




'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_							:	List[Any]	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	Union[str, Any]	     =		{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Dict										=      '''gpt_neo'''
       __lowercase	:   Optional[int]										=      ['''past_key_values''']
       __lowercase	:   List[str]										=      {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=5_0257     ,	__SCREAMING_SNAKE_CASE=2048     ,	__SCREAMING_SNAKE_CASE=2048     ,	__SCREAMING_SNAKE_CASE=24     ,	__SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=256     ,	__SCREAMING_SNAKE_CASE="gelu_new"     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=1E-5     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=5_0256     ,	__SCREAMING_SNAKE_CASE=5_0256     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	vocab_size
            __snake_case            =	max_position_embeddings
            __snake_case            =	hidden_size
            __snake_case            =	num_layers
            __snake_case            =	num_heads
            __snake_case            =	intermediate_size
            __snake_case            =	window_size
            __snake_case            =	activation_function
            __snake_case            =	resid_dropout
            __snake_case            =	embed_dropout
            __snake_case            =	attention_dropout
            __snake_case            =	classifier_dropout
            __snake_case            =	layer_norm_epsilon
            __snake_case            =	initializer_range
            __snake_case            =	use_cache

            __snake_case            =	bos_token_id
            __snake_case            =	eos_token_id

            __snake_case            =	attention_types
            __snake_case            =	self.expand_attention_types_params(__SCREAMING_SNAKE_CASE	)

            if len(self.attention_layers	) != self.num_layers:
                 raise ValueError(
                     '''Configuration for convolutional module is incorrect. '''
                     '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
                     F'''but is `len(config.attention_layers) = {len(self.attention_layers	)}`, '''
                     F'''`config.num_layers = {self.num_layers}`. '''
                     '''`config.attention_layers` is prepared using `config.attention_types`. '''
                     '''Please verify the value of `config.attention_types` argument.'''	)

            super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE     ,	eos_token_id=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)




       @staticmethod
       def    lowerCAmelCase   ( __SCREAMING_SNAKE_CASE	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	[]
            for item in attention_types:
                 for _ in range(item[1]	):
                      attentions.extend(item[0]	)
            return attentions







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Optional[Any]						)-> Tuple:




     '''simple docstring'''





     import torch

     __snake_case            =	input.size()
     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	shape[dimension]

     __snake_case            =	torch.arange(0 ,						_lowerCamelCase ,						_lowerCamelCase						)
     __snake_case            =	torch.div(sizedim - size ,						_lowerCamelCase ,						rounding_mode='''floor'''						) + 1
     __snake_case            =	torch.arange(_lowerCamelCase						) + low_indices[:min_length][:, None]

     __snake_case            =	[slice(_lowerCamelCase						)] * rank
     __snake_case            =	indices
     __snake_case            =	input[s]

     __snake_case            =	list(range(0 ,						rank + 1						)						)
     perm.append(perm.pop(dimension + 1						)						)

     return sliced.permute(_lowerCamelCase						)







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  str						)-> str:




     '''simple docstring'''





     import torch

     __snake_case            =	torch.arange(1 ,						_lowerCamelCase						)
     __snake_case            =	torch.remainder(_lowerCamelCase ,						_lowerCamelCase						)
     __snake_case            =	remainders == 0
     __snake_case            =	candidates[divisor_indices]
     __snake_case            =	torch.max(_lowerCamelCase						)
     return largest_divisor, torch.div(_lowerCamelCase ,						_lowerCamelCase ,						rounding_mode='''floor'''						)







class      lowerCAmelCase (			__lowerCAmelCase):


       @property
       def    lowerCAmelCase   ( self	)   ->	Mapping[str, Mapping[int, str]]:



            '''simple docstring'''




            __snake_case            =	OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
            if self.use_past:
                 self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE     ,	direction='''inputs'''	)
                 __snake_case            =	{0: '''batch''', 1: '''past_sequence + sequence'''}
            else:
                 __snake_case            =	{0: '''batch''', 1: '''sequence'''}

            return common_inputs


       @property
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            return self._config.num_heads


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = -1     ,	__SCREAMING_SNAKE_CASE = -1     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = None     ,	)   ->	Mapping[str, Any]:



            '''simple docstring'''




            __snake_case            =	super(__SCREAMING_SNAKE_CASE     ,	self	).generate_dummy_inputs(
                __SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE     ,	seq_length=__SCREAMING_SNAKE_CASE     ,	is_pair=__SCREAMING_SNAKE_CASE     ,	framework=__SCREAMING_SNAKE_CASE	)

            # We need to order the input in the way they appears in the forward()
            __snake_case            =	OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

            # Need to add the past_keys
            if self.use_past:
                 if not is_torch_available():
                      raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
                 else:
                      import torch

                      __snake_case       ,     __snake_case            =	common_inputs['''input_ids'''].shape
                      # Not using the same length for past_key_values
                      __snake_case            =	seqlen + 2
                      __snake_case            =	(
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      __snake_case            =	[
                          (torch.zeros(__SCREAMING_SNAKE_CASE	), torch.zeros(__SCREAMING_SNAKE_CASE	)) for _ in range(self.num_layers	)
                      ]

            __snake_case            =	common_inputs['''attention_mask''']
            if self.use_past:
                 __snake_case            =	ordered_inputs['''attention_mask'''].dtype
                 __snake_case            =	torch.cat(
                     [ordered_inputs['''attention_mask'''], torch.ones(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	dtype=__SCREAMING_SNAKE_CASE	)]     ,	dim=1	)

            return ordered_inputs




       @property
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            return 13












'''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


UpperCAmelCase_							:	Tuple	     =		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						):
          __snake_case            =	[image]

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

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







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





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

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







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




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




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 250     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

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

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

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

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






