
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     UpperCamelCase    (      _a       ,		_a=0.999       ,		_a="cosine"       ,		)					->							Dict:
     '''simple docstring'''





     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}"		)

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






class      UpperCamelCase					(		lowercase__						,      lowercase__     ):


   '''simple docstring'''







   lowercase					:	Tuple									=[e.name for e in KarrasDiffusionSchedulers]
   lowercase					:	Tuple									=2
   @register_to_config
   def __init__(     self   ,							UpperCamelCase_ = 1000   ,							UpperCamelCase_ = 0.0_0085   ,							UpperCamelCase_ = 0.012   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = "epsilon"   ,							UpperCamelCase_ = False   ,							UpperCamelCase_ = False   ,							UpperCamelCase_ = 1.0   ,							UpperCamelCase_ = "linspace"   ,							UpperCamelCase_ = 0   ,							):
        if trained_betas is not None:
             lowercase_       :int     =       torch.tensor(UpperCamelCase_   ,							dtype=torch.floataa		)
        elif beta_schedule == "linear":
             lowercase_       :List[str]     =       torch.linspace(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							dtype=torch.floataa		)
        elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             lowercase_       :int     =       (
                 torch.linspace(beta_start**0.5   ,							beta_end**0.5   ,							UpperCamelCase_   ,							dtype=torch.floataa		) ** 2
             )
        elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             lowercase_       :Optional[int]     =       betas_for_alpha_bar(UpperCamelCase_   ,							alpha_transform_type='''cosine'''		)
        elif beta_schedule == "exp":
             lowercase_       :Dict     =       betas_for_alpha_bar(UpperCamelCase_   ,							alpha_transform_type='''exp'''		)
        else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

        lowercase_       :str     =       1.0 - self.betas
        lowercase_       :Optional[int]     =       torch.cumprod(self.alphas   ,							dim=0		)

        #  set all values
        self.set_timesteps(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
        lowercase_       :str     =       use_karras_sigmas
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None		):
        if schedule_timesteps is None:
             lowercase_       :List[str]     =       self.timesteps

        lowercase_       :int     =       (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:
             lowercase_       :Dict     =       1 if len(UpperCamelCase_		) > 1 else 0
        else:
             lowercase_       :Any     =       timestep.cpu().item() if torch.is_tensor(UpperCamelCase_		) else timestep
             lowercase_       :Union[str, Any]     =       self._index_counter[timestep_int]

        return indices[pos].item()
   @property
   def 		UpperCamelCase  (     self		):
        # 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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							):
        lowercase_       :List[str]     =       self.index_for_timestep(UpperCamelCase_		)

        lowercase_       :Optional[Any]     =       self.sigmas[step_index]
        lowercase_       :Union[str, Any]     =       sample / ((sigma**2 + 1) ** 0.5)
        return sample
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							):
        lowercase_       :Optional[Any]     =       num_inference_steps

        lowercase_       :Dict     =       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":
             lowercase_       :Union[str, Any]     =       np.linspace(0   ,							num_train_timesteps - 1   ,							UpperCamelCase_   ,							dtype=UpperCamelCase_		)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
             lowercase_       :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
             lowercase_       :List[Any]     =       (np.arange(0   ,							UpperCamelCase_		) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_		)
             timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
             lowercase_       :Dict     =       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
             lowercase_       :Dict     =       (np.arange(UpperCamelCase_   ,							0   ,							-step_ratio		)).round().copy().astype(UpperCamelCase_		)
             timesteps -= 1
        else:
             raise ValueError(
                 f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."		)

        lowercase_       :Optional[int]     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
        lowercase_       :Tuple     =       np.log(UpperCamelCase_		)
        lowercase_       :Dict     =       np.interp(UpperCamelCase_   ,							np.arange(0   ,							len(UpperCamelCase_		)		)   ,							UpperCamelCase_		)

        if self.config.use_karras_sigmas:
             lowercase_       :int     =       self._convert_to_karras(in_sigmas=UpperCamelCase_   ,							num_inference_steps=self.num_inference_steps		)
             lowercase_       :Optional[int]     =       np.array([self._sigma_to_t(UpperCamelCase_   ,							UpperCamelCase_		) for sigma in sigmas]		)

        lowercase_       :Tuple     =       np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
        lowercase_       :Optional[int]     =       torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_		)
        lowercase_       :Tuple     =       torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

        lowercase_       :Any     =       torch.from_numpy(UpperCamelCase_		)
        lowercase_       :List[str]     =       torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

        if str(UpperCamelCase_		).startswith('''mps'''		):
             # mps does not support float64
             lowercase_       :int     =       timesteps.to(UpperCamelCase_   ,							dtype=torch.floataa		)
        else:
             lowercase_       :Optional[Any]     =       timesteps.to(device=UpperCamelCase_		)

        # empty dt and derivative
        lowercase_       :List[str]     =       None
        lowercase_       :List[Any]     =       None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        lowercase_       :int     =       defaultdict(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        # get log sigma
        lowercase_       :Union[str, Any]     =       np.log(UpperCamelCase_		)

        # get distribution
        lowercase_       :Optional[Any]     =       log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        lowercase_       :List[Any]     =       np.cumsum((dists >= 0)   ,							axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
        lowercase_       :str     =       low_idx + 1

        lowercase_       :Any     =       log_sigmas[low_idx]
        lowercase_       :int     =       log_sigmas[high_idx]

        # interpolate sigmas
        lowercase_       :Dict     =       (low - log_sigma) / (low - high)
        lowercase_       :str     =       np.clip(UpperCamelCase_   ,							0   ,							1		)

        # transform interpolation to time range
        lowercase_       :Dict     =       (1 - w) * low_idx + w * high_idx
        lowercase_       :int     =       t.reshape(sigma.shape		)
        return t
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :float     =       in_sigmas[-1].item()
        lowercase_       :float     =       in_sigmas[0].item()

        lowercase_       :int     =       7.0  # 7.0 is the value used in the paper
        lowercase_       :Optional[Any]     =       np.linspace(0   ,							1   ,							UpperCamelCase_		)
        lowercase_       :List[str]     =       sigma_min ** (1 / rho)
        lowercase_       :List[Any]     =       sigma_max ** (1 / rho)
        lowercase_       :Tuple     =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas
   @property
   def 		UpperCamelCase  (     self		):
        return self.dt is None
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_ = True   ,							):
        lowercase_       :Any     =       self.index_for_timestep(UpperCamelCase_		)

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

        if self.state_in_first_order:
             lowercase_       :Optional[int]     =       self.sigmas[step_index]
             lowercase_       :List[Any]     =       self.sigmas[step_index + 1]
        else:
             # 2nd order / Heun's method
             lowercase_       :Optional[int]     =       self.sigmas[step_index - 1]
             lowercase_       :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
        lowercase_       :List[Any]     =       0
        lowercase_       :List[str]     =       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":
             lowercase_       :Union[str, Any]     =       sigma_hat if self.state_in_first_order else sigma_next
             lowercase_       :List[Any]     =       sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
             lowercase_       :Dict     =       sigma_hat if self.state_in_first_order else sigma_next
             lowercase_       :Optional[Any]     =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
        elif self.config.prediction_type == "sample":
             lowercase_       :List[str]     =       model_output
        else:
             raise ValueError(
                 f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"		)

        if self.config.clip_sample:
             lowercase_       :str     =       pred_original_sample.clamp(
                 -self.config.clip_sample_range   ,							self.config.clip_sample_range		)

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

             # store for 2nd order step
             lowercase_       :str     =       derivative
             lowercase_       :Union[str, Any]     =       dt
             lowercase_       :Optional[int]     =       sample
        else:
             # 2. 2nd order / Heun's method
             lowercase_       :str     =       (sample - pred_original_sample) / sigma_next
             lowercase_       :List[str]     =       (self.prev_derivative + derivative) / 2

             # 3. take prev timestep & sample
             lowercase_       :Union[str, Any]     =       self.dt
             lowercase_       :Any     =       self.sample

             # free dt and derivative
             # Note, this puts the scheduler in "first order mode"
             lowercase_       :List[Any]     =       None
             lowercase_       :List[str]     =       None
             lowercase_       :Dict     =       None

        lowercase_       :int     =       sample + derivative * dt

        if not return_dict:
             return (prev_sample,)

        return SchedulerOutput(prev_sample=UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowercase_       :List[str]     =       self.sigmas.to(device=original_samples.device   ,							dtype=original_samples.dtype		)
        if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase_		):
             # mps does not support float64
             lowercase_       :Optional[Any]     =       self.timesteps.to(original_samples.device   ,							dtype=torch.floataa		)
             lowercase_       :Tuple     =       timesteps.to(original_samples.device   ,							dtype=torch.floataa		)
        else:
             lowercase_       :Union[str, Any]     =       self.timesteps.to(original_samples.device		)
             lowercase_       :int     =       timesteps.to(original_samples.device		)

        lowercase_       :int     =       [self.index_for_timestep(UpperCamelCase_   ,							UpperCamelCase_		) for t in timesteps]

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

        lowercase_       :List[str]     =       original_samples + noise * sigma
        return noisy_samples




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





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE       :		List[str]         =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE       :		Any         =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

