


from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
    from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )
         self.parent.assertTrue(hasattr(_lowercase		,						"embed_dim" ) )
         self.parent.assertTrue(hasattr(_lowercase		,						"num_heads" ) )




class     lowercase__  :






  def __init__(					self      :					Union[str, Any]		,						_lowercase      :					Tuple		,						_lowercase      :					List[str]=13		,						_lowercase      :					Dict=64		,						_lowercase      :					Optional[int]=3		,						_lowercase      :					Any=[16, 48, 96]		,						_lowercase      :					Tuple=[1, 3, 6]		,						_lowercase      :					List[str]=[1, 2, 10]		,						_lowercase      :					Dict=[7, 3, 3]		,						_lowercase      :					int=[4, 2, 2]		,						_lowercase      :					Dict=[2, 1, 1]		,						_lowercase      :					Dict=[2, 2, 2]		,						_lowercase      :					Optional[int]=[False, False, True]		,						_lowercase      :					str=[0.0, 0.0, 0.0]		,						_lowercase      :					List[Any]=0.0_2		,						_lowercase      :					int=1E-12		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					List[str]=2		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     image_size
         UpperCAmelCase__										=     patch_sizes
         UpperCAmelCase__										=     patch_stride
         UpperCAmelCase__										=     patch_padding
         UpperCAmelCase__										=     is_training
         UpperCAmelCase__										=     use_labels
         UpperCAmelCase__										=     num_labels
         UpperCAmelCase__										=     num_channels
         UpperCAmelCase__										=     embed_dim
         UpperCAmelCase__										=     num_heads
         UpperCAmelCase__										=     stride_kv
         UpperCAmelCase__										=     depth
         UpperCAmelCase__										=     cls_token
         UpperCAmelCase__										=     attention_drop_rate
         UpperCAmelCase__										=     initializer_range
         UpperCAmelCase__										=     layer_norm_eps






  def 			_UpperCAmelCase  (					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:
                # create a random int32 tensor of given shape
                UpperCAmelCase__										=     ids_tensor([self.batch_size]		,						self.num_labels )

         UpperCAmelCase__										=     self.get_config()
         return config, pixel_values, labels






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         return CvtConfig(
             image_size=self.image_size		,						num_labels=self.num_labels		,						num_channels=self.num_channels		,						embed_dim=self.embed_dim		,						num_heads=self.num_heads		,						patch_sizes=self.patch_sizes		,						patch_padding=self.patch_padding		,						patch_stride=self.patch_stride		,						stride_kv=self.stride_kv		,						depth=self.depth		,						cls_token=self.cls_token		,						attention_drop_rate=self.attention_drop_rate		,						initializer_range=self.initializer_range		,						)






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[int]		,						_lowercase      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     TFCvtModel(config=_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						training=_lowercase )
         UpperCAmelCase__										=     (self.image_size, self.image_size)
         UpperCAmelCase__						,			UpperCAmelCase__										=     image_size[0], image_size[1]
         for i in range(len(self.depth ) ):
                UpperCAmelCase__										=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
                UpperCAmelCase__										=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
         self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.embed_dim[-1], height, width) )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_labels
         UpperCAmelCase__										=     TFCvtForImageClassification(_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						labels=_lowercase		,						training=_lowercase )
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels) )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """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__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
  A__=  (
      {'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
      if is_tf_available()
      else {}
  )
  A__=  False
  A__=  False
  A__=  False
  A__=  False
  A__=  False






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     TFCvtModelTester(self )
         UpperCAmelCase__										=     TFCvtConfigTester(self		,						config_class=_lowercase		,						has_text_modality=_lowercase		,						hidden_size=37 )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         self.config_tester.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()






  @unittest.skip(reason="Cvt does not output attentions" )
  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         pass






  @unittest.skip(reason="Cvt does not use inputs_embeds" )
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         pass






  @unittest.skip(reason="Cvt does not support input and output embeddings" )
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         pass






  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0		,						reason="TF does not support backprop for grouped convolutions on CPU."		,						)
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         super().test_dataset_conversion()






  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0		,						reason="TF does not support backprop for grouped convolutions on CPU."		,						)
  @slow
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         super().test_keras_fit()






  @unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8" )
  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     tf.keras.mixed_precision.Policy("mixed_float16" )
         tf.keras.mixed_precision.set_global_policy(_lowercase )
         super().test_keras_fit()
         tf.keras.mixed_precision.set_global_policy("float32" )






  def 			_UpperCAmelCase  (					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(_lowercase )
                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]		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         def check_hidden_states_output(_lowercase      :					Optional[Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[Any] ):
                UpperCAmelCase__										=     model_class(_lowercase )

                UpperCAmelCase__										=     model(**self._prepare_for_class(_lowercase		,						_lowercase ) )
                UpperCAmelCase__										=     outputs.hidden_states

                UpperCAmelCase__										=     len(self.model_tester.depth )
                self.assertEqual(len(_lowercase )		,						_lowercase )

                # verify the first hidden states (first block)
                self.assertListEqual(
                    list(hidden_states[0].shape[-3:] )		,						[
                        self.model_tester.embed_dim[0],
                        self.model_tester.image_size // 4,
                        self.model_tester.image_size // 4,
                    ]		,						)

         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                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 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowercase )






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__										=     TFCvtModel.from_pretrained(_lowercase )
                self.assertIsNotNone(_lowercase )





def __UpperCAmelCase       (		)						->					Any:



       '''simple docstring'''





       UpperCAmelCase__										=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
       return image




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






  @cached_property
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )






  @slow
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

         UpperCAmelCase__										=     self.default_image_processor
         UpperCAmelCase__										=     prepare_img()
         UpperCAmelCase__										=     image_processor(images=_lowercase		,						return_tensors="tf" )

         # forward pass
         UpperCAmelCase__										=     model(**_lowercase )

         # verify the logits
         UpperCAmelCase__										=     tf.TensorShape((1, 10_00) )
         self.assertEqual(outputs.logits.shape		,						_lowercase )

         UpperCAmelCase__										=     tf.constant([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0] )
         self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,						_lowercase		,						atol=1E-4 ) )









from __future__ import annotations

import math





def __UpperCAmelCase       (		__A ,						__A			)						->					float:



       '''simple docstring'''





       UpperCAmelCase__										=     u
       for i in range(1 ,						__A			):
              UpperCAmelCase__										=     temp * (u - i)
       return temp





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     int(input("enter the numbers of values: "			)			)
       UpperCAmelCase__										=     []
       for _ in range(__A			):
              y.append([]			)
       for i in range(__A			):
              for j in range(__A			):
                     y[i].append(__A			)
                     UpperCAmelCase__										=     0

       print("enter the values of parameters in a list: "			)
       UpperCAmelCase__										=     list(map(__A ,						input().split()			)			)

       print("enter the values of corresponding parameters: "			)
       for i in range(__A			):
              UpperCAmelCase__										=     float(input()			)

       UpperCAmelCase__										=     int(input("enter the value to interpolate: "			)			)
       UpperCAmelCase__										=     (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1 ,						__A			):
              for j in range(n - i			):
                     UpperCAmelCase__										=     y[j + 1][i - 1] - y[j][i - 1]

       UpperCAmelCase__										=     y[0][0]
       for i in range(1 ,						__A			):
              summ += (ucal(__A ,						__A			) * y[0][i]) / math.factorial(__A			)

       print(F"""the value at {value} is {summ}"""			)


if __name__ == "__main__":
    main()





