

'''simple docstring'''






import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
      import tensorflow as tf

      tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset


snake_case_       =       5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Namespace    )							->   Optional[int]:
      """simple docstring"""

      return PTtoTFCommand(
          args.model_name  ,  args.local_dir  ,  args.max_error  ,  args.new_weights  ,  args.no_pr  ,  args.push  ,  args.extra_commit_description  ,  args.override_model_class  ,  )





class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):




    @staticmethod
    def   __lowerCamelCase      (					lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       parser.add_parser(
              "pt-to-tf"				,     help=(
                  "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
                  " Can also be used to validate existing weights without opening PRs, with --no-pr."
              )				,     )
          train_parser.add_argument(
              "--model-name"				,     type=lowercase__				,     required=lowercase__				,     help="The model name, including owner/organization, as seen on the hub."				,     )
          train_parser.add_argument(
              "--local-dir"				,     type=lowercase__				,     default=""				,     help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"				,     )
          train_parser.add_argument(
              "--max-error"				,     type=lowercase__				,     default=lowercase__				,     help=(
                  F"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
              )				,     )
          train_parser.add_argument(
              "--new-weights"				,     action="store_true"				,     help="Optional flag to create new TensorFlow weights, even if they already exist."				,     )
          train_parser.add_argument(
              "--no-pr"				,     action="store_true"				,     help="Optional flag to NOT open a PR with converted weights."			)
          train_parser.add_argument(
              "--push"				,     action="store_true"				,     help="Optional flag to push the weights directly to `main` (requires permissions)"				,     )
          train_parser.add_argument(
              "--extra-commit-description"				,     type=lowercase__				,     default=""				,     help="Optional additional commit description to use when opening a PR (e.g. to tag the owner)."				,     )
          train_parser.add_argument(
              "--override-model-class"				,     type=lowercase__				,     default=lowercase__				,     help="If you think you know better than the auto-detector, you can specify the model class here. "
              "Can be either an AutoModel class or a specific model class like BertForSequenceClassification."				,     )
          train_parser.set_defaults(func=lowercase__			)




    @staticmethod
    def   __lowerCamelCase      (					lowercase__				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       set(pt_outputs.keys()			)
          SCREAMING_SNAKE_CASE_      :			int     =       set(tf_outputs.keys()			)
          if pt_out_attrs != tf_out_attrs:
                raise ValueError(
                    F"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
                    F" {tf_out_attrs})"			)

          # 2. For each output attribute, computes the difference
          def _find_pt_tf_differences(lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=""			):
                # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
                # recursivelly, keeping the name of the attribute.
                if isinstance(lowercase__				,     torch.Tensor			):
                      SCREAMING_SNAKE_CASE_      :			Dict     =       np.max(np.abs(pt_out.numpy() - tf_out.numpy()			)			)
                      SCREAMING_SNAKE_CASE_      :			Any     =       tensor_difference
                else:
                      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       attr_name
                      for i, pt_item in enumerate(lowercase__			):
                            # If it is a named attribute, we keep the name. Otherwise, just its index.
                            if isinstance(lowercase__				,     lowercase__			):
                                  SCREAMING_SNAKE_CASE_      :			int     =       root_name + pt_item
                                  SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       tf_out[pt_item]
                                  SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       pt_out[pt_item]
                            else:
                                  SCREAMING_SNAKE_CASE_      :			int     =       root_name + F"[{i}]"
                                  SCREAMING_SNAKE_CASE_      :			Optional[int]     =       tf_out[i]
                            SCREAMING_SNAKE_CASE_      :			str     =       _find_pt_tf_differences(lowercase__				,     lowercase__				,     lowercase__				,     lowercase__			)

                return differences

          return _find_pt_tf_differences(lowercase__				,     lowercase__				,     {}			)




    def __init__(					self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     *lowercase__				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       logging.get_logger("transformers-cli/pt_to_tf"			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       model_name
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       local_dir if local_dir else os.path.join("/tmp"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       max_error
          SCREAMING_SNAKE_CASE_      :			Dict     =       new_weights
          SCREAMING_SNAKE_CASE_      :			str     =       no_pr
          SCREAMING_SNAKE_CASE_      :			Any     =       push
          SCREAMING_SNAKE_CASE_      :			List[str]     =       extra_commit_description
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       override_model_class




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          def _get_audio_input():
                SCREAMING_SNAKE_CASE_      :			Dict     =       load_dataset("hf-internal-testing/librispeech_asr_dummy"				,     "clean"				,     split="validation"			)
                SCREAMING_SNAKE_CASE_      :			str     =       ds.sort("id"			).select(range(2			)			)[:2]["audio"]
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       [x["array"] for x in speech_samples]
                return raw_samples

          SCREAMING_SNAKE_CASE_      :			List[str]     =       type(pt_model.config			)
          if model_config_class in PROCESSOR_MAPPING:
                SCREAMING_SNAKE_CASE_      :			Any     =       AutoProcessor.from_pretrained(self._local_dir			)
                if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
                      SCREAMING_SNAKE_CASE_      :			List[Any]     =       processor.tokenizer.eos_token
          elif model_config_class in IMAGE_PROCESSOR_MAPPING:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       AutoImageProcessor.from_pretrained(self._local_dir			)
          elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       AutoFeatureExtractor.from_pretrained(self._local_dir			)
          elif model_config_class in TOKENIZER_MAPPING:
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       AutoTokenizer.from_pretrained(self._local_dir			)
                if processor.pad_token is None:
                      SCREAMING_SNAKE_CASE_      :			str     =       processor.eos_token
          else:
                raise ValueError(F"Unknown data processing type (model config type: {model_config_class})"			)

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       set(inspect.signature(pt_model.forward			).parameters.keys()			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       {}
          if "input_ids" in model_forward_signature:
                processor_inputs.update(
                    {
                        "text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
                        "padding": True,
                        "truncation": True,
                    }			)
          if "pixel_values" in model_forward_signature:
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       load_dataset("cifar10"				,     "plain_text"				,     split="test"			)[:2]["img"]
                processor_inputs.update({"images": sample_images}			)
          if "input_features" in model_forward_signature:
                SCREAMING_SNAKE_CASE_      :			Dict     =       inspect.signature(processor.feature_extractor			).parameters
                # Pad to the largest input length by default but take feature extractor default
                # padding value if it exists e.g. "max_length" and is not False or None
                if "padding" in feature_extractor_signature:
                      SCREAMING_SNAKE_CASE_      :			int     =       feature_extractor_signature["padding"].default
                      if default_strategy is not False and default_strategy is not None:
                            SCREAMING_SNAKE_CASE_      :			Dict     =       default_strategy
                      else:
                            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       True
                else:
                      SCREAMING_SNAKE_CASE_      :			Any     =       True
                processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy}			)
          if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
                processor_inputs.update({"audio": _get_audio_input(), "padding": True}			)
          SCREAMING_SNAKE_CASE_      :			str     =       processor(**lowercase__				,     return_tensors="pt"			)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       processor(**lowercase__				,     return_tensors="tf"			)

          # Extra input requirements, in addition to the input modality
          if (
              config.is_encoder_decoder
              or (hasattr(lowercase__				,     "encoder"			) and hasattr(lowercase__				,     "decoder"			))
              or "decoder_input_ids" in tf_dummy_inputs
          ):
                SCREAMING_SNAKE_CASE_      :			int     =       np.asarray([[1], [1]]				,     dtype=lowercase__			) * (pt_model.config.decoder_start_token_id or 0)
                pt_input.update({"decoder_input_ids": torch.tensor(lowercase__			)}			)
                tf_input.update({"decoder_input_ids": tf.convert_to_tensor(lowercase__			)}			)

          return pt_input, tf_input







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if version.parse(huggingface_hub.__version__			) < version.parse("0.9.0"			):
                raise ImportError(
                    "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
                    " installation."			)
          else:
                from huggingface_hub import Repository, create_commit
                from huggingface_hub._commit_api import CommitOperationAdd

          # Fetch remote data
          SCREAMING_SNAKE_CASE_      :			Tuple     =       Repository(local_dir=self._local_dir				,     clone_from=self._model_name			)

          # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
          SCREAMING_SNAKE_CASE_      :			Tuple     =       AutoConfig.from_pretrained(self._local_dir			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       config.architectures
          if self._override_model_class is not None:
                if self._override_model_class.startswith("TF"			):
                      SCREAMING_SNAKE_CASE_      :			int     =       [self._override_model_class[2:]]
                else:
                      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       [self._override_model_class]
                try:
                      SCREAMING_SNAKE_CASE_      :			str     =       getattr(import_module("transformers"			)				,     architectures[0]			)
                except AttributeError:
                      raise ValueError(F"Model class {self._override_model_class} not found in transformers."			)
                try:
                      SCREAMING_SNAKE_CASE_      :			str     =       getattr(import_module("transformers"			)				,     "TF" + architectures[0]			)
                except AttributeError:
                      raise ValueError(F"TF model class TF{self._override_model_class} not found in transformers."			)
          elif architectures is None:  # No architecture defined -- use auto classes
                SCREAMING_SNAKE_CASE_      :			Dict     =       getattr(import_module("transformers"			)				,     "AutoModel"			)
                SCREAMING_SNAKE_CASE_      :			str     =       getattr(import_module("transformers"			)				,     "TFAutoModel"			)
                self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"			)
          else:  # Architecture defined -- use it
                if len(lowercase__			) > 1:
                      raise ValueError(F"More than one architecture was found, aborting. (architectures = {architectures})"			)
                self._logger.warning(F"Detected architecture: {architectures[0]}"			)
                SCREAMING_SNAKE_CASE_      :			Any     =       getattr(import_module("transformers"			)				,     architectures[0]			)
                try:
                      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       getattr(import_module("transformers"			)				,     "TF" + architectures[0]			)
                except AttributeError:
                      raise AttributeError(F"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."			)

        # Check the TF dummy inputs to see what keys we need in the forward pass
          SCREAMING_SNAKE_CASE_      :			List[str]     =       tf_class.from_config(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       tf_from_pt_model.dummy_inputs

          del tf_from_pt_model  # Try to keep only one model in memory at a time

          # Load the model and get some basic inputs
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       pt_class.from_pretrained(self._local_dir			)
          pt_model.eval()

          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			str     =       self.get_inputs(lowercase__				,     lowercase__				,     lowercase__			)

          with torch.no_grad():
                SCREAMING_SNAKE_CASE_      :			Tuple     =       pt_model(**lowercase__				,     output_hidden_states=lowercase__			)
          del pt_model  # will no longer be used, and may have a large memory footprint

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       tf_class.from_pretrained(self._local_dir				,     from_pt=lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       tf_from_pt_model(**lowercase__				,     output_hidden_states=lowercase__				,     training=lowercase__			)

          # Confirms that cross loading PT weights into TF worked.
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.find_pt_tf_differences(lowercase__				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       {k: v for k, v in crossload_differences.items() if "hidden" not in k}
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       {k: v for k, v in crossload_differences.items() if "hidden" in k}
          if len(lowercase__			) == 0 and architectures is not None:
                raise ValueError(
                    F"Something went wrong -- the config file has architectures ({architectures}), but no model head"
                    " output was found. All outputs start with 'hidden'"			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       max(output_differences.values()			) if output_differences else 0.0
          SCREAMING_SNAKE_CASE_      :			Tuple     =       max(hidden_differences.values()			)
          if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
                raise ValueError(
                    "The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
                    + F"\nList of maximum output differences above the threshold ({self._max_error}):\n"
                    + "\n".join([F"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]			)
                    + F"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
                    + "\n".join([F"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]			)			)

          # Save the weights in a TF format (if needed) and confirms that the results are still good
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       os.path.join(self._local_dir				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       os.path.join(self._local_dir				,     lowercase__			)
          if (not os.path.exists(lowercase__			) and not os.path.exists(lowercase__			)) or self._new_weights:
                tf_from_pt_model.save_pretrained(self._local_dir			)
          del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       tf_class.from_pretrained(self._local_dir			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       tf_model(**lowercase__				,     output_hidden_states=lowercase__			)

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.find_pt_tf_differences(lowercase__				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       {k: v for k, v in conversion_differences.items() if "hidden" not in k}
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       {k: v for k, v in conversion_differences.items() if "hidden" in k}
          if len(lowercase__			) == 0 and architectures is not None:
                raise ValueError(
                    F"Something went wrong -- the config file has architectures ({architectures}), but no model head"
                    " output was found. All outputs start with 'hidden'"			)
          SCREAMING_SNAKE_CASE_      :			Dict     =       max(output_differences.values()			) if output_differences else 0.0
          SCREAMING_SNAKE_CASE_      :			int     =       max(hidden_differences.values()			)
          if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
                raise ValueError(
                    "The converted TensorFlow model has different outputs, something went wrong!\n"
                    + F"\nList of maximum output differences above the threshold ({self._max_error}):\n"
                    + "\n".join([F"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]			)
                    + F"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
                    + "\n".join([F"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]			)			)

          SCREAMING_SNAKE_CASE_      :			Any     =       "Update TF weights" if self._new_weights else "Add TF weights"
          if self._push:
                repo.git_add(auto_lfs_track=lowercase__			)
                repo.git_commit(lowercase__			)
                repo.git_push(blocking=lowercase__			)  # this prints a progress bar with the upload
                self._logger.warning(F"TF weights pushed into {self._model_name}"			)
          elif not self._no_pr:
                self._logger.warning("Uploading the weights into a new PR..."			)
                SCREAMING_SNAKE_CASE_      :			List[str]     =       (
                    "Model converted by the [`transformers`' `pt_to_tf`"
                    " CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
                    "All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"
                    F"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
                    F"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
                    F"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
                    F"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
                )
                if self._max_error > MAX_ERROR:
                      commit_descrition += (
                          F"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
                      )
                if self._extra_commit_description:
                      commit_descrition += "\n\n" + self._extra_commit_description

                # sharded model -> adds all related files (index and .h5 shards)
                if os.path.exists(lowercase__			):
                      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       [
                          CommitOperationAdd(path_in_repo=lowercase__				,     path_or_fileobj=lowercase__			)
                      ]
                      for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"			):
                            operations += [
                                CommitOperationAdd(path_in_repo=os.path.basename(lowercase__			)				,     path_or_fileobj=lowercase__			)
                            ]
                else:
                      SCREAMING_SNAKE_CASE_      :			Tuple     =       [CommitOperationAdd(path_in_repo=lowercase__				,     path_or_fileobj=lowercase__			)]

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       create_commit(
                    repo_id=self._model_name				,     operations=lowercase__				,     commit_message=lowercase__				,     commit_description=lowercase__				,     repo_type="model"				,     create_pr=lowercase__				,     ).pr_url
                self._logger.warning(F"PR open in {hub_pr_url}"			)




'''simple docstring'''






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


snake_case_       =       logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							42
    _A			    =							None






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	int=0.999  ,  SCREAMING_SNAKE_CASE_     :	Optional[Any]="cosine"  ,  )							->   Optional[int]:
      """simple docstring"""

      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      SCREAMING_SNAKE_CASE_      :			List[str]     =       []
      for i in range(SCREAMING_SNAKE_CASE_    ):
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       (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 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,_UpperCAmelCase      ):
    _A			    =							[e.name for e in KarrasDiffusionSchedulers]
    _A			    =							1




    @register_to_config
    def __init__(					self				,     lowercase__ = 1000				,     lowercase__ = 0.0001				,     lowercase__ = 0.02				,     lowercase__ = "linear"				,     lowercase__ = None				,     lowercase__ = "epsilon"				,     lowercase__ = "linspace"				,     lowercase__ = 0				,     ):






          """simple docstring"""






          if trained_betas is not None:
                SCREAMING_SNAKE_CASE_      :			Any     =       torch.tensor(lowercase__				,     dtype=torch.floataa			)
          elif beta_schedule == "linear":
                SCREAMING_SNAKE_CASE_      :			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.
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       (
                    torch.linspace(beta_start**0.5				,     beta_end**0.5				,     lowercase__				,     dtype=torch.floataa			) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       betas_for_alpha_bar(lowercase__			)
          else:
                raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

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

          SCREAMING_SNAKE_CASE_      :			Dict     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.from_numpy(lowercase__			)

          # setable values
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       None
          SCREAMING_SNAKE_CASE_      :			str     =       np.linspace(0				,     num_train_timesteps - 1				,     lowercase__				,     dtype=lowercase__			)[::-1].copy()
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.from_numpy(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			int     =       False




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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

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




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          if isinstance(lowercase__				,     torch.Tensor			):
                SCREAMING_SNAKE_CASE_      :			Any     =       timestep.to(self.timesteps.device			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       (self.timesteps == timestep).nonzero().item()
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.sigmas[step_index]
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sample / ((sigma**2 + 1) ** 0.5)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       True
          return sample




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       num_inference_steps

          # "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_      :			Dict     =       np.linspace(0				,     self.config.num_train_timesteps - 1				,     lowercase__				,     dtype=lowercase__			)[
                    ::-1
                ].copy()
          elif self.config.timestep_spacing == "leading":
                SCREAMING_SNAKE_CASE_      :			Dict     =       self.config.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_      :			Union[str, Any]     =       (np.arange(0				,     lowercase__			) * step_ratio).round()[::-1].copy().astype(lowercase__			)
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.config.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_      :			Any     =       (np.arange(self.config.num_train_timesteps				,     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'."			)

          SCREAMING_SNAKE_CASE_      :			int     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          SCREAMING_SNAKE_CASE_      :			Any     =       np.interp(lowercase__				,     np.arange(0				,     len(lowercase__			)			)				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.from_numpy(lowercase__			).to(device=lowercase__			)
          if str(lowercase__			).startswith("mps"			):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.from_numpy(lowercase__			).to(lowercase__				,     dtype=torch.floataa			)
          else:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.from_numpy(lowercase__			).to(device=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     lowercase__ = True				,     ):






          """simple docstring"""






          if (
              isinstance(lowercase__				,     lowercase__			)
              or isinstance(lowercase__				,     torch.IntTensor			)
              or isinstance(lowercase__				,     torch.LongTensor			)
          ):
                raise ValueError(
                    (
                        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                        " one of the `scheduler.timesteps` as a timestep."
                    )				,     )

          if not self.is_scale_input_called:
                logger.warning(
                    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                    "See `StableDiffusionPipeline` for a usage example."			)

          if isinstance(lowercase__				,     torch.Tensor			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       timestep.to(self.timesteps.device			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       (self.timesteps == timestep).nonzero().item()
          SCREAMING_SNAKE_CASE_      :			str     =       self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                SCREAMING_SNAKE_CASE_      :			Any     =       sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                SCREAMING_SNAKE_CASE_      :			Any     =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`"			)

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.sigmas[step_index]
          SCREAMING_SNAKE_CASE_      :			Any     =       self.sigmas[step_index + 1]
          SCREAMING_SNAKE_CASE_      :			Any     =       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       (sample - pred_original_sample) / sigma

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sigma_down - sigma

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sample + derivative * dt

          SCREAMING_SNAKE_CASE_      :			Dict     =       model_output.device
          SCREAMING_SNAKE_CASE_      :			int     =       randn_tensor(model_output.shape				,     dtype=model_output.dtype				,     device=lowercase__				,     generator=lowercase__			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       prev_sample + noise * sigma_up

          if not return_dict:
                return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=lowercase__				,     pred_original_sample=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     ):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

          SCREAMING_SNAKE_CASE_      :			Dict     =       original_samples + noise * sigma
          return noisy_samples







    def __len__(					self			):






          """simple docstring"""






          return self.config.num_train_timesteps

