






'''simple docstring'''


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 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A   ,   _A   ,   _A   ,   _A   ,   _A = 0.93   ,   _A = True   ,   )    ->   Any:
    __a    :	Optional[Any]									=						mixing_coeff
    __a    :	Any									=						leapfrog_steps

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

    __a    :	List[str]									=						2 ** (len(self.vae.config.block_out_channels			) - 1)
    __a    :	List[Any]									=						VaeImageProcessor(vae_scale_factor=self.vae_scale_factor			)



   def    __magic_name__    ( self   ,   _A   ,   _A = None   ,   _A = False			)    ->   Tuple:
    __a    :	Any									=						self.tokenizer(
        _A   ,   padding='max_length'   ,   max_length=self.tokenizer.model_max_length   ,   truncation=_A   ,   return_tensors='pt'   ,   )

    __a    :	Union[str, Any]									=						self.text_encoder(text_inputs.input_ids.to(self.device			)			).last_hidden_state

    __a    :	List[Any]									=						prompt_embeds.to(dtype=self.text_encoder.dtype   ,   device=self.device			)

    if do_classifier_free_guidance:
     __a    :	Tuple									=						'' if negative_prompt is None else negative_prompt

     __a    :	Dict									=						self.tokenizer(
         _A   ,   padding='max_length'   ,   max_length=self.tokenizer.model_max_length   ,   truncation=_A   ,   return_tensors='pt'   ,   )

     __a    :	Any									=						self.text_encoder(uncond_input.input_ids.to(self.device			)			).last_hidden_state

     __a    :	Union[str, Any]									=						torch.cat([negative_prompt_embeds, prompt_embeds]			)

    return prompt_embeds



   def    __magic_name__    ( self   ,   _A   ,   _A			)    ->   Any:
    __a    :	Dict									=						self.mixing_coeff * x + (1 - self.mixing_coeff) * y
    __a    :	Optional[Any]									=						self.mixing_coeff * y + (1 - self.mixing_coeff) * x

    return [x, y]



   def    __magic_name__    ( self   ,   _A   ,   _A			)    ->   Optional[Any]:
    __a    :	List[Any]									=						(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
    __a    :	Tuple									=						(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

    return [x, y]



   def    __magic_name__    ( self   ,   _A			)    ->   List[Any]:
    # as self.alphas_cumprod is always in cpu
    __a    :	Any									=						int(_A			)

    __a    :	Optional[int]									=						self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

    return alpha_prod, 1 - alpha_prod



   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A   ,   _A   ,   )    ->   int:
    __a    :	int									=						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

    __a			,    __a    :	Optional[Any]									=						self._get_alpha_and_beta(_A			)
    __a			,    __a    :	Union[str, Any]									=						self._get_alpha_and_beta(_A			)

    __a    :	int									=						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
    __a    :	Optional[Any]									=						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

    __a    :	Dict									=						(base - b_t * model_output) / a_t

    return model_input, next_model_input.to(base.dtype			)



   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A   ,   _A   ,   )    ->   Any:
    __a    :	int									=						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

    __a			,    __a    :	List[str]									=						self._get_alpha_and_beta(_A			)
    __a			,    __a    :	Union[str, Any]									=						self._get_alpha_and_beta(_A			)

    __a    :	Optional[Any]									=						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
    __a    :	List[str]									=						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
    __a    :	Dict									=						a_t * base + b_t * model_output

    return model_input, next_model_input.to(base.dtype			)



   @torch.no_grad()
   def    __magic_name__    ( self   ,   _A			)    ->   str:
    __a    :	Optional[int]									=						1 / self.vae.config.scaling_factor * latents
    __a    :	Dict									=						self.vae.decode(_A			).sample
    __a    :	List[str]									=						(image / 2 + 0.5).clamp(0   ,   1			)
    return image



   @torch.no_grad()
   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A   ,   _A   ,   _A = None   ,   )    ->   Dict:
    __a    :	Any									=						guidance_scale > 1.0

    __a    :	str									=						image.to(device=self.device   ,   dtype=text_embeds.dtype			)
    __a    :	Any									=						self.vae.encode(_A			).latent_dist.sample(_A			)

    __a    :	Any									=						self.vae.config.scaling_factor * latent

    __a    :	int									=						[latent.clone(), latent.clone()]

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

     # j - model_input index, k - base index
     for j in range(2			):
      __a    :	List[Any]									=						j ^ 1

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

      __a    :	Dict									=						coupled_latents[j]
      __a    :	str									=						coupled_latents[k]

      __a    :	Union[str, Any]									=						torch.cat([model_input] * 2			) if do_classifier_free_guidance else model_input

      __a    :	Optional[int]									=						self.unet(_A   ,   _A   ,   encoder_hidden_states=_A			).sample

      if do_classifier_free_guidance:
       __a			,    __a    :	List[str]									=						noise_pred.chunk(2			)
       __a    :	Any									=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

      __a			,    __a    :	Any									=						self.noise_step(
          base=_A   ,   model_input=_A   ,   model_output=_A   ,   timestep=_A   ,   )

      __a    :	List[str]									=						model_input

    return coupled_latents



   @torch.no_grad()
   def __call__( self   ,   _A   ,   _A   ,   _A   ,   _A = 3.0   ,   _A = 50   ,   _A = 0.8   ,   _A = None   ,   _A = None   ,   _A = "pil"   ,   )    ->   Any:
    __a    :	str									=						guidance_scale > 1.0

    __a    :	List[Any]									=						self.image_processor.preprocess(_A			)

    __a    :	Tuple									=						self._encode_prompt(_A   ,   _A   ,   _A			)
    __a    :	Any									=						self._encode_prompt(_A   ,   _A   ,   _A			)

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

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

    __a    :	List[str]									=						self.prepare_latents(_A   ,   _A   ,   _A   ,   _A   ,   _A			)

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

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

      __a    :	Optional[Any]									=						coupled_latents[j]
      __a    :	Optional[int]									=						coupled_latents[k]

      __a    :	Any									=						torch.cat([model_input] * 2			) if do_classifier_free_guidance else model_input

      __a    :	Any									=						self.unet(_A   ,   _A   ,   encoder_hidden_states=_A			).sample

      if do_classifier_free_guidance:
       __a			,    __a    :	Union[str, Any]									=						noise_pred.chunk(2			)
       __a    :	Dict									=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

      __a			,    __a    :	str									=						self.denoise_step(
          base=_A   ,   model_input=_A   ,   model_output=_A   ,   timestep=_A   ,   )

      __a    :	List[Any]									=						model_input

     __a    :	Dict									=						self.denoise_mixing_layer(x=coupled_latents[0]   ,   y=coupled_latents[1]			)

    # either one is fine
    __a    :	int									=						coupled_latents[0]

    if output_type not in ["latent", "pt", "np", "pil"]:
     __a    :	List[Any]									=						(
         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'   ,   _A   ,   standard_warn=_A			)
     __a    :	Any									=						'np'

    if output_type == "latent":
     __a    :	List[Any]									=						final_latent
    else:
     __a    :	Optional[Any]									=						self.decode_latents(_A			)
     __a    :	List[Any]									=						self.image_processor.postprocess(_A   ,   output_type=_A			)

    return image












'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							{
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   _A						=   'squeezebert'



   def __init__( self   ,   _A=30522   ,   _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-1_2   ,   _A=0   ,   _A=768   ,   _A=4   ,   _A=4   ,   _A=4   ,   _A=1   ,   _A=4   ,   _A=4   ,   **_A   ,   )    ->   List[Any]:
    super().__init__(pad_token_id=_A   ,   **_A			)

    __a    :	Optional[int]									=						vocab_size
    __a    :	Optional[int]									=						hidden_size
    __a    :	int									=						num_hidden_layers
    __a    :	int									=						num_attention_heads
    __a    :	Tuple									=						hidden_act
    __a    :	Optional[Any]									=						intermediate_size
    __a    :	Any									=						hidden_dropout_prob
    __a    :	List[Any]									=						attention_probs_dropout_prob
    __a    :	List[str]									=						max_position_embeddings
    __a    :	Tuple									=						type_vocab_size
    __a    :	Tuple									=						initializer_range
    __a    :	List[str]									=						layer_norm_eps
    __a    :	str									=						embedding_size
    __a    :	Any									=						q_groups
    __a    :	Any									=						k_groups
    __a    :	Union[str, Any]									=						v_groups
    __a    :	List[str]									=						post_attention_groups
    __a    :	Dict									=						intermediate_groups
    __a    :	Optional[Any]									=						output_groups




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
     __a    :	Tuple									=						{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
     __a    :	List[Any]									=						{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]			)




