
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


lowercase__				:				Dict						=      5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors




def 						A_			(				snake_case				: Namespace			)   ->     List[str]:



						'''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__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							@staticmethod
							def      A__		(	SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      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=SCREAMING_SNAKE_CASE_  ,						required=SCREAMING_SNAKE_CASE_  ,						help='''The model name, including owner/organization, as seen on the hub.'''  ,						)
													train_parser.add_argument(
													    '''--local-dir'''  ,						type=SCREAMING_SNAKE_CASE_  ,						default=''''''  ,						help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''  ,						)
													train_parser.add_argument(
													    '''--max-error'''  ,						type=SCREAMING_SNAKE_CASE_  ,						default=SCREAMING_SNAKE_CASE_  ,						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=SCREAMING_SNAKE_CASE_  ,						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=SCREAMING_SNAKE_CASE_  ,						default=SCREAMING_SNAKE_CASE_  ,						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=SCREAMING_SNAKE_CASE_      )



							@staticmethod
							def      A__		(	SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      set(pt_outputs.keys()      )
													__UpperCamelCase  	=      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(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=""      ):
																			# 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(SCREAMING_SNAKE_CASE_  ,						torch.Tensor      ):
																									__UpperCamelCase  	=      np.max(np.abs(pt_out.numpy() - tf_out.numpy()      )      )
																									__UpperCamelCase  	=      tensor_difference
																			else:
																									__UpperCamelCase  	=      attr_name
																									for i, pt_item in enumerate(SCREAMING_SNAKE_CASE_      ):
																															# If it is a named attribute, we keep the name. Otherwise, just its index.
																															if isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																																					__UpperCamelCase  	=      root_name + pt_item
																																					__UpperCamelCase  	=      tf_out[pt_item]
																																					__UpperCamelCase  	=      pt_out[pt_item]
																															else:
																																					__UpperCamelCase  	=      root_name + F"[{i}]"
																																					__UpperCamelCase  	=      tf_out[i]
																															__UpperCamelCase  	=      _find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

																			return differences

													return _find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						{}      )



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						*SCREAMING_SNAKE_CASE_  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      logging.get_logger('''transformers-cli/pt_to_tf'''      )
													__UpperCamelCase  	=      model_name
													__UpperCamelCase  	=      local_dir if local_dir else os.path.join('''/tmp'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      max_error
													__UpperCamelCase  	=      new_weights
													__UpperCamelCase  	=      no_pr
													__UpperCamelCase  	=      push
													__UpperCamelCase  	=      extra_commit_description
													__UpperCamelCase  	=      override_model_class



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






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

													__UpperCamelCase  	=      type(pt_model.config      )
													if model_config_class in PROCESSOR_MAPPING:
																			__UpperCamelCase  	=      AutoProcessor.from_pretrained(self._local_dir      )
																			if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
																									__UpperCamelCase  	=      processor.tokenizer.eos_token
													elif model_config_class in IMAGE_PROCESSOR_MAPPING:
																			__UpperCamelCase  	=      AutoImageProcessor.from_pretrained(self._local_dir      )
													elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
																			__UpperCamelCase  	=      AutoFeatureExtractor.from_pretrained(self._local_dir      )
													elif model_config_class in TOKENIZER_MAPPING:
																			__UpperCamelCase  	=      AutoTokenizer.from_pretrained(self._local_dir      )
																			if processor.pad_token is None:
																									__UpperCamelCase  	=      processor.eos_token
													else:
																			raise ValueError(F"Unknown data processing type (model config type: {model_config_class})"      )

													__UpperCamelCase  	=      set(inspect.signature(pt_model.forward      ).parameters.keys()      )
													__UpperCamelCase  	=      {}
													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:
																			__UpperCamelCase  	=      load_dataset('''cifar10'''  ,						'''plain_text'''  ,						split='''test'''      )[:2]['''img''']
																			processor_inputs.update({'''images''': sample_images}      )
													if "input_features" in model_forward_signature:
																			__UpperCamelCase  	=      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:
																									__UpperCamelCase  	=      feature_extractor_signature['''padding'''].default
																									if default_strategy is not False and default_strategy is not None:
																															__UpperCamelCase  	=      default_strategy
																									else:
																															__UpperCamelCase  	=      True
																			else:
																									__UpperCamelCase  	=      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}      )
													__UpperCamelCase  	=      processor(**SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      )
													__UpperCamelCase  	=      processor(**SCREAMING_SNAKE_CASE_  ,						return_tensors='''tf'''      )

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

													return pt_input, tf_input



							def      A__		(	self      )-> int:





													'''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
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      AutoConfig.from_pretrained(self._local_dir      )
													__UpperCamelCase  	=      config.architectures
													if self._override_model_class is not None:
																			if self._override_model_class.startswith('''TF'''      ):
																									__UpperCamelCase  	=      [self._override_model_class[2:]]
																			else:
																									__UpperCamelCase  	=      [self._override_model_class]
																			try:
																									__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						architectures[0]      )
																			except AttributeError:
																									raise ValueError(F"Model class {self._override_model_class} not found in transformers."      )
																			try:
																									__UpperCamelCase  	=      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
																			__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						'''AutoModel'''      )
																			__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						'''TFAutoModel'''      )
																			self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''      )
													else:  # Architecture defined -- use it
																			if len(SCREAMING_SNAKE_CASE_      ) > 1:
																									raise ValueError(F"More than one architecture was found, aborting. (architectures = {architectures})"      )
																			self._logger.warning(F"Detected architecture: {architectures[0]}"      )
																			__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						architectures[0]      )
																			try:
																									__UpperCamelCase  	=      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
													__UpperCamelCase  	=      tf_class.from_config(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      pt_class.from_pretrained(self._local_dir      )
													pt_model.eval()

													__UpperCamelCase	,				__UpperCamelCase  	=      self.get_inputs(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      tf_class.from_pretrained(self._local_dir  ,						from_pt=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      tf_from_pt_model(**SCREAMING_SNAKE_CASE_  ,						output_hidden_states=SCREAMING_SNAKE_CASE_  ,						training=SCREAMING_SNAKE_CASE_      )

													# Confirms that cross loading PT weights into TF worked.
													__UpperCamelCase  	=      self.find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      {k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
													__UpperCamelCase  	=      {k: v for k, v in crossload_differences.items() if '''hidden''' in k}
													if len(SCREAMING_SNAKE_CASE_      ) == 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\''''      )
													__UpperCamelCase  	=      max(output_differences.values()      ) if output_differences else 0.0
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      os.path.join(self._local_dir  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      os.path.join(self._local_dir  ,						SCREAMING_SNAKE_CASE_      )
													if (not os.path.exists(SCREAMING_SNAKE_CASE_      ) and not os.path.exists(SCREAMING_SNAKE_CASE_      )) 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

													__UpperCamelCase  	=      tf_class.from_pretrained(self._local_dir      )
													__UpperCamelCase  	=      tf_model(**SCREAMING_SNAKE_CASE_  ,						output_hidden_states=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      {k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
													__UpperCamelCase  	=      {k: v for k, v in conversion_differences.items() if '''hidden''' in k}
													if len(SCREAMING_SNAKE_CASE_      ) == 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\''''      )
													__UpperCamelCase  	=      max(output_differences.values()      ) if output_differences else 0.0
													__UpperCamelCase  	=      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]      )      )

													__UpperCamelCase  	=      '''Update TF weights''' if self._new_weights else '''Add TF weights'''
													if self._push:
																			repo.git_add(auto_lfs_track=SCREAMING_SNAKE_CASE_      )
																			repo.git_commit(SCREAMING_SNAKE_CASE_      )
																			repo.git_push(blocking=SCREAMING_SNAKE_CASE_      )  # 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...'''      )
																			__UpperCamelCase  	=      (
																			    '''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(SCREAMING_SNAKE_CASE_      ):
																									__UpperCamelCase  	=      [
																									    CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE_  ,						path_or_fileobj=SCREAMING_SNAKE_CASE_      )
																									]
																									for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''      ):
																															operations += [
																															    CommitOperationAdd(path_in_repo=os.path.basename(SCREAMING_SNAKE_CASE_      )  ,						path_or_fileobj=SCREAMING_SNAKE_CASE_      )
																															]
																			else:
																									__UpperCamelCase  	=      [CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE_  ,						path_or_fileobj=SCREAMING_SNAKE_CASE_      )]

																			__UpperCamelCase  	=      create_commit(
																			    repo_id=self._model_name  ,						operations=SCREAMING_SNAKE_CASE_  ,						commit_message=SCREAMING_SNAKE_CASE_  ,						commit_description=SCREAMING_SNAKE_CASE_  ,						repo_type='''model'''  ,						create_pr=SCREAMING_SNAKE_CASE_  ,						).pr_url
																			self._logger.warning(F"PR open in {hub_pr_url}"      )







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

import numpy as np
import torch

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





@dataclass
class    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	42
   UpperCamelCase  	=	42






def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[int]=0.999							,					UpperCAmelCase				:     Tuple="cosine"							,					)    ->		List[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCAmelCase				:     List[str]     ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

  elif alpha_transform_type == "exp":

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

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

  UpperCAmelCase			:				Dict										=					[]
  for i in range(UpperCAmelCase     ):
    UpperCAmelCase			:				Any										=					i / num_diffusion_timesteps
    UpperCAmelCase			:				List[Any]										=					(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	(			lowerCamelCase__     ,    lowerCamelCase__		):
   UpperCamelCase  	=	1



   @register_to_config
   def __init__(				self				:	str,						__A				:	int = 1_0_0_0,						__A				:	float = 0.0_0_0_1,						__A				:	float = 0.0_2,						__A				:	str = "linear",						__A				:	float = 0.0,						__A				:	Optional[np.ndarray] = None,						__A				:	bool = True,						):
     if trained_betas is not None:
       UpperCAmelCase			:				int										=					torch.from_numpy(__A		)
     elif beta_schedule == "linear":
       UpperCAmelCase			:				str										=					torch.linspace(__A,						__A,						__A,						dtype=torch.floataa		)
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       UpperCAmelCase			:				List[str]										=					(
           torch.linspace(beta_start**0.5,						beta_end**0.5,						__A,						dtype=torch.floataa		) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       UpperCAmelCase			:				Tuple										=					betas_for_alpha_bar(__A		)
     elif beta_schedule == "sigmoid":
       # GeoDiff sigmoid schedule
       UpperCAmelCase			:				Any										=					torch.linspace(-6,						6,						__A		)
       UpperCAmelCase			:				Tuple										=					torch.sigmoid(__A		) * (beta_end - beta_start) + beta_start
     else:
       raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

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

     UpperCAmelCase			:				Union[str, Any]										=					torch.tensor(1.0		)

     # standard deviation of the initial noise distribution
     UpperCAmelCase			:				str										=					1.0

     # setable values
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Dict										=					torch.from_numpy(np.arange(0,						__A		)[::-1].copy()		)

     UpperCAmelCase			:				List[str]										=					eta



   def 			__magic_name__       (				self				:	List[str],						__A				:	torch.FloatTensor,						__A				:	Optional[int] = None		):
     return sample



   def 			__magic_name__       (				self				:	List[str],						__A				:	int,						__A				:	int = 1_0,						__A				:	int = 1_0,						__A				:	Union[str, torch.device] = None,						):
     UpperCAmelCase			:				Union[str, Any]										=					min(self.config.num_train_timesteps,						__A		)
     UpperCAmelCase			:				Optional[int]										=					num_inference_steps

     UpperCAmelCase			:				Union[str, Any]										=					[]

     UpperCAmelCase			:				Any										=					{}
     for j in range(0,						num_inference_steps - jump_length,						__A		):
       UpperCAmelCase			:				Tuple										=					jump_n_sample - 1

     UpperCAmelCase			:				str										=					num_inference_steps
     while t >= 1:
       UpperCAmelCase			:				Dict										=					t - 1
       timesteps.append(__A		)

       if jumps.get(__A,						0		) > 0:
         UpperCAmelCase			:				Dict										=					jumps[t] - 1
         for _ in range(__A		):
           UpperCAmelCase			:				Any										=					t + 1
           timesteps.append(__A		)

     UpperCAmelCase			:				Optional[Any]										=					np.array(__A		) * (self.config.num_train_timesteps // self.num_inference_steps)
     UpperCAmelCase			:				Dict										=					torch.from_numpy(__A		).to(__A		)



   def 			__magic_name__       (				self				:	List[Any],						__A				:	Any		):
     UpperCAmelCase			:				int										=					t - self.config.num_train_timesteps // self.num_inference_steps

     UpperCAmelCase			:				Optional[Any]										=					self.alphas_cumprod[t]
     UpperCAmelCase			:				Union[str, Any]										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     UpperCAmelCase			:				List[Any]										=					1 - alpha_prod_t
     UpperCAmelCase			:				str										=					1 - alpha_prod_t_prev

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from
     # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
     # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
     # variance to pred_sample
     # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
     # without eta.
     # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
     UpperCAmelCase			:				Union[str, Any]										=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

     return variance



   def 			__magic_name__       (				self				:	Tuple,						__A				:	torch.FloatTensor,						__A				:	int,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	Optional[torch.Generator] = None,						__A				:	bool = True,						):
     UpperCAmelCase			:				Union[str, Any]										=					timestep
     UpperCAmelCase			:				str										=					timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 1. compute alphas, betas
     UpperCAmelCase			:				List[Any]										=					self.alphas_cumprod[t]
     UpperCAmelCase			:				str										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     UpperCAmelCase			:				Any										=					1 - alpha_prod_t

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     UpperCAmelCase			:				int										=					(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
       UpperCAmelCase			:				Tuple										=					torch.clamp(__A,						-1,						1		)

     # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
     # substitute formula (7) in the algorithm coming from DDPM paper
     # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
     # DDIM schedule gives the same results as DDPM with eta = 1.0
     # Noise is being reused in 7. and 8., but no impact on quality has
     # been observed.

     # 5. Add noise
     UpperCAmelCase			:				Optional[Any]										=					model_output.device
     UpperCAmelCase			:				Optional[Any]										=					randn_tensor(model_output.shape,						generator=__A,						device=__A,						dtype=model_output.dtype		)
     UpperCAmelCase			:				List[str]										=					self.eta * self._get_variance(__A		) ** 0.5

     UpperCAmelCase			:				int										=					0
     if t > 0 and self.eta > 0:
       UpperCAmelCase			:				List[str]										=					std_dev_t * noise

     # 6. compute "direction pointing to x_t" of formula (12)
     # from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase			:				Any										=					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase			:				str										=					alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

     # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
     UpperCAmelCase			:				Union[str, Any]										=					(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

     # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
     UpperCAmelCase			:				Optional[Any]										=					mask * prev_known_part + (1.0 - mask) * prev_unknown_part

     if not return_dict:
       return (
           pred_prev_sample,
           pred_original_sample,
       )

     return RePaintSchedulerOutput(prev_sample=__A,						pred_original_sample=__A		)



   def 			__magic_name__       (				self				:	Dict,						__A				:	Dict,						__A				:	Tuple,						__A				:	Tuple=None		):
     UpperCAmelCase			:				Optional[int]										=					self.config.num_train_timesteps // self.num_inference_steps

     for i in range(__A		):
       UpperCAmelCase			:				Optional[Any]										=					self.betas[timestep + i]
       if sample.device.type == "mps":
         # randn does not work reproducibly on mps
         UpperCAmelCase			:				List[str]										=					randn_tensor(sample.shape,						dtype=sample.dtype,						generator=__A		)
         UpperCAmelCase			:				Optional[int]										=					noise.to(sample.device		)
       else:
         UpperCAmelCase			:				Union[str, Any]										=					randn_tensor(sample.shape,						generator=__A,						device=sample.device,						dtype=sample.dtype		)

       # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
       UpperCAmelCase			:				Optional[int]										=					(1 - beta) ** 0.5 * sample + beta**0.5 * noise

     return sample



   def 			__magic_name__       (				self				:	Optional[Any],						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	torch.IntTensor,						):
     raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''		)





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





