




from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase	,				lowerCamelCase    ) ->      Optional[Any]:





								"""simple docstring"""


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







	@torch.no_grad()
	def __call__(					self	,				lowerCamelCase = 1	,				lowerCamelCase = None	,				lowerCamelCase = 1000	,				lowerCamelCase = "pil"	,				lowerCamelCase = True	,				) ->      Union[ImagePipelineOutput, Tuple]:





								"""simple docstring"""


								if isinstance(self.unet.config.sample_size	,				lowerCamelCase    ):
															lowercase__  :	Tuple        =							(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															lowercase__  :	Union[str, Any]        =							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
															# randn does not work reproducibly on mps
															lowercase__  :	Optional[int]        =							randn_tensor(lowerCamelCase	,				generator=lowerCamelCase    )
															lowercase__  :	Tuple        =							image.to(self.device    )
								else:
															lowercase__  :	Dict        =							randn_tensor(lowerCamelCase	,				generator=lowerCamelCase	,				device=self.device    )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase    )

								for t in self.progress_bar(self.scheduler.timesteps    ):
															# 1. predict noise model_output
															lowercase__  :	str        =							self.unet(lowerCamelCase	,				lowerCamelCase    ).sample

															# 2. compute previous image: x_t -> x_t-1
															lowercase__  :	int        =							self.scheduler.step(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				generator=lowerCamelCase    ).prev_sample

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=lowerCamelCase    )





from collections import OrderedDict
from typing import Mapping

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


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : str	  =					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	a     : int	  =					"""squeezebert"""

	def __init__(					self	,				lowerCamelCase=30522	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=0	,				lowerCamelCase=768	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=1	,				lowerCamelCase=4	,				lowerCamelCase=4	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


								super().__init__(pad_token_id=lowerCamelCase	,				**lowerCamelCase    )

								lowercase__  :	Any        =							vocab_size
								lowercase__  :	Tuple        =							hidden_size
								lowercase__  :	str        =							num_hidden_layers
								lowercase__  :	Optional[int]        =							num_attention_heads
								lowercase__  :	List[Any]        =							hidden_act
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	List[Any]        =							attention_probs_dropout_prob
								lowercase__  :	Any        =							max_position_embeddings
								lowercase__  :	List[Any]        =							type_vocab_size
								lowercase__  :	Any        =							initializer_range
								lowercase__  :	Optional[Any]        =							layer_norm_eps
								lowercase__  :	Any        =							embedding_size
								lowercase__  :	Optional[Any]        =							q_groups
								lowercase__  :	List[str]        =							k_groups
								lowercase__  :	List[Any]        =							v_groups
								lowercase__  :	str        =							post_attention_groups
								lowercase__  :	Optional[int]        =							intermediate_groups
								lowercase__  :	Optional[int]        =							output_groups







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






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





								"""simple docstring"""


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