




'''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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



       """simple docstring"""

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

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

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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

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

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


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   42
    UpperCAmelCase__													=   42


    def __init__(    self   :			int      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Dict)		->str:





           '''simple docstring'''

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





    @torch.no_grad()
    def __call__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[torch.Tensor, PIL.Image.Image]      ,				UpperCAmelCase__   :			Union[torch.Tensor, PIL.Image.Image]      ,				UpperCAmelCase__   :			int = 250      ,				UpperCAmelCase__   :			float = 0.0      ,				UpperCAmelCase__   :			int = 10      ,				UpperCAmelCase__   :			int = 10      ,				UpperCAmelCase__   :			Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,				UpperCAmelCase__   :			Optional[str] = "pil"      ,				UpperCAmelCase__   :			bool = True      ,				)		->Union[ImagePipelineOutput, Tuple]:





           '''simple docstring'''

           A__   				=					image

           A__   				=					_preprocess_image(UpperCAmelCase__)
           A__   				=					original_image.to(device=self.device      ,				dtype=self.unet.dtype)
           A__   				=					_preprocess_mask(UpperCAmelCase__)
           A__   				=					mask_image.to(device=self.device      ,				dtype=self.unet.dtype)

           A__   				=					original_image.shape[0]

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

           A__   				=					original_image.shape
           A__   				=					randn_tensor(UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				device=self.device      ,				dtype=self.unet.dtype)

           # set step values
           self.scheduler.set_timesteps(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				self.device)
           A__   				=					eta

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

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

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

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=UpperCAmelCase__)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_lowerCamelCase			:	Dict							 =     {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''xlm'''
    UpperCAmelCase__													=   {
        '''hidden_size''': '''emb_dim''',
        '''num_attention_heads''': '''n_heads''',
        '''num_hidden_layers''': '''n_layers''',
        '''n_words''': '''vocab_size''',  # For backward compatibility
    }


    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			Optional[Any]=30_145      ,				UpperCAmelCase__   :			List[str]=2_048      ,				UpperCAmelCase__   :			str=12      ,				UpperCAmelCase__   :			List[Any]=16      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			str=True      ,				UpperCAmelCase__   :			Union[str, Any]=False      ,				UpperCAmelCase__   :			str=False      ,				UpperCAmelCase__   :			Optional[int]=False      ,				UpperCAmelCase__   :			List[str]=1      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Union[str, Any]=512      ,				UpperCAmelCase__   :			List[str]=2_048**-0.5      ,				UpperCAmelCase__   :			List[Any]=1e-12      ,				UpperCAmelCase__   :			Any=0.02      ,				UpperCAmelCase__   :			str=0      ,				UpperCAmelCase__   :			int=1      ,				UpperCAmelCase__   :			Union[str, Any]=2      ,				UpperCAmelCase__   :			List[Any]=3      ,				UpperCAmelCase__   :			int=5      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Tuple="first"      ,				UpperCAmelCase__   :			Optional[int]=True      ,				UpperCAmelCase__   :			List[str]=None      ,				UpperCAmelCase__   :			Tuple=True      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Any=5      ,				UpperCAmelCase__   :			List[Any]=5      ,				UpperCAmelCase__   :			int=0      ,				UpperCAmelCase__   :			Dict=0      ,				UpperCAmelCase__   :			Dict=2      ,				UpperCAmelCase__   :			str=0      ,				**UpperCAmelCase__   :			List[str]      ,				)		->str:





           '''simple docstring'''

           A__   				=					vocab_size
           A__   				=					emb_dim
           A__   				=					n_layers
           A__   				=					n_heads
           A__   				=					dropout
           A__   				=					attention_dropout
           A__   				=					gelu_activation
           A__   				=					sinusoidal_embeddings
           A__   				=					causal
           A__   				=					asm
           A__   				=					n_langs
           A__   				=					use_lang_emb
           A__   				=					layer_norm_eps
           A__   				=					bos_index
           A__   				=					eos_index
           A__   				=					pad_index
           A__   				=					unk_index
           A__   				=					mask_index
           A__   				=					is_encoder
           A__   				=					max_position_embeddings
           A__   				=					embed_init_std
           A__   				=					init_std
           A__   				=					summary_type
           A__   				=					summary_use_proj
           A__   				=					summary_activation
           A__   				=					summary_proj_to_labels
           A__   				=					summary_first_dropout
           A__   				=					start_n_top
           A__   				=					end_n_top
           A__   				=					mask_token_id
           A__   				=					lang_id

           if "n_words" in kwargs:
                  A__   				=					kwargs['''n_words''']

           super().__init__(pad_token_id=UpperCAmelCase__      ,				bos_token_id=UpperCAmelCase__      ,				**UpperCAmelCase__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






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





           '''simple docstring'''

           if self.task == "multiple-choice":
                  A__   				=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
                  A__   				=					{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
                   ('''token_type_ids''', dynamic_axis),
               ])


