

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        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Any=0.9_99		,							_UpperCamelCase		:  Optional[Any]="cosine"		,							)		->							Optional[Any]:
 '''simple docstring'''


 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_UpperCamelCase		:  int	):
   return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

 elif alpha_transform_type == "exp":

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

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

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




class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =[e.name for e in KarrasDiffusionSchedulers]
    __UpperCamelCase         =2

    @register_to_config
    def __init__(			self :      Union[str, Any]							,    snake_case__ :      int = 1_0_0_0							,    snake_case__ :      float = 0.00_085							,    snake_case__ :      float = 0.012							,    snake_case__ :      str = "linear"							,    snake_case__ :      Optional[Union[np.ndarray, List[float]]] = None							,    snake_case__ :      str = "epsilon"							,    snake_case__ :      Optional[bool] = False							,    snake_case__ :      Optional[bool] = False							,    snake_case__ :      float = 1.0							,    snake_case__ :      str = "linspace"							,    snake_case__ :      int = 0							,    ):

     """simple docstring"""






     if trained_betas is not None:
      SCREAMING_SNAKE_CASE								=			torch.tensor(snake_case__							,    dtype=torch.floataa    )
     elif beta_schedule == "linear":
      SCREAMING_SNAKE_CASE								=			torch.linspace(snake_case__							,    snake_case__							,    snake_case__							,    dtype=torch.floataa    )
     elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      SCREAMING_SNAKE_CASE								=			(
          torch.linspace(beta_start**0.5							,    beta_end**0.5							,    snake_case__							,    dtype=torch.floataa    ) ** 2
      )
     elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      SCREAMING_SNAKE_CASE								=			betas_for_alpha_bar(snake_case__							,    alpha_transform_type='cosine'    )
     elif beta_schedule == "exp":
      SCREAMING_SNAKE_CASE								=			betas_for_alpha_bar(snake_case__							,    alpha_transform_type='exp'    )
     else:
      raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

     #  set all values
     self.set_timesteps(snake_case__							,    snake_case__							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			use_karras_sigmas

    def UpperCamelCase			(			self :      str							,    snake_case__ :      Dict							,    snake_case__ :      List[Any]=None    ):

     """simple docstring"""






     if schedule_timesteps is None:
      SCREAMING_SNAKE_CASE								=			self.timesteps

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

     return indices[pos].item()

    @property
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






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

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

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      torch.FloatTensor							,    snake_case__ :      Union[float, torch.FloatTensor]							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.index_for_timestep(snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.sigmas[step_index]
     SCREAMING_SNAKE_CASE								=			sample / ((sigma**2 + 1) ** 0.5)
     return sample

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Union[str, torch.device] = None							,    snake_case__ :      Optional[int] = None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			num_inference_steps

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

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

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

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

     SCREAMING_SNAKE_CASE								=			torch.from_numpy(snake_case__    )
     SCREAMING_SNAKE_CASE								=			torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

     if str(snake_case__    ).startswith('mps'    ):
      # mps does not support float64
      SCREAMING_SNAKE_CASE								=			timesteps.to(snake_case__							,    dtype=torch.floataa    )
     else:
      SCREAMING_SNAKE_CASE								=			timesteps.to(device=snake_case__    )

     # empty dt and derivative
     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     SCREAMING_SNAKE_CASE								=			defaultdict(snake_case__    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			np.log(snake_case__    )

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

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

     SCREAMING_SNAKE_CASE								=			log_sigmas[low_idx]
     SCREAMING_SNAKE_CASE								=			log_sigmas[high_idx]

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

     # transform interpolation to time range
     SCREAMING_SNAKE_CASE								=			(1 - w) * low_idx + w * high_idx
     SCREAMING_SNAKE_CASE								=			t.reshape(sigma.shape    )
     return t

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      torch.FloatTensor							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			in_sigmas[-1].item()
     SCREAMING_SNAKE_CASE								=			in_sigmas[0].item()

     SCREAMING_SNAKE_CASE								=			7.0  # 7.0 is the value used in the paper
     SCREAMING_SNAKE_CASE								=			np.linspace(0							,    1							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			sigma_min ** (1 / rho)
     SCREAMING_SNAKE_CASE								=			sigma_max ** (1 / rho)
     SCREAMING_SNAKE_CASE								=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas

    @property
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     return self.dt is None

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Union[torch.FloatTensor, np.ndarray]							,    snake_case__ :      Union[float, torch.FloatTensor]							,    snake_case__ :      Union[torch.FloatTensor, np.ndarray]							,    snake_case__ :      bool = True							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.index_for_timestep(snake_case__    )

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

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

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

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

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

     SCREAMING_SNAKE_CASE								=			sample + derivative * dt

     if not return_dict:
      return (prev_sample,)

     return SchedulerOutput(prev_sample=snake_case__    )

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      torch.FloatTensor							,    snake_case__ :      torch.FloatTensor							,    snake_case__ :      torch.FloatTensor							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.sigmas.to(device=original_samples.device							,    dtype=original_samples.dtype    )
     if original_samples.device.type == "mps" and torch.is_floating_point(snake_case__    ):
      # mps does not support float64
      SCREAMING_SNAKE_CASE								=			self.timesteps.to(original_samples.device							,    dtype=torch.floataa    )
      SCREAMING_SNAKE_CASE								=			timesteps.to(original_samples.device							,    dtype=torch.floataa    )
     else:
      SCREAMING_SNAKE_CASE								=			self.timesteps.to(original_samples.device    )
      SCREAMING_SNAKE_CASE								=			timesteps.to(original_samples.device    )

     SCREAMING_SNAKE_CASE								=			[self.index_for_timestep(snake_case__							,    snake_case__    ) for t in timesteps]

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

     SCREAMING_SNAKE_CASE								=			original_samples + noise * sigma
     return noisy_samples




    def __len__(			self :      int    ):

     """simple docstring"""






     return self.config.num_train_timesteps




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="facebook/bart-large-mnli"
    __UpperCamelCase         =(
        "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
        "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
        "It returns the most likely label in the list of provided `labels` for the input text."
    )
    __UpperCamelCase         ="text_classifier"
    __UpperCamelCase         =AutoTokenizer
    __UpperCamelCase         =AutoModelForSequenceClassification

    __UpperCamelCase         =["text", ["text"]]
    __UpperCamelCase         =["text"]

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     super().setup()
     SCREAMING_SNAKE_CASE								=			self.model.config
     SCREAMING_SNAKE_CASE								=			-1
     for idx, label in config.idalabel.items():
      if label.lower().startswith('entail'    ):
       SCREAMING_SNAKE_CASE								=			int(snake_case__    )
     if self.entailment_id == -1:
      raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'    )

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			labels
     return self.pre_processor(
         [text] * len(snake_case__    )							,    [F"""This example is {label}""" for label in labels]							,    return_tensors='pt'							,    padding='max_length'							,    )




    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			outputs.logits
     SCREAMING_SNAKE_CASE								=			torch.argmax(logits[:, 2]    ).item()
     return self._labels[label_id]

