from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor


class 						lowercase    (     a				,       a     ):
							lowercase__						:       Tuple								=							"""pixel_values"""
							lowercase__						:       Union[str, Any]								=							False
							lowercase__						:       Dict								=							TimmBackboneConfig




							def __init__(						self   :					Dict	,							_UpperCamelCase   :					Tuple	,							**_UpperCamelCase   :					int      )					->				Optional[Any]:



												'''simple docstring'''

												requires_backends(self	,							"timm"      )
												super().__init__(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       config

												if config.backbone is None:
																	raise ValueError("backbone is not set in the config. Please set it to a timm model name."      )

												if config.backbone not in timm.list_models():
																	raise ValueError(F"backbone {config.backbone} is not supported by timm."      )

												if hasattr(_UpperCamelCase	,							"out_features"      ) and config.out_features is not None:
																	raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead."      )

												SCREAMING_SNAKE_CASE    			=       getattr(_UpperCamelCase	,							"use_pretrained_backbone"	,							_UpperCamelCase      )
												if pretrained is None:
																	raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False."      )

												# We just take the final layer by default. This matches the default for the transformers models.
												SCREAMING_SNAKE_CASE    			=       config.out_indices if getattr(_UpperCamelCase	,							"out_indices"	,							_UpperCamelCase      ) is not None else (-1,)

												SCREAMING_SNAKE_CASE    			=       timm.create_model(
												    config.backbone	,							pretrained=_UpperCamelCase	,							features_only=config.features_only	,							in_chans=config.num_channels	,							out_indices=_UpperCamelCase	,							**_UpperCamelCase	,							)
												# These are used to control the output of the model when called. If output_hidden_states is True, then
												# return_layers is modified to include all layers.
												SCREAMING_SNAKE_CASE    			=       self._backbone.return_layers
												SCREAMING_SNAKE_CASE    			=       {layer["module"]: str(_UpperCamelCase      ) for i, layer in enumerate(self._backbone.feature_info.info      )}
												super()._init_backbone(_UpperCamelCase      )




							@classmethod
							def 	__snake_case(						cls   :					int	,							_UpperCamelCase   :					Dict	,							*_UpperCamelCase   :					List[str]	,							**_UpperCamelCase   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												requires_backends(cls	,							["vision", "timm"]      )
												from ...models.timm_backbone import TimmBackboneConfig

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("config"	,							TimmBackboneConfig()      )

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("use_timm_backbone"	,							_UpperCamelCase      )
												if not use_timm:
																	raise ValueError("use_timm_backbone must be True for timm backbones"      )

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("num_channels"	,							config.num_channels      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("features_only"	,							config.features_only      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("use_pretrained_backbone"	,							config.use_pretrained_backbone      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("out_indices"	,							config.out_indices      )
												SCREAMING_SNAKE_CASE    			=       TimmBackboneConfig(
												    backbone=_UpperCamelCase	,							num_channels=_UpperCamelCase	,							features_only=_UpperCamelCase	,							use_pretrained_backbone=_UpperCamelCase	,							out_indices=_UpperCamelCase	,							)
												return super()._from_config(_UpperCamelCase	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					int      )					->				Optional[Any]:



												'''simple docstring'''

												pass





							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Dict=None	,							_UpperCamelCase   :					List[str]=None	,							_UpperCamelCase   :					str=None	,							**_UpperCamelCase   :					List[Any]      )					->				Union[BackboneOutput, Tuple[Tensor, ...]]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       return_dict if return_dict is not None else self.config.use_return_dict
												SCREAMING_SNAKE_CASE    			=       (
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												SCREAMING_SNAKE_CASE    			=       output_attentions if output_attentions is not None else self.config.output_attentions

												if output_attentions:
																	raise ValueError("Cannot output attentions for timm backbones at the moment"      )

												if output_hidden_states:
																	# We modify the return layers to include all the stages of the backbone
																	SCREAMING_SNAKE_CASE    			=       self._all_layers
																	SCREAMING_SNAKE_CASE    			=       self._backbone(_UpperCamelCase	,							**_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       self._return_layers
																	SCREAMING_SNAKE_CASE    			=       tuple(hidden_states[i] for i in self.out_indices      )
												else:
																	SCREAMING_SNAKE_CASE    			=       self._backbone(_UpperCamelCase	,							**_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       None

												SCREAMING_SNAKE_CASE    			=       tuple(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       tuple(_UpperCamelCase      ) if hidden_states is not None else None

												if not return_dict:
																	SCREAMING_SNAKE_CASE    			=       (feature_maps,)
																	if output_hidden_states:
																						SCREAMING_SNAKE_CASE    			=       output + (hidden_states,)
																	return output

												return BackboneOutput(feature_maps=_UpperCamelCase	,							hidden_states=_UpperCamelCase	,							attentions=_UpperCamelCase      )





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)




