"""simple docstring"""


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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


UpperCAmelCase__     						=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class 				a (						__snake_case      ):


    def __init__(    self		:     Optional[Any]      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Optional[int]      , ):
      super().__init__()

      if safety_checker is None:
        logger.warning(
            f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
            """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
            """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
            """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
            """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
            """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""    )

      self.register_modules(
          speech_model=A_      , speech_processor=A_      , vae=A_      , text_encoder=A_      , tokenizer=A_      , unet=A_      , scheduler=A_      , feature_extractor=A_      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     List[str] = "auto"    ):




      if slice_size == "auto":
        _UpperCAmelCase         =					self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(A_    )


    def    lowerCAmelCase_     (    self		:     int    ):
      self.enable_attention_slicing(A_    )


    @torch.no_grad()
    def __call__(    self		:     Optional[Any]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     List[str]=1_6000      , __lowerCAmelCase		:     List[Any] = 512      , __lowerCAmelCase		:     Optional[int] = 512      , __lowerCAmelCase		:     Optional[Any] = 50      , __lowerCAmelCase		:     Tuple = 7.5      , __lowerCAmelCase		:     Union[str, Any] = None      , __lowerCAmelCase		:     str = 1      , __lowerCAmelCase		:     Any = 0.0      , __lowerCAmelCase		:     Optional[Any] = None      , __lowerCAmelCase		:     Optional[int] = None      , __lowerCAmelCase		:     Any = "pil"      , __lowerCAmelCase		:     List[Any] = True      , __lowerCAmelCase		:     Any = None      , __lowerCAmelCase		:     Optional[int] = 1      , **__lowerCAmelCase		:     Any      , ):
      _UpperCAmelCase         =					self.speech_processor.feature_extractor(
          A_      , return_tensors="""pt"""      , sampling_rate=A_    ).input_features.to(self.device    )
      _UpperCAmelCase         =					self.speech_model.generate(A_      , max_length=48_0000    )

      _UpperCAmelCase         =					self.speech_processor.tokenizer.batch_decode(A_      , skip_special_tokens=A_      , normalize=A_    )[
          0
      ]

      if isinstance(A_      , A_    ):
        _UpperCAmelCase         =					1
      elif isinstance(A_      , A_    ):
        _UpperCAmelCase         =					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         =					self.tokenizer(
          A_      , padding="""max_length"""      , max_length=self.tokenizer.model_max_length      , return_tensors="""pt"""      , )
      _UpperCAmelCase         =					text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        _UpperCAmelCase         =					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         =					text_input_ids[:, : self.tokenizer.model_max_length]
      _UpperCAmelCase         =					self.text_encoder(text_input_ids.to(self.device    )    )[0]

      # duplicate text embeddings for each generation per prompt, using mps friendly method
      _UpperCAmelCase         =					text_embeddings.shape
      _UpperCAmelCase         =					text_embeddings.repeat(1      , A_      , 1    )
      _UpperCAmelCase         =					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         =					guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
        _UpperCAmelCase         =					42
        if negative_prompt is None:
          _UpperCAmelCase         =					[""] * batch_size
        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         =					[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         =					negative_prompt

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

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        _UpperCAmelCase         =					uncond_embeddings.shape[1]
        _UpperCAmelCase         =					uncond_embeddings.repeat(1      , A_      , 1    )
        _UpperCAmelCase         =					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         =					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         =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      _UpperCAmelCase         =					text_embeddings.dtype
      if latents is None:
        if self.device.type == "mps":
          # randn does not exist on mps
          _UpperCAmelCase         =					torch.randn(A_      , generator=A_      , device="""cpu"""      , dtype=A_    ).to(
              self.device    )
        else:
          _UpperCAmelCase         =					torch.randn(A_      , generator=A_      , device=self.device      , dtype=A_    )
      else:
        if latents.shape != latents_shape:
          raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''    )
        _UpperCAmelCase         =					latents.to(self.device    )

      # 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         =					self.scheduler.timesteps.to(self.device    )

      # scale the initial noise by the standard deviation required by the scheduler
      _UpperCAmelCase         =					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         =					"eta" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
      _UpperCAmelCase         =					{}
      if accepts_eta:
        _UpperCAmelCase         =					eta

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

        # predict the noise residual
        _UpperCAmelCase         =					self.unet(A_      , A_      , encoder_hidden_states=A_    ).sample

        # perform guidance
        if do_classifier_free_guidance:
          _UpperCAmelCase         =					noise_pred.chunk(2    )
          _UpperCAmelCase         =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        _UpperCAmelCase         =					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         =					1 / 0.18_215 * latents
      _UpperCAmelCase         =					self.vae.decode(A_    ).sample

      _UpperCAmelCase         =					(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         =					image.cpu().permute(0      , 2      , 3      , 1    ).float().numpy()

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

      if not return_dict:
        return image

      return StableDiffusionPipelineOutput(images=A_      , nsfw_content_detected=A_    )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase       :     List[Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Union[str, Any]						    =   'roberta'





      def __init__( self       ,							A_=5_0265       ,							A_=768       ,							A_=12       ,							A_=12       ,							A_=3072       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=2       ,							A_=0.02       ,							A_=1e-12       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_="absolute"       ,							A_=True       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(pad_token_id=A_       ,							bos_token_id=A_       ,							eos_token_id=A_       ,							**A_   )

       UpperCamelCase	:					Optional[int]       	=   vocab_size
       UpperCamelCase	:					Dict       	=   hidden_size
       UpperCamelCase	:					str       	=   num_hidden_layers
       UpperCamelCase	:					Any       	=   num_attention_heads
       UpperCamelCase	:					List[str]       	=   hidden_act
       UpperCamelCase	:					Optional[Any]       	=   intermediate_size
       UpperCamelCase	:					Tuple       	=   hidden_dropout_prob
       UpperCamelCase	:					Tuple       	=   attention_probs_dropout_prob
       UpperCamelCase	:					Tuple       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   type_vocab_size
       UpperCamelCase	:					int       	=   initializer_range
       UpperCamelCase	:					str       	=   layer_norm_eps
       UpperCamelCase	:					Dict       	=   position_embedding_type
       UpperCamelCase	:					Any       	=   use_cache
       UpperCamelCase	:					Union[str, Any]       	=   classifier_dropout



class 					A__   (       __snake_case			):





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




       if self.task == "multiple-choice":
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]   )
