_  :	Optional[int]				      =		(np.arange(0     ,lowercase) * step_ratio).round()[::-1].copy().astype(lowercase)
                   timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                   UpperCAmelCase_  :	str				      =		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
                   UpperCAmelCase_  :	Optional[Any]				      =		(np.arange(lowercase     ,0     ,-step_ratio)).round().copy().astype(lowercase)
                   timesteps -= 1
             else:
                   raise ValueError(
                       F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

             UpperCAmelCase_  :	str				      =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             UpperCAmelCase_  :	Any				      =		torch.from_numpy(np.log(lowercase)).to(lowercase)

             UpperCAmelCase_  :	Optional[Any]				      =		np.interp(lowercase     ,np.arange(0     ,len(lowercase))     ,lowercase)
             UpperCAmelCase_  :	Tuple				      =		np.concatenate([sigmas, [0.0]]).astype(np.floataa)
             UpperCAmelCase_  :	str				      =		torch.from_numpy(lowercase).to(device=lowercase)

             # interpolate sigmas
             UpperCAmelCase_  :	Optional[Any]				      =		sigmas.log().lerp(sigmas.roll(1).log()     ,0.5).exp()

             UpperCAmelCase_  :	List[str]				      =		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
             UpperCAmelCase_  :	List[Any]				      =		torch.cat(
                 [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

             if str(lowercase).startswith("mps"):
                   # mps does not support float64
                   UpperCAmelCase_  :	Any				      =		torch.from_numpy(lowercase).to(lowercase     ,dtype=torch.floataa)
             else:
                   UpperCAmelCase_  :	Optional[Any]				      =		torch.from_numpy(lowercase).to(lowercase)

             # interpolate timesteps
             UpperCAmelCase_  :	Tuple				      =		self.sigma_to_t(lowercase).to(lowercase     ,dtype=timesteps.dtype)
             UpperCAmelCase_  :	Tuple				      =		torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])     ,dim=-1).flatten()

             UpperCAmelCase_  :	List[Any]				      =		torch.cat([timesteps[:1], interleaved_timesteps])

             UpperCAmelCase_  :	int				      =		None

             # for exp beta schedules, such as the one for `pipeline_shap_e.py`
             # we need an index counter
             UpperCAmelCase_  :	Any				      =		defaultdict(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		sigma.log()

             # get distribution
             UpperCAmelCase_  :	List[Any]				      =		log_sigma - self.log_sigmas[:, None]

             # get sigmas range
             UpperCAmelCase_  :	Any				      =		dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
             UpperCAmelCase_  :	str				      =		low_idx + 1

             UpperCAmelCase_  :	Optional[int]				      =		self.log_sigmas[low_idx]
             UpperCAmelCase_  :	Optional[Any]				      =		self.log_sigmas[high_idx]

             # interpolate sigmas
             UpperCAmelCase_  :	Dict				      =		(low - log_sigma) / (low - high)
             UpperCAmelCase_  :	Tuple				      =		w.clamp(0     ,1)

             # transform interpolation to time range
             UpperCAmelCase_  :	Tuple				      =		(1 - w) * low_idx + w * high_idx
             UpperCAmelCase_  :	str				      =		t.view(sigma.shape)
             return t



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.sample is None



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase = True     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self.index_for_timestep(lowercase)

             # advance index counter by 1
             UpperCAmelCase_  :	str				      =		timestep.cpu().item() if torch.is_tensor(lowercase) else timestep
             self._index_counter[timestep_int] += 1

             if self.state_in_first_order:
                   UpperCAmelCase_  :	List[Any]				      =		self.sigmas[step_index]
                   UpperCAmelCase_  :	List[Any]				      =		self.sigmas_interpol[step_index + 1]
                   UpperCAmelCase_  :	Optional[Any]				      =		self.sigmas[step_index + 1]
             else:
                   # 2nd order / KDPM2's method
                   UpperCAmelCase_  :	Optional[Any]				      =		self.sigmas[step_index - 1]
                   UpperCAmelCase_  :	str				      =		self.sigmas_interpol[step_index]
                   UpperCAmelCase_  :	Dict				      =		self.sigmas[step_index]

             # currently only gamma=0 is supported. This usually works best anyways.
             # We can support gamma in the future but then need to scale the timestep before
             # passing it to the model which requires a change in API
             UpperCAmelCase_  :	Optional[int]				      =		0
             UpperCAmelCase_  :	Any				      =		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                   UpperCAmelCase_  :	List[Any]				      =		sigma_hat if self.state_in_first_order else sigma_interpol
                   UpperCAmelCase_  :	Dict				      =		sample - sigma_input * model_output
             elif self.config.prediction_type == "v_prediction":
                   UpperCAmelCase_  :	str				      =		sigma_hat if self.state_in_first_order else sigma_interpol
                   UpperCAmelCase_  :	Optional[int]				      =		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                       sample / (sigma_input**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`""")

             if self.state_in_first_order:
                   # 2. Convert to an ODE derivative for 1st order
                   UpperCAmelCase_  :	List[str]				      =		(sample - pred_original_sample) / sigma_hat
                   # 3. delta timestep
                   UpperCAmelCase_  :	Optional[int]				      =		sigma_interpol - sigma_hat

                   # store for 2nd order step
                   UpperCAmelCase_  :	Union[str, Any]				      =		sample
             else:
                   # DPM-Solver-2
                   # 2. Convert to an ODE derivative for 2nd order
                   UpperCAmelCase_  :	Dict				      =		(sample - pred_original_sample) / sigma_interpol

                   # 3. delta timestep
                   UpperCAmelCase_  :	int				      =		sigma_next - sigma_hat

                   UpperCAmelCase_  :	int				      =		self.sample
                   UpperCAmelCase_  :	Dict				      =		None

             UpperCAmelCase_  :	Any				      =		sample + derivative * dt

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		self.sigmas.to(device=original_samples.device     ,dtype=original_samples.dtype)
             if original_samples.device.type == "mps" and torch.is_floating_point(lowercase):
                   # mps does not support float64
                   UpperCAmelCase_  :	int				      =		self.timesteps.to(original_samples.device     ,dtype=torch.floataa)
                   UpperCAmelCase_  :	int				      =		timesteps.to(original_samples.device     ,dtype=torch.floataa)
             else:
                   UpperCAmelCase_  :	str				      =		self.timesteps.to(original_samples.device)
                   UpperCAmelCase_  :	int				      =		timesteps.to(original_samples.device)

             UpperCAmelCase_  :	str				      =		[self.index_for_timestep(lowercase     ,lowercase) for t in timesteps]

             UpperCAmelCase_  :	List[Any]				      =		sigmas[step_indices].flatten()
             while len(sigma.shape) < len(original_samples.shape):
                   UpperCAmelCase_  :	Dict				      =		sigma.unsqueeze(-1)

             UpperCAmelCase_  :	Dict				      =		original_samples + noise * sigma
             return noisy_samples



       def __len__(     self):

             """simple docstring"""





             return self.config.num_train_timesteps





import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		FunnelConfig.from_json_file(__snake_case    )
      print(F"""Building PyTorch model from configuration: {config}"""    )
      UpperCAmelCase_  :	str				      =		FunnelBaseModel(__snake_case    ) if base_model else FunnelModel(__snake_case    )

      # Load weights from tf checkpoint
      load_tf_weights_in_funnel(__snake_case					,  __snake_case					,  __snake_case    )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""    )
      torch.save(model.state_dict()					,  __snake_case    )


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
   )
   __lowerCamelCase         =			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
   )
