




'''simple docstring'''







import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import RegNetForImageClassification, RegNetModel
   from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class     _a						:
       def __init__(			self		:			Union[str, Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			str=3     ,       lowercase		:			Tuple=32     ,       lowercase		:			str=3     ,       lowercase		:			int=10     ,       lowercase		:			List[str]=[10, 20, 30, 40]     ,       lowercase		:			str=[1, 1, 2, 1]     ,       lowercase		:			List[Any]=True     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			Optional[Any]="relu"     ,       lowercase		:			Dict=3     ,       lowercase		:			Tuple=None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	image_size
           UpperCAmelCase     =	num_channels
           UpperCAmelCase     =	embeddings_size
           UpperCAmelCase     =	hidden_sizes
           UpperCAmelCase     =	depths
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	scope
           UpperCAmelCase     =	len(lowercase       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           UpperCAmelCase     =	None
           if self.use_labels:
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.num_labels       )

           UpperCAmelCase     =	self.get_config()

           return config, pixel_values, labels
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           return RegNetConfig(
               num_channels=self.num_channels     ,       embeddings_size=self.embeddings_size     ,       hidden_sizes=self.hidden_sizes     ,       depths=self.depths     ,       hidden_act=self.hidden_act     ,       num_labels=self.num_labels     ,       )
       def 					A   (			self		:			int     ,       lowercase		:			Tuple     ,       lowercase		:			List[str]     ,       lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	RegNetModel(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase       )
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape     ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,       )
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[Any]     ,       lowercase		:			str     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	RegNetForImageClassification(lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)       )







       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	config_and_inputs
           UpperCAmelCase     =	{'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_torch
class     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  int            =      (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
       __a       :  Optional[int]            =      (
           {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
           if is_torch_available()
           else {}
       )

       __a       :  Optional[Any]            =      False
       __a       :  int            =      False
       __a       :  Any            =      False
       __a       :  Union[str, Any]            =      False
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	RegNetModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       has_text_modality=lowercase       )
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           self.create_and_test_config_common_properties()
           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		:			int       ):



           '''simple docstring'''

           return
       @unittest.skip(reason='''RegNet does not use inputs_embeds'''       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           pass
       @unittest.skip(reason='''RegNet does not support input and output embeddings'''       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase     =	['''pixel_values''']
               self.assertListEqual(arg_names[:1]     ,       lowercase       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     =	model_class(config=lowercase       )
               for name, module in model.named_modules():
                   if isinstance(lowercase     ,       (nn.BatchNormad, nn.GroupNorm)       ):
                       self.assertTrue(
                           torch.all(module.weight == 1       )     ,       msg=f"Parameter {name} of model {model_class} seems not properly initialized"     ,       )
                       self.assertTrue(
                           torch.all(module.bias == 0       )     ,       msg=f"Parameter {name} of model {model_class} seems not properly initialized"     ,       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           def check_hidden_states_output(lowercase		:			List[str]     ,       lowercase		:			int     ,       lowercase		:			Optional[int]       ):
               UpperCAmelCase     =	model_class(lowercase       )
               model.to(lowercase       )
               model.eval()

               with torch.no_grad():
                   UpperCAmelCase     =	model(**self._prepare_for_class(lowercase     ,       lowercase       )       )

               UpperCAmelCase     =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               UpperCAmelCase     =	self.model_tester.num_stages
               self.assertEqual(len(lowercase       )     ,       expected_num_stages + 1       )

               # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]       )     ,       [self.model_tester.image_size // 2, self.model_tester.image_size // 2]     ,       )

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase     =	['''basic''', '''bottleneck''']
           for model_class in self.all_model_classes:
               for layer_type in layers_type:
                   UpperCAmelCase     =	layer_type
                   UpperCAmelCase     =	True
                   check_hidden_states_output(lowercase     ,       lowercase     ,       lowercase       )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   UpperCAmelCase     =	True

                   check_hidden_states_output(lowercase     ,       lowercase     ,       lowercase       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowercase       )







       @slow
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     =	RegNetModel.from_pretrained(lowercase       )
               self.assertIsNotNone(lowercase       )






def 		snake_case_							():
    UpperCAmelCase     =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
    return image







@require_torch
@require_vision
class     _a						(			unittest.TestCase				):
       @cached_property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
               if is_vision_available()
               else None
           )







       @slow
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(lowercase       )

           UpperCAmelCase     =	self.default_image_processor
           UpperCAmelCase     =	prepare_img()
           UpperCAmelCase     =	image_processor(images=lowercase     ,       return_tensors='''pt'''       ).to(lowercase       )

           # forward pass
           with torch.no_grad():
               UpperCAmelCase     =	model(**lowercase       )

           # verify the logits
           UpperCAmelCase     =	torch.Size((1, 1_000)       )
           self.assertEqual(outputs.logits.shape     ,       lowercase       )

           UpperCAmelCase     =	torch.tensor([-0.4180, -1.5051, -3.4836]       ).to(lowercase       )

           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,       lowercase     ,       atol=1E-4       )       )










'''simple docstring'''







from __future__ import annotations





def 		snake_case_							(_a			:							str					,     _a			:							list[str] | None = None  ):
    UpperCAmelCase     =	word_bank or []
    # create a table
    UpperCAmelCase     =	len(_a  ) + 1

    UpperCAmelCase     =	[]
    for _ in range(_a  ):
        table.append([]  )
    # seed value
    UpperCAmelCase     =	[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(_a  ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(_a  )] == word:
                    UpperCAmelCase     =	[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(_a  )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(_a  )]:
        combination.reverse()

    return table[len(_a  )]


if __name__ == "__main__":
   print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
   print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
   print(
       all_construct(
           'hexagonosaurus',
           ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
       )
   )




