

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				(VQDiffusionScheduler,)


    def 		snake_case__	( self					:			Any,**lowercase_					:			str						)->       int:





        '''simple docstring'''







        A__        =  {
            '''num_vec_classes''': 4_0_9_7,
            '''num_train_timesteps''': 1_0_0,
        }

        config.update(**_UpperCAmelCase						)
        return config


    def 		snake_case__	( self					:			Optional[int],lowercase_					:			Tuple						)->       List[Any]:





        '''simple docstring'''







        A__        =  4
        A__        =  8
        A__        =  8

        A__        =  torch.randint(0,_UpperCAmelCase,(batch_size, height * width)						)

        return sample


    @property
    def 		snake_case__	( self					:			Optional[int]						)->       Optional[Any]:





        '''simple docstring'''







        assert False


    def 		snake_case__	( self					:			Tuple,lowercase_					:			Union[str, Any]						)->       Optional[Any]:





        '''simple docstring'''







        def model(lowercase_					:			List[Any],lowercase_					:			Tuple,*lowercase_					:			Optional[Any]						):
            A__        =  sample.shape
            A__        =  torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
            A__        =  F.log_softmax(logits.double(),dim=1						).float()
            return return_value

        return model


    def 		snake_case__	( self					:			Optional[int]						)->       int:





        '''simple docstring'''







        for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)


    def 		snake_case__	( self					:			List[Any]						)->       Optional[Any]:





        '''simple docstring'''







        for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
            self.check_over_configs(num_vec_classes=_UpperCAmelCase						)


    def 		snake_case__	( self					:			List[Any]						)->       Tuple:





        '''simple docstring'''







        for t in [0, 5_0, 9_9]:
            self.check_over_forward(time_step=_UpperCAmelCase						)


    def 		snake_case__	( self					:			str						)->       Optional[Any]:





        '''simple docstring'''







        pass








'''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


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """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 	_lowercase    (				a					):
   _UpperCamelCase									= """mobilevit"""


   def __init__(			self     , _UpperCAmelCase=3     , _UpperCAmelCase=256     , _UpperCAmelCase=2     , _UpperCAmelCase=[144, 192, 240]     , _UpperCAmelCase=[16, 32, 64, 96, 128, 160, 640]     , _UpperCAmelCase=4     , _UpperCAmelCase=2.0     , _UpperCAmelCase=4.0     , _UpperCAmelCase="silu"     , _UpperCAmelCase=3     , _UpperCAmelCase=32     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.0     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.02     , _UpperCAmelCase=1E-5     , _UpperCAmelCase=True     , _UpperCAmelCase=256     , _UpperCAmelCase=[6, 12, 18]     , _UpperCAmelCase=0.1     , _UpperCAmelCase=255     , **_UpperCAmelCase     , ):
       super().__init__(**_UpperCAmelCase       )

       A							:							Union[str, Any]				=    num_channels
       A							:							Union[str, Any]				=    image_size
       A							:							List[str]				=    patch_size
       A							:							Any				=    hidden_sizes
       A							:							Dict				=    neck_hidden_sizes
       A							:							int				=    num_attention_heads
       A							:							List[str]				=    mlp_ratio
       A							:							List[Any]				=    expand_ratio
       A							:							Dict				=    hidden_act
       A							:							List[Any]				=    conv_kernel_size
       A							:							List[str]				=    output_stride
       A							:							Dict				=    hidden_dropout_prob
       A							:							List[str]				=    attention_probs_dropout_prob
       A							:							Optional[Any]				=    classifier_dropout_prob
       A							:							Any				=    initializer_range
       A							:							Union[str, Any]				=    layer_norm_eps
       A							:							Union[str, Any]				=    qkv_bias

       # decode head attributes for semantic segmentation
       A							:							int				=    aspp_out_channels
       A							:							Any				=    atrous_rates
       A							:							Tuple				=    aspp_dropout_prob
       A							:							List[Any]				=    semantic_loss_ignore_index







class 	_lowercase    (				a					):
   _UpperCamelCase									= version.parse("""1.11"""					)


   @property
   def        snake_case					(			self       ):
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]       )


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


   @property
   def        snake_case					(			self       ):
       return 1E-4
