
'''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 					a_					(_a      ):




       def __init__(       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_ = 0.93				,				snake_case_ = True				,				):
         _lowerCAmelCase    :					List[str]							=   mixing_coeff
         _lowerCAmelCase    :					int							=   leapfrog_steps

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

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




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_ = None				,				snake_case_ = False					):
         _lowerCAmelCase    :					int							=   self.tokenizer(
             snake_case_				,				padding="""max_length"""				,				max_length=self.tokenizer.model_max_length				,				truncation=snake_case_				,				return_tensors="""pt"""				,				)

         _lowerCAmelCase    :					int							=   self.text_encoder(text_inputs.input_ids.to(self.device					)					).last_hidden_state

         _lowerCAmelCase    :					Tuple							=   prompt_embeds.to(dtype=self.text_encoder.dtype				,				device=self.device					)

         if do_classifier_free_guidance:
           _lowerCAmelCase    :					Any							=   """""" if negative_prompt is None else negative_prompt

           _lowerCAmelCase    :					Any							=   self.tokenizer(
               snake_case_				,				padding="""max_length"""				,				max_length=self.tokenizer.model_max_length				,				truncation=snake_case_				,				return_tensors="""pt"""				,				)

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

           _lowerCAmelCase    :					List[str]							=   torch.cat([negative_prompt_embeds, prompt_embeds]					)

         return prompt_embeds




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
         _lowerCAmelCase    :					str							=   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         _lowerCAmelCase    :					Optional[Any]							=   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
         _lowerCAmelCase    :					Tuple							=   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         _lowerCAmelCase    :					Union[str, Any]							=   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         # as self.alphas_cumprod is always in cpu
         _lowerCAmelCase    :					int							=   int(snake_case_					)

         _lowerCAmelCase    :					Any							=   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

         return alpha_prod, 1 - alpha_prod




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				):
         _lowerCAmelCase    :					Dict							=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         _lowerCAmelCase			,   _lowerCAmelCase    :					Union[str, Any]							=   self._get_alpha_and_beta(snake_case_					)
         _lowerCAmelCase			,   _lowerCAmelCase    :					Dict							=   self._get_alpha_and_beta(snake_case_					)

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

         _lowerCAmelCase    :					Optional[int]							=   (base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype					)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				):
         _lowerCAmelCase    :					List[str]							=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         _lowerCAmelCase			,   _lowerCAmelCase    :					List[Any]							=   self._get_alpha_and_beta(snake_case_					)
         _lowerCAmelCase			,   _lowerCAmelCase    :					Union[str, Any]							=   self._get_alpha_and_beta(snake_case_					)

         _lowerCAmelCase    :					Dict							=   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
         _lowerCAmelCase    :					Any							=   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         _lowerCAmelCase    :					Dict							=   a_t * base + b_t * model_output

         return model_input, next_model_input.to(base.dtype					)




       @torch.no_grad()
       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					List[Any]							=   1 / self.vae.config.scaling_factor * latents
         _lowerCAmelCase    :					List[Any]							=   self.vae.decode(snake_case_					).sample
         _lowerCAmelCase    :					Any							=   (image / 2 + 0.5).clamp(0				,				1					)
         return image




       @torch.no_grad()
       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_ = None				,				):
         _lowerCAmelCase    :					Dict							=   guidance_scale > 1.0

         _lowerCAmelCase    :					Any							=   image.to(device=self.device				,				dtype=text_embeds.dtype					)
         _lowerCAmelCase    :					Dict							=   self.vae.encode(snake_case_					).latent_dist.sample(snake_case_					)

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

         _lowerCAmelCase    :					str							=   [latent.clone(), latent.clone()]

         for i, t in tqdm(enumerate(snake_case_					)				,				total=len(snake_case_					)					):
           _lowerCAmelCase    :					Dict							=   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    :					int							=   j ^ 1

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

             _lowerCAmelCase    :					Any							=   coupled_latents[j]
             _lowerCAmelCase    :					Optional[Any]							=   coupled_latents[k]

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

             _lowerCAmelCase    :					Union[str, Any]							=   self.unet(snake_case_				,				snake_case_				,				encoder_hidden_states=snake_case_					).sample

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

             _lowerCAmelCase			,   _lowerCAmelCase    :					str							=   self.noise_step(
                 base=snake_case_				,				model_input=snake_case_				,				model_output=snake_case_				,				timestep=snake_case_				,				)

             _lowerCAmelCase    :					Any							=   model_input

         return coupled_latents







       @torch.no_grad()
       def __call__(       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_ = 3.0				,				snake_case_ = 5_0				,				snake_case_ = 0.8				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = "pil"				,				):
         _lowerCAmelCase    :					Any							=   guidance_scale > 1.0

         _lowerCAmelCase    :					int							=   self.image_processor.preprocess(snake_case_					)

         _lowerCAmelCase    :					List[str]							=   self._encode_prompt(snake_case_				,				snake_case_				,				snake_case_					)
         _lowerCAmelCase    :					Dict							=   self._encode_prompt(snake_case_				,				snake_case_				,				snake_case_					)

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

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

         _lowerCAmelCase    :					Any							=   self.prepare_latents(snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					)

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

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

             _lowerCAmelCase    :					Any							=   coupled_latents[j]
             _lowerCAmelCase    :					Optional[Any]							=   coupled_latents[k]

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

             _lowerCAmelCase    :					Optional[Any]							=   self.unet(snake_case_				,				snake_case_				,				encoder_hidden_states=snake_case_					).sample

             if do_classifier_free_guidance:
               _lowerCAmelCase			,   _lowerCAmelCase    :					Optional[int]							=   noise_pred.chunk(2					)
               _lowerCAmelCase    :					Dict							=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             _lowerCAmelCase			,   _lowerCAmelCase    :					str							=   self.denoise_step(
                 base=snake_case_				,				model_input=snake_case_				,				model_output=snake_case_				,				timestep=snake_case_				,				)

             _lowerCAmelCase    :					Optional[int]							=   model_input

           _lowerCAmelCase    :					Optional[Any]							=   self.denoise_mixing_layer(x=coupled_latents[0]				,				y=coupled_latents[1]					)

         # either one is fine
         _lowerCAmelCase    :					Union[str, Any]							=   coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
           _lowerCAmelCase    :					Dict							=   (
               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"""				,				snake_case_				,				standard_warn=snake_case_					)
           _lowerCAmelCase    :					Dict							=   """np"""

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

         return image




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase_            =    {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTJForCausalLM""",
         """GPTJForQuestionAnswering""",
         """GPTJForSequenceClassification""",
         """GPTJModel""",
         """GPTJPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """TFGPTJForCausalLM""",
         """TFGPTJForQuestionAnswering""",
         """TFGPTJForSequenceClassification""",
         """TFGPTJModel""",
         """TFGPTJPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """FlaxGPTJForCausalLM""",
         """FlaxGPTJModel""",
         """FlaxGPTJPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase_            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


