



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    _lowercase :
				'''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  ,			):

									'''simple docstring'''


									UpperCamelCase_			= parent
									UpperCamelCase_			= batch_size
									UpperCamelCase_			= seq_length
									UpperCamelCase_			= is_training
									UpperCamelCase_			= use_input_mask
									UpperCamelCase_			= use_labels
									UpperCamelCase_			= vocab_size
									UpperCamelCase_			= d_model
									UpperCamelCase_			= num_hidden_layers
									UpperCamelCase_			= num_attention_heads
									UpperCamelCase_			= ffn_dim
									UpperCamelCase_			= activation_function
									UpperCamelCase_			= activation_dropout
									UpperCamelCase_			= attention_dropout
									UpperCamelCase_			= max_position_embeddings
									UpperCamelCase_			= initializer_range
									UpperCamelCase_			= None
									UpperCamelCase_			= 0
									UpperCamelCase_			= 2
									UpperCamelCase_			= 1

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= np.clip(ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size     )  ,			3  ,			self.vocab_size     )

									UpperCamelCase_			= None
									if self.use_input_mask:
														UpperCamelCase_			= random_attention_mask([self.batch_size, self.seq_length]     )

									UpperCamelCase_			= 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=snake_case__  ,			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     ):

									'''simple docstring'''


									UpperCamelCase_			= self.prepare_config_and_inputs()
									UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= config_and_inputs
									UpperCamelCase_			= {"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= self.prepare_config_and_inputs()

									UpperCamelCase_			= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
									UpperCamelCase_			= 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__     ):

									'''simple docstring'''


									UpperCamelCase_			= 20
									UpperCamelCase_			= model_class_name(snake_case__     )

									UpperCamelCase_			= model.init_cache(input_ids.shape[0]  ,			snake_case__     )
									UpperCamelCase_			= jnp.ones((input_ids.shape[0], max_decoder_length)  ,			dtype="i4"     )

									UpperCamelCase_			= jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,			(input_ids.shape[0], input_ids.shape[-1] - 1)     )
									UpperCamelCase_			= model(
									    input_ids[:, :-1]  ,			attention_mask=snake_case__  ,			past_key_values=snake_case__  ,			position_ids=snake_case__  ,			)

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

									UpperCamelCase_			= model(snake_case__     )

									UpperCamelCase_			= 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__     ):

									'''simple docstring'''


									UpperCamelCase_			= 20
									UpperCamelCase_			= model_class_name(snake_case__     )

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

									UpperCamelCase_			= model.init_cache(input_ids.shape[0]  ,			snake_case__     )
									UpperCamelCase_			= jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,			(input_ids.shape[0], input_ids.shape[-1] - 1)     )

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

									UpperCamelCase_			= model(snake_case__  ,			attention_mask=snake_case__     )
									UpperCamelCase_			= 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    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				lowercase__     =    (FlaxXGLMForCausalLM,) if is_flax_available() else ()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= FlaxXGLMModelTester(self     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= XGLMTokenizer.from_pretrained("XGLM"  ,			padding_side="left"     )
									UpperCamelCase_			= tokenizer(["Hello this is a long string", "Hey"]  ,			return_tensors="np"  ,			padding=snake_case__  ,			truncation=snake_case__     )

									UpperCamelCase_			= FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )
									UpperCamelCase_			= 1
									UpperCamelCase_			= False

									UpperCamelCase_			= jax.jit(model.generate     )

									UpperCamelCase_			= jit_generate(inputs["input_ids"]  ,			attention_mask=inputs["attention_mask"]     ).sequences

									UpperCamelCase_			= tokenizer.batch_decode(snake_case__  ,			skip_special_tokens=snake_case__     )

									UpperCamelCase_			= [
									    "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(snake_case__  ,			snake_case__     )

				@is_pt_flax_cross_test
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= 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
																			UpperCamelCase_			= self._prepare_for_class(snake_case__  ,			snake_case__     )
																			UpperCamelCase_			= {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			UpperCamelCase_			= model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			UpperCamelCase_			= getattr(snake_case__  ,			snake_case__     )

																			UpperCamelCase_	,     UpperCamelCase_			= pt_inputs["input_ids"].shape
																			UpperCamelCase_			= np.random.randint(0  ,			seq_length - 1  ,			size=(batch_size,)     )
																			for batch_idx, start_index in enumerate(snake_case__     ):
																								UpperCamelCase_			= 0
																								UpperCamelCase_			= 1
																								UpperCamelCase_			= 0
																								UpperCamelCase_			= 1
																			UpperCamelCase_			= pt_model_class(snake_case__     ).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.
																			UpperCamelCase_			= False
																			UpperCamelCase_			= model_class(snake_case__  ,			dtype=jnp.floataa     )
																			UpperCamelCase_			= convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,			snake_case__     )
																			UpperCamelCase_			= fx_state

																			with torch.no_grad():
																								UpperCamelCase_			= pt_model(**snake_case__     ).to_tuple()

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								pt_model.save_pretrained(snake_case__     )
																								UpperCamelCase_			= model_class.from_pretrained(snake_case__  ,			from_pt=snake_case__     )

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

				@is_pt_flax_cross_test
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= 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
																			UpperCamelCase_			= self._prepare_for_class(snake_case__  ,			snake_case__     )
																			UpperCamelCase_			= {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			UpperCamelCase_			= model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			UpperCamelCase_			= getattr(snake_case__  ,			snake_case__     )

																			UpperCamelCase_			= pt_model_class(snake_case__     ).eval()
																			UpperCamelCase_			= False
																			UpperCamelCase_			= model_class(snake_case__  ,			dtype=jnp.floataa     )

																			UpperCamelCase_			= load_flax_weights_in_pytorch_model(snake_case__  ,			fx_model.params     )
																			UpperCamelCase_	,     UpperCamelCase_			= pt_inputs["input_ids"].shape
																			UpperCamelCase_			= np.random.randint(0  ,			seq_length - 1  ,			size=(batch_size,)     )
																			for batch_idx, start_index in enumerate(snake_case__     ):
																								UpperCamelCase_			= 0
																								UpperCamelCase_			= 1
																								UpperCamelCase_			= 0
																								UpperCamelCase_			= 1

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

																			with torch.no_grad():
																								UpperCamelCase_			= pt_model(**snake_case__     ).to_tuple()

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								fx_model.save_pretrained(snake_case__     )
																								UpperCamelCase_			= pt_model_class.from_pretrained(snake_case__  ,			from_flax=snake_case__     )

																			with torch.no_grad():
																								UpperCamelCase_			= pt_model_loaded(**snake_case__     ).to_tuple()

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

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_			= model_class_name.from_pretrained("facebook/xglm-564M"     )
														UpperCamelCase_			= model(np.ones((1, 1)     )     )
														self.assertIsNotNone(snake_case__     )








"""simple docstring"""


_UpperCamelCase							:       str       					=		10**8




def      snake_case    (A_ :int ):







  '''simple docstring'''

  if n % 1_0 == 0:
    return False
  a	:     Optional[int]							=  str(A_ )
  return s == s[::-1]




def      snake_case    ():







  '''simple docstring'''

  a	:     Tuple							=  set()
  a	:     Optional[Any]							=  1
  a	:     List[str]							=  5
  while sum_squares < LIMIT:
    a	:     int							=  first_square + 1
    while sum_squares < LIMIT:
      if is_palindrome(A_ ):
        answer.add(A_ )
      last_square += 1
      sum_squares += last_square**2
    first_square += 1
    a	:     List[str]							=  first_square**2 + (first_square + 1) ** 2

  return sum(A_ )


if __name__ == "__main__":
   print(solution())




