





'''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_     :

    '''simple docstring'''


    def __init__(		self ,		A ,		A=None ,		A=None ,		A=None ,		A="resnet50" ,		A=3 ,		A=32 ,		A=3 ,		A=True ,		A=True ,		)		->   Any:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			out_indices if out_indices is not None else [4]
        _SCREAMING_SNAKE_CASE									=			stage_names
        _SCREAMING_SNAKE_CASE									=			out_features
        _SCREAMING_SNAKE_CASE									=			backbone
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			use_pretrained_backbone
        _SCREAMING_SNAKE_CASE									=			is_training


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
        _SCREAMING_SNAKE_CASE									=			self.get_config()

        return config, pixel_values


    def 			snake_case_(		self       )		->   List[str]:
        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 			snake_case_(		self ,		A ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			TimmBackbone(config=A       )
        model.to(A       )
        model.eval()
        with torch.no_grad():
            _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(
            result.feature_map[-1].shape ,		(self.batch_size, model.channels[-1], 14, 14) ,		)





    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.prepare_config_and_inputs()
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			config_and_inputs
        _SCREAMING_SNAKE_CASE									=			{"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_torch
@require_timm
class 					a_     (				snake_case_      ,    snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = (TimmBackbone,) if is_torch_available() else ()
    UpperCamelCase     = {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			TimmBackboneModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A ,		has_text_modality=A       )


    def 			snake_case_(		self       )		->   str:
        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 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			"""resnet18"""
        _SCREAMING_SNAKE_CASE									=			"""microsoft/resnet-18"""

        _SCREAMING_SNAKE_CASE									=			AutoBackbone.from_pretrained(A ,		use_timm_backbone=A       )
        _SCREAMING_SNAKE_CASE									=			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]       )

        _SCREAMING_SNAKE_CASE									=			AutoBackbone.from_pretrained(A ,		use_timm_backbone=A ,		out_indices=[1, 2, 3]       )
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self       )		->   Optional[Any]:
        pass


    @unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""       )
    def 			snake_case_(		self       )		->   Optional[Any]:
        pass


    @unittest.skip("""TimmBackbone initialization is managed on the timm side"""       )
    def 			snake_case_(		self       )		->   int:
        pass


    @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""       )
    def 			snake_case_(		self       )		->   int:
        pass


    @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""       )
    def 			snake_case_(		self       )		->   Any:
        pass


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


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


    @unittest.skip("""model weights aren't tied in TimmBackbone."""       )
    def 			snake_case_(		self       )		->   List[str]:
        pass


    @unittest.skip("""model weights aren't tied in TimmBackbone."""       )
    def 			snake_case_(		self       )		->   Union[str, Any]:
        pass


    @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""       )
    def 			snake_case_(		self       )		->   Optional[int]:
        pass


    @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""       )
    def 			snake_case_(		self       )		->   Union[str, Any]:
        pass


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


    @unittest.skip("""TimmBackbone doesn't support output_attentions."""       )
    def 			snake_case_(		self       )		->   Tuple:
        pass


    @unittest.skip("""Safetensors is not supported by timm."""       )
    def 			snake_case_(		self       )		->   Tuple:
        pass


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


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

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

            _SCREAMING_SNAKE_CASE									=			["""pixel_values"""]
            self.assertListEqual(arg_names[:1] ,		A       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			self.has_attentions

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

        _SCREAMING_SNAKE_CASE									=			self._prepare_for_class(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			model(**A       )
        _SCREAMING_SNAKE_CASE									=			outputs[0][-1]

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

        if self.has_attentions:
            _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )
            model.to(A       )
            model.eval()
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			copy.deepcopy(A       )
            _SCREAMING_SNAKE_CASE									=			None
            _SCREAMING_SNAKE_CASE									=			model_class(A       )
            model.to(A       )
            model.eval()
            _SCREAMING_SNAKE_CASE									=			model(**A       )

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

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







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A      :    List[str]		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def __init__(   self							:			Optional[int]					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



             warnings.warn(
                 "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                 " use BeitImageProcessor instead."					,      __lowerCamelCase					,      )
             super().__init__(*__lowerCamelCase					,      **__lowerCamelCase       )


