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					:   int							=		logging.get_logger(__name__)

_lowerCamelCase					:   Optional[Any]							=		{
    '''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 						lowercase    (     a     ):
							lowercase__						:       Dict								=							"""segformer"""




							def __init__(						self   :					str	,							_UpperCamelCase   :					str=3	,							_UpperCamelCase   :					int=4	,							_UpperCamelCase   :					Any=[2, 2, 2, 2]	,							_UpperCamelCase   :					str=[8, 4, 2, 1]	,							_UpperCamelCase   :					List[Any]=[32, 64, 160, 256]	,							_UpperCamelCase   :					Optional[Any]=[7, 3, 3, 3]	,							_UpperCamelCase   :					Tuple=[4, 2, 2, 2]	,							_UpperCamelCase   :					List[Any]=[1, 2, 5, 8]	,							_UpperCamelCase   :					int=[4, 4, 4, 4]	,							_UpperCamelCase   :					List[Any]="gelu"	,							_UpperCamelCase   :					int=0.0	,							_UpperCamelCase   :					Any=0.0	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					Any=0.0_2	,							_UpperCamelCase   :					List[Any]=0.1	,							_UpperCamelCase   :					Optional[Any]=1e-6	,							_UpperCamelCase   :					Tuple=256	,							_UpperCamelCase   :					Optional[Any]=255	,							**_UpperCamelCase   :					Dict	,							)					->				List[Any]:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase      )

												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."	,							_UpperCamelCase	,							)

												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       num_encoder_blocks
												SCREAMING_SNAKE_CASE    			=       depths
												SCREAMING_SNAKE_CASE    			=       sr_ratios
												SCREAMING_SNAKE_CASE    			=       hidden_sizes
												SCREAMING_SNAKE_CASE    			=       patch_sizes
												SCREAMING_SNAKE_CASE    			=       strides
												SCREAMING_SNAKE_CASE    			=       mlp_ratios
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       hidden_act
												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    			=       drop_path_rate
												SCREAMING_SNAKE_CASE    			=       layer_norm_eps
												SCREAMING_SNAKE_CASE    			=       decoder_hidden_size
												SCREAMING_SNAKE_CASE    			=       kwargs.get("reshape_last_stage"	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       semantic_loss_ignore_index



class 						lowercase    (     a     ):
							lowercase__						:       str								=							version.parse("""1.11"""     )




							@property
							def 	__snake_case(						self   :					str      )					->				Mapping[str, Mapping[int, str]]:



												'''simple docstring'''

												return OrderedDict(
												    [
												        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
												    ]      )




							@property
							def 	__snake_case(						self   :					Any      )					->				float:



												'''simple docstring'''

												return 1e-4





							@property
							def 	__snake_case(						self   :					Any      )					->				int:



												'''simple docstring'''

												return 12





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       inspect.getfile(accelerate.test_utils      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_script.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(
												    mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_distributed_data_loop.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_ops.py"]      )




							@require_multi_gpu
							def 	__snake_case(						self   :					Optional[int]      )					->				Any:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
												print(F"Command: {cmd}"      )
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )





							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
												with patch_environment(omp_num_threads=1	,							cuda_visible_devices="0,1"      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )


if __name__ == "__main__":
							_lowerCamelCase					:   str							=		Accelerator()
							_lowerCamelCase					:   List[str]							=		(accelerator.state.process_index + 2, 10)
							_lowerCamelCase					:   str							=		torch.randint(0, 10, shape).to(accelerator.device)

							_lowerCamelCase					:   Optional[Any]							=		''''''

							_lowerCamelCase					:   str							=		accelerator.pad_across_processes(tensor)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
														error_msg += "Padding was not done with the right value (0)."

							_lowerCamelCase					:   Any							=		accelerator.pad_across_processes(tensor, pad_first=True)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							_lowerCamelCase					:   int							=		accelerator.state.num_processes - accelerator.state.process_index - 1
							if not torch.equal(tensora[index:], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[:index] == 0):
														error_msg += "Padding was not done with the right value (0)."

							# Raise error at the end to make sure we don't stop at the first failure.
							if len(error_msg) > 0:
														raise ValueError(error_msg)




