





'''simple docstring'''


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor

class 				lowerCAmelCase_						:




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=13     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=3     ,       _lowerCAmelCase=4     ,       _lowerCAmelCase=[10, 20, 30, 40]     ,       _lowerCAmelCase=[2, 2, 3, 2]     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=37     ,       _lowerCAmelCase="gelu"     ,       _lowerCAmelCase=10     ,       _lowerCAmelCase=0.02     ,       _lowerCAmelCase=3     ,       _lowerCAmelCase=None     ,       )      ->							int:
     _lowerCAmelCase		     = parent
     _lowerCAmelCase		     = batch_size
     _lowerCAmelCase		     = image_size
     _lowerCAmelCase		     = num_channels
     _lowerCAmelCase		     = num_stages
     _lowerCAmelCase		     = hidden_sizes
     _lowerCAmelCase		     = depths
     _lowerCAmelCase		     = is_training
     _lowerCAmelCase		     = use_labels
     _lowerCAmelCase		     = intermediate_size
     _lowerCAmelCase		     = hidden_act
     _lowerCAmelCase		     = type_sequence_label_size
     _lowerCAmelCase		     = initializer_range
     _lowerCAmelCase		     = scope




 def 							_snake_case				(		self						)      ->							List[Any]:
     _lowerCAmelCase		     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     _lowerCAmelCase		     = None
     if self.use_labels:
         _lowerCAmelCase		     = ids_tensor([self.batch_size]     ,       self.type_sequence_label_size						)

     _lowerCAmelCase		     = self.get_config()

     return config, pixel_values, labels




 def 							_snake_case				(		self						)      ->							Tuple:
     return ConvNextConfig(
         num_channels=self.num_channels     ,       hidden_sizes=self.hidden_sizes     ,       depths=self.depths     ,       num_stages=self.num_stages     ,       hidden_act=self.hidden_act     ,       is_decoder=_lowerCAmelCase     ,       initializer_range=self.initializer_range     ,       )




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							List[Any]:
     _lowerCAmelCase		     = TFConvNextModel(config=_lowerCAmelCase						)
     _lowerCAmelCase		     = model(_lowerCAmelCase     ,       training=_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 // 32, self.image_size // 32)     ,       )




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = self.type_sequence_label_size
     _lowerCAmelCase		     = TFConvNextForImageClassification(_lowerCAmelCase						)
     _lowerCAmelCase		     = model(_lowerCAmelCase     ,       labels=_lowerCAmelCase     ,       training=_lowerCAmelCase						)
     self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)						)





 def 							_snake_case				(		self						)      ->							Dict:
     _lowerCAmelCase		     = self.prepare_config_and_inputs()
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = config_and_inputs
     _lowerCAmelCase		     = {"pixel_values": pixel_values}
     return config, inputs_dict







@require_tf
class 				lowerCAmelCase_						(						__magic_name__  ,__magic_name__  ,unittest.TestCase						):
 __lowerCamelCase  :							Tuple    					=   (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
 __lowerCamelCase  :							Any    					=   (
     {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
     if is_tf_available()
     else {}
 )

 __lowerCamelCase  :							Optional[int]    					=   False
 __lowerCamelCase  :							List[str]    					=   False
 __lowerCamelCase  :							Dict    					=   False
 __lowerCamelCase  :							int    					=   False
 __lowerCamelCase  :							str    					=   False




 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = TFConvNextModelTester(self						)
     _lowerCAmelCase		     = ConfigTester(
         self     ,       config_class=_lowerCAmelCase     ,       has_text_modality=_lowerCAmelCase     ,       hidden_size=37     ,       )




 @unittest.skip(reason="ConvNext does not use inputs_embeds"						)
 def 							_snake_case				(		self						)      ->							Optional[int]:
     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."     ,       )
 @slow
 def 							_snake_case				(		self						)      ->							List[str]:
     super().test_keras_fit()




 @unittest.skip(reason="ConvNext does not support input and output embeddings"						)
 def 							_snake_case				(		self						)      ->							Optional[int]:
     pass




 def 							_snake_case				(		self						)      ->							List[str]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

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

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




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase						)




 @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 							_snake_case				(		self						)      ->							Optional[int]:
     super().test_dataset_conversion()




 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     def check_hidden_states_output(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						):
         _lowerCAmelCase		     = model_class(_lowerCAmelCase						)

         _lowerCAmelCase		     = model(**self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase						)						)
         _lowerCAmelCase		     = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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

         # ConvNext'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]     ,       )

     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase		     = True
         check_hidden_states_output(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)

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

         check_hidden_states_output(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Optional[Any]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

     def check_equivalence(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase={}						):
         _lowerCAmelCase		     = model(_lowerCAmelCase     ,       return_dict=_lowerCAmelCase     ,       **_lowerCAmelCase						)
         _lowerCAmelCase		     = model(_lowerCAmelCase     ,       return_dict=_lowerCAmelCase     ,       **_lowerCAmelCase						).to_tuple()

         def recursive_check(_lowerCAmelCase     ,       _lowerCAmelCase						):
             if isinstance(_lowerCAmelCase     ,       (List, Tuple)						):
                 for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase     ,       _lowerCAmelCase						):
                     recursive_check(_lowerCAmelCase     ,       _lowerCAmelCase						)
             elif tuple_object is None:
                 return
             else:
                 self.assertTrue(
                     all(tf.equal(_lowerCAmelCase     ,       _lowerCAmelCase						)						)     ,       msg=(
                         "Tuple and dict output are not equal. Difference:"
                         f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}'''
                     )     ,       )

             recursive_check(_lowerCAmelCase     ,       _lowerCAmelCase						)

     for model_class in self.all_model_classes:
         _lowerCAmelCase		     = model_class(_lowerCAmelCase						)

         _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase						)
         _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase						)
         check_equivalence(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)

         _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase     ,       return_labels=_lowerCAmelCase						)
         _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase     ,       return_labels=_lowerCAmelCase						)
         check_equivalence(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)

         _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase						)
         _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase						)
         check_equivalence(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       {"output_hidden_states": True}						)

         _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase     ,       return_labels=_lowerCAmelCase						)
         _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase     ,       return_labels=_lowerCAmelCase						)
         check_equivalence(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       {"output_hidden_states": True}						)




 def 							_snake_case				(		self						)      ->							Dict:
     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)





 @slow
 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase		     = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
     self.assertIsNotNone(_lowerCAmelCase						)







def 		__a():



    '''simple docstring'''





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





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




 @cached_property
 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None





 @slow
 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

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

     # forward pass
     _lowerCAmelCase		     = model(**_lowerCAmelCase						)

     # verify the logits
     _lowerCAmelCase		     = tf.TensorShape((1, 1000)						)
     self.assertEqual(outputs.logits.shape     ,       _lowerCAmelCase						)

     _lowerCAmelCase		     = tf.constant([-0.0260, -0.4739, 0.1911]						)

     tf.debugging.assert_near(outputs.logits[0, :3]     ,       _lowerCAmelCase     ,       atol=1E-4						)









'''simple docstring'''


import math


def 		__a(SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    if number == 0:
        _lowerCAmelCase		     = "Zero"
        return words
    else:
        _lowerCAmelCase		     = math.logaa(SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = digits + 1
        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "One"
        _lowerCAmelCase		     = "Two"
        _lowerCAmelCase		     = "Three"
        _lowerCAmelCase		     = "Four"
        _lowerCAmelCase		     = "Five"
        _lowerCAmelCase		     = "Six"
        _lowerCAmelCase		     = "Seven"
        _lowerCAmelCase		     = "Eight"
        _lowerCAmelCase		     = "Nine"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "Twenty"
        _lowerCAmelCase		     = "Thirty"
        _lowerCAmelCase		     = "Forty"
        _lowerCAmelCase		     = "Fifty"
        _lowerCAmelCase		     = "Sixty"
        _lowerCAmelCase		     = "Seventy"
        _lowerCAmelCase		     = "Eighty"
        _lowerCAmelCase		     = "Ninety"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Ten"
        _lowerCAmelCase		     = "Eleven"
        _lowerCAmelCase		     = "Twelve"
        _lowerCAmelCase		     = "Thirteen"
        _lowerCAmelCase		     = "Fourteen"
        _lowerCAmelCase		     = "Fifteen"
        _lowerCAmelCase		     = "Sixteen"
        _lowerCAmelCase		     = "Seventeen"
        _lowerCAmelCase		     = "Eighteen"
        _lowerCAmelCase		     = "Nineteen"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Hundred,"
        _lowerCAmelCase		     = "Thousand,"
        _lowerCAmelCase		     = "Lakh,"
        _lowerCAmelCase		     = "Crore,"

        _lowerCAmelCase		     = number
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = 0
        _lowerCAmelCase		     = int(SCREAMING_SNAKE_CASE_      )
        while counter < digits:
            _lowerCAmelCase		     = temp_num % 10
            if counter % 2 == 0:
                _lowerCAmelCase		     = ""
                if counter in placevalue and current != 0:
                    _lowerCAmelCase		     = placevalue[counter]
                if counter == 2:
                    _lowerCAmelCase		     = singles[current] + addition + words
                elif counter == 0:
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words

                else:
                    _lowerCAmelCase		     = doubles[current] + addition + words

            else:
                if counter == 1:
                    if current == 1:
                        _lowerCAmelCase		     = teens[number % 10] + words
                    else:
                        _lowerCAmelCase		     = ""
                        if counter in placevalue:
                            _lowerCAmelCase		     = placevalue[counter]
                        _lowerCAmelCase		     = doubles[current] + addition + words
                else:
                    _lowerCAmelCase		     = ""
                    if counter in placevalue:
                        if current == 0 and ((temp_num % 100) // 10) == 0:
                            _lowerCAmelCase		     = ""
                        else:
                            _lowerCAmelCase		     = placevalue[counter]
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words
            counter += 1
            _lowerCAmelCase		     = temp_num // 10
    return words


if __name__ == "__main__":
       import doctest

       doctest.testmod()


