






"""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 					_UpperCAmelCase       ( _A							):




					def   A  (				self     :						str   )  ->  Dict:
							lowercase_ : str							       =   self.config_class(**self.inputs_dict   )
							self.parent.assertTrue(hasattr(A    ,						'''embed_dim'''   )   )
							self.parent.assertTrue(hasattr(A    ,						'''num_heads'''   )   )





class 					_UpperCAmelCase       :




					def __init__(				self     :						Tuple    ,						A     :						Union[str, Any]    ,						A     :						Union[str, Any]=13    ,						A     :						Optional[int]=64    ,						A     :						Optional[int]=3    ,						A     :						Any=[16, 48, 96]    ,						A     :						Any=[1, 3, 6]    ,						A     :						str=[1, 2, 10]    ,						A     :						List[str]=[7, 3, 3]    ,						A     :						Dict=[4, 2, 2]    ,						A     :						List[str]=[2, 1, 1]    ,						A     :						List[Any]=[2, 2, 2]    ,						A     :						List[Any]=[False, False, True]    ,						A     :						str=[0.0, 0.0, 0.0]    ,						A     :						Dict=0.02    ,						A     :						Union[str, Any]=1e-12    ,						A     :						int=True    ,						A     :						Tuple=True    ,						A     :						Any=2    ,						)  ->  Union[str, Any]:
							lowercase_ : str							       =   parent
							lowercase_ : Optional[Any]							       =   batch_size
							lowercase_ : int							       =   image_size
							lowercase_ : Optional[Any]							       =   patch_sizes
							lowercase_ : Tuple							       =   patch_stride
							lowercase_ : List[Any]							       =   patch_padding
							lowercase_ : Optional[int]							       =   is_training
							lowercase_ : Optional[int]							       =   use_labels
							lowercase_ : Optional[Any]							       =   num_labels
							lowercase_ : int							       =   num_channels
							lowercase_ : List[Any]							       =   embed_dim
							lowercase_ : Optional[Any]							       =   num_heads
							lowercase_ : Union[str, Any]							       =   stride_kv
							lowercase_ : str							       =   depth
							lowercase_ : Tuple							       =   cls_token
							lowercase_ : List[str]							       =   attention_drop_rate
							lowercase_ : List[str]							       =   initializer_range
							lowercase_ : Optional[int]							       =   layer_norm_eps




					def   A  (				self     :						str   )  ->  Any:
							lowercase_ : List[str]							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

							lowercase_ : Any							       =   self.get_config()
							return config, pixel_values, labels




					def   A  (				self     :						Optional[int]   )  ->  Optional[Any]:
							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   A  (				self     :						List[str]    ,						A     :						Dict    ,						A     :						Optional[int]    ,						A     :						Tuple   )  ->  Any:
							lowercase_ : Optional[int]							       =   TFCvtModel(config=A   )
							lowercase_ : Any							       =   model(A    ,						training=A   )
							lowercase_ : List[str]							       =   (self.image_size, self.image_size)
							lowercase_       ,					lowercase_ : Optional[Any]							       =   image_size[0], image_size[1]
							for i in range(len(self.depth   )   ):
									lowercase_ : List[Any]							       =   floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1   )
									lowercase_ : List[Any]							       =   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   A  (				self     :						Tuple    ,						A     :						Optional[int]    ,						A     :						int    ,						A     :						Tuple   )  ->  Any:
							lowercase_ : Any							       =   self.num_labels
							lowercase_ : Union[str, Any]							       =   TFCvtForImageClassification(A   )
							lowercase_ : List[Any]							       =   model(A    ,						labels=A    ,						training=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )






					def   A  (				self     :						Optional[Any]   )  ->  Any:
							lowercase_ : Tuple							       =   self.prepare_config_and_inputs()
							lowercase_       ,					lowercase_       ,					lowercase_ : List[str]							       =   config_and_inputs
							lowercase_ : Union[str, Any]							       =   {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_tf
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   (
					    {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   False
					SCREAMING_SNAKE_CASE_		:				Dict							    =   False
					SCREAMING_SNAKE_CASE_		:				Dict							    =   False
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   False
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   False




					def   A  (				self     :						Optional[int]   )  ->  Tuple:
							lowercase_ : Union[str, Any]							       =   TFCvtModelTester(self   )
							lowercase_ : int							       =   TFCvtConfigTester(self    ,						config_class=A    ,						has_text_modality=A    ,						hidden_size=37   )




					def   A  (				self     :						Tuple   )  ->  Dict:
							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   A  (				self     :						Optional[Any]   )  ->  str:
							pass




					@unittest.skip(reason='''Cvt does not use inputs_embeds'''   )
					def   A  (				self     :						Dict   )  ->  Optional[Any]:
							pass




					@unittest.skip(reason='''Cvt does not support input and output embeddings'''   )
					def   A  (				self     :						Tuple   )  ->  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.'''    ,						)
					def   A  (				self     :						List[str]   )  ->  Optional[int]:
							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   A  (				self     :						Optional[Any]   )  ->  Union[str, Any]:
							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   A  (				self     :						Optional[int]   )  ->  Optional[int]:
							lowercase_ : Optional[Any]							       =   tf.keras.mixed_precision.Policy('''mixed_float16'''   )
							tf.keras.mixed_precision.set_global_policy(A   )
							super().test_keras_fit()
							tf.keras.mixed_precision.set_global_policy('''float32'''   )




					def   A  (				self     :						Dict   )  ->  List[str]:
							lowercase_       ,					lowercase_ : Any							       =   self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase_ : int							       =   ['''pixel_values''']
									self.assertListEqual(arg_names[:1]    ,						A   )




					def   A  (				self     :						Tuple   )  ->  str:
							def check_hidden_states_output(A     :						Dict    ,						A     :						List[Any]    ,						A     :						Optional[Any]   ):
									lowercase_ : Tuple							       =   model_class(A   )

									lowercase_ : Any							       =   model(**self._prepare_for_class(A    ,						A   )   )
									lowercase_ : Union[str, Any]							       =   outputs.hidden_states

									lowercase_ : Optional[int]							       =   len(self.model_tester.depth   )
									self.assertEqual(len(A   )    ,						A   )

									# 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,
									    ]    ,						)

							lowercase_       ,					lowercase_ : Tuple							       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase_ : Tuple							       =   True
									check_hidden_states_output(A    ,						A    ,						A   )

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

									check_hidden_states_output(A    ,						A    ,						A   )




					def   A  (				self     :						int   )  ->  List[str]:
							lowercase_ : Optional[int]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A   )




					def   A  (				self     :						Optional[int]   )  ->  Optional[Any]:
							lowercase_ : Any							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A   )






					@slow
					def   A  (				self     :						Any   )  ->  List[Any]:
							for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Optional[int]							       =   TFCvtModel.from_pretrained(A   )
									self.assertIsNotNone(A   )



def 						lowercase    (				):
		lowercase_ : int							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image






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




					@cached_property
					def   A  (				self     :						List[str]   )  ->  Optional[int]:
							return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )






					@slow
					def   A  (				self     :						str   )  ->  List[Any]:
							lowercase_ : List[str]							       =   TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

							lowercase_ : Optional[int]							       =   self.default_image_processor
							lowercase_ : str							       =   prepare_img()
							lowercase_ : Tuple							       =   image_processor(images=A    ,						return_tensors='''tf'''   )

							# forward pass
							lowercase_ : Union[str, Any]							       =   model(**A   )

							# verify the logits
							lowercase_ : Optional[int]							       =   tf.TensorShape((1, 10_00)   )
							self.assertEqual(outputs.logits.shape    ,						A   )

							lowercase_ : Union[str, Any]							       =   tf.constant([0.9285, 0.9015, -0.3150]   )
							self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,						A    ,						atol=1e-4   )   )







'''simple docstring'''



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]							)     ->		str:
     __lowerCamelCase   :	Tuple								=		0
     __lowerCamelCase   :	Optional[int]								=		len(UpperCAmelCase_							)

     for i in range(n - 1							):
          for j in range(i + 1      ,    UpperCAmelCase_							):
               if arr[i] > arr[j]:
                    num_inversions += 1

     return num_inversions



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		Optional[int]:
     if len(UpperCAmelCase_							) <= 1:
          return arr, 0
     __lowerCamelCase   :	str								=		len(UpperCAmelCase_							) // 2
     __lowerCamelCase   :	List[Any]								=		arr[0:mid]
     __lowerCamelCase   :	List[str]								=		arr[mid:]

     __lowerCamelCase				,       __lowerCamelCase   :	int								=		count_inversions_recursive(UpperCAmelCase_							)
     __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		count_inversions_recursive(UpperCAmelCase_							)
     __lowerCamelCase				,       __lowerCamelCase   :	Any								=		_count_cross_inversions(UpperCAmelCase_      ,    UpperCAmelCase_							)

     __lowerCamelCase   :	Optional[int]								=		inversion_p + inversions_q + cross_inversions
     return c, num_inversions



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				List[str]							)     ->		Optional[Any]:
     __lowerCamelCase   :	List[str]								=		[]
     __lowerCamelCase   :	Optional[int]								=		0
     while i < len(UpperCAmelCase_							) and j < len(UpperCAmelCase_							):
          if p[i] > q[j]:
               # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
               # These are all inversions. The claim emerges from the
               # property that P is sorted.
               num_inversion += len(UpperCAmelCase_							) - i
               r.append(q[j]							)
               j += 1
          else:
               r.append(p[i]							)
               i += 1

     if i < len(UpperCAmelCase_							):
          r.extend(p[i:]							)
     else:
          r.extend(q[j:]							)

     return r, num_inversion



def 			UpperCAmelCase__					(	)     ->		List[str]:
     __lowerCamelCase   :	Any								=		[10, 2, 1, 5, 5, 2, 11]

     # this arr has 8 inversions:
     # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

     __lowerCamelCase   :	Optional[Any]								=		count_inversions_bf(UpperCAmelCase_							)
     __lowerCamelCase				,       __lowerCamelCase   :	Dict								=		count_inversions_recursive(UpperCAmelCase_							)

     assert num_inversions_bf == num_inversions_recursive == 8

     print('number of inversions = '      ,    UpperCAmelCase_							)

     # testing an array with zero inversion (a sorted arr_1)

     arr_a.sort()
     __lowerCamelCase   :	Optional[Any]								=		count_inversions_bf(UpperCAmelCase_							)
     __lowerCamelCase				,       __lowerCamelCase   :	int								=		count_inversions_recursive(UpperCAmelCase_							)

     assert num_inversions_bf == num_inversions_recursive == 0
     print('number of inversions = '      ,    UpperCAmelCase_							)

     # an empty list should also have zero inversions
     __lowerCamelCase   :	Dict								=		[]
     __lowerCamelCase   :	Optional[Any]								=		count_inversions_bf(UpperCAmelCase_							)
     __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		count_inversions_recursive(UpperCAmelCase_							)

     assert num_inversions_bf == num_inversions_recursive == 0
     print('number of inversions = '      ,    UpperCAmelCase_							)


if __name__ == "__main__":
      main()






