


"""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__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}





class  lowerCamelCase__    (		_a     ):
      a			:  Dict           =		"""mobilenet_v2"""
      def __init__(		self						:						Dict   ,							A_						:						Any=3   ,							A_						:						Tuple=2_2_4   ,							A_						:						int=1.0   ,							A_						:						List[Any]=8   ,							A_						:						Tuple=8   ,							A_						:						Dict=6   ,							A_						:						Dict=3_2   ,							A_						:						Union[str, Any]=True   ,							A_						:						Union[str, Any]=True   ,							A_						:						Union[str, Any]="relu6"   ,							A_						:						List[str]=True   ,							A_						:						int=0.8   ,							A_						:						Tuple=0.02   ,							A_						:						List[Any]=0.0_01   ,							A_						:						Any=2_5_5   ,							**A_						:						int   ,							):






       '''simple docstring'''


       super().__init__(**A_				)

       if depth_multiplier <= 0:
        raise ValueError("""depth_multiplier must be greater than zero."""				)

       __lowercase						=						num_channels
       __lowercase						=						image_size
       __lowercase						=						depth_multiplier
       __lowercase						=						depth_divisible_by
       __lowercase						=						min_depth
       __lowercase						=						expand_ratio
       __lowercase						=						output_stride
       __lowercase						=						first_layer_is_expansion
       __lowercase						=						finegrained_output
       __lowercase						=						hidden_act
       __lowercase						=						tf_padding
       __lowercase						=						classifier_dropout_prob
       __lowercase						=						initializer_range
       __lowercase						=						layer_norm_eps
       __lowercase						=						semantic_loss_ignore_index






class  lowerCamelCase__    (		_a     ):
      a			:  int           =		version.parse("""1.11"""     )
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


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






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       return 1e-4




"""simple docstring"""

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCAmelCase__					=os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
UpperCAmelCase__					="    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"





class  lowerCamelCase__    (		unittest.TestCase     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       __lowercase						=						tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir   ,							"""schedulers/"""				)				)
       __lowercase						=						self.diffusers_dir
       shutil.copy(
           os.path.join(A_   ,							"""src/diffusers/schedulers/scheduling_ddpm.py"""				)   ,							os.path.join(self.diffusers_dir   ,							"""schedulers/scheduling_ddpm.py"""				)   ,							)
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						"""src/diffusers"""
       shutil.rmtree(self.diffusers_dir				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						List[str]   ,							A_						:						int   ,							A_						:						Optional[Any]   ,							A_						:						str=None				):






       '''simple docstring'''


       __lowercase						=						comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
       if overwrite_result is not None:
        __lowercase						=						comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
       __lowercase						=						black.Mode(target_versions={black.TargetVersion.PYaa}   ,							line_length=1_1_9				)
       __lowercase						=						black.format_str(A_   ,							mode=A_				)
       __lowercase						=						os.path.join(self.diffusers_dir   ,							"""new_code.py"""				)
       with open(A_   ,							"""w"""   ,							newline="""\n"""				) as f:
        f.write(A_				)
       if overwrite_result is None:
        self.assertTrue(len(check_copies.is_copy_consistent(A_				)				) == 0				)
       else:
        check_copies.is_copy_consistent(f.name   ,							overwrite=A_				)
        with open(A_   ,							"""r"""				) as f:
         self.assertTrue(f.read()   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput"""				)
       self.assertEqual(A_   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       self.check_copy_consistency(
           """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"""   ,							"""DDPMSchedulerOutput"""   ,							REFERENCE_CODE + """\n"""   ,							)

       # With no empty line at the end
       self.check_copy_consistency(
           """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"""   ,							"""DDPMSchedulerOutput"""   ,							A_   ,							)

       # Copy consistency with rename
       self.check_copy_consistency(
           """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"""   ,							"""TestSchedulerOutput"""   ,							re.sub("""DDPM"""   ,							"""Test"""   ,							A_				)   ,							)

       # Copy consistency with a really long name
       __lowercase						=						"""TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
       self.check_copy_consistency(
           F'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'''   ,							F'''{long_class_name}SchedulerOutput'''   ,							re.sub("""Bert"""   ,							A_   ,							A_				)   ,							)

       # Copy consistency with overwrite
       self.check_copy_consistency(
           """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"""   ,							"""TestSchedulerOutput"""   ,							A_   ,							overwrite_result=re.sub("""DDPM"""   ,							"""Test"""   ,							A_				)   ,							)
