




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


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




def    __SCREAMING_SNAKE_CASE				(					self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase											=  image.to(device=lowercase_ )
    __UpperCamelCase											=  image  # take image as image_embeddings
    __UpperCamelCase											=  image_embeddings.unsqueeze(1 )

    # duplicate image embeddings for each generation per prompt, using mps friendly method
    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  image_embeddings.shape
    __UpperCamelCase											=  image_embeddings.repeat(1   ,       lowercase_   ,       1 )
    __UpperCamelCase											=  image_embeddings.view(bs_embed * num_images_per_prompt   ,       lowercase_   ,       -1 )

    if do_classifier_free_guidance:
        __UpperCamelCase											=  torch.zeros_like(lowercase_ )

        # 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, image_embeddings] )

    return image_embeddings


class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      def __init__(       self   :	str	,    snake_case   :	PriorTransformer	,    snake_case   :	CLIPTokenizer	,    snake_case   :	CLIPTextModelWithProjection	,    snake_case   :	UnCLIPScheduler	,    snake_case   :	Optional[dict] = None	,    ):
          super().__init__()

          __UpperCamelCase											=  {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

          __UpperCamelCase											=  decoder_pipe_kwargs.get('''torch_dtype'''	,    snake_case			) or prior.dtype

          __UpperCamelCase											=  StableDiffusionImageVariationPipeline.from_pretrained(
              '''lambdalabs/sd-image-variations-diffusers'''	,    **snake_case			)

          # replace `_encode_image` method
          __UpperCamelCase											=  types.MethodType(_encode_image	,    self.decoder_pipe			)

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







      def  snake_case		(       self   :	Optional[int]	,    snake_case   :	Dict	,    snake_case   :	List[str]	,    snake_case   :	str	,    snake_case   :	Union[str, Any]	,    snake_case   :	Optional[Union[CLIPTextModelOutput, Tuple]] = None	,    snake_case   :	Optional[torch.Tensor] = None	,    ):
          if text_model_output is None:
              __UpperCamelCase											=  len(snake_case			) if isinstance(snake_case	,    snake_case			) else 1
              # get prompt text embeddings
              __UpperCamelCase											=  self.tokenizer(
                  snake_case	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    return_tensors='''pt'''	,    )
              __UpperCamelCase											=  text_inputs.input_ids
              __UpperCamelCase											=  text_inputs.attention_mask.bool().to(snake_case			)

              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(snake_case			)			)

              __UpperCamelCase											=  text_encoder_output.text_embeds
              __UpperCamelCase											=  text_encoder_output.last_hidden_state

          else:
              __UpperCamelCase											=  text_model_output[0].shape[0]
              __UpperCamelCase						, __UpperCamelCase											=  text_model_output[0], text_model_output[1]
              __UpperCamelCase											=  text_attention_mask

          __UpperCamelCase											=  text_embeddings.repeat_interleave(snake_case	,    dim=0			)
          __UpperCamelCase											=  text_encoder_hidden_states.repeat_interleave(snake_case	,    dim=0			)
          __UpperCamelCase											=  text_mask.repeat_interleave(snake_case	,    dim=0			)

          if do_classifier_free_guidance:
              __UpperCamelCase											=  [''''''] * batch_size

              __UpperCamelCase											=  self.tokenizer(
                  snake_case	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=snake_case	,    return_tensors='''pt'''	,    )
              __UpperCamelCase											=  uncond_input.attention_mask.bool().to(snake_case			)
              __UpperCamelCase											=  self.text_encoder(uncond_input.input_ids.to(snake_case			)			)

              __UpperCamelCase											=  uncond_embeddings_text_encoder_output.text_embeds
              __UpperCamelCase											=  uncond_embeddings_text_encoder_output.last_hidden_state

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

              __UpperCamelCase											=  uncond_embeddings.shape[1]
              __UpperCamelCase											=  uncond_embeddings.repeat(1	,    snake_case			)
              __UpperCamelCase											=  uncond_embeddings.view(batch_size * num_images_per_prompt	,    snake_case			)

              __UpperCamelCase											=  uncond_text_encoder_hidden_states.shape[1]
              __UpperCamelCase											=  uncond_text_encoder_hidden_states.repeat(1	,    snake_case	,    1			)
              __UpperCamelCase											=  uncond_text_encoder_hidden_states.view(
                  batch_size * num_images_per_prompt	,    snake_case	,    -1			)
              __UpperCamelCase											=  uncond_text_mask.repeat_interleave(snake_case	,    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											=  torch.cat([uncond_embeddings, text_embeddings]			)
              __UpperCamelCase											=  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

              __UpperCamelCase											=  torch.cat([uncond_text_mask, text_mask]			)

          return text_embeddings, text_encoder_hidden_states, text_mask







      @property
      def  snake_case		(       self   :	int			):
          if self.device != torch.device('''meta'''			) or not hasattr(self.prior	,    '''_hf_hook'''			):
              return self.device
          for module in self.prior.modules():
              if (
                  hasattr(snake_case	,    '''_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  snake_case		(       self   :	Any	,    snake_case   :	Dict	,    snake_case   :	int	,    snake_case   :	Optional[int]	,    snake_case   :	Dict	,    snake_case   :	Tuple	,    snake_case   :	Any			):
          if latents is None:
              __UpperCamelCase											=  randn_tensor(snake_case	,    generator=snake_case	,    device=snake_case	,    dtype=snake_case			)
          else:
              if latents.shape != shape:
                  raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"			)
              __UpperCamelCase											=  latents.to(snake_case			)

          __UpperCamelCase											=  latents * scheduler.init_noise_sigma
          return latents







      def  snake_case		(       self   :	int	,    snake_case   :	Optional[Union[str, torch.device]] = None			):
          self.decoder_pipe.to(snake_case			)
          super().to(snake_case			)







      @torch.no_grad()
      def __call__(       self   :	Dict	,    snake_case   :	Optional[Union[str, List[str]]] = None	,    snake_case   :	Optional[int] = None	,    snake_case   :	Optional[int] = None	,    snake_case   :	int = 1	,    snake_case   :	int = 25	,    snake_case   :	Optional[torch.Generator] = None	,    snake_case   :	Optional[torch.FloatTensor] = None	,    snake_case   :	Optional[Union[CLIPTextModelOutput, Tuple]] = None	,    snake_case   :	Optional[torch.Tensor] = None	,    snake_case   :	float = 4.0	,    snake_case   :	float = 8.0	,    snake_case   :	int = 50	,    snake_case   :	Optional[int] = 1	,    snake_case   :	float = 0.0	,    snake_case   :	Optional[str] = "pil"	,    snake_case   :	bool = True	,    ):
          if prompt is not None:
              if isinstance(snake_case	,    snake_case			):
                  __UpperCamelCase											=  1
              elif isinstance(snake_case	,    snake_case			):
                  __UpperCamelCase											=  len(snake_case			)
              else:
                  raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(snake_case			)}"			)
          else:
              __UpperCamelCase											=  text_model_output[0].shape[0]

          __UpperCamelCase											=  self._execution_device

          __UpperCamelCase											=  batch_size * num_images_per_prompt

          __UpperCamelCase											=  prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

          __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  self._encode_prompt(
              snake_case	,    snake_case	,    snake_case	,    snake_case	,    snake_case	,    snake_case			)

          # prior

          self.prior_scheduler.set_timesteps(snake_case	,    device=snake_case			)
          __UpperCamelCase											=  self.prior_scheduler.timesteps

          __UpperCamelCase											=  self.prior.config.embedding_dim

          __UpperCamelCase											=  self.prepare_latents(
              (batch_size, embedding_dim)	,    text_embeddings.dtype	,    snake_case	,    snake_case	,    snake_case	,    self.prior_scheduler	,    )

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

              __UpperCamelCase											=  self.prior(
                  snake_case	,    timestep=snake_case	,    proj_embedding=snake_case	,    encoder_hidden_states=snake_case	,    attention_mask=snake_case	,    ).predicted_image_embedding

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

              if i + 1 == prior_timesteps_tensor.shape[0]:
                  __UpperCamelCase											=  None
              else:
                  __UpperCamelCase											=  prior_timesteps_tensor[i + 1]

              __UpperCamelCase											=  self.prior_scheduler.step(
                  snake_case	,    timestep=snake_case	,    sample=snake_case	,    generator=snake_case	,    prev_timestep=snake_case	,    ).prev_sample

          __UpperCamelCase											=  self.prior.post_process_latents(snake_case			)

          __UpperCamelCase											=  prior_latents

          __UpperCamelCase											=  self.decoder_pipe(
              image=snake_case	,    height=snake_case	,    width=snake_case	,    num_inference_steps=snake_case	,    guidance_scale=snake_case	,    generator=snake_case	,    output_type=snake_case	,    return_dict=snake_case	,    num_images_per_prompt=snake_case	,    eta=snake_case	,    )
          return output












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


a_										=       "Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   "google/flan-t5-base"
      lowerCAmelCase__     :	Union[str, Any]      =   (
          "This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the "
          "text where to find the answer, and `question`, which is the question, and returns the answer to the question."
      )
      lowerCAmelCase__     :	List[Any]      =   "text_qa"
      lowerCAmelCase__     :	int      =   AutoTokenizer
      lowerCAmelCase__     :	Optional[Any]      =   AutoModelForSeqaSeqLM

      lowerCAmelCase__     :	Tuple      =   ["text", "text"]
      lowerCAmelCase__     :	Any      =   ["text"]







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	str	,    snake_case   :	str			):
          __UpperCamelCase											=  QA_PROMPT.format(text=snake_case	,    question=snake_case			)
          return self.pre_processor(snake_case	,    return_tensors='''pt'''			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  self.model.generate(**snake_case			)

          __UpperCamelCase						, __UpperCamelCase											=  inputs['''input_ids'''].shape
          __UpperCamelCase											=  output_ids.shape[0]

          return output_ids.reshape(snake_case	,    out_b // in_b	,    *output_ids.shape[1:]			)[0][0]







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[Any]			):
          return self.pre_processor.decode(snake_case	,    skip_special_tokens=snake_case	,    clean_up_tokenization_spaces=snake_case			)






