


from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor





class   __a      :
	def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=13    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=37    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=10    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=None    ,			) ->    int:




								'''simple docstring'''
								lowercase__:       str        =       parent
								lowercase__:       Optional[Any]        =       batch_size
								lowercase__:       List[Any]        =       image_size
								lowercase__:       Tuple        =       patch_size
								lowercase__:       str        =       num_channels
								lowercase__:       List[Any]        =       is_training
								lowercase__:       List[str]        =       use_labels
								lowercase__:       Union[str, Any]        =       hidden_size
								lowercase__:       List[str]        =       num_hidden_layers
								lowercase__:       Optional[Any]        =       num_attention_heads
								lowercase__:       int        =       intermediate_size
								lowercase__:       List[str]        =       hidden_act
								lowercase__:       Optional[int]        =       hidden_dropout_prob
								lowercase__:       Union[str, Any]        =       attention_probs_dropout_prob
								lowercase__:       List[str]        =       type_sequence_label_size
								lowercase__:       Union[str, Any]        =       initializer_range
								lowercase__:       int        =       scope

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								lowercase__:       List[str]        =       (image_size // patch_size) ** 2
								lowercase__:       List[str]        =       num_patches + 1
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




								'''simple docstring'''
								lowercase__:       Optional[int]        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

								lowercase__:       List[Any]        =       None
								if self.use_labels:
															lowercase__:       Optional[int]        =       ids_tensor([self.batch_size]    ,			self.type_sequence_label_size			)

								lowercase__:       List[Any]        =       self.get_config()

								return config, pixel_values, labels
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




								'''simple docstring'''
								return ViTConfig(
								    image_size=self.image_size    ,			patch_size=self.patch_size    ,			num_channels=self.num_channels    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			is_decoder=lowerCAmelCase__    ,			initializer_range=self.initializer_range    ,			)
	def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Dict:




								'''simple docstring'''
								lowercase__:       Optional[Any]        =       TFViTModel(config=lowerCAmelCase__			)
								lowercase__:       Union[str, Any]        =       model(lowerCAmelCase__    ,			training=lowerCAmelCase__			)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size)			)

								# Test with an image with different size than the one specified in config.
								lowercase__:       Optional[Any]        =       self.image_size // 2
								lowercase__:       Dict        =       pixel_values[:, :, :image_size, :image_size]
								lowercase__:       Any        =       model(lowerCAmelCase__    ,			interpolate_pos_encoding=lowerCAmelCase__    ,			training=lowerCAmelCase__			)
								lowercase__:       Optional[int]        =       (image_size // self.patch_size) ** 2 + 1
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, seq_length, self.hidden_size)			)
	def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Union[str, Any]:




								'''simple docstring'''
								lowercase__:       Optional[Any]        =       self.type_sequence_label_size
								lowercase__:       Dict        =       TFViTForImageClassification(lowerCAmelCase__			)
								lowercase__:       List[Any]        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__    ,			training=lowerCAmelCase__			)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

								# Test with an image with different size than the one specified in config.
								lowercase__:       Optional[Any]        =       self.image_size // 2
								lowercase__:       Optional[int]        =       pixel_values[:, :, :image_size, :image_size]
								lowercase__:       str        =       model(lowerCAmelCase__    ,			interpolate_pos_encoding=lowerCAmelCase__    ,			training=lowerCAmelCase__			)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

								# test greyscale images
								lowercase__:       List[str]        =       1
								lowercase__:       Any        =       TFViTForImageClassification(lowerCAmelCase__			)
								lowercase__:       List[Any]        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
								lowercase__:       Optional[int]        =       model(lowerCAmelCase__			)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




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





@require_tf
class   __a      (   __UpperCamelCase	,	__UpperCamelCase	,	unittest.TestCase				):
	__lowercase			:							Optional[Any]       					=			(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
	__lowercase			:							List[str]       					=			(
	    {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
	    if is_tf_available()
	    else {}
	)

	__lowercase			:							Tuple       					=			False
	__lowercase			:							List[Any]       					=			False
	__lowercase			:							int       					=			False
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




								'''simple docstring'''
								lowercase__:       Optional[Any]        =       TFViTModelTester(self			)
								lowercase__:       Dict        =       ConfigTester(self    ,			config_class=lowerCAmelCase__    ,			has_text_modality=lowerCAmelCase__    ,			hidden_size=37			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




								'''simple docstring'''
								self.config_tester.run_common_tests()
	@unittest.skip(reason='ViT does not use inputs_embeds'			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




								'''simple docstring'''
								pass
	@unittest.skip(reason='ViT does not use inputs_embeds'			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




								'''simple docstring'''
								pass
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




								'''simple docstring'''
								lowercase__:       Any        =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowercase__:       List[Any]        =       model_class(lowerCAmelCase__			)
															self.assertIsInstance(model.get_input_embeddings()    ,			(tf.keras.layers.Layer)			)
															lowercase__:       Any        =       model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(lowerCAmelCase__    ,			tf.keras.layers.Layer			)			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




								'''simple docstring'''
								lowercase__:       Dict        =       self.model_tester.prepare_config_and_inputs_for_common()

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

															lowercase__:       int        =       ['pixel_values']
															self.assertListEqual(arg_names[:1]    ,			lowerCAmelCase__			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




								'''simple docstring'''
								lowercase__:       Optional[int]        =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase__			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




								'''simple docstring'''
								lowercase__:       Union[str, Any]        =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
	@slow
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




								'''simple docstring'''
								lowercase__:       int        =       TFViTModel.from_pretrained('google/vit-base-patch16-224'			)
								self.assertIsNotNone(lowerCAmelCase__			)





def        snake_case_					(  )  ->    List[str]:
							lowercase__:       List[Any]        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							return image





@require_tf
@require_vision
class   __a      (   unittest.TestCase				):
	@cached_property
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




								'''simple docstring'''
								return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'			) if is_vision_available() else None
	@slow
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




								'''simple docstring'''
								lowercase__:       str        =       TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'			)

								lowercase__:       Tuple        =       self.default_image_processor
								lowercase__:       List[Any]        =       prepare_img()
								lowercase__:       List[str]        =       image_processor(images=lowerCAmelCase__    ,			return_tensors='tf'			)

								# forward pass
								lowercase__:       str        =       model(**lowerCAmelCase__			)

								# verify the logits
								lowercase__:       Optional[int]        =       tf.TensorShape((1, 1_000)			)
								self.assertEqual(outputs.logits.shape    ,			lowerCAmelCase__			)

								lowercase__:       Optional[Any]        =       tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]			)

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






from __future__ import annotations




def        snake_case_					(  snake_case      ,							snake_case					)  ->    list[str]:
       if nth_term == "":
              return [""]
       lowercase__:       Tuple        =       int(snake_case					)
       lowercase__:       int        =       int(snake_case					)
       lowercase__:       list[str]        =       []
       for temp in range(int(snake_case					)					):
              series.append(f'1 / {pow(temp + 1      ,							int(snake_case					)					)}' if series else '1'					)
       return series


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __lowerCAmelCase			=							int(input('''Enter the last number (nth term) of the P-Series'''))
 __lowerCAmelCase			=							int(input('''Enter the power for  P-Series'''))
 print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
 print(p_series(nth_term, power))


