





'''simple docstring'''





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__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.config_class(**self.inputs_dict							)
       self.parent.assertTrue(hasattr(__magic_name__,	'''embed_dim'''							)							)
       self.parent.assertTrue(hasattr(__magic_name__,	'''num_heads'''							)							)






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=13,	__magic_name__=64,	__magic_name__=3,	__magic_name__=[16, 48, 96],	__magic_name__=[1, 3, 6],	__magic_name__=[1, 2, 10],	__magic_name__=[7, 3, 3],	__magic_name__=[4, 2, 2],	__magic_name__=[2, 1, 1],	__magic_name__=[2, 2, 2],	__magic_name__=[False, False, True],	__magic_name__=[0.0, 0.0, 0.0],	__magic_name__=0.02,	__magic_name__=1E-12,	__magic_name__=True,	__magic_name__=True,	__magic_name__=2,	)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   parent
       UpperCamelCase__							:       List[str]      =   batch_size
       UpperCamelCase__							:       Dict      =   image_size
       UpperCamelCase__							:       List[Any]      =   patch_sizes
       UpperCamelCase__							:       str      =   patch_stride
       UpperCamelCase__							:       List[str]      =   patch_padding
       UpperCamelCase__							:       Tuple      =   is_training
       UpperCamelCase__							:       Optional[Any]      =   use_labels
       UpperCamelCase__							:       List[str]      =   num_labels
       UpperCamelCase__							:       str      =   num_channels
       UpperCamelCase__							:       Optional[Any]      =   embed_dim
       UpperCamelCase__							:       Optional[int]      =   num_heads
       UpperCamelCase__							:       str      =   stride_kv
       UpperCamelCase__							:       List[Any]      =   depth
       UpperCamelCase__							:       Tuple      =   cls_token
       UpperCamelCase__							:       str      =   attention_drop_rate
       UpperCamelCase__							:       Tuple      =   initializer_range
       UpperCamelCase__							:       List[Any]      =   layer_norm_eps




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       str      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCamelCase__							:       Any      =   None
       if self.use_labels:
           # create a random int32 tensor of given shape
           UpperCamelCase__							:       int      =   ids_tensor([self.batch_size],	self.num_labels							)

       UpperCamelCase__							:       Optional[Any]      =   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,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   TFCvtModel(config=__magic_name__							)
       UpperCamelCase__							:       int      =   model(__magic_name__,	training=__magic_name__							)
       UpperCamelCase__							:       Tuple      =   (self.image_size, self.image_size)
       UpperCamelCase__							:       Optional[Any]      =   image_size[0], image_size[1]
       for i in range(len(self.depth							)							):
           UpperCamelCase__							:       Any      =   floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
           UpperCamelCase__							:       List[str]      =   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,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.num_labels
       UpperCamelCase__							:       Dict      =   TFCvtForImageClassification(__magic_name__							)
       UpperCamelCase__							:       int      =   model(__magic_name__,	labels=__magic_name__,	training=__magic_name__							)
       self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)							)




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self.prepare_config_and_inputs()
       UpperCamelCase__							:       Dict      =   config_and_inputs
       UpperCamelCase__							:       Optional[Any]      =   {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							List[str]     	=	(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
   a		:							int     	=	(
       {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
       if is_tf_available()
       else {}
   )
   a		:							Optional[Any]     	=	False
   a		:							Union[str, Any]     	=	False
   a		:							str     	=	False
   a		:							List[str]     	=	False
   a		:							Optional[int]     	=	False




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   TFCvtModelTester(self							)
       UpperCamelCase__							:       int      =   TFCvtConfigTester(self,	config_class=__magic_name__,	has_text_modality=__magic_name__,	hidden_size=37							)




   def    UpperCamelCase__						(					self							)				->   Any:






       """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							)				->   str:






       """simple docstring"""
       pass




   @unittest.skip(reason='''Cvt does not support input and output embeddings'''							)
   def    UpperCamelCase__						(					self							)				->   int:






       """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							)				->   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							)				->   Optional[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__							:       List[str]      =   tf.keras.mixed_precision.Policy('''mixed_float16'''							)
       tf.keras.mixed_precision.set_global_policy(__magic_name__							)
       super().test_keras_fit()
       tf.keras.mixed_precision.set_global_policy('''float32'''							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__							:       str      =   ['''pixel_values''']
           self.assertListEqual(arg_names[:1],	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       def check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   model(**self._prepare_for_class(__magic_name__,	__magic_name__							)							)
           UpperCamelCase__							:       Optional[Any]      =   outputs.hidden_states

           UpperCamelCase__							:       Any      =   len(self.model_tester.depth							)
           self.assertEqual(len(__magic_name__							),	__magic_name__							)

           # 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__							:       Optional[int]      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   True
           check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCamelCase__							:       int      =   True

           check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__magic_name__							)




   @slow
   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__							:       Tuple      =   TFCvtModel.from_pretrained(__magic_name__							)
           self.assertIsNotNone(__magic_name__							)




def 	lowerCAmelCase_					(						)   ->			Any:
    UpperCamelCase__							:       str      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
    return image






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

   '''simple docstring'''


   @cached_property
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)




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






       """simple docstring"""
       UpperCamelCase__							:       str      =   TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

       UpperCamelCase__							:       int      =   self.default_image_processor
       UpperCamelCase__							:       Union[str, Any]      =   prepare_img()
       UpperCamelCase__							:       Optional[int]      =   image_processor(images=__magic_name__,	return_tensors='''tf'''							)

       # forward pass
       UpperCamelCase__							:       Any      =   model(**__magic_name__							)

       # verify the logits
       UpperCamelCase__							:       Any      =   tf.TensorShape((1, 1000)							)
       self.assertEqual(outputs.logits.shape,	__magic_name__							)

       UpperCamelCase__							:       Dict      =   tf.constant([0.9285, 0.9015, -0.3150]							)
       self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(),	__magic_name__,	atol=1E-4							)							)








from __future__ import annotations

from math import gcd




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    int = 2	,     __UpperCAmelCase:    int = 1	,     __UpperCAmelCase:    int = 3	,     )   ->			int | None:
    # A value less than 2 can cause an infinite loop in the algorithm.
    if num < 2:
        raise ValueError('''The input value cannot be less than 2'''   )

    # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
    # algorithm struggles to find factors that are divisible by two.
    # As a workaround, we specifically check for two and even inputs.
    #   See: https://math.stackexchange.com/a/2856214/165820
    if num > 2 and num % 2 == 0:
        return 2

    # Pollard's Rho algorithm requires a function that returns pseudorandom
    # values between 0 <= X < ``num``.  It doesn't need to be random in the
    # sense that the output value is cryptographically secure or difficult
    # to calculate, it only needs to be random in the sense that all output
    # values should be equally likely to appear.
    # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
    # However, the success of Pollard's algorithm isn't guaranteed and is
    # determined in part by the initial seed and the chosen random function.
    # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
    # where ``C`` is a value that we can modify between each attempt.
    def rand_fn(__UpperCAmelCase:    int	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    int   ) -> int:
        return (pow(__UpperCAmelCase	,     2   ) + step) % modulus

    for _ in range(__UpperCAmelCase   ):
        # These track the position within the cycle detection logic.
        UpperCamelCase__							:       List[Any]      =   seed
        UpperCamelCase__							:       List[str]      =   seed

        while True:
            # At each iteration, the tortoise moves one step and the hare moves two.
            UpperCamelCase__							:       Optional[int]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
            UpperCamelCase__							:       List[str]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
            UpperCamelCase__							:       Optional[int]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

            # At some point both the tortoise and the hare will enter a cycle whose
            # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
            # the tortoise and hare will end up on the same value modulo ``p``.
            # We can detect when this happens because the position difference between
            # the tortoise and the hare will share a common divisor with ``num``.
            UpperCamelCase__							:       Optional[int]      =   gcd(hare - tortoise	,     __UpperCAmelCase   )

            if divisor == 1:
                # No common divisor yet, just keep searching.
                continue
            else:
                # We found a common divisor!
                if divisor == num:
                    # Unfortunately, the divisor is ``num`` itself and is useless.
                    break
                else:
                    # The divisor is a nontrivial factor of ``num``!
                    return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
        UpperCamelCase__							:       List[str]      =   hare

        # The new step value for the random function can just be incremented.
        # At first the results will be similar to what the old function would
        # have produced, but the value will quickly diverge after a bit.
        step += 1

    # We haven't found a divisor within the requested number of attempts.
    # We were unlucky or ``num`` itself is actually prime.
    return None


if __name__ == "__main__":
  import argparse

  UpperCAmelCase_  							=				argparse.ArgumentParser()
  parser.add_argument(
      'num',
      type=int,
      help='The value to find a divisor of',
  )
  parser.add_argument(
      '--attempts',
      type=int,
      default=3,
      help='The number of attempts before giving up',
  )
  UpperCAmelCase_  							=				parser.parse_args()

  UpperCAmelCase_  							=				pollard_rho(args.num, attempts=args.attempts)
  if divisor is None:
    print(F'''{args.num} is probably prime''')
  else:
    UpperCAmelCase_  							=				args.num // divisor
    print(F'''{args.num} = {divisor} * {quotient}''')

