
from math import pi, sqrt


def       A       (					lowercase )   ->  float:
		'''simple docstring'''

		if num <= 0:
				raise ValueError('math domain error' )
		if num > 1_7_1.5:
				raise OverflowError('math range error' )
		elif num - int(lowercase ) not in (0, 0.5):
				raise NotImplementedError('num must be an integer or a half-integer' )
		elif num == 0.5:
				return sqrt(lowercase )
		else:
				return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )


def       A       (					)   ->  None:
		'''simple docstring'''

		assert gamma(0.5 ) == sqrt(lowercase )
		assert gamma(1 ) == 1.0
		assert gamma(2 ) == 1.0


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					_UpperCAmelCase					: List[str]      =       1.0
					while num:
										_UpperCAmelCase					: int      =       float(input("Gamma of: "))
										print(F'''gamma({num}) = {gamma(num)}''')
										print("\nEnter 0 to exit...")


import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import 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       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     self.config_class(**self.inputs_dict       )
				self.parent.assertTrue(hasattr(A_				,      'embed_dim'       )       )
				self.parent.assertTrue(hasattr(A_				,      'num_heads'       )       )






class 				lowercase					:



		def __init__( self				,      A_				,      A_=13				,      A_=64				,      A_=3				,      A_=[16, 48, 96]				,      A_=[1, 3, 6]				,      A_=[1, 2, 10]				,      A_=[7, 3, 3]				,      A_=[4, 2, 2]				,      A_=[2, 1, 1]				,      A_=[2, 2, 2]				,      A_=[False, False, True]				,      A_=[0.0, 0.0, 0.0]				,      A_=0.02				,      A_=1e-12				,      A_=True				,      A_=True				,      A_=2				,      )  ->					int:
				"""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[str]:
				"""simple docstring"""
				UpperCamelCase		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

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



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""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				,      A_				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     CvtModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				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				,      A_				,      A_				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     CvtForImageClassification(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     config_and_inputs
				UpperCamelCase		=     {'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       List[str]	     =				(CvtModel, CvtForImageClassification) if is_torch_available() else ()
		__lowercase       :       Tuple	     =				(
		    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__lowercase       :       Tuple	     =				False
		__lowercase       :       Union[str, Any]	     =				False
		__lowercase       :       Optional[Any]	     =				False
		__lowercase       :       List[str]	     =				False
		__lowercase       :       Dict	     =				False



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     CvtModelTester(self       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_				,      has_text_modality=A_				,      hidden_size=37       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self.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()



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				return



		@unittest.skip(reason='Cvt does not output attentions'       )
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not use inputs_embeds'       )
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not support input and output embeddings'       )
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				pass



		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(A_       )
						UpperCamelCase		=     inspect.signature(model.forward       )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						UpperCamelCase		=     [*signature.parameters.keys()]

						UpperCamelCase		=     ['pixel_values']
						self.assertListEqual(arg_names[:1]				,      A_       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				def check_hidden_states_output(A_				,      A_				,      A_       ):
						UpperCamelCase		=     model_class(A_       )
						model.to(A_       )
						model.eval()

						with torch.no_grad():
								UpperCamelCase		=     model(**self._prepare_for_class(A_				,      A_       )       )

						UpperCamelCase		=     outputs.hidden_states

						UpperCamelCase		=     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,
						    ]				,      )

				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(A_				,      A_				,      A_       )

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

						check_hidden_states_output(A_				,      A_				,      A_       )



		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(*A_       )



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase		=     CvtModel.from_pretrained(A_       )
						self.assertIsNotNone(A_       )






def       A       (					)   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image


@require_torch
@require_vision
class 				lowercase					(		unittest.TestCase   ):



		@cached_property
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )



		@slow
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(A_       )

				UpperCamelCase		=     self.default_image_processor
				UpperCamelCase		=     prepare_img()
				UpperCamelCase		=     image_processor(images=A_				,      return_tensors='pt'       ).to(A_       )

				# forward pass
				with torch.no_grad():
						UpperCamelCase		=     model(**A_       )

				# verify the logits
				UpperCamelCase		=     torch.Size((1, 1_000)       )
				self.assertEqual(outputs.logits.shape				,      A_       )

				UpperCamelCase		=     torch.tensor([0.9285, 0.9015, -0.3150]       ).to(A_       )

				self.assertTrue(torch.allclose(outputs.logits[0, :3]				,      A_				,      atol=1e-4       )       )
