import warnings
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          =						{
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class UpperCAmelCase		(   __lowercase    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''segformer'''
				def __init__(		self						,      lowercase=3						,      lowercase=4						,      lowercase=[2, 2, 2, 2]						,      lowercase=[8, 4, 2, 1]						,      lowercase=[3_2, 6_4, 1_6_0, 2_5_6]						,      lowercase=[7, 3, 3, 3]						,      lowercase=[4, 2, 2, 2]						,      lowercase=[1, 2, 5, 8]						,      lowercase=[4, 4, 4, 4]						,      lowercase="gelu"						,      lowercase=0.0						,      lowercase=0.0						,      lowercase=0.1						,      lowercase=0.02						,      lowercase=0.1						,      lowercase=1E-6						,      lowercase=2_5_6						,      lowercase=2_5_5						,      **lowercase						,      ):



						"""simple docstring"""





						super().__init__(**_A   )

						if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
								warnings.warn(
								    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
								    ' removed, as the behaviour will default to that of reshape_last_stage = True.'						,      _A						,      )

						A_						:							Union[str, Any]      = num_channels
						A_						:							Any      = num_encoder_blocks
						A_						:							int      = depths
						A_						:							List[str]      = sr_ratios
						A_						:							int      = hidden_sizes
						A_						:							str      = patch_sizes
						A_						:							List[str]      = strides
						A_						:							Any      = mlp_ratios
						A_						:							str      = num_attention_heads
						A_						:							int      = hidden_act
						A_						:							Optional[int]      = hidden_dropout_prob
						A_						:							Optional[int]      = attention_probs_dropout_prob
						A_						:							Tuple      = classifier_dropout_prob
						A_						:							int      = initializer_range
						A_						:							Dict      = drop_path_rate
						A_						:							Optional[Any]      = layer_norm_eps
						A_						:							Tuple      = decoder_hidden_size
						A_						:							List[str]      = kwargs.get('reshape_last_stage'						,      _A   )
						A_						:							int      = semantic_loss_ignore_index





class UpperCAmelCase		(   __lowercase    ):


				'''simple docstring'''






				lowerCamelCase_        =   version.parse('''1.11'''    )
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





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



						"""simple docstring"""





						return 1E-4







				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return 1_2







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

import black


_lowerCAmelCase       =		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.
_lowerCAmelCase       =		"    \"\"\"\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  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     tempfile.mkdtemp()
												os.makedirs(os.path.join(self.diffusers_dir ,			'''schedulers/'''		)		)
												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     '''src/diffusers'''
												shutil.rmtree(self.diffusers_dir		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     List[str] ,			_A      :     Optional[Any] ,			_A      :     List[str] ,			_A      :     Optional[int]=None		):
												_UpperCamelCase			     =     comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
												if overwrite_result is not None:
																	_UpperCamelCase			     =     comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
												_UpperCamelCase			     =     black.Mode(target_versions={black.TargetVersion.PYaa} ,			line_length=119		)
												_UpperCamelCase			     =     black.format_str(_A ,			mode=_A		)
												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''		)
												self.assertEqual(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												# Base copy consistency
												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
												_UpperCamelCase			     =     '''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		) ,			)
