




'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class 	SCREAMING_SNAKE_CASE(    __A					):

       def __init__(   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		)   -> List[str]:

           """simple docstring"""



           super().__init__()

           self.register_modules(vae=lowerCamelCase__ ,		text_encoder=lowerCamelCase__ ,		tokenizer=lowerCamelCase__ ,		unet=lowerCamelCase__ ,		scheduler=lowerCamelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> List[str]:

           """simple docstring"""



           with torch.no_grad():
               __lowercase          =						self.vae.encode(tfms.ToTensor()(lowerCamelCase__					).unsqueeze(0					).to(self.device					) * 2 - 1					)
               __lowercase          =						0.1_82_15 * latent.latent_dist.sample()
           return latent

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						(1 / 0.1_82_15) * latent
           with torch.no_grad():
               __lowercase          =						self.vae.decode(lowerCamelCase__					).sample
           __lowercase          =						(img / 2 + 0.5).clamp(0 ,		1					)
           __lowercase          =						img.detach().cpu().permute(0 ,		2 ,		3 ,		1					).numpy()
           __lowercase          =						(img * 255).round().astype("""uint8"""					)
           return Image.fromarray(img[0]					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Optional[Any]:

           """simple docstring"""



           __lowercase          =						self.tokenizer(
               lowerCamelCase__ ,		padding="""max_length""" ,		max_length=self.tokenizer.model_max_length ,		truncation=lowerCamelCase__ ,		return_tensors="""pt""" ,		)

           __lowercase          =						self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

           __lowercase          =						self.tokenizer(
               """""" ,		padding="""max_length""" ,		max_length=self.tokenizer.model_max_length ,		truncation=lowerCamelCase__ ,		return_tensors="""pt""" ,		)

           __lowercase          =						self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

           return torch.cat([uncond_embedding, text_embedding]					)




       def __call__(   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ = 0.3 ,		lowerCamelCase__ = 0.6 ,		lowerCamelCase__ = 0.5 ,		lowerCamelCase__ = 42 ,		lowerCamelCase__ = 50 ,		lowerCamelCase__ = 7.5 ,		)   -> Image.Image:

           """simple docstring"""



           __lowercase          =						steps - int(kmin * steps					)
           __lowercase          =						steps - int(kmax * steps					)

           __lowercase          =						self.prep_text(lowerCamelCase__					)

           self.scheduler.set_timesteps(lowerCamelCase__					)

           __lowercase    ,__lowercase          =						img.size
           __lowercase          =						self.encode(lowerCamelCase__					)

           torch.manual_seed(lowerCamelCase__					)
           __lowercase          =						torch.randn(
               (1, self.unet.config.in_channels, height // 8, width // 8) ,		).to(self.device					)

           __lowercase          =						self.scheduler.add_noise(
               lowerCamelCase__ ,		lowerCamelCase__ ,		timesteps=self.scheduler.timesteps[tmax] ,		)

           __lowercase          =						torch.cat([latents] * 2					)

           __lowercase          =						self.scheduler.scale_model_input(lowerCamelCase__ ,		self.scheduler.timesteps[tmax]					)

           with torch.no_grad():
               __lowercase          =						self.unet(
                   lowerCamelCase__ ,		self.scheduler.timesteps[tmax] ,		encoder_hidden_states=lowerCamelCase__ ,		).sample

           __lowercase    ,__lowercase          =						pred.chunk(2					)
           __lowercase          =						pred_uncond + guidance_scale * (pred_text - pred_uncond)

           __lowercase          =						self.scheduler.step(lowerCamelCase__ ,		self.scheduler.timesteps[tmax] ,		lowerCamelCase__					).prev_sample

           for i, t in enumerate(tqdm(self.scheduler.timesteps					)					):
               if i > tmax:
                   if i < tmin:  # layout generation phase
                       __lowercase          =						self.scheduler.add_noise(
                           lowerCamelCase__ ,		lowerCamelCase__ ,		timesteps=lowerCamelCase__ ,		)

                       __lowercase          =						(mix_factor * latents) + (
                           1 - mix_factor
                       ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                       __lowercase          =						torch.cat([input] * 2					)

                   else:  # content generation phase
                       __lowercase          =						torch.cat([latents] * 2					)

                   __lowercase          =						self.scheduler.scale_model_input(lowerCamelCase__ ,		lowerCamelCase__					)

                   with torch.no_grad():
                       __lowercase          =						self.unet(
                           lowerCamelCase__ ,		lowerCamelCase__ ,		encoder_hidden_states=lowerCamelCase__ ,		).sample

                   __lowercase    ,__lowercase          =						pred.chunk(2					)
                   __lowercase          =						pred_uncond + guidance_scale * (pred_text - pred_uncond)

                   __lowercase          =						self.scheduler.step(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__					).prev_sample

           return self.decode(lowerCamelCase__					)










'''simple docstring'''
# 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							:				Dict			     =	"""Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""






class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					List[Any]   = """google/flan-t5-base"""
       snake_case_			:					int   = (
           """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."""
       )
       snake_case_			:					Optional[int]   = """text_qa"""
       snake_case_			:					List[Any]   = AutoTokenizer
       snake_case_			:					List[str]   = AutoModelForSeqaSeqLM

       snake_case_			:					int   = ["""text""", """text"""]
       snake_case_			:					Dict   = ["""text"""]

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						QA_PROMPT.format(text=lowerCamelCase__ ,		question=lowerCamelCase__					)
           return self.pre_processor(lowerCamelCase__ ,		return_tensors="""pt"""					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> int:

           """simple docstring"""



           __lowercase          =						self.model.generate(**lowerCamelCase__					)

           __lowercase    ,__lowercase          =						inputs["""input_ids"""].shape
           __lowercase          =						output_ids.shape[0]

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




       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Tuple:

           """simple docstring"""



           return self.pre_processor.decode(lowerCamelCase__ ,		skip_special_tokens=lowerCamelCase__ ,		clean_up_tokenization_spaces=lowerCamelCase__					)




