





'''simple docstring'''

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				(							_lowerCamelCase					:					Any						,       _lowerCamelCase					:					List[Any]=0.9_9_9						,       _lowerCamelCase					:					Tuple="cosine"						,       ):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_lowerCamelCase					:					str       ):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_lowerCamelCase					:					Dict       ):
            return math.exp(t * -1_2.0       )

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

    A__				=			[]
    for i in range(_lowerCamelCase       ):
        A__				=			i / num_diffusion_timesteps
        A__				=			(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	(       UpperCamelCase__			,       UpperCamelCase__				):
 __lowercase   		=				[e.name for e in KarrasDiffusionSchedulers]
 __lowercase   		=				2
 @register_to_config
 def __init__(				self      :List[str]						,       lowercase_      :int = 10_00						,       lowercase_      :float = 0.0_0_0_8_5						,       lowercase_      :float = 0.0_1_2						,       lowercase_      :str = "linear"						,       lowercase_      :Optional[Union[np.ndarray, List[float]]] = None						,       lowercase_      :str = "epsilon"						,       lowercase_      :Optional[bool] = False						,       lowercase_      :Optional[bool] = False						,       lowercase_      :float = 1.0						,       lowercase_      :str = "linspace"						,       lowercase_      :int = 0						,       )->							List[str]:
     if trained_betas is not None:
         A__				=			torch.tensor(lowercase_						,       dtype=torch.floataa							)
     elif beta_schedule == "linear":
         A__				=			torch.linspace(lowercase_						,       lowercase_						,       lowercase_						,       dtype=torch.floataa							)
     elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         A__				=			(
             torch.linspace(beta_start**0.5						,       beta_end**0.5						,       lowercase_						,       dtype=torch.floataa							) ** 2
         )
     elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         A__				=			betas_for_alpha_bar(lowercase_						,       alpha_transform_type="cosine"							)
     elif beta_schedule == "exp":
         A__				=			betas_for_alpha_bar(lowercase_						,       alpha_transform_type="exp"							)
     else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"							)

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

     #  set all values
     self.set_timesteps(lowercase_						,       lowercase_						,       lowercase_							)
     A__				=			use_karras_sigmas
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Any						,       lowercase_      :Dict=None							)->							str:
     if schedule_timesteps is None:
         A__				=			self.timesteps

     A__				=			(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:
         A__				=			1 if len(lowercase_							) > 1 else 0
     else:
         A__				=			timestep.cpu().item() if torch.is_tensor(lowercase_							) else timestep
         A__				=			self._index_counter[timestep_int]

     return indices[pos].item()
 @property
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							int:
     # 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      :Dict						,       lowercase_      :torch.FloatTensor						,       lowercase_      :Union[float, torch.FloatTensor]						,       )->							torch.FloatTensor:
     A__				=			self.index_for_timestep(lowercase_							)

     A__				=			self.sigmas[step_index]
     A__				=			sample / ((sigma**2 + 1) ** 0.5)
     return sample
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :int						,       lowercase_      :Union[str, torch.device] = None						,       lowercase_      :Optional[int] = None						,       )->							Union[str, Any]:
     A__				=			num_inference_steps

     A__				=			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":
         A__				=			np.linspace(0						,       num_train_timesteps - 1						,       lowercase_						,       dtype=lowercase_							)[::-1].copy()
     elif self.config.timestep_spacing == "leading":
         A__				=			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__				=			(np.arange(0						,       lowercase_							) * step_ratio).round()[::-1].copy().astype(lowercase_							)
         timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
         A__				=			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__				=			(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'."							)

     A__				=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
     A__				=			np.log(lowercase_							)
     A__				=			np.interp(lowercase_						,       np.arange(0						,       len(lowercase_							)							)						,       lowercase_							)

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

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

     A__				=			torch.from_numpy(lowercase_							)
     A__				=			torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2							)]							)

     if str(lowercase_							).startswith("mps"							):
         # mps does not support float64
         A__				=			timesteps.to(lowercase_						,       dtype=torch.floataa							)
     else:
         A__				=			timesteps.to(device=lowercase_							)

     # empty dt and derivative
     A__				=			None
     A__				=			None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     A__				=			defaultdict(lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :int						,       lowercase_      :Any							)->							List[Any]:
     # get log sigma
     A__				=			np.log(lowercase_							)

     # get distribution
     A__				=			log_sigma - log_sigmas[:, np.newaxis]

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

     A__				=			log_sigmas[low_idx]
     A__				=			log_sigmas[high_idx]

     # interpolate sigmas
     A__				=			(low - log_sigma) / (low - high)
     A__				=			np.clip(lowercase_						,       0						,       1							)

     # transform interpolation to time range
     A__				=			(1 - w) * low_idx + w * high_idx
     A__				=			t.reshape(sigma.shape							)
     return t
 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :torch.FloatTensor						,       lowercase_      :Optional[Any]							)->							torch.FloatTensor:
     A__				=			in_sigmas[-1].item()
     A__				=			in_sigmas[0].item()

     A__				=			7.0  # 7.0 is the value used in the paper
     A__				=			np.linspace(0						,       1						,       lowercase_							)
     A__				=			sigma_min ** (1 / rho)
     A__				=			sigma_max ** (1 / rho)
     A__				=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Optional[int]:
     return self.dt is None
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Union[torch.FloatTensor, np.ndarray]						,       lowercase_      :Union[float, torch.FloatTensor]						,       lowercase_      :Union[torch.FloatTensor, np.ndarray]						,       lowercase_      :bool = True						,       )->							Union[SchedulerOutput, Tuple]:
     A__				=			self.index_for_timestep(lowercase_							)

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

     if self.state_in_first_order:
         A__				=			self.sigmas[step_index]
         A__				=			self.sigmas[step_index + 1]
     else:
         # 2nd order / Heun's method
         A__				=			self.sigmas[step_index - 1]
         A__				=			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
     A__				=			0
     A__				=			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":
         A__				=			sigma_hat if self.state_in_first_order else sigma_next
         A__				=			sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
         A__				=			sigma_hat if self.state_in_first_order else sigma_next
         A__				=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
     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 `v_prediction`"							)

     if self.config.clip_sample:
         A__				=			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
         A__				=			(sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         A__				=			sigma_next - sigma_hat

         # store for 2nd order step
         A__				=			derivative
         A__				=			dt
         A__				=			sample
     else:
         # 2. 2nd order / Heun's method
         A__				=			(sample - pred_original_sample) / sigma_next
         A__				=			(self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         A__				=			self.dt
         A__				=			self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         A__				=			None
         A__				=			None
         A__				=			None

     A__				=			sample + derivative * dt

     if not return_dict:
         return (prev_sample,)

     return SchedulerOutput(prev_sample=lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[Any]						,       lowercase_      :torch.FloatTensor						,       lowercase_      :torch.FloatTensor						,       lowercase_      :torch.FloatTensor						,       )->							torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     A__				=			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
         A__				=			self.timesteps.to(original_samples.device						,       dtype=torch.floataa							)
         A__				=			timesteps.to(original_samples.device						,       dtype=torch.floataa							)
     else:
         A__				=			self.timesteps.to(original_samples.device							)
         A__				=			timesteps.to(original_samples.device							)

     A__				=			[self.index_for_timestep(lowercase_						,       lowercase_							) for t in timesteps]

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

     A__				=			original_samples + noise * sigma
     return noisy_samples



 def __len__(				self      :Any							)->							int:
     return self.config.num_train_timesteps










'''simple docstring'''

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
  import torch






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""char"""
 __lowercase   		=				"""bpe"""
 __lowercase   		=				"""wp"""

__lowerCAmelCase							:	Union[str, Any]			=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				["""image_processor""", """char_tokenizer"""]
 __lowercase   		=				"""ViTImageProcessor"""
 __lowercase   		=				"""MgpstrTokenizer"""
 def __init__(				self      :int						,       lowercase_      :int=None						,       lowercase_      :List[str]=None						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     A__				=			None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."						,       lowercase_						,       )
         A__				=			kwargs.pop("feature_extractor"							)

     A__				=			image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."							)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."							)

     A__				=			tokenizer
     A__				=			AutoTokenizer.from_pretrained("gpt2"							)
     A__				=			AutoTokenizer.from_pretrained("bert-base-uncased"							)

     super().__init__(lowercase_						,       lowercase_							)
 def __call__(				self      :Optional[Any]						,       lowercase_      :Any=None						,       lowercase_      :Tuple=None						,       lowercase_      :List[str]=None						,       **lowercase_      :Union[str, Any]							)->							Optional[Any]:
     if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."							)

     if images is not None:
         A__				=			self.image_processor(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)
     if text is not None:
         A__				=			self.char_tokenizer(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)

     if text is None:
         return inputs
     elif images is None:
         return encodings
     else:
         A__				=			encodings["input_ids"]
         return inputs
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :int							)->							int:
     A__,	A__,	A__				=			sequences
     A__				=			char_preds.size(0							)

     A__,	A__				=			self._decode_helper(lowercase_						,       "char"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "bpe"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "wp"							)

     A__				=			[]
     A__				=			[]
     for i in range(lowercase_							):
         A__				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
         A__				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
         A__				=			scores.index(max(lowercase_							)							)
         final_strs.append(strs[max_score_index]							)
         final_scores.append(scores[max_score_index]							)

     A__				=			{}
     A__				=			final_strs
     A__				=			final_scores
     A__				=			char_strs
     A__				=			bpe_strs
     A__				=			wp_strs
     return out
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :List[str]						,       lowercase_      :str							)->							Optional[Any]:
     if format == DecodeType.CHARACTER:
         A__				=			self.char_decode
         A__				=			1
         A__				=			"[s]"
     elif format == DecodeType.BPE:
         A__				=			self.bpe_decode
         A__				=			2
         A__				=			"#"
     elif format == DecodeType.WORDPIECE:
         A__				=			self.wp_decode
         A__				=			1_02
         A__				=			"[SEP]"
     else:
         raise ValueError(F"Format {format} is not supported."							)

     A__,	A__				=			[], []
     A__				=			pred_logits.size(0							)
     A__				=			pred_logits.size(1							)
     A__,	A__				=			pred_logits.topk(1						,       dim=-1						,       largest=lowercase_						,       sorted=lowercase_							)
     A__				=			preds_index.view(-1						,       lowercase_							)[:, 1:]
     A__				=			decoder(lowercase_							)
     A__,	A__				=			torch.nn.functional.softmax(lowercase_						,       dim=2							).max(dim=2							)
     A__				=			preds_max_prob[:, 1:]

     for index in range(lowercase_							):
         A__				=			preds_str[index].find(lowercase_							)
         A__				=			preds_str[index][:pred_eos]
         A__				=			preds_index[index].cpu().tolist()
         A__				=			pred_index.index(lowercase_							) if eos_token in pred_index else -1
         A__				=			preds_max_prob[index][: pred_eos_index + 1]
         A__				=			pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(lowercase_							)
         conf_scores.append(lowercase_							)

     return dec_strs, conf_scores
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							int:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.char_tokenizer.batch_decode(lowercase_							)]
     return decode_strs
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :Optional[Any]							)->							List[str]:
     return self.bpe_tokenizer.batch_decode(lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :List[str]							)->							Union[str, Any]:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.wp_tokenizer.batch_decode(lowercase_							)]
     return decode_strs



