
import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class       __A:

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     List[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					__UpperCamelCase					:     str="resnet50"   ,					__UpperCamelCase					:     Union[str, Any]=3   ,					__UpperCamelCase					:     Optional[Any]=3_2   ,					__UpperCamelCase					:     Dict=3   ,					__UpperCamelCase					:     Union[str, Any]=True   ,					__UpperCamelCase					:     Union[str, Any]=True   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					out_indices if out_indices is not None else [4]
												lowerCamelCase_							  =					stage_names
												lowerCamelCase_							  =					out_features
												lowerCamelCase_							  =					backbone
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					image_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					use_pretrained_backbone
												lowerCamelCase_							  =					is_training

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
												lowerCamelCase_							  =					self.get_config()

												return config, pixel_values

					def    lowercase__      (	self					:     Any   ):
												return TimmBackboneConfig(
												    image_size=self.image_size   ,					num_channels=self.num_channels   ,					out_features=self.out_features   ,					out_indices=self.out_indices   ,					stage_names=self.stage_names   ,					use_pretrained_backbone=self.use_pretrained_backbone   ,					backbone=self.backbone   ,					)

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_							  =					TimmBackbone(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												with torch.no_grad():
																			lowerCamelCase_							  =					model(__UpperCamelCase   )
												self.parent.assertEqual(
												    result.feature_map[-1].shape   ,					(self.batch_size, model.channels[-1], 1_4, 1_4)   ,					)



					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_torch
@require_timm
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (TimmBackbone,) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE			    =    {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					TimmBackboneModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					has_text_modality=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					"""resnet18"""
												lowerCamelCase_							  =					"""microsoft/resnet-18"""

												lowerCamelCase_							  =					AutoBackbone.from_pretrained(__UpperCamelCase   ,					use_timm_backbone=__UpperCamelCase   )
												lowerCamelCase_							  =					AutoBackbone.from_pretrained(__UpperCamelCase   )

												self.assertEqual(len(timm_model.out_features   )   ,					len(transformers_model.out_features   )   )
												self.assertEqual(len(timm_model.stage_names   )   ,					len(transformers_model.stage_names   )   )
												self.assertEqual(timm_model.channels   ,					transformers_model.channels   )
												# Out indices are set to the last layer by default. For timm models, we don't know
												# the number of layers in advance, so we set it to (-1,), whereas for transformers
												# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
												self.assertEqual(timm_model.out_indices   ,					(-1,)   )
												self.assertEqual(transformers_model.out_indices   ,					[len(timm_model.stage_names   ) - 1]   )

												lowerCamelCase_							  =					AutoBackbone.from_pretrained(__UpperCamelCase   ,					use_timm_backbone=__UpperCamelCase   ,					out_indices=[1, 2, 3]   )
												lowerCamelCase_							  =					AutoBackbone.from_pretrained(__UpperCamelCase   ,					out_indices=[1, 2, 3]   )

												self.assertEqual(timm_model.out_indices   ,					transformers_model.out_indices   )
												self.assertEqual(len(timm_model.out_features   )   ,					len(transformers_model.out_features   )   )
												self.assertEqual(timm_model.channels   ,					transformers_model.channels   )

					@unittest.skip("""TimmBackbone doesn't support feed forward chunking"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""   )
					def    lowercase__      (	self					:     List[Any]   ):
												pass

					@unittest.skip("""TimmBackbone initialization is managed on the timm side"""   )
					def    lowercase__      (	self					:     Dict   ):
												pass

					@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""   )
					def    lowercase__      (	self					:     Union[str, Any]   ):
												pass

					@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""   )
					def    lowercase__      (	self					:     Optional[Any]   ):
												pass

					@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""   )
					def    lowercase__      (	self					:     Any   ):
												pass

					@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""   )
					def    lowercase__      (	self					:     Any   ):
												pass

					@unittest.skip("""model weights aren't tied in TimmBackbone."""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@unittest.skip("""model weights aren't tied in TimmBackbone."""   )
					def    lowercase__      (	self					:     List[Any]   ):
												pass

					@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""   )
					def    lowercase__      (	self					:     str   ):
												pass

					@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""   )
					def    lowercase__      (	self					:     List[Any]   ):
												pass

					@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@unittest.skip("""TimmBackbone doesn't support output_attentions."""   )
					def    lowercase__      (	self					:     List[Any]   ):
												pass

					@unittest.skip("""Safetensors is not supported by timm."""   )
					def    lowercase__      (	self					:     List[Any]   ):
												pass

					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			lowerCamelCase_							  =					inspect.signature(model.forward   )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			lowerCamelCase_							  =					[*signature.parameters.keys()]

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_							  =					True
												lowerCamelCase_							  =					self.has_attentions

												# no need to test all models as different heads yield the same functionality
												lowerCamelCase_							  =					self.all_model_classes[0]
												lowerCamelCase_							  =					model_class(__UpperCamelCase   )
												model.to(__UpperCamelCase   )

												lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					model(**__UpperCamelCase   )
												lowerCamelCase_							  =					outputs[0][-1]

												# Encoder-/Decoder-only models
												lowerCamelCase_							  =					outputs.hidden_states[0]
												hidden_states.retain_grad()

												if self.has_attentions:
																			lowerCamelCase_							  =					outputs.attentions[0]
																			attentions.retain_grad()

												output.flatten()[0].backward(retain_graph=__UpperCamelCase   )

												self.assertIsNotNone(hidden_states.grad   )

												if self.has_attentions:
																			self.assertIsNotNone(attentions.grad   )



					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.eval()
																			lowerCamelCase_							  =					model(**__UpperCamelCase   )

																			self.assertEqual(len(result.feature_maps   )   ,					len(config.out_indices   )   )
																			self.assertEqual(len(model.channels   )   ,					len(config.out_indices   )   )

																			# Check output of last stage is taken if out_features=None, out_indices=None
																			lowerCamelCase_							  =					copy.deepcopy(__UpperCamelCase   )
																			lowerCamelCase_							  =					None
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.eval()
																			lowerCamelCase_							  =					model(**__UpperCamelCase   )

																			self.assertEqual(len(result.feature_maps   )   ,					1   )
																			self.assertEqual(len(model.channels   )   ,					1   )

																			# Check backbone can be initialized with fresh weights
																			lowerCamelCase_							  =					copy.deepcopy(__UpperCamelCase   )
																			lowerCamelCase_							  =					False
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.eval()
																			lowerCamelCase_							  =					model(**__UpperCamelCase   )







from abc import ABC, abstractmethod
from argparse import ArgumentParser





class       __A(				UpperCAmelCase       ):

					@staticmethod
					@abstractmethod
					def    lowercase__      (	__UpperCamelCase					:     ArgumentParser   ):
												raise NotImplementedError()



					@abstractmethod
					def    lowercase__      (	self					:     Any   ):
												raise NotImplementedError()





