





def     a_       (					lowerCAmelCase_ :     int,      lowerCAmelCase_ :     int      ):
   return "\n".join(
       F"""{number} * {i} = {number * i}""" for i in range(1,      number_of_terms + 1      )      )


if __name__ == "__main__":
     print(multiplication_table(number=5, number_of_terms=10))





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 	__lowercase					:





      def __init__(		self    :       str       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[int]=3       ,							__lowerCamelCase    :       Union[str, Any]=3_2       ,							__lowerCamelCase    :       List[str]=3       ,							__lowerCamelCase    :       Dict=1_0       ,							__lowerCamelCase    :       Optional[int]=[1_0, 2_0, 3_0, 4_0]       ,							__lowerCamelCase    :       Optional[int]=[1, 1, 2, 1]       ,							__lowerCamelCase    :       int=True       ,							__lowerCamelCase    :       str=True       ,							__lowerCamelCase    :       str="relu"       ,							__lowerCamelCase    :       str=3       ,							__lowerCamelCase    :       int=None       ,							)     -> Dict:






         """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(__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[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 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







         return ResNetConfig(
             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       ,							image_size=self.image_size       ,							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Tuple							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModel(config=__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         # 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 // 3_2, self.image_size // 3_2)       ,							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       int							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.num_labels
         UpperCAmelCase      					=  TFResNetForImageClassification(__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase       ,							labels=__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> Tuple:






         """simple docstring"""







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






@require_tf
class 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      UpperCamelCase										=							(
          {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModelTester(self							)
         UpperCAmelCase      					=  ConfigTester(self       ,							config_class=__lowerCamelCase       ,							has_text_modality=__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """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 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         return





      @unittest.skip(reason="""ResNet does not use inputs_embeds"""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> Optional[int]:






         """simple docstring"""







         pass





      @unittest.skip(reason="""ResNet does not support input and output embeddings"""							)
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Tuple:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCAmelCase      					=  ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[Any]							)     -> List[str]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase    :       str       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Union[str, Any]							):
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  model(**self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase							)							)

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

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

            # ResNet'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 // 4, self.model_tester.image_size // 4]       ,							)

         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(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

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

               check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> int:






         """simple docstring"""







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





      @slow
      def 							_lowercase					(		self    :       Tuple							)     -> List[str]:






         """simple docstring"""







         for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase      					=  TFResNetModel.from_pretrained(__lowerCamelCase							)
            self.assertIsNotNone(__lowerCamelCase							)

def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







@require_tf
@require_vision
class 	__lowercase					(	unittest.TestCase	):





      @cached_property
      def 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







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





      @slow
      def 							_lowercase					(		self    :       Dict							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

         UpperCAmelCase      					=  self.default_image_processor
         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""tf"""							)

         # forward pass
         UpperCAmelCase      					=  model(**__lowerCamelCase							)

         # verify the logits
         UpperCAmelCase      					=  tf.TensorShape((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  tf.constant([-11.1_069, -9.7_877, -8.3_777]							)

         self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,							__lowerCamelCase       ,							atol=1e-4							)							)



