

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						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__=None   ,	A__=None   ,	A__=None   ,	A__="resnet50"   ,	A__=3   ,	A__=32   ,	A__=3   ,	A__=True   ,	A__=True   ,	):
						A__							:   List[Any]					       =		parent
						A__							:   Any					       =		out_indices if out_indices is not None else [4]
						A__							:   Optional[Any]					       =		stage_names
						A__							:   Optional[int]					       =		out_features
						A__							:   List[Any]					       =		backbone
						A__							:   List[str]					       =		batch_size
						A__							:   List[Any]					       =		image_size
						A__							:   Optional[Any]					       =		num_channels
						A__							:   List[str]					       =		use_pretrained_backbone
						A__							:   Union[str, Any]					       =		is_training






					def   __A      (			self      ):
						A__							:   Tuple					       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
						A__							:   Optional[int]					       =		self.get_config()

						return config, pixel_values






					def   __A      (			self      ):
						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   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Optional[int]					       =		TimmBackbone(config=A__      )
						model.to(A__      )
						model.eval()
						with torch.no_grad():
							A__							:   str					       =		model(A__      )
						self.parent.assertEqual(
						    result.feature_map[-1].shape   ,	(self.batch_size, model.channels[-1], 14, 14)   ,	)






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.prepare_config_and_inputs()
						A__       ,       A__							:   List[str]					       =		config_and_inputs
						A__							:   Tuple					       =		{"""pixel_values""": pixel_values}
						return config, inputs_dict



@require_torch
@require_timm
class    _a						(__magic_name__		,       __magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	(TimmBackbone,) if is_torch_available() else ()
					UpperCAmelCase__:       Any  							=	{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
					UpperCAmelCase__:       Union[str, Any]  							=	False
					UpperCAmelCase__:       int  							=	False
					UpperCAmelCase__:       List[str]  							=	False
					UpperCAmelCase__:       Optional[Any]  							=	False






					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		TimmBackboneModelTester(self      )
						A__							:   List[Any]					       =		ConfigTester(self   ,	config_class=A__   ,	has_text_modality=A__      )






					def   __A      (			self      ):
						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   __A      (			self      ):
						A__							:   Union[str, Any]					       =		"""resnet18"""
						A__							:   Union[str, Any]					       =		"""microsoft/resnet-18"""

						A__							:   Optional[Any]					       =		AutoBackbone.from_pretrained(A__   ,	use_timm_backbone=A__      )
						A__							:   List[str]					       =		AutoBackbone.from_pretrained(A__      )

						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]      )

						A__							:   str					       =		AutoBackbone.from_pretrained(A__   ,	use_timm_backbone=A__   ,	out_indices=[1, 2, 3]      )
						A__							:   Optional[Any]					       =		AutoBackbone.from_pretrained(A__   ,	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   __A      (			self      ):
						pass






					@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""      )
					def   __A      (			self      ):
						pass






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






					@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""      )
					def   __A      (			self      ):
						pass






					@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""      )
					def   __A      (			self      ):
						pass






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






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






					@unittest.skip("""model weights aren't tied in TimmBackbone."""      )
					def   __A      (			self      ):
						pass






					@unittest.skip("""model weights aren't tied in TimmBackbone."""      )
					def   __A      (			self      ):
						pass






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






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






					@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""      )
					def   __A      (			self      ):
						pass






					@unittest.skip("""TimmBackbone doesn't support output_attentions."""      )
					def   __A      (			self      ):
						pass






					@unittest.skip("""Safetensors is not supported by timm."""      )
					def   __A      (			self      ):
						pass






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






					def   __A      (			self      ):
						A__       ,       A__							:   List[Any]					       =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:   Dict					       =		model_class(A__      )
							A__							:   Union[str, Any]					       =		inspect.signature(model.forward      )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A__							:   Dict					       =		[*signature.parameters.keys()]

							A__							:   Union[str, Any]					       =		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]   ,	A__      )






					def   __A      (			self      ):
						A__       ,       A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   Union[str, Any]					       =		True
						A__							:   Any					       =		self.has_attentions

						# no need to test all models as different heads yield the same functionality
						A__							:   Tuple					       =		self.all_model_classes[0]
						A__							:   List[Any]					       =		model_class(A__      )
						model.to(A__      )

						A__							:   Optional[Any]					       =		self._prepare_for_class(A__   ,	A__      )
						A__							:   int					       =		model(**A__      )
						A__							:   Any					       =		outputs[0][-1]

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

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

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

						self.assertIsNotNone(hidden_states.grad      )

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






					def   __A      (			self      ):
						A__       ,       A__							:   int					       =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:   Any					       =		model_class(A__      )
							model.to(A__      )
							model.eval()
							A__							:   Union[str, Any]					       =		model(**A__      )

							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
							A__							:   Optional[int]					       =		copy.deepcopy(A__      )
							A__							:   Optional[int]					       =		None
							A__							:   int					       =		model_class(A__      )
							model.to(A__      )
							model.eval()
							A__							:   Optional[int]					       =		model(**A__      )

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

							# Check backbone can be initialized with fresh weights
							A__							:   Any					       =		copy.deepcopy(A__      )
							A__							:   List[Any]					       =		False
							A__							:   Dict					       =		model_class(A__      )
							model.to(A__      )
							model.eval()
							A__							:   Optional[int]					       =		model(**A__      )






from typing import Any

def 						UpperCamelCase (lowercase_:			list )					->				list[Any]:
	if not input_list:
		return []
	A__							:   Any					       =		[input_list.count(lowercase_ ) for value in input_list]
	A__							:   List[Any]					       =		max(lowercase_ )  # Gets the maximum count in the input list.
	# Gets values of modes
	return sorted({input_list[i] for i, value in enumerate(lowercase_ ) if value == y} )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



