






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  __lowercase (		lowercase_					):







     '''simple docstring'''




     def __init__(			self    :     Union[str, Any]	,		UpperCamelCase_    :     AutoencoderKL	,		UpperCamelCase_    :     CLIPTextModel	,		UpperCamelCase_    :     CLIPTokenizer	,		UpperCamelCase_    :     UNetaDConditionModel	,		UpperCamelCase_    :     DDIMScheduler	,		UpperCamelCase_    :     float = 0.93	,		UpperCamelCase_    :     bool = True	,		):


         """simple docstring"""



         __A             =		mixing_coeff
         __A             =		leapfrog_steps

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

         __A             =		2 ** (len(self.vae.config.block_out_channels			) - 1)
         __A             =		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor			)




     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     Optional[str] = None	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		self.tokenizer(
             UpperCamelCase_	,		padding="""max_length"""	,		max_length=self.tokenizer.model_max_length	,		truncation=UpperCamelCase_	,		return_tensors="""pt"""	,		)

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

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

         if do_classifier_free_guidance:
             __A             =		"""""" if negative_prompt is None else negative_prompt

             __A             =		self.tokenizer(
                 UpperCamelCase_	,		padding="""max_length"""	,		max_length=self.tokenizer.model_max_length	,		truncation=UpperCamelCase_	,		return_tensors="""pt"""	,		)

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

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

         return prompt_embeds




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor			):


         """simple docstring"""



         __A             =		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         __A             =		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]




     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor			):


         """simple docstring"""



         __A             =		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         __A             =		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     torch.Tensor			):


         """simple docstring"""



         __A             =		int(UpperCamelCase_			)

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

         return alpha_prod, 1 - alpha_prod




     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		):


         """simple docstring"""



         __A             =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         __A      ,		__A             =		self._get_alpha_and_beta(UpperCamelCase_			)
         __A      ,		__A             =		self._get_alpha_and_beta(UpperCamelCase_			)

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

         __A             =		(base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype			)




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		):


         """simple docstring"""



         __A             =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         __A      ,		__A             =		self._get_alpha_and_beta(UpperCamelCase_			)
         __A      ,		__A             =		self._get_alpha_and_beta(UpperCamelCase_			)

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

         return model_input, next_model_input.to(base.dtype			)




     @torch.no_grad()
     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     torch.Tensor			):


         """simple docstring"""



         __A             =		1 / self.vae.config.scaling_factor * latents
         __A             =		self.vae.decode(UpperCamelCase_			).sample
         __A             =		(image / 2 + 0.5).clamp(0	,		1			)
         return image




     @torch.no_grad()
     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     Image.Image	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     float	,		UpperCamelCase_    :     Optional[torch.Generator] = None	,		):


         """simple docstring"""



         __A             =		guidance_scale > 1.0

         __A             =		image.to(device=self.device	,		dtype=text_embeds.dtype			)
         __A             =		self.vae.encode(UpperCamelCase_			).latent_dist.sample(UpperCamelCase_			)

         __A             =		self.vae.config.scaling_factor * latent

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

         for i, t in tqdm(enumerate(UpperCamelCase_			)	,		total=len(UpperCamelCase_			)			):
             __A             =		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             =		j ^ 1

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

                 __A             =		coupled_latents[j]
                 __A             =		coupled_latents[k]

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

                 __A             =		self.unet(UpperCamelCase_	,		UpperCamelCase_	,		encoder_hidden_states=UpperCamelCase_			).sample

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

                 __A      ,		__A             =		self.noise_step(
                     base=UpperCamelCase_	,		model_input=UpperCamelCase_	,		model_output=UpperCamelCase_	,		timestep=UpperCamelCase_	,		)

                 __A             =		model_input

         return coupled_latents





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


         """simple docstring"""



         __A             =		guidance_scale > 1.0

         __A             =		self.image_processor.preprocess(UpperCamelCase_			)

         __A             =		self._encode_prompt(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)
         __A             =		self._encode_prompt(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)

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

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

         __A             =		self.prepare_latents(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)

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

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

                 __A             =		coupled_latents[j]
                 __A             =		coupled_latents[k]

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

                 __A             =		self.unet(UpperCamelCase_	,		UpperCamelCase_	,		encoder_hidden_states=UpperCamelCase_			).sample

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

                 __A      ,		__A             =		self.denoise_step(
                     base=UpperCamelCase_	,		model_input=UpperCamelCase_	,		model_output=UpperCamelCase_	,		timestep=UpperCamelCase_	,		)

                 __A             =		model_input

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

         # either one is fine
         __A             =		coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
             __A             =		(
                 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"""	,		UpperCamelCase_	,		standard_warn=UpperCamelCase_			)
             __A             =		"""np"""

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

         return image












# Copyright 2022 The HuggingFace 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.
import os
from typing import List



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]     ,			__lowercase				:     str      )  -> Optional[int]:





    """simple docstring"""

    if name != "training_function" and name != "main":
        raise ValueError(f"Incorrect function name passed: {name}, choose either 'main' or 'training_function'"      )
    __A      ,		__A             =		[], False
    for line in lines:
        if not found_start and f"def {name}" in line:
            __A             =		True
            good_lines.append(__lowercase      )
            continue
        if found_start:
            if name == "training_function" and "def main" in line:
                return good_lines
            if name == "main" and "if __name__" in line:
                return good_lines
            good_lines.append(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]      )  -> Optional[int]:





    """simple docstring"""

    return [line for line in lines if not line.lstrip().startswith("""#"""      ) and line != "\n"]






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     str     ,			__lowercase				:     bool     ,			__lowercase				:     str = None      )  -> List[Any]:





    """simple docstring"""

    with open(__lowercase     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    with open(os.path.abspath(os.path.join("""examples"""     ,			"""nlp_example.py"""      )      )     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    with open(__lowercase     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    if secondary_filename is not None:
        with open(__lowercase     ,			"""r"""      ) as f:
            __A             =		f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
    if parser_only:
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        if secondary_filename is not None:
            __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
    else:
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        if secondary_filename is not None:
            __A             =		clean_lines(
                get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )

    __A             =		"""train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

    # Specific code in our script that differs from the full version, aka what is new
    __A             =		[]
    __A             =		[]  # We keep track of the idxs just in case it's a repeated statement
    __A             =		iter(__lowercase      )
    for i in range(len(__lowercase      ) - 1      ):
        if i not in passed_idxs:
            __A             =		next(__lowercase      )
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_feature_code.append(__lowercase      )
                    passed_idxs.append(__lowercase      )
                else:
                    # Skip over the `config['num_epochs'] = 2` statement
                    __A             =		next(__lowercase      )

    # Extract out just the new parts from the full_file_training_func
    __A             =		[]
    __A             =		[]  # We keep track of the idxs just in case it's a repeated statement
    for i, line in enumerate(__lowercase      ):
        if i not in passed_idxs:
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_full_example_parts.append(__lowercase      )
                    passed_idxs.append(__lowercase      )

    # Finally, get the overall diff
    __A             =		[line for line in new_feature_code if line not in new_full_example_parts]
    if secondary_filename is not None:
        __A             =		[line for line in full_file_contents if line not in secondary_file_func]
        __A             =		[line for line in diff_from_example if line not in diff_from_two]

    return diff_from_example




