



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 SCREAMING_SNAKE_CASE__     :




    '''simple docstring'''






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






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
        A :       List[Any]          =   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,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   TimmBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        with torch.no_grad():
            A :       List[Any]          =   model(lowercase_		)
        self.parent.assertEqual(
            result.feature_map[-1].shape,		(self.batch_size, model.channels[-1], 14, 14),		)






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.prepare_config_and_inputs()
        A :       Optional[Any]          =   config_and_inputs
        A :       List[Any]          =   {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
@require_timm
class SCREAMING_SNAKE_CASE__     (		UpperCAmelCase__ ,						UpperCAmelCase__ ,						UpperCAmelCase__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      (TimmBackbone,) if is_torch_available() else ()
    __lowerCamelCase			: Optional[Any]					=      {"feature-extraction": TimmBackbone} if is_torch_available() else {}
    __lowerCamelCase			: Optional[int]					=      False
    __lowerCamelCase			: int					=      False
    __lowerCamelCase			: List[str]					=      False
    __lowerCamelCase			: str					=      False






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   TimmBackboneModelTester(self		)
        A :       Optional[int]          =   ConfigTester(self,		config_class=lowercase_,		has_text_modality=lowercase_		)






    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		):
        A :       Union[str, Any]          =   '''resnet18'''
        A :       List[Any]          =   '''microsoft/resnet-18'''

        A :       str          =   AutoBackbone.from_pretrained(lowercase_,		use_timm_backbone=lowercase_		)
        A :       str          =   AutoBackbone.from_pretrained(lowercase_		)

        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 :       Optional[int]          =   AutoBackbone.from_pretrained(lowercase_,		use_timm_backbone=lowercase_,		out_indices=[1, 2, 3]		)
        A :       Optional[Any]          =   AutoBackbone.from_pretrained(lowercase_,		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		):
        A :       Union[str, Any]          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            A :       Optional[int]          =   ['''pixel_values''']
            self.assertListEqual(arg_names[:1],		lowercase_		)






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.model_tester.prepare_config_and_inputs_for_common()
        A :       List[str]          =   True
        A :       Optional[Any]          =   self.has_attentions

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

        A :       Optional[Any]          =   self._prepare_for_class(lowercase_,		lowercase_		)
        A :       List[str]          =   model(**lowercase_		)
        A :       int          =   outputs[0][-1]

        # Encoder-/Decoder-only models
        A :       List[str]          =   outputs.hidden_states[0]
        hidden_states.retain_grad()

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

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

        self.assertIsNotNone(hidden_states.grad		)

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






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A :       int          =   model_class(lowercase_		)
            model.to(lowercase_		)
            model.eval()
            A :       int          =   model(**lowercase_		)

            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 :       List[str]          =   copy.deepcopy(lowercase_		)
            A :       int          =   None
            A :       Any          =   model_class(lowercase_		)
            model.to(lowercase_		)
            model.eval()
            A :       Union[str, Any]          =   model(**lowercase_		)

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

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










"""simple docstring"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


