
"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__ :       Tuple				= logging.get_logger(__name__)

A__ :       Union[str, Any]				= {
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'open-llama'
       def __init__(  self			,					A_=10_0000			,					A_=4096			,					A_=1_1008			,					A_=32			,					A_=32			,					A_="silu"			,					A_=2048			,					A_=0.02			,					A_=1E-6			,					A_=True			,					A_=0			,					A_=1			,					A_=2			,					A_=False			,					A_=True			,					A_=0.1			,					A_=0.1			,					A_=True			,					A_=True			,					A_=None			,					**A_			,					)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      str   			=  vocab_size
         _lowercase:      Tuple   			=  max_position_embeddings
         _lowercase:      Optional[int]   			=  hidden_size
         _lowercase:      List[Any]   			=  intermediate_size
         _lowercase:      str   			=  num_hidden_layers
         _lowercase:      Optional[Any]   			=  num_attention_heads
         _lowercase:      Tuple   			=  hidden_act
         _lowercase:      str   			=  initializer_range
         _lowercase:      List[Any]   			=  rms_norm_eps
         _lowercase:      Optional[int]   			=  use_cache
         _lowercase:      Tuple   			=  kwargs.pop(
             '''use_memorry_efficient_attention'''			,					__lowerCAmelCase							)
         _lowercase:      Optional[int]   			=  hidden_dropout_prob
         _lowercase:      List[Any]   			=  attention_dropout_prob
         _lowercase:      int   			=  use_stable_embedding
         _lowercase:      Dict   			=  shared_input_output_embedding
         _lowercase:      int   			=  rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=__lowerCAmelCase			,					bos_token_id=__lowerCAmelCase			,					eos_token_id=__lowerCAmelCase			,					tie_word_embeddings=__lowerCAmelCase			,					**__lowerCAmelCase			,					)



       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         if self.rope_scaling is None:
           return

         if not isinstance(self.rope_scaling			,					__lowerCAmelCase							) or len(self.rope_scaling							) != 2:
           raise ValueError(
               '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
               f'''got {self.rope_scaling}'''							)
         _lowercase:      Dict   			=  self.rope_scaling.get('''type'''			,					__lowerCAmelCase							)
         _lowercase:      List[str]   			=  self.rope_scaling.get('''factor'''			,					__lowerCAmelCase							)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''							)
         if rope_scaling_factor is None or not isinstance(__lowerCAmelCase			,					__lowerCAmelCase							) or rope_scaling_factor <= 1.0:
           raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''							)

"""simple docstring"""






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase       :			Optional[Any]   	=logging.get_logger(__name__)  # pylint: disable=invalid-name


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Optional[Any]:
      '''simple docstring'''


      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""   ,						lowerCAmelCase__   ,						)
      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return image
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [image]

      if isinstance(image[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      image[0].size
            lowercase				,				lowercase								=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            lowercase								=      [np.array(i.resize((w, h)   ,						resample=PIL_INTERPOLATION["""lanczos"""]						)						)[None, :] for i in image]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      np.array(lowerCAmelCase__						).astype(np.floataa						) / 255.0
            lowercase								=      image.transpose(0   ,						3   ,						1   ,						2						)
            lowercase								=      2.0 * image - 1.0
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(image[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return image


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Dict:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return mask
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [mask]

      if isinstance(mask[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      mask[0].size
            lowercase				,				lowercase								=      (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
            lowercase								=      [np.array(m.convert("""L"""						).resize((w, h)   ,						resample=PIL_INTERPOLATION["""nearest"""]						)						)[None, :] for m in mask]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      mask.astype(np.floataa						) / 255.0
            lowercase								=      0
            lowercase								=      1
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(mask[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return mask






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  UNetaDModel
      snake_case__     :  RePaintScheduler

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            self.register_modules(unet=__lowerCAmelCase						,			scheduler=__lowerCAmelCase		)







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 250						,			__lowerCAmelCase = 0.0						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			):







            """simple docstring"""



            lowercase								=      image

            lowercase								=      _preprocess_image(__lowerCAmelCase		)
            lowercase								=      original_image.to(device=self.device						,			dtype=self.unet.dtype		)
            lowercase								=      _preprocess_mask(__lowerCAmelCase		)
            lowercase								=      mask_image.to(device=self.device						,			dtype=self.unet.dtype		)

            lowercase								=      original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) and len(__lowerCAmelCase		) != batch_size:
                  raise ValueError(
                      f'You have passed a list of generators of length {len(__lowerCAmelCase		)}, but requested an effective batch'
                      f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

            lowercase								=      original_image.shape
            lowercase								=      randn_tensor(__lowerCAmelCase						,			generator=__lowerCAmelCase						,			device=self.device						,			dtype=self.unet.dtype		)

            # set step values
            self.scheduler.set_timesteps(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			self.device		)
            lowercase								=      eta

            lowercase								=      self.scheduler.timesteps[0] + 1
            lowercase								=      generator[0] if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
                  if t < t_last:
                        # predict the noise residual
                        lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample
                        # compute previous image: x_t -> x_t-1
                        lowercase								=      self.scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

                  else:
                        # compute the reverse: x_t-1 -> x_t
                        lowercase								=      self.scheduler.undo_step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      t

            lowercase								=      (image / 2 + 0.5).clamp(0						,			1		)
            lowercase								=      image.cpu().permute(0						,			2						,			3						,			1		).numpy()
            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



