




from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     def __init__(	self  :		Optional[Any]					,	__lowerCamelCase  :		AutoencoderKL					,	__lowerCamelCase  :		CLIPTextModel					,	__lowerCamelCase  :		CLIPTokenizer					,	__lowerCamelCase  :		UNetaDConditionModel					,	__lowerCamelCase  :		DDIMScheduler					,	__lowerCamelCase  :		float = 0.93					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""
       lowerCAmelCase__								=			mixing_coeff
       lowerCAmelCase__								=			leapfrog_steps

       super().__init__()
       self.register_modules(
           vae=__lowerCamelCase					,	text_encoder=__lowerCamelCase					,	tokenizer=__lowerCamelCase					,	unet=__lowerCamelCase					,	scheduler=__lowerCamelCase					,	)

       lowerCAmelCase__								=			2 ** (len(self.vae.config.block_out_channels	) - 1)
       lowerCAmelCase__								=			VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Optional[str] = None					,	__lowerCamelCase  :		bool = False	):



       """simple docstring"""
       lowerCAmelCase__								=			self.tokenizer(
           __lowerCamelCase					,	padding='''max_length'''					,	max_length=self.tokenizer.model_max_length					,	truncation=__lowerCamelCase					,	return_tensors='''pt'''					,	)

       lowerCAmelCase__								=			self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

       lowerCAmelCase__								=			prompt_embeds.to(dtype=self.text_encoder.dtype					,	device=self.device	)

       if do_classifier_free_guidance:
         lowerCAmelCase__								=			'''''' if negative_prompt is None else negative_prompt

         lowerCAmelCase__								=			self.tokenizer(
             __lowerCamelCase					,	padding='''max_length'''					,	max_length=self.tokenizer.model_max_length					,	truncation=__lowerCamelCase					,	return_tensors='''pt'''					,	)

         lowerCAmelCase__								=			self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

         lowerCAmelCase__								=			torch.cat([negative_prompt_embeds, prompt_embeds]	)

       return prompt_embeds
     def 					A__			(	self  :		Optional[Any]					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor	):



       """simple docstring"""
       lowerCAmelCase__								=			self.mixing_coeff * x + (1 - self.mixing_coeff) * y
       lowerCAmelCase__								=			self.mixing_coeff * y + (1 - self.mixing_coeff) * x

       return [x, y]
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor	):



       """simple docstring"""
       lowerCAmelCase__								=			(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
       lowerCAmelCase__								=			(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

       return [x, y]
     def 					A__			(	self  :		Optional[int]					,	__lowerCamelCase  :		torch.Tensor	):



       """simple docstring"""
       lowerCAmelCase__								=			int(__lowerCamelCase	)

       lowerCAmelCase__								=			self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

       return alpha_prod, 1 - alpha_prod
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor					,	):



       """simple docstring"""
       lowerCAmelCase__								=			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       lowerCAmelCase__							,			lowerCAmelCase__								=			self._get_alpha_and_beta(__lowerCamelCase	)
       lowerCAmelCase__							,			lowerCAmelCase__								=			self._get_alpha_and_beta(__lowerCamelCase	)

       lowerCAmelCase__								=			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       lowerCAmelCase__								=			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

       lowerCAmelCase__								=			(base - b_t * model_output) / a_t

       return model_input, next_model_input.to(base.dtype	)
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor					,	):



       """simple docstring"""
       lowerCAmelCase__								=			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       lowerCAmelCase__							,			lowerCAmelCase__								=			self._get_alpha_and_beta(__lowerCamelCase	)
       lowerCAmelCase__							,			lowerCAmelCase__								=			self._get_alpha_and_beta(__lowerCamelCase	)

       lowerCAmelCase__								=			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       lowerCAmelCase__								=			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
       lowerCAmelCase__								=			a_t * base + b_t * model_output

       return model_input, next_model_input.to(base.dtype	)
     @torch.no_grad()
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		torch.Tensor	):



       """simple docstring"""
       lowerCAmelCase__								=			1 / self.vae.config.scaling_factor * latents
       lowerCAmelCase__								=			self.vae.decode(__lowerCamelCase	).sample
       lowerCAmelCase__								=			(image / 2 + 0.5).clamp(0					,	1	)
       return image
     @torch.no_grad()
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		Image.Image					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		torch.Tensor					,	__lowerCamelCase  :		float					,	__lowerCamelCase  :		Optional[torch.Generator] = None					,	):



       """simple docstring"""
       lowerCAmelCase__								=			guidance_scale > 1.0

       lowerCAmelCase__								=			image.to(device=self.device					,	dtype=text_embeds.dtype	)
       lowerCAmelCase__								=			self.vae.encode(__lowerCamelCase	).latent_dist.sample(__lowerCamelCase	)

       lowerCAmelCase__								=			self.vae.config.scaling_factor * latent

       lowerCAmelCase__								=			[latent.clone(), latent.clone()]

       for i, t in tqdm(enumerate(__lowerCamelCase	)					,	total=len(__lowerCamelCase	)	):
         lowerCAmelCase__								=			self.noise_mixing_layer(x=coupled_latents[0]					,	y=coupled_latents[1]	)

         # j - model_input index, k - base index
         for j in range(2	):
           lowerCAmelCase__								=			j ^ 1

           if self.leapfrog_steps:
             if i % 2 == 0:
               lowerCAmelCase__							,			lowerCAmelCase__								=			j, k

           lowerCAmelCase__								=			coupled_latents[j]
           lowerCAmelCase__								=			coupled_latents[k]

           lowerCAmelCase__								=			torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

           lowerCAmelCase__								=			self.unet(__lowerCamelCase					,	__lowerCamelCase					,	encoder_hidden_states=__lowerCamelCase	).sample

           if do_classifier_free_guidance:
             lowerCAmelCase__							,			lowerCAmelCase__								=			noise_pred.chunk(2	)
             lowerCAmelCase__								=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           lowerCAmelCase__							,			lowerCAmelCase__								=			self.noise_step(
               base=__lowerCamelCase					,	model_input=__lowerCamelCase					,	model_output=__lowerCamelCase					,	timestep=__lowerCamelCase					,	)

           lowerCAmelCase__								=			model_input

       return coupled_latents



     @torch.no_grad()
     def __call__(	self  :		Tuple					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Image.Image					,	__lowerCamelCase  :		float = 3.0					,	__lowerCamelCase  :		int = 50					,	__lowerCamelCase  :		float = 0.8					,	__lowerCamelCase  :		Optional[str] = None					,	__lowerCamelCase  :		Optional[torch.Generator] = None					,	__lowerCamelCase  :		Optional[str] = "pil"					,	):



       """simple docstring"""
       lowerCAmelCase__								=			guidance_scale > 1.0

       lowerCAmelCase__								=			self.image_processor.preprocess(__lowerCamelCase	)

       lowerCAmelCase__								=			self._encode_prompt(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	)
       lowerCAmelCase__								=			self._encode_prompt(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	)

       self.scheduler.set_timesteps(__lowerCamelCase					,	self.device	)

       lowerCAmelCase__								=			num_inference_steps - int(num_inference_steps * strength	)
       lowerCAmelCase__								=			self.scheduler.timesteps[t_limit:]
       lowerCAmelCase__								=			fwd_timesteps.flip(0	)

       lowerCAmelCase__								=			self.prepare_latents(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	)

       for i, t in tqdm(enumerate(__lowerCamelCase	)					,	total=len(__lowerCamelCase	)	):
         # j - model_input index, k - base index
         for k in range(2	):
           lowerCAmelCase__								=			k ^ 1

           if self.leapfrog_steps:
             if i % 2 == 1:
               lowerCAmelCase__							,			lowerCAmelCase__								=			j, k

           lowerCAmelCase__								=			coupled_latents[j]
           lowerCAmelCase__								=			coupled_latents[k]

           lowerCAmelCase__								=			torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

           lowerCAmelCase__								=			self.unet(__lowerCamelCase					,	__lowerCamelCase					,	encoder_hidden_states=__lowerCamelCase	).sample

           if do_classifier_free_guidance:
             lowerCAmelCase__							,			lowerCAmelCase__								=			noise_pred.chunk(2	)
             lowerCAmelCase__								=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           lowerCAmelCase__							,			lowerCAmelCase__								=			self.denoise_step(
               base=__lowerCamelCase					,	model_input=__lowerCamelCase					,	model_output=__lowerCamelCase					,	timestep=__lowerCamelCase					,	)

           lowerCAmelCase__								=			model_input

         lowerCAmelCase__								=			self.denoise_mixing_layer(x=coupled_latents[0]					,	y=coupled_latents[1]	)

       # either one is fine
       lowerCAmelCase__								=			coupled_latents[0]

       if output_type not in ["latent", "pt", "np", "pil"]:
         lowerCAmelCase__								=			(
             F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
             '''`pil`, `np`, `pt`, `latent`'''
         )
         deprecate('''Unsupported output_type'''					,	'''1.0.0'''					,	__lowerCamelCase					,	standard_warn=__lowerCamelCase	)
         lowerCAmelCase__								=			'''np'''

       if output_type == "latent":
         lowerCAmelCase__								=			final_latent
       else:
         lowerCAmelCase__								=			self.decode_latents(__lowerCamelCase	)
         lowerCAmelCase__								=			self.image_processor.postprocess(__lowerCamelCase					,	output_type=__lowerCamelCase	)

       return image







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


__magic_name__     :     str  							=					logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     def __init__(	self  :		Any					,	__lowerCamelCase  :		Any					,	__lowerCamelCase  :		Optional[int]=None	):



       """simple docstring"""
       lowerCAmelCase__								=			language
       lowerCAmelCase__								=			train_language
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.language if self.train_language is None else self.train_language
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			F"""train-{i}"""
         lowerCAmelCase__								=			line[0]
         lowerCAmelCase__								=			line[1]
         lowerCAmelCase__								=			'''contradiction''' if line[2] == '''contradictory''' else line[2]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	'''XNLI-1.0/xnli.test.tsv'''	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			line[0]
         if language != self.language:
           continue
         lowerCAmelCase__								=			F"""test-{i}"""
         lowerCAmelCase__								=			line[6]
         lowerCAmelCase__								=			line[7]
         lowerCAmelCase__								=			line[1]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples



     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       return ["contradiction", "entailment", "neutral"]



__magic_name__     :     Tuple  							=					{
    """xnli""": XnliProcessor,
}

__magic_name__     :     List[Any]  							=					{
    """xnli""": """classification""",
}

__magic_name__     :     str  							=					{
    """xnli""": 3,
}

