


import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
	import jax

	from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class   __a      (   unittest.TestCase				):
	def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=13    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=5    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=37    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=10    ,			lowerCAmelCase__=0.0_2    ,			) ->    Optional[int]:




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

								# 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__:       Optional[int]        =       num_patches + 1
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




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

								lowercase__:       Optional[Any]        =       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    ,			)

								return config, pixel_values
	def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Tuple:




								'''simple docstring'''
								lowercase__:       int        =       FlaxViTModel(config=lowerCAmelCase__			)
								lowercase__:       Tuple        =       model(lowerCAmelCase__			)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								lowercase__:       Optional[Any]        =       (self.image_size, self.image_size)
								lowercase__:       List[str]        =       (self.patch_size, self.patch_size)
								lowercase__:       List[Any]        =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, num_patches + 1, self.hidden_size)			)
	def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Optional[int]:




								'''simple docstring'''
								lowercase__:       str        =       self.type_sequence_label_size
								lowercase__:       Dict        =       FlaxViTForImageClassification(config=lowerCAmelCase__			)
								lowercase__:       Any        =       model(lowerCAmelCase__			)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

								# test greyscale images
								lowercase__:       List[Any]        =       1
								lowercase__:       int        =       FlaxViTForImageClassification(lowerCAmelCase__			)

								lowercase__:       List[Any]        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
								lowercase__:       List[Any]        =       model(lowerCAmelCase__			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




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





@require_flax
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
	__lowercase			:							str       					=			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    None:




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




								'''simple docstring'''
								self.config_tester.run_common_tests()
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




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




								'''simple docstring'''
								lowercase__:       Any        =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




								'''simple docstring'''
								lowercase__:       Union[str, Any]        =       self.model_tester.prepare_config_and_inputs_for_common()

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

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




								'''simple docstring'''
								lowercase__:       Union[str, Any]        =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__			):
																						lowercase__:       List[Any]        =       self._prepare_for_class(lowerCAmelCase__    ,			lowerCAmelCase__			)
																						lowercase__:       Tuple        =       model_class(lowerCAmelCase__			)

																						@jax.jit
																						def model_jitted(lowerCAmelCase__    ,			**lowerCAmelCase__			):
																													return model(pixel_values=lowerCAmelCase__    ,			**lowerCAmelCase__			)

																						with self.subTest('JIT Enabled'			):
																													lowercase__:       Tuple        =       model_jitted(**lowerCAmelCase__			).to_tuple()

																						with self.subTest('JIT Disabled'			):
																													with jax.disable_jit():
																																				lowercase__:       Dict        =       model_jitted(**lowerCAmelCase__			).to_tuple()

																						self.assertEqual(len(lowerCAmelCase__			)    ,			len(lowerCAmelCase__			)			)
																						for jitted_output, output in zip(lowerCAmelCase__    ,			lowerCAmelCase__			):
																													self.assertEqual(jitted_output.shape    ,			output.shape			)
	@slow
	def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




								'''simple docstring'''
								for model_class_name in self.all_model_classes:
															lowercase__:       Optional[int]        =       model_class_name.from_pretrained('google/vit-base-patch16-224'			)
															lowercase__:       List[Any]        =       model(np.ones((1, 3, 224, 224)			)			)
															self.assertIsNotNone(lowerCAmelCase__			)






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))


