
"""simple docstring"""







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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
			__lowerCAmelCase    :		Optional[int]		    =   self.get_config()

			return config, pixel_values

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







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   config_and_inputs
			__lowerCAmelCase    :		Union[str, Any]		    =   {'pixel_values': pixel_values}
			return config, inputs_dict




@require_torch
@require_timm
class 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: List[str]            =			(TimmBackbone,) if is_torch_available() else ()
		A_		: List[Any]            =			{'feature-extraction': TimmBackbone} if is_torch_available() else {}
		A_		: Tuple            =			False
		A_		: List[str]            =			False
		A_		: int            =			False
		A_		: List[Any]            =			False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   TimmBackboneModelTester(self				)
			__lowerCAmelCase    :		Optional[int]		    =   ConfigTester(self ,	config_class=_SCREAMING_SNAKE_CASE ,	has_text_modality=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   'resnet18'
			__lowerCAmelCase    :		Optional[Any]		    =   'microsoft/resnet-18'

			__lowerCAmelCase    :		Optional[int]		    =   AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE ,	use_timm_backbone=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE				)

			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    :		Dict		    =   AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE ,	use_timm_backbone=_SCREAMING_SNAKE_CASE ,	out_indices=[1, 2, 3]				)
			__lowerCAmelCase    :		Optional[Any]		    =   AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE ,	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 							__lowerCamelCase							(   self				):
			pass

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

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

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

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

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

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

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

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

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

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

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

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

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

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		int		    =   model_class(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Tuple		    =   inspect.signature(model.forward				)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__lowerCAmelCase    :		Optional[Any]		    =   [*signature.parameters.keys()]

				__lowerCAmelCase    :		Any		    =   ['pixel_values']
				self.assertListEqual(arg_names[:1] ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase    :		Dict		    =   True
			__lowerCAmelCase    :		Optional[int]		    =   self.has_attentions

			# no need to test all models as different heads yield the same functionality
			__lowerCAmelCase    :		Union[str, Any]		    =   self.all_model_classes[0]
			__lowerCAmelCase    :		Union[str, Any]		    =   model_class(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)

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

			# Encoder-/Decoder-only models
			__lowerCAmelCase    :		Optional[int]		    =   outputs.hidden_states[0]
			hidden_states.retain_grad()

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

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

			self.assertIsNotNone(hidden_states.grad				)

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







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Tuple		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.eval()
				__lowerCAmelCase    :		List[Any]		    =   model(**_SCREAMING_SNAKE_CASE				)

				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    :		List[Any]		    =   copy.deepcopy(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		List[str]		    =   None
				__lowerCAmelCase    :		List[str]		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.eval()
				__lowerCAmelCase    :		Optional[int]		    =   model(**_SCREAMING_SNAKE_CASE				)

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

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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()