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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "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     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "segformer"







							def __init__(   self      :     Optional[Any] ,			_A      :     List[Any]=3 ,			_A      :     Tuple=4 ,			_A      :     Any=[2, 2, 2, 2] ,			_A      :     Dict=[8, 4, 2, 1] ,			_A      :     int=[32, 64, 160, 256] ,			_A      :     Any=[7, 3, 3, 3] ,			_A      :     Any=[4, 2, 2, 2] ,			_A      :     str=[1, 2, 5, 8] ,			_A      :     Optional[Any]=[4, 4, 4, 4] ,			_A      :     Dict="gelu" ,			_A      :     Optional[Any]=0.0 ,			_A      :     Any=0.0 ,			_A      :     Tuple=0.1 ,			_A      :     Optional[Any]=0.02 ,			_A      :     Dict=0.1 ,			_A      :     Dict=1e-6 ,			_A      :     int=256 ,			_A      :     Dict=255 ,			**_A      :     List[Any] ,			):
												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 ,			)

												_UpperCamelCase			     =     num_channels
												_UpperCamelCase			     =     num_encoder_blocks
												_UpperCamelCase			     =     depths
												_UpperCamelCase			     =     sr_ratios
												_UpperCamelCase			     =     hidden_sizes
												_UpperCamelCase			     =     patch_sizes
												_UpperCamelCase			     =     strides
												_UpperCamelCase			     =     mlp_ratios
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     hidden_act
												_UpperCamelCase			     =     hidden_dropout_prob
												_UpperCamelCase			     =     attention_probs_dropout_prob
												_UpperCamelCase			     =     classifier_dropout_prob
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     drop_path_rate
												_UpperCamelCase			     =     layer_norm_eps
												_UpperCamelCase			     =     decoder_hidden_size
												_UpperCamelCase			     =     kwargs.get('''reshape_last_stage''' ,			_A		)
												_UpperCamelCase			     =     semantic_loss_ignore_index




class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     version.parse("1.11" )







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







							@property
							def  UpperCamelCase_						(   self      :     str		):
												return 1e-4







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return 12

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		) ,			)
