



"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class    lowercase__     (					_lowerCamelCase   ):







							'''simple docstring'''


							UpperCamelCase		=  42
							UpperCamelCase		=  42



							def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Optional[Any]					)				->		List[str]:


													'''simple docstring'''




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







							@torch.no_grad()
							def __call__(							self    :						List[str]	,       _UpperCAmelCase    :						str = 1	,       _UpperCAmelCase    :						List[Any] = 50	,       _UpperCAmelCase    :						Union[str, Any] = None	,       _UpperCAmelCase    :						Optional[Any] = "pil"	,       _UpperCAmelCase    :						Optional[int] = True	,       **_UpperCAmelCase    :						Dict	,       )				->		Union[Tuple, ImagePipelineOutput]:


													'''simple docstring'''




													UpperCAmelCase_    				=							self.unet.config.sample_size
													UpperCAmelCase_    				=							(batch_size, 3, img_size, img_size)

													UpperCAmelCase_    				=							self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													UpperCAmelCase_    				=							randn_tensor(A__	,       generator=A__	,       device=self.device					) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(A__					)

													for t in self.progress_bar(self.scheduler.timesteps					):
																			# here sigma_t == t_i from the paper
																			UpperCAmelCase_    				=							self.scheduler.schedule[t]
																			UpperCAmelCase_    				=							self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			UpperCAmelCase_				,					UpperCAmelCase_    				=							self.scheduler.add_noise_to_input(A__	,       A__	,       generator=A__					)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			UpperCAmelCase_    				=							(sigma_hat / 2) * model((sample_hat + 1) / 2	,       sigma_hat / 2					).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			UpperCAmelCase_    				=							self.scheduler.step(A__	,       A__	,       A__	,       A__					)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									UpperCAmelCase_    				=							(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2	,       sigma_prev / 2					).sample
																									UpperCAmelCase_    				=							self.scheduler.step_correct(
																									    A__	,       A__	,       A__	,       A__	,       step_output.prev_sample	,       step_output["derivative"]	,       )
																			UpperCAmelCase_    				=							step_output.prev_sample

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

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=A__					)









"""simple docstring"""





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      =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''convbert'''



       def __init__(							self    :						Any	,       _UpperCAmelCase    :						Optional[int]=30522	,       _UpperCAmelCase    :						Tuple=768	,       _UpperCAmelCase    :						Optional[int]=12	,       _UpperCAmelCase    :						Dict=12	,       _UpperCAmelCase    :						Any=3072	,       _UpperCAmelCase    :						Dict="gelu"	,       _UpperCAmelCase    :						Optional[int]=0.1	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Tuple=512	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						Union[str, Any]=0.02	,       _UpperCAmelCase    :						Dict=1e-12	,       _UpperCAmelCase    :						Optional[int]=1	,       _UpperCAmelCase    :						int=0	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						List[Any]=768	,       _UpperCAmelCase    :						Optional[int]=2	,       _UpperCAmelCase    :						str=9	,       _UpperCAmelCase    :						List[Any]=1	,       _UpperCAmelCase    :						Optional[Any]=None	,       **_UpperCAmelCase    :						str	,       )				->		List[Any]:


             '''simple docstring'''




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

             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							hidden_size
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							intermediate_size
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							hidden_dropout_prob
             UpperCAmelCase_    				=							attention_probs_dropout_prob
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							type_vocab_size
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps
             UpperCAmelCase_    				=							embedding_size
             UpperCAmelCase_    				=							head_ratio
             UpperCAmelCase_    				=							conv_kernel_size
             UpperCAmelCase_    				=							num_groups
             UpperCAmelCase_    				=							classifier_dropout



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


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


             '''simple docstring'''




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




