


'''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


_UpperCamelCase		:		List[Any]								=			5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
def __UpperCAmelCase (			A			:	Namespace						)  -> Union[str, Any]:
			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 				snake_case__     (			UpperCamelCase):




					@staticmethod
					def 						A     (       _A       :						Optional[int]     )  ->    Tuple:
								UpperCAmelCase_     :   List[str]       =   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=_A							,				required=_A							,				help='''The model name, including owner/organization, as seen on the hub.'''							,				)
								train_parser.add_argument(
								    '''--local-dir'''							,				type=_A							,				default=''''''							,				help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''							,				)
								train_parser.add_argument(
								    '''--max-error'''							,				type=_A							,				default=_A							,				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=_A							,				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=_A							,				default=_A							,				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=_A     )




					@staticmethod
					def 						A     (       _A       :						Optional[Any]							,				_A       :						int     )  ->    Any:
								UpperCAmelCase_     :   List[str]       =   set(pt_outputs.keys()     )
								UpperCAmelCase_     :   Optional[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(_A       :						str							,				_A       :						Optional[Any]							,				_A       :						str							,				_A       :						Tuple=""     ):
											# 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(_A							,				torch.Tensor     ):
														UpperCAmelCase_     :   List[str]       =   np.max(np.abs(pt_out.numpy() - tf_out.numpy()     )     )
														UpperCAmelCase_     :   str       =   tensor_difference
											else:
														UpperCAmelCase_     :   List[Any]       =   attr_name
														for i, pt_item in enumerate(_A     ):
																	# If it is a named attribute, we keep the name. Otherwise, just its index.
																	if isinstance(_A							,				_A     ):
																				UpperCAmelCase_     :   Tuple       =   root_name + pt_item
																				UpperCAmelCase_     :   List[Any]       =   tf_out[pt_item]
																				UpperCAmelCase_     :   Tuple       =   pt_out[pt_item]
																	else:
																				UpperCAmelCase_     :   List[Any]       =   root_name + F"[{i}]"
																				UpperCAmelCase_     :   List[str]       =   tf_out[i]
																	UpperCAmelCase_     :   List[Any]       =   _find_pt_tf_differences(_A							,				_A							,				_A							,				_A     )

											return differences

								return _find_pt_tf_differences(_A							,				_A							,				{}     )




					def __init__(       self       :						List[Any]							,				_A       :						Tuple							,				_A       :						Any							,				_A       :						List[Any]							,				_A       :						int							,				_A       :						Any							,				_A       :						Union[str, Any]							,				_A       :						str							,				_A       :						Dict							,				*_A       :						Union[str, Any]							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   Any       =   logging.get_logger('''transformers-cli/pt_to_tf'''     )
								UpperCAmelCase_     :   Optional[int]       =   model_name
								UpperCAmelCase_     :   List[str]       =   local_dir if local_dir else os.path.join('''/tmp'''							,				_A     )
								UpperCAmelCase_     :   Tuple       =   max_error
								UpperCAmelCase_     :   Dict       =   new_weights
								UpperCAmelCase_     :   int       =   no_pr
								UpperCAmelCase_     :   Optional[Any]       =   push
								UpperCAmelCase_     :   Optional[int]       =   extra_commit_description
								UpperCAmelCase_     :   List[Any]       =   override_model_class




					def 						A     (       self       :						Optional[int]							,				_A       :						Union[str, Any]							,				_A       :						Any							,				_A       :						List[Any]     )  ->    List[str]:






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

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

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

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

								return pt_input, tf_input





					def 						A     (       self       :						Union[str, Any]     )  ->    int:






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

								UpperCAmelCase_  ,     UpperCAmelCase_     :   List[str]       =   self.get_inputs(_A							,				_A							,				_A     )

								with torch.no_grad():
											UpperCAmelCase_     :   List[Any]       =   pt_model(**_A							,				output_hidden_states=_A     )
								del pt_model  # will no longer be used, and may have a large memory footprint

								UpperCAmelCase_     :   Dict       =   tf_class.from_pretrained(self._local_dir							,				from_pt=_A     )
								UpperCAmelCase_     :   Dict       =   tf_from_pt_model(**_A							,				output_hidden_states=_A							,				training=_A     )

								# Confirms that cross loading PT weights into TF worked.
								UpperCAmelCase_     :   List[Any]       =   self.find_pt_tf_differences(_A							,				_A     )
								UpperCAmelCase_     :   Optional[Any]       =   {k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
								UpperCAmelCase_     :   str       =   {k: v for k, v in crossload_differences.items() if '''hidden''' in k}
								if len(_A     ) == 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_     :   Tuple       =   max(output_differences.values()     ) if output_differences else 0.0
								UpperCAmelCase_     :   int       =   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_     :   Optional[int]       =   os.path.join(self._local_dir							,				_A     )
								UpperCAmelCase_     :   str       =   os.path.join(self._local_dir							,				_A     )
								if (not os.path.exists(_A     ) and not os.path.exists(_A     )) 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_     :   str       =   tf_class.from_pretrained(self._local_dir     )
								UpperCAmelCase_     :   Optional[Any]       =   tf_model(**_A							,				output_hidden_states=_A     )

								UpperCAmelCase_     :   Tuple       =   self.find_pt_tf_differences(_A							,				_A     )
								UpperCAmelCase_     :   str       =   {k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
								UpperCAmelCase_     :   str       =   {k: v for k, v in conversion_differences.items() if '''hidden''' in k}
								if len(_A     ) == 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_     :   Tuple       =   max(output_differences.values()     ) if output_differences else 0.0
								UpperCAmelCase_     :   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]     )     )

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

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










'''simple docstring'''





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 				snake_case__     (			UpperCamelCase):




					def __init__(       self       :						Optional[int]							,				_A       :						AutoencoderKL							,				_A       :						CLIPTextModel							,				_A       :						CLIPTokenizer							,				_A       :						UNetaDConditionModel							,				_A       :						DDIMScheduler							,				_A       :						float = 0.93							,				_A       :						bool = True							,				)  ->    Dict:
								UpperCAmelCase_     :   Tuple       =   mixing_coeff
								UpperCAmelCase_     :   Union[str, Any]       =   leapfrog_steps

								super().__init__()
								self.register_modules(
								    vae=_A							,				text_encoder=_A							,				tokenizer=_A							,				unet=_A							,				scheduler=_A							,				)

								UpperCAmelCase_     :   Optional[Any]       =   2 ** (len(self.vae.config.block_out_channels     ) - 1)
								UpperCAmelCase_     :   Optional[Any]       =   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )




					def 						A     (       self       :						Optional[int]							,				_A       :						str							,				_A       :						Optional[str] = None							,				_A       :						bool = False     )  ->    Dict:
								UpperCAmelCase_     :   int       =   self.tokenizer(
								    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

								UpperCAmelCase_     :   Tuple       =   self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

								UpperCAmelCase_     :   Tuple       =   prompt_embeds.to(dtype=self.text_encoder.dtype							,				device=self.device     )

								if do_classifier_free_guidance:
											UpperCAmelCase_     :   Optional[int]       =   '''''' if negative_prompt is None else negative_prompt

											UpperCAmelCase_     :   Union[str, Any]       =   self.tokenizer(
											    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

											UpperCAmelCase_     :   List[str]       =   self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

											UpperCAmelCase_     :   Tuple       =   torch.cat([negative_prompt_embeds, prompt_embeds]     )

								return prompt_embeds




					def 						A     (       self       :						Optional[int]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Tuple:
								UpperCAmelCase_     :   Union[str, Any]       =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								UpperCAmelCase_     :   Any       =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]




					def 						A     (       self       :						Union[str, Any]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								UpperCAmelCase_     :   int       =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




					def 						A     (       self       :						List[str]							,				_A       :						torch.Tensor     )  ->    List[Any]:
								# as self.alphas_cumprod is always in cpu
								UpperCAmelCase_     :   Tuple       =   int(_A     )

								UpperCAmelCase_     :   List[str]       =   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

								return alpha_prod, 1 - alpha_prod




					def 						A     (       self       :						Dict							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Tuple:
								UpperCAmelCase_     :   str       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Optional[Any]       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Tuple       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

								UpperCAmelCase_     :   List[str]       =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype     )




					def 						A     (       self       :						Any							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   Tuple       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Any       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Optional[int]       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
								UpperCAmelCase_     :   List[Any]       =   a_t * base + b_t * model_output

								return model_input, next_model_input.to(base.dtype     )




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						torch.Tensor     )  ->    List[Any]:
								UpperCAmelCase_     :   Union[str, Any]       =   1 / self.vae.config.scaling_factor * latents
								UpperCAmelCase_     :   str       =   self.vae.decode(_A     ).sample
								UpperCAmelCase_     :   Any       =   (image / 2 + 0.5).clamp(0							,				1     )
								return image




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						Image.Image							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						float							,				_A       :						Optional[torch.Generator] = None							,				)  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   str       =   image.to(device=self.device							,				dtype=text_embeds.dtype     )
								UpperCAmelCase_     :   int       =   self.vae.encode(_A     ).latent_dist.sample(_A     )

								UpperCAmelCase_     :   List[str]       =   self.vae.config.scaling_factor * latent

								UpperCAmelCase_     :   Optional[int]       =   [latent.clone(), latent.clone()]

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											UpperCAmelCase_     :   List[str]       =   self.noise_mixing_layer(x=coupled_latents[0]							,				y=coupled_latents[1]     )

											# j - model_input index, k - base index
											for j in range(2     ):
														UpperCAmelCase_     :   List[Any]       =   j ^ 1

														if self.leapfrog_steps:
																	if i % 2 == 0:
																				UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   j, k

														UpperCAmelCase_     :   str       =   coupled_latents[j]
														UpperCAmelCase_     :   List[Any]       =   coupled_latents[k]

														UpperCAmelCase_     :   Optional[int]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   Dict       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

														if do_classifier_free_guidance:
																	UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   noise_pred.chunk(2     )
																	UpperCAmelCase_     :   List[str]       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														UpperCAmelCase_  ,     UpperCAmelCase_     :   Union[str, Any]       =   self.noise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

								return coupled_latents





					@torch.no_grad()
					def __call__(       self       :						Any							,				_A       :						str							,				_A       :						str							,				_A       :						Image.Image							,				_A       :						float = 3.0							,				_A       :						int = 50							,				_A       :						float = 0.8							,				_A       :						Optional[str] = None							,				_A       :						Optional[torch.Generator] = None							,				_A       :						Optional[str] = "pil"							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   List[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   Optional[Any]       =   self.image_processor.preprocess(_A     )

								UpperCAmelCase_     :   str       =   self._encode_prompt(_A							,				_A							,				_A     )
								UpperCAmelCase_     :   int       =   self._encode_prompt(_A							,				_A							,				_A     )

								self.scheduler.set_timesteps(_A							,				self.device     )

								UpperCAmelCase_     :   Any       =   num_inference_steps - int(num_inference_steps * strength     )
								UpperCAmelCase_     :   Tuple       =   self.scheduler.timesteps[t_limit:]
								UpperCAmelCase_     :   Any       =   fwd_timesteps.flip(0     )

								UpperCAmelCase_     :   Optional[int]       =   self.prepare_latents(_A							,				_A							,				_A							,				_A							,				_A     )

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											# j - model_input index, k - base index
											for k in range(2     ):
														UpperCAmelCase_     :   Union[str, Any]       =   k ^ 1

														if self.leapfrog_steps:
																	if i % 2 == 1:
																				UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   j, k

														UpperCAmelCase_     :   Tuple       =   coupled_latents[j]
														UpperCAmelCase_     :   Optional[int]       =   coupled_latents[k]

														UpperCAmelCase_     :   Union[str, Any]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   int       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

														if do_classifier_free_guidance:
																	UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   noise_pred.chunk(2     )
																	UpperCAmelCase_     :   Any       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   self.denoise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

											UpperCAmelCase_     :   str       =   self.denoise_mixing_layer(x=coupled_latents[0]							,				y=coupled_latents[1]     )

								# either one is fine
								UpperCAmelCase_     :   Union[str, Any]       =   coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   (
											    F"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
											    '''`pil`, `np`, `pt`, `latent`'''
											)
											deprecate('''Unsupported output_type'''							,				'''1.0.0'''							,				_A							,				standard_warn=_A     )
											UpperCAmelCase_     :   int       =   '''np'''

								if output_type == "latent":
											UpperCAmelCase_     :   Tuple       =   final_latent
								else:
											UpperCAmelCase_     :   Optional[int]       =   self.decode_latents(_A     )
											UpperCAmelCase_     :   str       =   self.image_processor.postprocess(_A							,				output_type=_A     )

								return image


