





from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """xlm-roberta"""


      def __init__(		self   ,				a__=3_0522   ,				a__=768   ,				a__=12   ,				a__=12   ,				a__=3072   ,				a__="gelu"   ,				a__=0.1   ,				a__=0.1   ,				a__=512   ,				a__=2   ,				a__=0.02   ,				a__=1e-12   ,				a__=1   ,				a__=0   ,				a__=2   ,				a__="absolute"   ,				a__=True   ,				a__=None   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(pad_token_id=a__   ,				bos_token_id=a__   ,				eos_token_id=a__   ,				**a__)

             _lowerCamelCase				:  Tuple						=		vocab_size
             _lowerCamelCase				:  Dict						=		hidden_size
             _lowerCamelCase				:  Tuple						=		num_hidden_layers
             _lowerCamelCase				:  int						=		num_attention_heads
             _lowerCamelCase				:  Union[str, Any]						=		hidden_act
             _lowerCamelCase				:  int						=		intermediate_size
             _lowerCamelCase				:  List[str]						=		hidden_dropout_prob
             _lowerCamelCase				:  Optional[Any]						=		attention_probs_dropout_prob
             _lowerCamelCase				:  List[Any]						=		max_position_embeddings
             _lowerCamelCase				:  List[str]						=		type_vocab_size
             _lowerCamelCase				:  Any						=		initializer_range
             _lowerCamelCase				:  Union[str, Any]						=		layer_norm_eps
             _lowerCamelCase				:  Any						=		position_embedding_type
             _lowerCamelCase				:  List[str]						=		use_cache
             _lowerCamelCase				:  int						=		classifier_dropout







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      @property
      def 					__snake_case      (		self):



             """simple docstring"""





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








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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()
             self.register_modules(vqvae=a__   ,				unet=a__   ,				scheduler=a__)



      @torch.no_grad()
      def __call__(		self   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = "pil"   ,				a__ = True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,				generator=a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		latents.to(self.device)

             # scale the initial noise by the standard deviation required by the scheduler
             _lowerCamelCase				:  Tuple						=		latents * self.scheduler.init_noise_sigma

             self.scheduler.set_timesteps(a__)

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             _lowerCamelCase				:  Optional[int]						=		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())

             _lowerCamelCase				:  Union[str, Any]						=		{}
             if accepts_eta:
                    _lowerCamelCase				:  List[Any]						=		eta

             for t in self.progress_bar(self.scheduler.timesteps):
                    _lowerCamelCase				:  List[Any]						=		self.scheduler.scale_model_input(a__   ,				a__)
                    # predict the noise residual
                    _lowerCamelCase				:  Optional[int]						=		self.unet(a__   ,				a__).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    _lowerCamelCase				:  List[str]						=		self.scheduler.step(a__   ,				a__   ,				a__   ,				**a__).prev_sample

             # decode the image latents with the VAE
             _lowerCamelCase				:  Optional[int]						=		self.vqvae.decode(a__).sample

             _lowerCamelCase				:  Dict						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  List[str]						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  List[Any]						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a__)

