



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		_lowercase		:				Union[str, Any]      		="0.12"  # assumed parallelism: 8


if is_torch_available():
		import torch

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       Union[str, Any]						,       _lowercase    :       Optional[Any]=None)							->	Any:







			"""simple docstring"""
			if rng is None:
						a__       :						Tuple							=  random.Random()

			a__       :						str							=  1
			for dim in shape:
						total_dims *= dim

			a__       :						Dict							=  []
			for _ in range(_lowercase):
						values.append(rng.randint(0						,       vocab_size - 1))

			a__       :						int							=  np.array(_lowercase						,       dtype=jnp.intaa).reshape(_lowercase)

			return output

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       int=None)							->	int:







			"""simple docstring"""
			a__       :						Dict							=  ids_tensor(_lowercase						,       vocab_size=2						,       rng=_lowercase)
			# make sure that at least one token is attended to for each batch
			a__       :						Union[str, Any]							=  1
			return attn_mask

@require_flax
class        snake_case__    :







						"""simple docstring"""




						__lowerCAmelCase					:Any      =					None
						__lowerCAmelCase					:int      =					()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__	,						a__       :						Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()

									# cut to half length & take max batch_size 3
									a__       :						Optional[int]							=  2
									a__       :						str							=  inputs["""input_ids"""].shape[-1] // 2
									a__       :						Dict							=  inputs["""input_ids"""][:max_batch_size, :sequence_length]

									a__       :						int							=  jnp.ones_like(__lowercase  )
									a__       :						Any							=  attention_mask[:max_batch_size, :sequence_length]

									# generate max 5 tokens
									a__       :						Optional[Any]							=  input_ids.shape[-1] + 5
									if config.eos_token_id is not None and config.pad_token_id is None:
												# hack to allow generate for models such as GPT2 as is done in `generate()`
												a__       :						Dict							=  config.eos_token_id
									return config, input_ids, attention_mask, max_length

						@is_pt_flax_cross_test
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						List[str]							=  self._get_input_ids_and_config()
									a__       :						List[str]							=  False
									a__       :						Optional[int]							=  max_length
									a__       :						List[Any]							=  0

									for model_class in self.all_generative_model_classes:
												a__       :						Optional[int]							=  model_class(__lowercase  )

												a__       :						Optional[int]							=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
												a__       :						int							=  getattr(__lowercase ,       __lowercase  )
												a__       :						Any							=  pt_model_class(__lowercase  ).eval()
												a__       :						Union[str, Any]							=  load_flax_weights_in_pytorch_model(__lowercase ,       flax_model.params  )

												a__       :						Optional[Any]							=  flax_model.generate(__lowercase  ).sequences
												a__       :						List[str]							=  pt_model.generate(torch.tensor(__lowercase ,       dtype=torch.long  )  )

												if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
															a__       :						Tuple							=  flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

												self.assertListEqual(pt_generation_outputs.numpy().tolist() ,       flax_generation_outputs.tolist()  )

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




									a__	,						a__	,						a__	,						a__       :						List[Any]							=  self._get_input_ids_and_config()
									a__       :						str							=  False
									a__       :						Dict							=  max_length

									for model_class in self.all_generative_model_classes:
												a__       :						str							=  model_class(__lowercase  )

												a__       :						Dict							=  model.generate(__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						str							=  jit(model.generate  )
												a__       :						Optional[Any]							=  jit_generate(__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						List[str]							=  self._get_input_ids_and_config()
									a__       :						Any							=  True
									a__       :						int							=  max_length

									for model_class in self.all_generative_model_classes:
												a__       :						Optional[Any]							=  model_class(__lowercase  )

												a__       :						List[Any]							=  model.generate(__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						List[Any]							=  jit(model.generate  )
												a__       :						int							=  jit_generate(__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						Optional[int]							=  self._get_input_ids_and_config()
									a__       :						Union[str, Any]							=  False
									a__       :						int							=  max_length
									a__       :						Any							=  2

									for model_class in self.all_generative_model_classes:
												a__       :						int							=  model_class(__lowercase  )

												a__       :						Tuple							=  model.generate(__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						Dict							=  jit(model.generate  )
												a__       :						Tuple							=  jit_generate(__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						Optional[int]							=  self._get_input_ids_and_config()
									a__       :						Union[str, Any]							=  False
									a__       :						Tuple							=  max_length
									a__       :						int							=  2
									a__       :						Dict							=  2

									for model_class in self.all_generative_model_classes:
												a__       :						Union[str, Any]							=  model_class(__lowercase  )

												a__       :						List[Any]							=  model.generate(__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[0] ,       input_ids.shape[0] * config.num_return_sequences  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						List[str]							=  self._get_input_ids_and_config()
									a__       :						str							=  True
									a__       :						Tuple							=  max_length
									a__       :						Optional[Any]							=  0.8
									a__       :						Any							=  1_0
									a__       :						List[Any]							=  0.3
									a__       :						List[str]							=  1
									a__       :						Optional[int]							=  8
									a__       :						List[str]							=  9

									for model_class in self.all_generative_model_classes:
												a__       :						Optional[int]							=  model_class(__lowercase  )

												a__       :						Any							=  model.generate(__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						Optional[int]							=  jit(model.generate  )
												a__       :						int							=  jit_generate(__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						Dict							=  self._get_input_ids_and_config()
									a__       :						List[str]							=  max_length
									a__       :						Tuple							=  1
									a__       :						Optional[int]							=  8
									a__       :						Any							=  9

									for model_class in self.all_generative_model_classes:
												a__       :						List[Any]							=  model_class(__lowercase  )

												a__       :						Any							=  model.generate(__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						Optional[Any]							=  jit(model.generate  )
												a__       :						Tuple							=  jit_generate(__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						int							=  self._get_input_ids_and_config()
									a__       :						Tuple							=  max_length
									a__       :						List[Any]							=  2
									a__       :						Optional[Any]							=  1
									a__       :						List[str]							=  8
									a__       :						Tuple							=  9

									for model_class in self.all_generative_model_classes:
												a__       :						str							=  model_class(__lowercase  )

												a__       :						Tuple							=  model.generate(__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						Any							=  jit(model.generate  )
												a__       :						Optional[int]							=  jit_generate(__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						str							=  self._get_input_ids_and_config()

									# pad attention mask on the left
									a__       :						str							=  attention_mask.at[(0, 0)].set(0  )

									a__       :						Optional[Any]							=  False
									a__       :						int							=  max_length

									for model_class in self.all_generative_model_classes:
												a__       :						Union[str, Any]							=  model_class(__lowercase  )

												a__       :						List[Any]							=  model.generate(__lowercase ,       attention_mask=__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						List[str]							=  jit(model.generate  )
												a__       :						Optional[int]							=  jit_generate(__lowercase ,       attention_mask=__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )

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




									a__	,						a__	,						a__	,						a__       :						List[str]							=  self._get_input_ids_and_config()

									# pad attention mask on the left
									a__       :						Union[str, Any]							=  attention_mask.at[(0, 0)].set(0  )

									a__       :						Dict							=  True
									a__       :						Dict							=  max_length

									for model_class in self.all_generative_model_classes:
												a__       :						Dict							=  model_class(__lowercase  )

												a__       :						Dict							=  model.generate(__lowercase ,       attention_mask=__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						List[str]							=  jit(model.generate  )
												a__       :						Union[str, Any]							=  jit_generate(__lowercase ,       attention_mask=__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__	,						a__	,						a__	,						a__       :						Dict							=  self._get_input_ids_and_config()

									# pad attention mask on the left
									a__       :						int							=  attention_mask.at[(0, 0)].set(0  )

									a__       :						Any							=  2
									a__       :						int							=  max_length

									for model_class in self.all_generative_model_classes:
												a__       :						List[Any]							=  model_class(__lowercase  )

												a__       :						Union[str, Any]							=  model.generate(__lowercase ,       attention_mask=__lowercase  ).sequences
												self.assertEqual(generation_outputs.shape[-1] ,       __lowercase  )

												a__       :						Any							=  jit(model.generate  )
												a__       :						List[str]							=  jit_generate(__lowercase ,       attention_mask=__lowercase  ).sequences

												self.assertListEqual(generation_outputs.tolist() ,       jit_generation_outputs.tolist()  )




@require_flax
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""  )
									a__       :						Optional[int]							=  FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""  )

									a__       :						List[Any]							=  """Hello world"""
									a__       :						Dict							=  tokenizer(__lowercase ,       return_tensors="""np"""  ).input_ids

									# typos are quickly detected (the correct argument is `do_sample`)
									with self.assertRaisesRegex(__lowercase ,       """do_samples"""  ):
												model.generate(__lowercase ,       do_samples=__lowercase  )

									# arbitrary arguments that will not be used anywhere are also not accepted
									with self.assertRaisesRegex(__lowercase ,       """foo"""  ):
												a__       :						Any							=  {"""foo""": """bar"""}
												model.generate(__lowercase ,       **__lowercase  )









from __future__ import annotations

import math

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       int)							->	float:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  u
			for i in range(1						,       _lowercase):
						a__       :						Optional[int]							=  temp * (u - i)
			return temp

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						Tuple							=  int(input("""enter the numbers of values: """))
			a__       :						list[list[float]]							=  []
			for _ in range(_lowercase):
						y.append([])
			for i in range(_lowercase):
						for j in range(_lowercase):
									y[i].append(_lowercase)
									a__       :						Optional[Any]							=  0

			print("""enter the values of parameters in a list: """)
			a__       :						List[Any]							=  list(map(_lowercase						,       input().split()))

			print("""enter the values of corresponding parameters: """)
			for i in range(_lowercase):
						a__       :						Optional[Any]							=  float(input())

			a__       :						Tuple							=  int(input("""enter the value to interpolate: """))
			a__       :						int							=  (value - x[0]) / (x[1] - x[0])

			# for calculating forward difference table

			for i in range(1						,       _lowercase):
						for j in range(n - i):
									a__       :						int							=  y[j + 1][i - 1] - y[j][i - 1]

			a__       :						Optional[int]							=  y[0][0]
			for i in range(1						,       _lowercase):
						summ += (ucal(_lowercase						,       _lowercase) * y[0][i]) / math.factorial(_lowercase)

			print(F'''the value at {value} is {summ}''')


if __name__ == "__main__":
		main()




