
import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowercase  :					List[str]						    =							"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowercase  :					List[Any]						    =							direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowercase  :					str						    =							re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowercase  :					Tuple						    =							re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowercase  :					Optional[Any]						    =							re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowercase  :					Tuple						    =							[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Tuple       	=      re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""					,	SCREAMING_SNAKE_CASE__				)
       return [m.group(0				) for m in matches]







def 						_snake_case(  ) ->    int:
       lowercase							:     Optional[Any]       	=      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       lowercase							:     Optional[Any]       	=      {
           config.replace("""Config"""					,	""""""				): model_type for model_type, config in config_maping_names.items()
       }

       # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
       lowercase							:     int       	=      collections.defaultdict(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      collections.defaultdict(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Any       	=      collections.defaultdict(SCREAMING_SNAKE_CASE__				)

       # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
       for attr_name in dir(SCREAMING_SNAKE_CASE__				):
              lowercase							:     Tuple       	=      None
              if _re_tf_models.match(SCREAMING_SNAKE_CASE__				) is not None:
                     lowercase							:     Any       	=      tf_models
                     lowercase							:     List[str]       	=      _re_tf_models.match(SCREAMING_SNAKE_CASE__				).groups()[0]
              elif _re_flax_models.match(SCREAMING_SNAKE_CASE__				) is not None:
                     lowercase							:     Optional[Any]       	=      flax_models
                     lowercase							:     Optional[int]       	=      _re_flax_models.match(SCREAMING_SNAKE_CASE__				).groups()[0]
              elif _re_pt_models.match(SCREAMING_SNAKE_CASE__				) is not None:
                     lowercase							:     Union[str, Any]       	=      pt_models
                     lowercase							:     List[Any]       	=      _re_pt_models.match(SCREAMING_SNAKE_CASE__				).groups()[0]

              if lookup_dict is not None:
                     while len(SCREAMING_SNAKE_CASE__				) > 0:
                            if attr_name in model_prefix_to_model_type:
                                   lowercase							:     List[Any]       	=      True
                                   break
                            # Try again after removing the last word in the name
                            lowercase							:     List[str]       	=      """""".join(camel_case_split(SCREAMING_SNAKE_CASE__				)[:-1]				)

       lowercase							:     Optional[int]       	=      set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
       lowercase							:     Tuple       	=      list(SCREAMING_SNAKE_CASE__				)
       all_models.sort()

       lowercase							:     Any       	=      {"""model_type""": all_models}
       lowercase							:     Tuple       	=      [pt_models[t] for t in all_models]
       lowercase							:     Tuple       	=      [tf_models[t] for t in all_models]
       lowercase							:     Optional[int]       	=      [flax_models[t] for t in all_models]

       # Now let's use the auto-mapping names to make sure
       lowercase							:     str       	=      {}
       for t in all_models:
              if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                     lowercase							:     int       	=      """AutoProcessor"""
              elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
                     lowercase							:     int       	=      """AutoTokenizer"""
              elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
                     lowercase							:     List[str]       	=      """AutoFeatureExtractor"""
              else:
                     # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                     lowercase							:     List[Any]       	=      """AutoTokenizer"""

       lowercase							:     int       	=      [processors[t] for t in all_models]

       return pd.DataFrame(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Any:
       lowercase							:     Optional[Any]       	=      [
           transformers_module.models.auto.modeling_auto,
           transformers_module.models.auto.modeling_tf_auto,
           transformers_module.models.auto.modeling_flax_auto,
       ]
       for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
              lowercase							:     Optional[int]       	=      [model_mapping, f"TF_{model_mapping}", f"FLAX_{model_mapping}"]
              lowercase							:     Any       	=      [auto_class, f"TF_{auto_class}", f"Flax_{auto_class}"]
              # Loop through all three frameworks
              for module, cls, mapping in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     # The type of pipeline may not exist in this framework
                     if not hasattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                            continue
                     # First extract all model_names
                     lowercase							:     Any       	=      []
                     for name in getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				).values():
                            if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                                   model_names.append(SCREAMING_SNAKE_CASE__				)
                            else:
                                   model_names.extend(list(SCREAMING_SNAKE_CASE__				)				)

            # Add pipeline tag and auto model class for those models
                     table.update({model_name: (pipeline_tag, cls) for model_name in model_names}				)

       return table







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     Union[str, Any]       	=      get_frameworks_table()
       lowercase							:     Tuple       	=      Dataset.from_pandas(SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[Any]       	=      hf_hub_download(
           """huggingface/transformers-metadata"""					,	"""pipeline_tags.json"""					,	repo_type="""dataset"""					,	token=SCREAMING_SNAKE_CASE__				)
       lowercase							:     int       	=      Dataset.from_json(SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      {
           tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
           for i in range(len(SCREAMING_SNAKE_CASE__				)				)
       }
       lowercase							:     Tuple       	=      update_pipeline_and_auto_class_table(SCREAMING_SNAKE_CASE__				)

       # Sort the model classes to avoid some nondeterministic updates to create false update commits.
       lowercase							:     Union[str, Any]       	=      sorted(table.keys()				)
       lowercase							:     int       	=      pd.DataFrame(
           {
               """model_class""": model_classes,
               """pipeline_tag""": [table[m][0] for m in model_classes],
               """auto_class""": [table[m][1] for m in model_classes],
           }				)
       lowercase							:     Optional[int]       	=      Dataset.from_pandas(SCREAMING_SNAKE_CASE__				)

       with tempfile.TemporaryDirectory() as tmp_dir:
              frameworks_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE__					,	"""frameworks.json"""				)				)
              tags_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE__					,	"""pipeline_tags.json"""				)				)

              if commit_sha is not None:
                     lowercase							:     int       	=      (
                         f"Update with commit {commit_sha}\n\nSee: "
                         f"https://github.com/huggingface/transformers/commit/{commit_sha}"
                     )
              else:
                     lowercase							:     Dict       	=      """Update"""

              upload_folder(
                  repo_id="""huggingface/transformers-metadata"""					,	folder_path=SCREAMING_SNAKE_CASE__					,	repo_type="""dataset"""					,	token=SCREAMING_SNAKE_CASE__					,	commit_message=SCREAMING_SNAKE_CASE__					,	)







def 						_snake_case(  ) ->    int:
       lowercase							:     List[Any]       	=      {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
       lowercase							:     str       	=      transformers_module.pipelines.SUPPORTED_TASKS
       lowercase							:     Optional[Any]       	=      []
       for key in pipeline_tasks:
              if key not in in_table:
                     lowercase							:     str       	=      pipeline_tasks[key]["""pt"""]
                     if isinstance(SCREAMING_SNAKE_CASE__					,	(list, tuple)				):
                            lowercase							:     Tuple       	=      model[0]
                     lowercase							:     List[Any]       	=      model.__name__
                     if model not in in_table.values():
                            missing.append(SCREAMING_SNAKE_CASE__				)

       if len(SCREAMING_SNAKE_CASE__				) > 0:
              lowercase							:     List[Any]       	=      """, """.join(SCREAMING_SNAKE_CASE__				)
              raise ValueError(
                  """The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
                  f"`utils/update_metadata.py`: {msg}. Please add them!"				)


if __name__ == "__main__":
  lowercase  :					str						    =							argparse.ArgumentParser()
  parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
  parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
  parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
  lowercase  :					Dict						    =							parser.parse_args()

  if args.check_only:
    check_pipeline_tags()
  else:
    update_metadata(args.token, args.commit_sha)


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(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=0.999					,	SCREAMING_SNAKE_CASE__="cosine"					,	) ->    List[Any]:
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       lowercase							:     int       	=      []
       for i in range(SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      i / num_diffusion_timesteps
              lowercase							:     Optional[int]       	=      (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 __snake_case		(      lowerCAmelCase					,	lowerCAmelCase     ):
    _a		:						Tuple=				[e.name for e in KarrasDiffusionSchedulers]
    _a		:						int=				2
    @register_to_config
    def __init__(  self		,snake_case = 1000		,snake_case = 0.00_085		,snake_case = 0.012		,snake_case = "linear"		,snake_case = None		,snake_case = "epsilon"		,snake_case = False		,snake_case = False		,snake_case = 1.0		,snake_case = "linspace"		,snake_case = 0		,):



           '''simple docstring'''







           if trained_betas is not None:
                  lowercase							:     List[str]       	=      torch.tensor(snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "linear":
                  lowercase							:     Optional[Any]       	=      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.
                  lowercase							:     int       	=      (
                      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
                  lowercase							:     Union[str, Any]       	=      betas_for_alpha_bar(snake_case		,alpha_transform_type="""cosine"""      )
           elif beta_schedule == "exp":
                  lowercase							:     int       	=      betas_for_alpha_bar(snake_case		,alpha_transform_type="""exp"""      )
           else:
                  raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"      )

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

           #  set all values
           self.set_timesteps(snake_case		,snake_case		,snake_case      )
           lowercase							:     Tuple       	=      use_karras_sigmas
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           if schedule_timesteps is None:
                  lowercase							:     Union[str, Any]       	=      self.timesteps

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

           return indices[pos].item()
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

           return (self.sigmas.max() ** 2 + 1) ** 0.5
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.index_for_timestep(snake_case      )

           lowercase							:     Dict       	=      self.sigmas[step_index]
           lowercase							:     List[str]       	=      sample / ((sigma**2 + 1) ** 0.5)
           return sample
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      num_inference_steps

           lowercase							:     List[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":
                  lowercase							:     Optional[int]       	=      np.linspace(0		,num_train_timesteps - 1		,snake_case		,dtype=snake_case      )[::-1].copy()
           elif self.config.timestep_spacing == "leading":
                  lowercase							:     int       	=      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[str]       	=      (np.arange(0		,snake_case      ) * step_ratio).round()[::-1].copy().astype(snake_case      )
                  timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                  lowercase							:     List[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
                  lowercase							:     Optional[int]       	=      (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'."      )

           lowercase							:     Optional[int]       	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
           lowercase							:     Dict       	=      np.log(snake_case      )
           lowercase							:     Union[str, Any]       	=      np.interp(snake_case		,np.arange(0		,len(snake_case      )      )		,snake_case      )

           if self.config.use_karras_sigmas:
                  lowercase							:     List[Any]       	=      self._convert_to_karras(in_sigmas=snake_case		,num_inference_steps=self.num_inference_steps      )
                  lowercase							:     Tuple       	=      np.array([self._sigma_to_t(snake_case		,snake_case      ) for sigma in sigmas]      )

           lowercase							:     Any       	=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
           lowercase							:     List[Any]       	=      torch.from_numpy(snake_case      ).to(device=snake_case      )
           lowercase							:     List[Any]       	=      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

           lowercase							:     Dict       	=      torch.from_numpy(snake_case      )
           lowercase							:     List[Any]       	=      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

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

           # empty dt and derivative
           lowercase							:     Union[str, Any]       	=      None
           lowercase							:     Any       	=      None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           lowercase							:     str       	=      defaultdict(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      np.log(snake_case      )

           # get distribution
           lowercase							:     Union[str, Any]       	=      log_sigma - log_sigmas[:, np.newaxis]

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

           lowercase							:     str       	=      log_sigmas[low_idx]
           lowercase							:     Dict       	=      log_sigmas[high_idx]

           # interpolate sigmas
           lowercase							:     int       	=      (low - log_sigma) / (low - high)
           lowercase							:     Dict       	=      np.clip(snake_case		,0		,1      )

           # transform interpolation to time range
           lowercase							:     Optional[Any]       	=      (1 - w) * low_idx + w * high_idx
           lowercase							:     Tuple       	=      t.reshape(sigma.shape      )
           return t
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     float       	=      in_sigmas[-1].item()
           lowercase							:     float       	=      in_sigmas[0].item()

           lowercase							:     Dict       	=      7.0  # 7.0 is the value used in the paper
           lowercase							:     Optional[int]       	=      np.linspace(0		,1		,snake_case      )
           lowercase							:     int       	=      sigma_min ** (1 / rho)
           lowercase							:     Any       	=      sigma_max ** (1 / rho)
           lowercase							:     Tuple       	=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self.dt is None
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = True		,):



           '''simple docstring'''







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

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

           if self.state_in_first_order:
                  lowercase							:     str       	=      self.sigmas[step_index]
                  lowercase							:     Optional[int]       	=      self.sigmas[step_index + 1]
           else:
                  # 2nd order / Heun's method
                  lowercase							:     Dict       	=      self.sigmas[step_index - 1]
                  lowercase							:     Optional[Any]       	=      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							:     Union[str, Any]       	=      0
           lowercase							:     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":
                  lowercase							:     Any       	=      sigma_hat if self.state_in_first_order else sigma_next
                  lowercase							:     int       	=      sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
                  lowercase							:     Union[str, Any]       	=      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							:     Optional[Any]       	=      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							:     Union[str, Any]       	=      sigma_next - sigma_hat

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

                  # 3. take prev timestep & sample
                  lowercase							:     Tuple       	=      self.dt
                  lowercase							:     Optional[Any]       	=      self.sample

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

           lowercase							:     List[Any]       	=      sample + derivative * dt

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      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
                  lowercase							:     List[Any]       	=      self.timesteps.to(original_samples.device		,dtype=torch.floataa      )
                  lowercase							:     List[str]       	=      timesteps.to(original_samples.device		,dtype=torch.floataa      )
           else:
                  lowercase							:     List[str]       	=      self.timesteps.to(original_samples.device      )
                  lowercase							:     Tuple       	=      timesteps.to(original_samples.device      )

           lowercase							:     Any       	=      [self.index_for_timestep(snake_case		,snake_case      ) for t in timesteps]

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

           lowercase							:     Optional[int]       	=      original_samples + noise * sigma
           return noisy_samples
    def __len__(  self      ):



           '''simple docstring'''







           return self.config.num_train_timesteps
