






"""simple docstring"""





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

							__lowerCamelCase							=  '0.12'  # assumed parallelism: 8


if is_torch_available():
							import torch



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    Any=None  )    ->							Tuple:
						if rng is None:
												__magic_name__:	Union[str, Any]							=       random.Random()

						__magic_name__:	Tuple							=       1
						for dim in shape:
												total_dims *= dim

						__magic_name__:	int							=       []
						for _ in range(__UpperCAmelCase  ):
												values.append(rng.randint(0      ,   vocab_size - 1  )  )

						__magic_name__:	int							=       np.array(__UpperCAmelCase      ,   dtype=jnp.intaa  ).reshape(__UpperCAmelCase  )

						return output



def   a    (     __UpperCAmelCase      :    Any      ,   __UpperCAmelCase      :    Optional[int]=None  )    ->							int:
						__magic_name__:	Tuple							=       ids_tensor(__UpperCAmelCase      ,   vocab_size=2      ,   rng=__UpperCAmelCase  )
						# make sure that at least one token is attended to for each batch
						__magic_name__:	List[str]							=       1
						return attn_mask



@require_flax
class       __A		:
					UpperCAmelCase__								=				None
					UpperCAmelCase__								=				()

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Tuple:
											__magic_name__,			__magic_name__:	Optional[int]							=       self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											__magic_name__:	Tuple							=       2
											__magic_name__:	Tuple							=       inputs["""input_ids"""].shape[-1] // 2
											__magic_name__:	Tuple							=       inputs["""input_ids"""][:max_batch_size, :sequence_length]

											__magic_name__:	Any							=       jnp.ones_like(__snake_case					)
											__magic_name__:	Any							=       attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											__magic_name__:	int							=       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()`
																	__magic_name__:	Dict							=       config.eos_token_id
											return config, input_ids, attention_mask, max_length

					@is_pt_flax_cross_test
					def   lowerCamelCase__   (			self  :      str					)			->							str:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Union[str, Any]							=       self._get_input_ids_and_config()
											__magic_name__:	Optional[Any]							=       False
											__magic_name__:	List[Any]							=       max_length
											__magic_name__:	Optional[Any]							=       0

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	Optional[Any]							=       model_class(__snake_case					)

																	__magic_name__:	Any							=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	__magic_name__:	Any							=       getattr(__snake_case     ,		__snake_case					)
																	__magic_name__:	List[Any]							=       pt_model_class(__snake_case					).eval()
																	__magic_name__:	Tuple							=       load_flax_weights_in_pytorch_model(__snake_case     ,		flax_model.params					)

																	__magic_name__:	Any							=       flax_model.generate(__snake_case					).sequences
																	__magic_name__:	int							=       pt_model.generate(torch.tensor(__snake_case     ,		dtype=torch.long					)					)

																	if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																							__magic_name__:	Any							=       flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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

					def   lowerCamelCase__   (			self  :      Any					)			->							Optional[Any]:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Dict							=       self._get_input_ids_and_config()
											__magic_name__:	List[Any]							=       False
											__magic_name__:	str							=       max_length

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	Any							=       model_class(__snake_case					)

																	__magic_name__:	Optional[int]							=       model.generate(__snake_case					).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,		__snake_case					)

																	__magic_name__:	Union[str, Any]							=       jit(model.generate					)
																	__magic_name__:	int							=       jit_generate(__snake_case					).sequences

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

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Tuple:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Optional[int]							=       self._get_input_ids_and_config()
											__magic_name__:	Union[str, Any]							=       True
											__magic_name__:	Any							=       max_length

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	Any							=       model_class(__snake_case					)

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

																	__magic_name__:	Optional[Any]							=       jit(model.generate					)
																	__magic_name__:	Tuple							=       jit_generate(__snake_case					).sequences

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

					def   lowerCamelCase__   (			self  :      Tuple					)			->							List[Any]:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	str							=       self._get_input_ids_and_config()
											__magic_name__:	Tuple							=       False
											__magic_name__:	str							=       max_length
											__magic_name__:	List[str]							=       2

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	Optional[Any]							=       model_class(__snake_case					)

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

																	__magic_name__:	Dict							=       jit(model.generate					)
																	__magic_name__:	List[Any]							=       jit_generate(__snake_case					).sequences

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

					def   lowerCamelCase__   (			self  :      str					)			->							List[str]:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       self._get_input_ids_and_config()
											__magic_name__:	List[Any]							=       False
											__magic_name__:	int							=       max_length
											__magic_name__:	Tuple							=       2
											__magic_name__:	Optional[int]							=       2

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	int							=       model_class(__snake_case					)

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

					def   lowerCamelCase__   (			self  :      Dict					)			->							List[Any]:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Tuple							=       self._get_input_ids_and_config()
											__magic_name__:	Any							=       True
											__magic_name__:	Optional[int]							=       max_length
											__magic_name__:	int							=       0.8
											__magic_name__:	Any							=       1_0
											__magic_name__:	Dict							=       0.3
											__magic_name__:	List[Any]							=       1
											__magic_name__:	Any							=       8
											__magic_name__:	List[str]							=       9

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	Optional[int]							=       model_class(__snake_case					)

																	__magic_name__:	Optional[Any]							=       model.generate(__snake_case					).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,		__snake_case					)

																	__magic_name__:	Dict							=       jit(model.generate					)
																	__magic_name__:	int							=       jit_generate(__snake_case					).sequences

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

					def   lowerCamelCase__   (			self  :      str					)			->							str:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Any							=       self._get_input_ids_and_config()
											__magic_name__:	Optional[Any]							=       max_length
											__magic_name__:	str							=       1
											__magic_name__:	Tuple							=       8
											__magic_name__:	str							=       9

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	Any							=       model_class(__snake_case					)

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

																	__magic_name__:	str							=       jit(model.generate					)
																	__magic_name__:	Tuple							=       jit_generate(__snake_case					).sequences

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

					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       self._get_input_ids_and_config()
											__magic_name__:	Optional[int]							=       max_length
											__magic_name__:	List[str]							=       2
											__magic_name__:	List[Any]							=       1
											__magic_name__:	Dict							=       8
											__magic_name__:	Union[str, Any]							=       9

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	Any							=       model_class(__snake_case					)

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

																	__magic_name__:	Tuple							=       jit(model.generate					)
																	__magic_name__:	Union[str, Any]							=       jit_generate(__snake_case					).sequences

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

					def   lowerCamelCase__   (			self  :      List[str]					)			->							Dict:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Dict							=       self._get_input_ids_and_config()

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

											__magic_name__:	int							=       False
											__magic_name__:	Tuple							=       max_length

											for model_class in self.all_generative_model_classes:
																	__magic_name__:	Tuple							=       model_class(__snake_case					)

																	__magic_name__:	Any							=       model.generate(__snake_case     ,		attention_mask=__snake_case					).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,		__snake_case					)

																	__magic_name__:	Dict							=       jit(model.generate					)
																	__magic_name__:	List[Any]							=       jit_generate(__snake_case     ,		attention_mask=__snake_case					).sequences

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

					def   lowerCamelCase__   (			self  :      Any					)			->							Tuple:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       self._get_input_ids_and_config()

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

											__magic_name__:	Dict							=       True
											__magic_name__:	List[str]							=       max_length

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

																	__magic_name__:	Optional[Any]							=       model.generate(__snake_case     ,		attention_mask=__snake_case					).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,		__snake_case					)

																	__magic_name__:	int							=       jit(model.generate					)
																	__magic_name__:	int							=       jit_generate(__snake_case     ,		attention_mask=__snake_case					).sequences

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






					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[Any]:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       self._get_input_ids_and_config()

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

											__magic_name__:	int							=       2
											__magic_name__:	str							=       max_length

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

																	__magic_name__:	List[str]							=       model.generate(__snake_case     ,		attention_mask=__snake_case					).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,		__snake_case					)

																	__magic_name__:	Optional[int]							=       jit(model.generate					)
																	__magic_name__:	str							=       jit_generate(__snake_case     ,		attention_mask=__snake_case					).sequences

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



@require_flax
class       __A		(   unittest.TestCase			):

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[int]:
											__magic_name__:	List[Any]							=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""					)
											__magic_name__:	List[Any]							=       FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)

											__magic_name__:	Union[str, Any]							=       """Hello world"""
											__magic_name__:	List[str]							=       tokenizer(__snake_case     ,		return_tensors="""np"""					).input_ids

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

											# arbitrary arguments that will not be used anywhere are also not accepted
											with self.assertRaisesRegex(__snake_case     ,		"""foo"""					):
																	__magic_name__:	List[str]							=       {"""foo""": """bar"""}
																	model.generate(__snake_case     ,		**__snake_case					)











"""simple docstring"""





def   a    (     __UpperCAmelCase      :    int = 1_0_0  )    ->							int:
						__magic_name__:	str							=       0
						__magic_name__:	Any							=       0
						for i in range(1      ,   n + 1  ):
												sum_of_squares += i**2
												sum_of_ints += i
						return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
							print(f'''{solution() = }''')



