

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Dict=3     , __lowerCamelCase					:			List[Any]=32     , __lowerCamelCase					:			Optional[Any]=3     , __lowerCamelCase					:			Optional[Any]=10     , __lowerCamelCase					:			Optional[int]=[10, 20, 30, 40]     , __lowerCamelCase					:			Any=[1, 1, 2, 1]     , __lowerCamelCase					:			int=True     , __lowerCamelCase					:			List[str]=True     , __lowerCamelCase					:			int="relu"     , __lowerCamelCase					:			Optional[int]=3     , __lowerCamelCase					:			Optional[int]=None     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   embeddings_size
       SCREAMING_SNAKE_CASE										=   hidden_sizes
       SCREAMING_SNAKE_CASE										=   depths
       SCREAMING_SNAKE_CASE										=   is_training
       SCREAMING_SNAKE_CASE										=   use_labels
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   num_labels
       SCREAMING_SNAKE_CASE										=   scope
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

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





   def      _snake_case		(       self					:			Optional[int]			):
       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      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			int     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   TFRegNetModel(config=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   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					:			str     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   self.num_labels
       SCREAMING_SNAKE_CASE										=   TFRegNetForImageClassification(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , labels=__lowerCamelCase     , training=__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)			)





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   config_and_inputs
       SCREAMING_SNAKE_CASE										=   {"pixel_values": pixel_values}
       return config, inputs_dict





@require_tf
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
   lowerCamelCase__				=      (
       {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
       if is_tf_available()
       else {}
   )

   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   TFRegNetModelTester(self			)
       SCREAMING_SNAKE_CASE										=   ConfigTester(self     , config_class=__lowerCamelCase     , has_text_modality=__lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       return





   @unittest.skip(reason="RegNet does not use inputs_embeds"			)
   def      _snake_case		(       self					:			Tuple			):
       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					:			str			):
       super().test_keras_fit()





   @unittest.skip(reason="RegNet does not support input and output embeddings"			)
   def      _snake_case		(       self					:			List[str]			):
       pass





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

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

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





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       def check_hidden_states_output(__lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			str			):
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   model(**self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase			)     , training=__lowerCamelCase			)

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

           SCREAMING_SNAKE_CASE										=   self.model_tester.num_stages
           self.assertEqual(len(__lowerCamelCase			)     , 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]     , )

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE										=   ["basic", "bottleneck"]
       for model_class in self.all_model_classes:
           for layer_type in layers_type:
               SCREAMING_SNAKE_CASE										=   layer_type
               SCREAMING_SNAKE_CASE										=   True
               check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

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

               check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       def check_equivalence(__lowerCamelCase					:			str     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Dict={}			):
           SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , return_dict=__lowerCamelCase     , **__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , return_dict=__lowerCamelCase     , **__lowerCamelCase			).to_tuple()

           def recursive_check(__lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Optional[int]			):
               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:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase			)
           check_equivalence(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase     , return_labels=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase     , return_labels=__lowerCamelCase			)
           check_equivalence(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

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

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





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





   @slow
   def      _snake_case		(       self					:			str			):
       for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE										=   TFRegNetModel.from_pretrained(__lowerCamelCase			)
           self.assertIsNotNone(__lowerCamelCase			)





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image





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







   '''simple docstring'''




   @cached_property
   def      _snake_case		(       self					:			Union[str, Any]			):
       return (
           AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
           if is_vision_available()
           else None
       )





   @slow
   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

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

       # forward pass
       SCREAMING_SNAKE_CASE										=   model(**__lowerCamelCase     , training=__lowerCamelCase			)

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

       SCREAMING_SNAKE_CASE										=   tf.constant([-0.4_180, -1.5_051, -3.4_836]			)

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






'''simple docstring'''

from __future__ import annotations







def    a__							(							lowercase   :	list,   lowercase   :	int )						->						List[Any]:





     """simple docstring"""

     if len(lowercase ) <= 1 or n <= 1:
          return

     insert_next(lowercase,   n - 1 )
     rec_insertion_sort(lowercase,   n - 1 )







def    a__							(							lowercase   :	list,   lowercase   :	int )						->						Optional[Any]:





     """simple docstring"""

     if index >= len(lowercase ) or collection[index - 1] <= collection[index]:
          return

     # Swaps adjacent elements since they are not in ascending order
     _UpperCamelCase       ,							_UpperCamelCase							       =			(
         collection[index],
         collection[index - 1],
     )

     insert_next(lowercase,   index + 1 )


if __name__ == "__main__":
   lowercase__		:     str			=							input('Enter integers separated by spaces: ')
   lowercase__		:     list[int]			=							[int(num) for num in numbers.split()]
   rec_insertion_sort(number_list, len(number_list))
   print(number_list)



