'''simple docstring'''

import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
						import torch




@require_flax
class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_=14							, snake_case_=7							, snake_case_=True							, snake_case_=True							, snake_case_=True							, snake_case_=99							, snake_case_=32							, snake_case_=5							, snake_case_=4							, snake_case_=37							, snake_case_="gelu"							, snake_case_=0.1							, snake_case_=0.1							, snake_case_=512							, snake_case_=0.02							, snake_case_=None							, ):
									snake_case   :					List[str]							=						parent
									snake_case   :					Any							=						batch_size
									snake_case   :					List[Any]							=						seq_length
									snake_case   :					Union[str, Any]							=						is_training
									snake_case   :					List[Any]							=						use_input_mask
									snake_case   :					Optional[int]							=						use_labels
									snake_case   :					Dict							=						vocab_size
									snake_case   :					int							=						d_model
									snake_case   :					List[Any]							=						num_hidden_layers
									snake_case   :					List[Any]							=						num_attention_heads
									snake_case   :					Optional[int]							=						ffn_dim
									snake_case   :					List[str]							=						activation_function
									snake_case   :					Tuple							=						activation_dropout
									snake_case   :					Dict							=						attention_dropout
									snake_case   :					Optional[Any]							=						max_position_embeddings
									snake_case   :					Optional[Any]							=						initializer_range
									snake_case   :					List[Any]							=						None
									snake_case   :					Optional[int]							=						0
									snake_case   :					int							=						2
									snake_case   :					Union[str, Any]							=						1



			def      lowerCamelCase   (       self				):
									snake_case   :					Optional[int]							=						np.clip(ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size				)							, 3							, self.vocab_size				)

									snake_case   :					str							=						None
									if self.use_input_mask:
															snake_case   :					Optional[Any]							=						random_attention_mask([self.batch_size, self.seq_length]				)

									snake_case   :					str							=						XGLMConfig(
									    vocab_size=self.vocab_size							, d_model=self.hidden_size							, num_layers=self.num_hidden_layers							, attention_heads=self.num_attention_heads							, ffn_dim=self.ffn_dim							, activation_function=self.activation_function							, activation_dropout=self.activation_dropout							, attention_dropout=self.attention_dropout							, max_position_embeddings=self.max_position_embeddings							, initializer_range=self.initializer_range							, use_cache=__UpperCamelCase							, bos_token_id=self.bos_token_id							, eos_token_id=self.eos_token_id							, pad_token_id=self.pad_token_id							, )

									return (config, input_ids, input_mask)



			def      lowerCamelCase   (       self				):
									snake_case   :					List[Any]							=						self.prepare_config_and_inputs()
									snake_case							,				snake_case							,				snake_case   :					int							=						config_and_inputs
									snake_case   :					Union[str, Any]							=						{"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict



			def      lowerCamelCase   (       self				):
									snake_case							,				snake_case							,				snake_case   :					int							=						self.prepare_config_and_inputs()

									snake_case   :					Any							=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
									snake_case   :					List[Any]							=						ids_tensor([self.batch_size, self.seq_length]							, vocab_size=2				)

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					Any							=						20
									snake_case   :					Union[str, Any]							=						model_class_name(__UpperCamelCase				)

									snake_case   :					List[Any]							=						model.init_cache(input_ids.shape[0]							, __UpperCamelCase				)
									snake_case   :					Any							=						jnp.ones((input_ids.shape[0], max_decoder_length)							, dtype="i4"				)

									snake_case   :					Tuple							=						jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1				)[None, :]							, (input_ids.shape[0], input_ids.shape[-1] - 1)				)
									snake_case   :					str							=						model(
									    input_ids[:, :-1]							, attention_mask=__UpperCamelCase							, past_key_values=__UpperCamelCase							, position_ids=__UpperCamelCase							, )

									snake_case   :					List[str]							=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							, dtype="i4"				)
									snake_case   :					Tuple							=						model(
									    input_ids[:, -1:]							, attention_mask=__UpperCamelCase							, past_key_values=outputs_cache.past_key_values							, position_ids=__UpperCamelCase							, )

									snake_case   :					List[str]							=						model(__UpperCamelCase				)

									snake_case   :					List[Any]							=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
									self.parent.assertTrue(diff < 1e-3							, msg=F"""Max diff is {diff}"""				)






			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					Tuple							=						20
									snake_case   :					List[Any]							=						model_class_name(__UpperCamelCase				)

									snake_case   :					Tuple							=						jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]							, axis=-1							, )

									snake_case   :					Union[str, Any]							=						model.init_cache(input_ids.shape[0]							, __UpperCamelCase				)
									snake_case   :					Optional[Any]							=						jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1				)[None, :]							, (input_ids.shape[0], input_ids.shape[-1] - 1)				)

									snake_case   :					Any							=						model(
									    input_ids[:, :-1]							, attention_mask=__UpperCamelCase							, past_key_values=__UpperCamelCase							, position_ids=__UpperCamelCase							, )
									snake_case   :					Any							=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							, dtype="i4"				)
									snake_case   :					Optional[Any]							=						model(
									    input_ids[:, -1:]							, past_key_values=outputs_cache.past_key_values							, attention_mask=__UpperCamelCase							, position_ids=__UpperCamelCase							, )

									snake_case   :					Optional[Any]							=						model(__UpperCamelCase							, attention_mask=__UpperCamelCase				)
									snake_case   :					Optional[int]							=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
									self.parent.assertTrue(diff < 1e-3							, msg=F"""Max diff is {diff}"""				)







@require_sentencepiece
@require_flax
class 		SCREAMING_SNAKE_CASE_				(UpperCAmelCase_       ,							UpperCAmelCase_       ,							unittest.TestCase			):



			"""simple docstring"""






			__magic_name__   :							Union[str, Any]             =					(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			__magic_name__   :							int             =					(FlaxXGLMForCausalLM,) if is_flax_available() else ()



			def      lowerCamelCase   (       self				):
									snake_case   :					Optional[int]							=						FlaxXGLMModelTester(self				)



			def      lowerCamelCase   (       self				):
									for model_class_name in self.all_model_classes:
															snake_case							,				snake_case							,				snake_case   :					Tuple							=						self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(__UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase				)



			def      lowerCamelCase   (       self				):
									for model_class_name in self.all_model_classes:
															snake_case							,				snake_case							,				snake_case   :					Optional[int]							=						self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase				)



			@slow
			def      lowerCamelCase   (       self				):
									snake_case   :					Any							=						XGLMTokenizer.from_pretrained("XGLM"							, padding_side="left"				)
									snake_case   :					Optional[int]							=						tokenizer(["Hello this is a long string", "Hey"]							, return_tensors="np"							, padding=__UpperCamelCase							, truncation=__UpperCamelCase				)

									snake_case   :					str							=						FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"				)
									snake_case   :					List[str]							=						1
									snake_case   :					Optional[int]							=						False

									snake_case   :					Optional[Any]							=						jax.jit(model.generate				)

									snake_case   :					Optional[Any]							=						jit_generate(inputs["input_ids"]							, attention_mask=inputs["attention_mask"]				).sequences

									snake_case   :					List[Any]							=						tokenizer.batch_decode(__UpperCamelCase							, skip_special_tokens=__UpperCamelCase				)

									snake_case   :					Optional[int]							=						[
									    "Hello this is a long string of questions, but I'm not sure if I'm",
									    "Hey, I'm a newbie to the forum and I'",
									]

									self.assertListEqual(__UpperCamelCase							, __UpperCamelCase				)



			@is_pt_flax_cross_test
			def      lowerCamelCase   (       self				):
									snake_case							,				snake_case   :					List[str]							=						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																					# prepare inputs
																					snake_case   :					int							=						self._prepare_for_class(__UpperCamelCase							, __UpperCamelCase				)
																					snake_case   :					Tuple							=						{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					snake_case   :					Union[str, Any]							=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					snake_case   :					Any							=						getattr(__UpperCamelCase							, __UpperCamelCase				)

																					snake_case							,				snake_case   :					str							=						pt_inputs["input_ids"].shape
																					snake_case   :					Optional[int]							=						np.random.randint(0							, seq_length - 1							, size=(batch_size,)				)
																					for batch_idx, start_index in enumerate(__UpperCamelCase				):
																											snake_case   :					Tuple							=						0
																											snake_case   :					Optional[Any]							=						1
																											snake_case   :					Optional[int]							=						0
																											snake_case   :					List[Any]							=						1
																					snake_case   :					Any							=						pt_model_class(__UpperCamelCase				).eval()
																					# Flax models don't use the `use_cache` option and cache is not returned as a default.
																					# So we disable `use_cache` here for PyTorch model.
																					snake_case   :					Optional[Any]							=						False
																					snake_case   :					List[Any]							=						model_class(__UpperCamelCase							, dtype=jnp.floataa				)
																					snake_case   :					Union[str, Any]							=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()							, __UpperCamelCase				)
																					snake_case   :					List[str]							=						fx_state

																					with torch.no_grad():
																											snake_case   :					Optional[Any]							=						pt_model(**__UpperCamelCase				).to_tuple()

																					snake_case   :					List[Any]							=						fx_model(**__UpperCamelCase				).to_tuple()
																					self.assertEqual(len(__UpperCamelCase				)							, len(__UpperCamelCase				)							, "Output lengths differ between Flax and PyTorch"				)
																					for fx_output, pt_output in zip(__UpperCamelCase							, __UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]							, pt_output[:, -1].numpy()							, 4e-2				)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(__UpperCamelCase				)
																											snake_case   :					Tuple							=						model_class.from_pretrained(__UpperCamelCase							, from_pt=__UpperCamelCase				)

																					snake_case   :					List[Any]							=						fx_model_loaded(**__UpperCamelCase				).to_tuple()
																					self.assertEqual(
																					    len(__UpperCamelCase				)							, len(__UpperCamelCase				)							, "Output lengths differ between Flax and PyTorch"				)
																					for fx_output_loaded, pt_output in zip(__UpperCamelCase							, __UpperCamelCase				):
																											self.assert_almost_equals(fx_output_loaded[:, -1]							, pt_output[:, -1].numpy()							, 4e-2				)



			@is_pt_flax_cross_test
			def      lowerCamelCase   (       self				):
									snake_case							,				snake_case   :					List[Any]							=						self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																					# prepare inputs
																					snake_case   :					Any							=						self._prepare_for_class(__UpperCamelCase							, __UpperCamelCase				)
																					snake_case   :					List[Any]							=						{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					snake_case   :					Any							=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					snake_case   :					Optional[Any]							=						getattr(__UpperCamelCase							, __UpperCamelCase				)

																					snake_case   :					List[str]							=						pt_model_class(__UpperCamelCase				).eval()
																					snake_case   :					List[str]							=						False
																					snake_case   :					List[str]							=						model_class(__UpperCamelCase							, dtype=jnp.floataa				)

																					snake_case   :					Tuple							=						load_flax_weights_in_pytorch_model(__UpperCamelCase							, fx_model.params				)
																					snake_case							,				snake_case   :					Dict							=						pt_inputs["input_ids"].shape
																					snake_case   :					Optional[Any]							=						np.random.randint(0							, seq_length - 1							, size=(batch_size,)				)
																					for batch_idx, start_index in enumerate(__UpperCamelCase				):
																											snake_case   :					List[Any]							=						0
																											snake_case   :					Tuple							=						1
																											snake_case   :					Tuple							=						0
																											snake_case   :					Dict							=						1

																					# make sure weights are tied in PyTorch
																					pt_model.tie_weights()

																					with torch.no_grad():
																											snake_case   :					Any							=						pt_model(**__UpperCamelCase				).to_tuple()

																					snake_case   :					Optional[int]							=						fx_model(**__UpperCamelCase				).to_tuple()
																					self.assertEqual(len(__UpperCamelCase				)							, len(__UpperCamelCase				)							, "Output lengths differ between Flax and PyTorch"				)
																					for fx_output, pt_output in zip(__UpperCamelCase							, __UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]							, pt_output[:, -1].numpy()							, 4e-2				)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(__UpperCamelCase				)
																											snake_case   :					List[Any]							=						pt_model_class.from_pretrained(__UpperCamelCase							, from_flax=__UpperCamelCase				)

																					with torch.no_grad():
																											snake_case   :					str							=						pt_model_loaded(**__UpperCamelCase				).to_tuple()

																					self.assertEqual(
																					    len(__UpperCamelCase				)							, len(__UpperCamelCase				)							, "Output lengths differ between Flax and PyTorch"				)
																					for fx_output, pt_output in zip(__UpperCamelCase							, __UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]							, pt_output[:, -1].numpy()							, 4e-2				)






			@slow
			def      lowerCamelCase   (       self				):
									for model_class_name in self.all_model_classes:
															snake_case   :					Union[str, Any]							=						model_class_name.from_pretrained("facebook/xglm-564M"				)
															snake_case   :					List[str]							=						model(np.ones((1, 1)				)				)
															self.assertIsNotNone(__UpperCamelCase				)






'''simple docstring'''

from math import sqrt


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		list[int]:



						"""simple docstring"""

						snake_case   :					Dict							=						[1]
						for i in range(2,			int(sqrt(_lowerCAmelCase)) + 1,			1):
												if number % i == 0:
																		values.append(_lowerCAmelCase)
																		if int(number // i) != i:
																								values.append(int(number // i))
						return sorted(_lowerCAmelCase)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						return sum(factors(_lowerCAmelCase)) > n


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						snake_case   :					Dict							=						factors(_lowerCAmelCase)
						snake_case   :					List[str]							=						len(_lowerCAmelCase)
						snake_case   :					Optional[Any]							=						[[0 for i in range(number + 1)] for j in range(r + 1)]
						for i in range(r + 1):
												snake_case   :					int							=						True

						for i in range(1,			number + 1):
												snake_case   :					List[Any]							=						False

						for i in range(1,			r + 1):
												for j in range(1,			number + 1):
																		if j < values[i - 1]:
																								snake_case   :					Union[str, Any]							=						subset[i - 1][j]
																		else:
																								snake_case   :					Tuple							=						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

						return subset[r][number] != 0


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						return abundant(_lowerCAmelCase) and not semi_perfect(_lowerCAmelCase)


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)
						for number in (69, 70, 71):
												print(f"{number} is {'' if weird(number) else 'not '}weird.")





