'''simple docstring'''

import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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



def  _lowerCAmelCase				(				self			,      __a			,      __a			,      __a			,      __a      )  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :Dict       =image.to(device=__a      )
     _UpperCamelCase  :Union[str, Any]       =image  # take image as image_embeddings
     _UpperCamelCase  :Dict       =image_embeddings.unsqueeze(1      )

     # duplicate image embeddings for each generation per prompt, using mps friendly method
     _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :str       =image_embeddings.shape
     _UpperCamelCase  :int       =image_embeddings.repeat(1			,      __a			,      1      )
     _UpperCamelCase  :int       =image_embeddings.view(bs_embed * num_images_per_prompt			,      __a			,      -1      )

     if do_classifier_free_guidance:
          _UpperCamelCase  :str       =torch.zeros_like(__a      )

          # 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  :List[Any]       =torch.cat([uncond_embeddings, image_embeddings]      )

     return image_embeddings




class    lowerCamelCase__  (   __snake_case     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      )				->     Union[str, Any]:
        """simple docstring"""





        super().__init__()

        _UpperCamelCase  :Any       ={"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        _UpperCamelCase  :List[Any]       =decoder_pipe_kwargs.get("""torch_dtype"""					,      lowerCAmelCase__     ) or prior.dtype

        _UpperCamelCase  :Union[str, Any]       =StableDiffusionImageVariationPipeline.from_pretrained(
            """lambdalabs/sd-image-variations-diffusers"""					,      **lowerCAmelCase__     )

        # replace `_encode_image` method
        _UpperCamelCase  :Tuple       =types.MethodType(_encode_image					,      self.decoder_pipe     )

        self.register_modules(
            prior=lowerCAmelCase__					,      tokenizer=lowerCAmelCase__					,      text_encoder=lowerCAmelCase__					,      prior_scheduler=lowerCAmelCase__					,      )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      )				->     str:
        """simple docstring"""





        if text_model_output is None:
             _UpperCamelCase  :Optional[int]       =len(lowerCAmelCase__     ) if isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ) else 1
             # get prompt text embeddings
             _UpperCamelCase  :Any       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      return_tensors="""pt"""					,      )
             _UpperCamelCase  :List[str]       =text_inputs.input_ids
             _UpperCamelCase  :Tuple       =text_inputs.attention_mask.bool().to(lowerCAmelCase__     )

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                  _UpperCamelCase  :Any       =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  :Tuple       =text_input_ids[:, : self.tokenizer.model_max_length]

             _UpperCamelCase  :str       =self.text_encoder(text_input_ids.to(lowerCAmelCase__     )     )

             _UpperCamelCase  :str       =text_encoder_output.text_embeds
             _UpperCamelCase  :Any       =text_encoder_output.last_hidden_state

        else:
             _UpperCamelCase  :Dict       =text_model_output[0].shape[0]
             _UpperCamelCase       ,      _UpperCamelCase  :Dict       =text_model_output[0], text_model_output[1]
             _UpperCamelCase  :Tuple       =text_attention_mask

        _UpperCamelCase  :Optional[Any]       =text_embeddings.repeat_interleave(lowerCAmelCase__					,      dim=0     )
        _UpperCamelCase  :Optional[int]       =text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__					,      dim=0     )
        _UpperCamelCase  :List[Any]       =text_mask.repeat_interleave(lowerCAmelCase__					,      dim=0     )

        if do_classifier_free_guidance:
             _UpperCamelCase  :Optional[int]       =[""""""] * batch_size

             _UpperCamelCase  :Dict       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      )
             _UpperCamelCase  :Any       =uncond_input.attention_mask.bool().to(lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__     )     )

             _UpperCamelCase  :Optional[int]       =uncond_embeddings_text_encoder_output.text_embeds
             _UpperCamelCase  :Optional[Any]       =uncond_embeddings_text_encoder_output.last_hidden_state

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

             _UpperCamelCase  :List[Any]       =uncond_embeddings.shape[1]
             _UpperCamelCase  :Dict       =uncond_embeddings.repeat(1					,      lowerCAmelCase__     )
             _UpperCamelCase  :Optional[int]       =uncond_embeddings.view(batch_size * num_images_per_prompt					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[Any]       =uncond_text_encoder_hidden_states.shape[1]
             _UpperCamelCase  :List[Any]       =uncond_text_encoder_hidden_states.repeat(1					,      lowerCAmelCase__					,      1     )
             _UpperCamelCase  :Optional[Any]       =uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt					,      lowerCAmelCase__					,      -1     )
             _UpperCamelCase  :Tuple       =uncond_text_mask.repeat_interleave(lowerCAmelCase__					,      dim=0     )

             # done duplicates

             # 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  :Any       =torch.cat([uncond_embeddings, text_embeddings]     )
             _UpperCamelCase  :Any       =torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

             _UpperCamelCase  :Dict       =torch.cat([uncond_text_mask, text_mask]     )

        return text_embeddings, text_encoder_hidden_states, text_mask



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        if self.device != torch.device("""meta"""     ) or not hasattr(self.prior					,      """_hf_hook"""     ):
             return self.device
        for module in self.prior.modules():
             if (
                 hasattr(lowerCAmelCase__					,      """_hf_hook"""     )
                 and hasattr(module._hf_hook					,      """execution_device"""     )
                 and module._hf_hook.execution_device is not None
             ):
                  return torch.device(module._hf_hook.execution_device     )
        return self.device



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        if latents is None:
             _UpperCamelCase  :Optional[int]       =randn_tensor(lowerCAmelCase__					,      generator=lowerCAmelCase__					,      device=lowerCAmelCase__					,      dtype=lowerCAmelCase__     )
        else:
             if latents.shape != shape:
                  raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
             _UpperCamelCase  :List[str]       =latents.to(lowerCAmelCase__     )

        _UpperCamelCase  :Any       =latents * scheduler.init_noise_sigma
        return latents



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__ = None     )				->     int:
        """simple docstring"""





        self.decoder_pipe.to(lowerCAmelCase__     )
        super().to(lowerCAmelCase__     )




   @torch.no_grad()
   def __call__(    self					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 25					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 4.0					,      lowerCAmelCase__ = 8.0					,      lowerCAmelCase__ = 50					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 0.0					,      lowerCAmelCase__ = "pil"					,      lowerCAmelCase__ = True					,      )				->     Dict:
        """simple docstring"""





        if prompt is not None:
             if isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ):
                  _UpperCamelCase  :List[Any]       =1
             elif isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ):
                  _UpperCamelCase  :List[Any]       =len(lowerCAmelCase__     )
             else:
                  raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__     )}'''     )
        else:
             _UpperCamelCase  :int       =text_model_output[0].shape[0]

        _UpperCamelCase  :List[str]       =self._execution_device

        _UpperCamelCase  :List[str]       =batch_size * num_images_per_prompt

        _UpperCamelCase  :Optional[Any]       =prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self._encode_prompt(
            lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

        # prior

        self.prior_scheduler.set_timesteps(lowerCAmelCase__					,      device=lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =self.prior_scheduler.timesteps

        _UpperCamelCase  :int       =self.prior.config.embedding_dim

        _UpperCamelCase  :Union[str, Any]       =self.prepare_latents(
            (batch_size, embedding_dim)					,      text_embeddings.dtype					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      self.prior_scheduler					,      )

        for i, t in enumerate(self.progress_bar(lowerCAmelCase__     )     ):
             # expand the latents if we are doing classifier free guidance
             _UpperCamelCase  :Optional[int]       =torch.cat([prior_latents] * 2     ) if do_classifier_free_guidance else prior_latents

             _UpperCamelCase  :Dict       =self.prior(
                 lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      proj_embedding=lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__					,      attention_mask=lowerCAmelCase__					,      ).predicted_image_embedding

             if do_classifier_free_guidance:
                  _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =predicted_image_embedding.chunk(2     )
                  _UpperCamelCase  :Dict       =predicted_image_embedding_uncond + prior_guidance_scale * (
                      predicted_image_embedding_text - predicted_image_embedding_uncond
                  )

             if i + 1 == prior_timesteps_tensor.shape[0]:
                  _UpperCamelCase  :Tuple       =None
             else:
                  _UpperCamelCase  :Optional[int]       =prior_timesteps_tensor[i + 1]

             _UpperCamelCase  :Tuple       =self.prior_scheduler.step(
                 lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      sample=lowerCAmelCase__					,      generator=lowerCAmelCase__					,      prev_timestep=lowerCAmelCase__					,      ).prev_sample

        _UpperCamelCase  :Any       =self.prior.post_process_latents(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =prior_latents

        _UpperCamelCase  :Tuple       =self.decoder_pipe(
            image=lowerCAmelCase__					,      height=lowerCAmelCase__					,      width=lowerCAmelCase__					,      num_inference_steps=lowerCAmelCase__					,      guidance_scale=lowerCAmelCase__					,      generator=lowerCAmelCase__					,      output_type=lowerCAmelCase__					,      return_dict=lowerCAmelCase__					,      num_images_per_prompt=lowerCAmelCase__					,      eta=lowerCAmelCase__					,      )
        return output
'''simple docstring'''

from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCamelCase :				int														= [
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

_lowerCamelCase :				int														= get_logger(__name__)


_lowerCamelCase :				List[str]														= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def  _lowerCAmelCase				(				__a      )  ->      Optional[TaskTemplate]:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =task_template_dict.get("""task"""      )
     if task_name is None:
          logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(__a      )}'''      )
          return None
     _UpperCamelCase  :List[str]       =NAMEaTEMPLATE.get(__a      )
     return template.from_dict(__a      )