





import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
       import numpy as np
       import torch

       from . import (
           AlbertForPreTraining,
           BartForConditionalGeneration,
           BertForPreTraining,
           BertForQuestionAnswering,
           BertForSequenceClassification,
           CamembertForMaskedLM,
           CTRLLMHeadModel,
           DistilBertForMaskedLM,
           DistilBertForQuestionAnswering,
           DPRContextEncoder,
           DPRQuestionEncoder,
           DPRReader,
           ElectraForPreTraining,
           FlaubertWithLMHeadModel,
           GPTaLMHeadModel,
           LayoutLMForMaskedLM,
           LxmertForPreTraining,
           LxmertVisualFeatureEncoder,
           OpenAIGPTLMHeadModel,
           RobertaForMaskedLM,
           RobertaForSequenceClassification,
           TaForConditionalGeneration,
           TransfoXLLMHeadModel,
           XLMRobertaForMaskedLM,
           XLMWithLMHeadModel,
           XLNetLMHeadModel,
       )


logging.set_verbosity_info()

__lowerCamelCase	:					Tuple			  =					{
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=False							,       _lowerCAmelCase=True					)						->			Union[str, Any]:
 if model_type not in MODEL_CLASSES:
  raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()					)}."""					)

 UpperCamelCase     : str	     =       MODEL_CLASSES[model_type]

 # Initialise TF model
 if config_file in aws_config_map:
  UpperCamelCase     : Union[str, Any]	     =       cached_file(_lowerCAmelCase							,       _lowerCAmelCase							,       force_download=not use_cached_models					)
 UpperCamelCase     : Union[str, Any]	     =       config_class.from_json_file(_lowerCAmelCase					)
 UpperCamelCase     : List[Any]	     =       True
 UpperCamelCase     : Optional[Any]	     =       True
 print(F"""Building TensorFlow model from configuration: {config}"""					)
 UpperCamelCase     : Tuple	     =       model_class(_lowerCAmelCase					)

 # Load weights from tf checkpoint
 if pytorch_checkpoint_path in aws_config_map.keys():
  UpperCamelCase     : int	     =       cached_file(
      _lowerCAmelCase							,       _lowerCAmelCase							,       force_download=not use_cached_models					)
 # Load PyTorch checkpoint in tf2 model:
 UpperCamelCase     : Union[str, Any]	     =       load_pytorch_checkpoint_in_tfa_model(_lowerCAmelCase							,       _lowerCAmelCase					)

 if compare_with_pt_model:
  UpperCamelCase     : Optional[int]	     =       tf_model(tf_model.dummy_inputs							,       training=_lowerCAmelCase					)  # build the network

  UpperCamelCase     : Optional[int]	     =       torch.load(_lowerCAmelCase							,       map_location="cpu"					)
  UpperCamelCase     : List[Any]	     =       pt_model_class.from_pretrained(
      pretrained_model_name_or_path=_lowerCAmelCase							,       config=_lowerCAmelCase							,       state_dict=_lowerCAmelCase					)

  with torch.no_grad():
   UpperCamelCase     : Any	     =       pt_model(**pt_model.dummy_inputs					)

  UpperCamelCase     : Optional[int]	     =       pto[0].numpy()
  UpperCamelCase     : Optional[Any]	     =       tfo[0].numpy()
  UpperCamelCase     : Optional[Any]	     =       np.amax(np.abs(np_pt - np_tf					)					)
  print(F"""Max absolute difference between models outputs {diff}"""					)
  assert diff <= 2e-2, F"""Error, model absolute difference is >2e-2: {diff}"""

 # Save pytorch-model
 print(F"""Save TensorFlow model to {tf_dump_path}"""					)
 tf_model.save_weights(_lowerCAmelCase							,       save_format="h5"					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       )						->			List[Any]:
 if args_model_type is None:
  UpperCamelCase     : Optional[Any]	     =       list(MODEL_CLASSES.keys()					)
 else:
  UpperCamelCase     : str	     =       [args_model_type]

 for j, model_type in enumerate(_lowerCAmelCase							,       start=1					):
  print("=" * 100					)
  print(F""" Converting model type {j}/{len(_lowerCAmelCase					)}: {model_type}"""					)
  print("=" * 100					)
  if model_type not in MODEL_CLASSES:
   raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()					)}."""					)

  UpperCamelCase     : int	     =       MODEL_CLASSES[model_type]

  if model_shortcut_names_or_path is None:
   UpperCamelCase     : Union[str, Any]	     =       list(aws_model_maps.keys()					)
  if config_shortcut_names_or_path is None:
   UpperCamelCase     : Optional[int]	     =       model_shortcut_names_or_path

  for i, (model_shortcut_name, config_shortcut_name) in enumerate(
      zip(_lowerCAmelCase							,       _lowerCAmelCase					)							,       start=1					):
   print("-" * 100					)
   if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
    if not only_convert_finetuned_models:
     print(F"""    Skipping finetuned checkpoint {model_shortcut_name}"""					)
     continue
    UpperCamelCase     : Optional[Any]	     =       model_shortcut_name
   elif only_convert_finetuned_models:
    print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""					)
    continue
   print(
       F"""    Converting checkpoint {i}/{len(_lowerCAmelCase					)}: {model_shortcut_name} - model_type {model_type}"""					)
   print("-" * 100					)

   if config_shortcut_name in aws_config_map:
    UpperCamelCase     : str	     =       cached_file(_lowerCAmelCase							,       _lowerCAmelCase							,       force_download=not use_cached_models					)
   else:
    UpperCamelCase     : Union[str, Any]	     =       config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    UpperCamelCase     : Union[str, Any]	     =       cached_file(_lowerCAmelCase							,       _lowerCAmelCase							,       force_download=not use_cached_models					)
   else:
    UpperCamelCase     : List[str]	     =       model_shortcut_name

   if os.path.isfile(_lowerCAmelCase					):
    UpperCamelCase     : Optional[Any]	     =       "converted_model"

   convert_pt_checkpoint_to_tf(
       model_type=_lowerCAmelCase							,       pytorch_checkpoint_path=_lowerCAmelCase							,       config_file=_lowerCAmelCase							,       tf_dump_path=os.path.join(_lowerCAmelCase							,       model_shortcut_name + "-tf_model.h5"					)							,       compare_with_pt_model=_lowerCAmelCase							,       )
   if remove_cached_files:
    os.remove(_lowerCAmelCase					)
    os.remove(_lowerCAmelCase					)


if __name__ == "__main__":
       __lowerCamelCase	:					Union[str, Any]			  =					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
       )
       parser.add_argument(
           """--model_type""",
           default=None,
           type=str,
           help=(
               f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
               """convert all the models from AWS."""
           ),
       )
       parser.add_argument(
           """--pytorch_checkpoint_path""",
           default=None,
           type=str,
           help=(
               """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
               """If not given, will download and convert all the checkpoints from AWS."""
           ),
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           help=(
               """The config json file corresponding to the pre-trained model. \n"""
               """This specifies the model architecture. If not given and """
               """--pytorch_checkpoint_path is not given or is a shortcut name """
               """use the configuration associated to the shortcut name on the AWS"""
           ),
       )
       parser.add_argument(
           """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
       )
       parser.add_argument(
           """--use_cached_models""",
           action="""store_true""",
           help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
       )
       parser.add_argument(
           """--remove_cached_files""",
           action="""store_true""",
           help="""Remove pytorch models after conversion (save memory when converting in batches).""",
       )
       parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
       __lowerCamelCase	:					int			  =					parser.parse_args()

       # if args.pytorch_checkpoint_path is not None:
       #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
       #                                 args.pytorch_checkpoint_path,
       #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
       #                                 args.tf_dump_path,
       #                                 compare_with_pt_model=args.compare_with_pt_model,
       #                                 use_cached_models=args.use_cached_models)
       # else:
       convert_all_pt_checkpoints_to_tf(
           args.model_type.lower() if args.model_type is not None else None,
           args.tf_dump_path,
           model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
           if args.pytorch_checkpoint_path is not None
           else None,
           config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
           compare_with_pt_model=args.compare_with_pt_model,
           use_cached_models=args.use_cached_models,
           remove_cached_files=args.remove_cached_files,
           only_convert_finetuned_models=args.only_convert_finetuned_models,
       )







import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__lowerCamelCase	:					List[str]			  =					logging.get_logger(__name__)  # pylint: disable=invalid-name



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




       super().__init__()
       self.register_modules(
           vae=A_	,							text_encoder=A_	,							tokenizer=A_	,							unet=A_	,							scheduler=A_	,							safety_checker=A_	,							feature_extractor=A_	,							)





      def 	__UpperCamelCase(			self	,							A_ = "auto"       ):
       '''simple docstring'''




       if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        UpperCamelCase     : Union[str, Any]	     =       self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.enable_attention_slicing(A_       )





      @torch.no_grad()
      def __call__(			self	,							A_	,							A_ = 512	,							A_ = 512	,							A_ = 50	,							A_ = 7.5	,							A_ = None	,							A_ = 1	,							A_ = 0.0	,							A_ = None	,							A_ = None	,							A_ = "pil"	,							A_ = True	,							A_ = None	,							A_ = 1	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       if isinstance(A_	,							A_       ):
        UpperCamelCase     : Any	     =       1
       elif isinstance(A_	,							A_       ):
        UpperCamelCase     : Optional[Any]	     =       len(A_       )
       else:
        raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(A_       )}"""       )

       if height % 8 != 0 or width % 8 != 0:
        raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""       )

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(A_	,							A_       ) or callback_steps <= 0)
       ):
        raise ValueError(
            F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
            F""" {type(A_       )}."""       )

       # get prompt text embeddings
       UpperCamelCase     : int	     =       self.tokenizer(
           A_	,							padding="max_length"	,							max_length=self.tokenizer.model_max_length	,							return_tensors="pt"	,							)
       UpperCamelCase     : Optional[Any]	     =       text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        UpperCamelCase     : List[str]	     =       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
        logger.warning(
            "The following part of your input was truncated because CLIP can only handle sequences up to"
            F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""       )
        UpperCamelCase     : Any	     =       text_input_ids[:, : self.tokenizer.model_max_length]

       if text_embeddings is None:
        UpperCamelCase     : str	     =       self.text_encoder(text_input_ids.to(self.device       )       )[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : str	     =       text_embeddings.shape
       UpperCamelCase     : int	     =       text_embeddings.repeat(1	,							A_	,							1       )
       UpperCamelCase     : str	     =       text_embeddings.view(bs_embed * num_images_per_prompt	,							A_	,							-1       )

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       UpperCamelCase     : Optional[int]	     =       guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        UpperCamelCase     : List[str]
        if negative_prompt is None:
         UpperCamelCase     : Tuple	     =       [""]
        elif type(A_       ) is not type(A_       ):
         raise TypeError(
             F"""`negative_prompt` should be the same type to `prompt`, but got {type(A_       )} !="""
             F""" {type(A_       )}."""       )
        elif isinstance(A_	,							A_       ):
         UpperCamelCase     : Optional[int]	     =       [negative_prompt]
        elif batch_size != len(A_       ):
         raise ValueError(
             F"""`negative_prompt`: {negative_prompt} has batch size {len(A_       )}, but `prompt`:"""
             F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
             " the batch size of `prompt`."       )
        else:
         UpperCamelCase     : Union[str, Any]	     =       negative_prompt

        UpperCamelCase     : Tuple	     =       text_input_ids.shape[-1]
        UpperCamelCase     : str	     =       self.tokenizer(
            A_	,							padding="max_length"	,							max_length=A_	,							truncation=A_	,							return_tensors="pt"	,							)
        UpperCamelCase     : str	     =       self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        UpperCamelCase     : Optional[int]	     =       uncond_embeddings.shape[1]
        UpperCamelCase     : Optional[Any]	     =       uncond_embeddings.repeat(A_	,							A_	,							1       )
        UpperCamelCase     : Dict	     =       uncond_embeddings.view(batch_size * num_images_per_prompt	,							A_	,							-1       )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        UpperCamelCase     : Optional[int]	     =       torch.cat([uncond_embeddings, text_embeddings]       )

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       UpperCamelCase     : Union[str, Any]	     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCamelCase     : Tuple	     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       UpperCamelCase     : Any	     =       text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not exist on mps
         UpperCamelCase     : Optional[Any]	     =       torch.randn(
             A_	,							generator=A_	,							device="cpu"	,							dtype=A_       ).to(self.device       )
         UpperCamelCase     : Dict	     =       torch.randn(A_	,							generator=A_	,							device="cpu"	,							dtype=A_       ).to(
             self.device       )
        else:
         UpperCamelCase     : Tuple	     =       torch.randn(
             A_	,							generator=A_	,							device=self.device	,							dtype=A_       )
         UpperCamelCase     : str	     =       torch.randn(A_	,							generator=A_	,							device=self.device	,							dtype=A_       )
       else:
        if latents_reference.shape != latents_shape:
         raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
        UpperCamelCase     : List[Any]	     =       latents_reference.to(self.device       )
        UpperCamelCase     : Optional[Any]	     =       latents.to(self.device       )

       # This is the key part of the pipeline where we
       # try to ensure that the generated images w/ the same seed
       # but different sizes actually result in similar images
       UpperCamelCase     : List[str]	     =       (latents_shape[3] - latents_shape_reference[3]) // 2
       UpperCamelCase     : str	     =       (latents_shape[2] - latents_shape_reference[2]) // 2
       UpperCamelCase     : List[Any]	     =       latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       UpperCamelCase     : Optional[Any]	     =       latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       UpperCamelCase     : str	     =       0 if dx < 0 else dx
       UpperCamelCase     : Union[str, Any]	     =       0 if dy < 0 else dy
       UpperCamelCase     : Union[str, Any]	     =       max(-dx	,							0       )
       UpperCamelCase     : Tuple	     =       max(-dy	,							0       )
       # import pdb
       # pdb.set_trace()
       UpperCamelCase     : Optional[Any]	     =       latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(A_       )

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       UpperCamelCase     : Union[str, Any]	     =       self.scheduler.timesteps.to(self.device       )

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCamelCase     : str	     =       latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       UpperCamelCase     : List[str]	     =       "eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
       UpperCamelCase     : str	     =       {}
       if accepts_eta:
        UpperCamelCase     : int	     =       eta

       for i, t in enumerate(self.progress_bar(A_       )       ):
        # expand the latents if we are doing classifier free guidance
        UpperCamelCase     : Any	     =       torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
        UpperCamelCase     : Tuple	     =       self.scheduler.scale_model_input(A_	,							A_       )

        # predict the noise residual
        UpperCamelCase     : List[str]	     =       self.unet(A_	,							A_	,							encoder_hidden_states=A_       ).sample

        # perform guidance
        if do_classifier_free_guidance:
         UpperCamelCase     ,   UpperCamelCase     : Any	     =       noise_pred.chunk(2       )
         UpperCamelCase     : List[str]	     =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        UpperCamelCase     : Optional[Any]	     =       self.scheduler.step(A_	,							A_	,							A_	,							**A_       ).prev_sample

        # call the callback, if provided
        if callback is not None and i % callback_steps == 0:
         callback(A_	,							A_	,							A_       )

       UpperCamelCase     : int	     =       1 / 0.1_82_15 * latents
       UpperCamelCase     : Tuple	     =       self.vae.decode(A_       ).sample

       UpperCamelCase     : Any	     =       (image / 2 + 0.5).clamp(0	,							1       )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       UpperCamelCase     : Tuple	     =       image.cpu().permute(0	,							2	,							3	,							1       ).float().numpy()

       if self.safety_checker is not None:
        UpperCamelCase     : int	     =       self.feature_extractor(self.numpy_to_pil(A_       )	,							return_tensors="pt"       ).to(
            self.device       )
        UpperCamelCase     ,   UpperCamelCase     : int	     =       self.safety_checker(
            images=A_	,							clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype       )       )
       else:
        UpperCamelCase     : int	     =       None

       if output_type == "pil":
        UpperCamelCase     : Tuple	     =       self.numpy_to_pil(A_       )

       if not return_dict:
        return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=A_	,							nsfw_content_detected=A_       )
