


"""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(     lowerCamelCase__		):

  """simple docstring"""





  def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = 0.9_3				,							SCREAMING_SNAKE_CASE__ = True				,							)			->  int:

     """simple docstring"""



     _UpperCamelCase				:Optional[int]				    =  mixing_coeff
     _UpperCamelCase				:Tuple				    =  leapfrog_steps

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

     _UpperCamelCase				:List[str]				    =  2 ** (len(self.vae.config.block_out_channels      ) - 1)
     _UpperCamelCase				:Tuple				    =  VaeImageProcessor(vae_scale_factor=self.vae_scale_factor      )
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = False      )			->  Tuple:

     """simple docstring"""



     _UpperCamelCase				:Optional[Any]				    =  self.tokenizer(
         SCREAMING_SNAKE_CASE__				,							padding='''max_length'''				,							max_length=self.tokenizer.model_max_length				,							truncation=SCREAMING_SNAKE_CASE__				,							return_tensors='''pt'''				,							)

     _UpperCamelCase				:str				    =  self.text_encoder(text_inputs.input_ids.to(self.device      )      ).last_hidden_state

     _UpperCamelCase				:Any				    =  prompt_embeds.to(dtype=self.text_encoder.dtype				,							device=self.device      )

     if do_classifier_free_guidance:
        _UpperCamelCase				:List[Any]				    =  '''''' if negative_prompt is None else negative_prompt

        _UpperCamelCase				:Tuple				    =  self.tokenizer(
            SCREAMING_SNAKE_CASE__				,							padding='''max_length'''				,							max_length=self.tokenizer.model_max_length				,							truncation=SCREAMING_SNAKE_CASE__				,							return_tensors='''pt'''				,							)

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

        _UpperCamelCase				:Tuple				    =  torch.cat([negative_prompt_embeds, prompt_embeds]      )

     return prompt_embeds
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Tuple:

     """simple docstring"""



     _UpperCamelCase				:Union[str, Any]				    =  self.mixing_coeff * x + (1 - self.mixing_coeff) * y
     _UpperCamelCase				:Any				    =  self.mixing_coeff * y + (1 - self.mixing_coeff) * x

     return [x, y]
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  str:

     """simple docstring"""



     _UpperCamelCase				:Optional[Any]				    =  (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
     _UpperCamelCase				:str				    =  (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

     return [x, y]
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

     """simple docstring"""



     _UpperCamelCase				:str				    =  int(SCREAMING_SNAKE_CASE__      )

     _UpperCamelCase				:Dict				    =  self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

     return alpha_prod, 1 - alpha_prod
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							)			->  Dict:

     """simple docstring"""



     _UpperCamelCase				:Any				    =  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     _UpperCamelCase				:Tuple				    =  self._get_alpha_and_beta(SCREAMING_SNAKE_CASE__      )
     _UpperCamelCase				:str				    =  self._get_alpha_and_beta(SCREAMING_SNAKE_CASE__      )

     _UpperCamelCase				:Optional[int]				    =  (alpha_prod_t_prev / alpha_prod_t) ** 0.5
     _UpperCamelCase				:Union[str, Any]				    =  -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

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

     return model_input, next_model_input.to(base.dtype      )
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							)			->  Any:

     """simple docstring"""



     _UpperCamelCase				:Dict				    =  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     _UpperCamelCase				:int				    =  self._get_alpha_and_beta(SCREAMING_SNAKE_CASE__      )
     _UpperCamelCase				:Optional[Any]				    =  self._get_alpha_and_beta(SCREAMING_SNAKE_CASE__      )

     _UpperCamelCase				:List[str]				    =  (alpha_prod_t_prev / alpha_prod_t) ** 0.5
     _UpperCamelCase				:Tuple				    =  -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
     _UpperCamelCase				:Tuple				    =  a_t * base + b_t * model_output

     return model_input, next_model_input.to(base.dtype      )
  @torch.no_grad()
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Tuple:

     """simple docstring"""



     _UpperCamelCase				:Tuple				    =  1 / self.vae.config.scaling_factor * latents
     _UpperCamelCase				:List[Any]				    =  self.vae.decode(SCREAMING_SNAKE_CASE__      ).sample
     _UpperCamelCase				:List[Any]				    =  (image / 2 + 0.5).clamp(0				,							1      )
     return image
  @torch.no_grad()
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None				,							)			->  Union[str, Any]:

     """simple docstring"""



     _UpperCamelCase				:List[Any]				    =  guidance_scale > 1.0

     _UpperCamelCase				:List[Any]				    =  image.to(device=self.device				,							dtype=text_embeds.dtype      )
     _UpperCamelCase				:Union[str, Any]				    =  self.vae.encode(SCREAMING_SNAKE_CASE__      ).latent_dist.sample(SCREAMING_SNAKE_CASE__      )

     _UpperCamelCase				:Union[str, Any]				    =  self.vae.config.scaling_factor * latent

     _UpperCamelCase				:Optional[int]				    =  [latent.clone(), latent.clone()]

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

        # j - model_input index, k - base index
        for j in range(2      ):
           _UpperCamelCase				:Optional[int]				    =  j ^ 1

           if self.leapfrog_steps:
              if i % 2 == 0:
                 _UpperCamelCase				:Union[str, Any]				    =  j, k

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

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

           _UpperCamelCase				:Any				    =  self.unet(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							encoder_hidden_states=SCREAMING_SNAKE_CASE__      ).sample

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

           _UpperCamelCase				:Dict				    =  self.noise_step(
               base=SCREAMING_SNAKE_CASE__				,							model_input=SCREAMING_SNAKE_CASE__				,							model_output=SCREAMING_SNAKE_CASE__				,							timestep=SCREAMING_SNAKE_CASE__				,							)

           _UpperCamelCase				:Optional[int]				    =  model_input

     return coupled_latents





  @torch.no_grad()
  def __call__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = 3.0				,							SCREAMING_SNAKE_CASE__ = 50				,							SCREAMING_SNAKE_CASE__ = 0.8				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = "pil"				,							)			->  List[Any]:

     """simple docstring"""



     _UpperCamelCase				:Dict				    =  guidance_scale > 1.0

     _UpperCamelCase				:Optional[Any]				    =  self.image_processor.preprocess(SCREAMING_SNAKE_CASE__      )

     _UpperCamelCase				:Tuple				    =  self._encode_prompt(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
     _UpperCamelCase				:str				    =  self._encode_prompt(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

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

     _UpperCamelCase				:Union[str, Any]				    =  num_inference_steps - int(num_inference_steps * strength      )
     _UpperCamelCase				:str				    =  self.scheduler.timesteps[t_limit:]
     _UpperCamelCase				:List[str]				    =  fwd_timesteps.flip(0      )

     _UpperCamelCase				:Tuple				    =  self.prepare_latents(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

     for i, t in tqdm(enumerate(SCREAMING_SNAKE_CASE__      )				,							total=len(SCREAMING_SNAKE_CASE__      )      ):
        # j - model_input index, k - base index
        for k in range(2      ):
           _UpperCamelCase				:List[Any]				    =  k ^ 1

           if self.leapfrog_steps:
              if i % 2 == 1:
                 _UpperCamelCase				:Optional[int]				    =  j, k

           _UpperCamelCase				:Union[str, Any]				    =  coupled_latents[j]
           _UpperCamelCase				:List[str]				    =  coupled_latents[k]

           _UpperCamelCase				:Tuple				    =  torch.cat([model_input] * 2      ) if do_classifier_free_guidance else model_input

           _UpperCamelCase				:Union[str, Any]				    =  self.unet(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							encoder_hidden_states=SCREAMING_SNAKE_CASE__      ).sample

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

           _UpperCamelCase				:Dict				    =  self.denoise_step(
               base=SCREAMING_SNAKE_CASE__				,							model_input=SCREAMING_SNAKE_CASE__				,							model_output=SCREAMING_SNAKE_CASE__				,							timestep=SCREAMING_SNAKE_CASE__				,							)

           _UpperCamelCase				:Optional[int]				    =  model_input

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

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

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

     if output_type == "latent":
        _UpperCamelCase				:Union[str, Any]				    =  final_latent
     else:
        _UpperCamelCase				:List[str]				    =  self.decode_latents(SCREAMING_SNAKE_CASE__      )
        _UpperCamelCase				:Dict				    =  self.image_processor.postprocess(SCREAMING_SNAKE_CASE__				,							output_type=SCREAMING_SNAKE_CASE__      )

     return image





"""simple docstring"""






import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json





@pytest.fixture
def 							A_					(   snake_case__		)				->     Union[str, Any]:
			_UpperCamelCase				:int				    =  tmp_path / '''file.jsonl'''
			_UpperCamelCase				:Dict				    =  textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''		)
			with open(snake_case__					,				'''w'''		) as f:
						f.write(snake_case__		)
			return str(snake_case__		)





@pytest.fixture
def 							A_					(   snake_case__		)				->     Optional[int]:
			_UpperCamelCase				:Dict				    =  tmp_path / '''file_utf16_encoded.jsonl'''
			_UpperCamelCase				:Dict				    =  textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''		)
			with open(snake_case__					,				'''w'''					,				encoding='''utf-16'''		) as f:
						f.write(snake_case__		)
			return str(snake_case__		)





@pytest.fixture
def 							A_					(   snake_case__		)				->     Optional[int]:
			_UpperCamelCase				:Optional[Any]				    =  tmp_path / '''file_with_list_of_dicts.json'''
			_UpperCamelCase				:str				    =  textwrap.dedent(
			    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''		)
			with open(snake_case__					,				'''w'''		) as f:
						f.write(snake_case__		)
			return str(snake_case__		)





@pytest.fixture
def 							A_					(   snake_case__		)				->     Union[str, Any]:
			_UpperCamelCase				:Dict				    =  tmp_path / '''file_with_list_of_dicts_field.json'''
			_UpperCamelCase				:Optional[int]				    =  textwrap.dedent(
			    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''		)
			with open(snake_case__					,				'''w'''		) as f:
						f.write(snake_case__		)
			return str(snake_case__		)





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''					,				[
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]					,				)
def 							A_					(   snake_case__					,				snake_case__					,				snake_case__		)				->     int:
			_UpperCamelCase				:Any				    =  Json(**snake_case__		)
			_UpperCamelCase				:str				    =  json._generate_tables([[request.getfixturevalue(snake_case__		)]]		)
			_UpperCamelCase				:List[Any]				    =  pa.concat_tables([table for _, table in generator]		)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''					,				[
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''		), '''col_2''': Value('''int64'''		), '''missing_col''': Value('''string'''		)}		)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''		), '''col_2''': Value('''int64'''		), '''missing_col''': Value('''string'''		)}		)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''		), '''col_2''': Value('''int64'''		), '''missing_col''': Value('''string'''		)}		),
            },
        ),
    ]					,				)
def 							A_					(   snake_case__					,				snake_case__					,				snake_case__		)				->     Optional[int]:
			_UpperCamelCase				:Union[str, Any]				    =  Json(**snake_case__		)
			_UpperCamelCase				:List[str]				    =  json._generate_tables([[request.getfixturevalue(snake_case__		)]]		)
			_UpperCamelCase				:str				    =  pa.concat_tables([table for _, table in generator]		)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}

