
'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCamelCase__					:						Dict											=     logging.get_logger(__name__)

UpperCamelCase__					:						Any											=     {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mobilevit'






       def __init__(							self				,							A__=3				,							A__=2_56				,							A__=2				,							A__=[1_44, 1_92, 2_40]				,							A__=[16, 32, 64, 96, 1_28, 1_60, 6_40]				,							A__=4				,							A__=2.0				,							A__=4.0				,							A__="silu"				,							A__=3				,							A__=32				,							A__=0.1				,							A__=0.0				,							A__=0.1				,							A__=0.02				,							A__=1E-5				,							A__=True				,							A__=2_56				,							A__=[6, 12, 18]				,							A__=0.1				,							A__=2_55				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(**A__					)

          _SCREAMING_SNAKE_CASE          = num_channels
          _SCREAMING_SNAKE_CASE          = image_size
          _SCREAMING_SNAKE_CASE          = patch_size
          _SCREAMING_SNAKE_CASE          = hidden_sizes
          _SCREAMING_SNAKE_CASE          = neck_hidden_sizes
          _SCREAMING_SNAKE_CASE          = num_attention_heads
          _SCREAMING_SNAKE_CASE          = mlp_ratio
          _SCREAMING_SNAKE_CASE          = expand_ratio
          _SCREAMING_SNAKE_CASE          = hidden_act
          _SCREAMING_SNAKE_CASE          = conv_kernel_size
          _SCREAMING_SNAKE_CASE          = output_stride
          _SCREAMING_SNAKE_CASE          = hidden_dropout_prob
          _SCREAMING_SNAKE_CASE          = attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE          = classifier_dropout_prob
          _SCREAMING_SNAKE_CASE          = initializer_range
          _SCREAMING_SNAKE_CASE          = layer_norm_eps
          _SCREAMING_SNAKE_CASE          = qkv_bias

          # decode head attributes for semantic segmentation
          _SCREAMING_SNAKE_CASE          = aspp_out_channels
          _SCREAMING_SNAKE_CASE          = atrous_rates
          _SCREAMING_SNAKE_CASE          = aspp_dropout_prob
          _SCREAMING_SNAKE_CASE          = semantic_loss_ignore_index






class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     version.parse('1.11')






       @property
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]					)






       @property
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          if self.task == "image-classification":
             return OrderedDict([("""logits""", {0: """batch"""})]					)
          else:
             return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]					)






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 1E-4








'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

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

class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__					)			->	Union[str, Any]:
          super().__init__()
          self.register_modules(unet=A__				,							scheduler=A__					)






       @torch.no_grad()
       def __call__(							self				,							A__ = 1				,							A__ = None				,							A__ = 10_00				,							A__ = "pil"				,							A__ = True				,							)			->	Union[ImagePipelineOutput, Tuple]:
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,							A__					):
             _SCREAMING_SNAKE_CASE          = (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
          else:
             _SCREAMING_SNAKE_CASE          = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__					)
             _SCREAMING_SNAKE_CASE          = image.to(self.device					)
          else:
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__				,							device=self.device					)

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

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

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

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

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__					)






