code: 
  """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    _lowerCAmelCase						:
  
  
  
  							def __init__(      self ,						a_ ,						a_=14 ,						a_=7 ,						a_=True ,						a_=True ,						a_=True ,						a_=99 ,						a_=32 ,						a_=5 ,						a_=4 ,						a_=37 ,						a_="gelu" ,						a_=0.1 ,						a_=0.1 ,						a_=512 ,						a_=0.02 ,						a_=None ,						)							->							List[Any]:
  								_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       _a   (      self   )							->							Union[str, Any]:
  								_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=a_ ,						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       _a   (      self   )							->							Dict:
  								_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       _a   (      self   )							->							int:
  								_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       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_   )							->							Optional[Any]:
  								_UpperCAmelCase =						20
  								_UpperCAmelCase =						model_class_name(a_   )
  
  								_UpperCAmelCase =						model.init_cache(input_ids.shape[0] ,						a_   )
  								_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=a_ ,						past_key_values=a_ ,						position_ids=a_ ,						)
  
  								_UpperCAmelCase =						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,						dtype="i4"   )
  								_UpperCAmelCase =						model(
  								    input_ids[:, -1:] ,						attention_mask=a_ ,						past_key_values=outputs_cache.past_key_values ,						position_ids=a_ ,						)
  
  								_UpperCAmelCase =						model(a_   )
  
  								_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       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_   )							->							int:
  								_UpperCAmelCase =						20
  								_UpperCAmelCase =						model_class_name(a_   )
  
  								_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] ,						a_   )
  								_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=a_ ,						past_key_values=a_ ,						position_ids=a_ ,						)
  								_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=a_ ,						position_ids=a_ ,						)
  
  								_UpperCAmelCase =						model(a_ ,						attention_mask=a_   )
  								_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    _lowerCAmelCase						(						lowerCamelCase    ,       lowerCamelCase    ,       unittest.TestCase						):
  							lowercase_     :     Union[str, Any]       =							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  							lowercase_     :     Tuple       =							(FlaxXGLMForCausalLM,) if is_flax_available() else ()
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						FlaxXGLMModelTester(self   )
  
  
  
  							def       _a   (      self   )							->							int:
  								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(a_ ,						a_ ,						a_ ,						a_   )
  
  
  
  							def       _a   (      self   )							->							Union[str, Any]:
  								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(
  									    a_ ,						a_ ,						a_ ,						a_   )
  
  
  
  							@slow
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						XGLMTokenizer.from_pretrained("XGLM" ,						padding_side="left"   )
  								_UpperCAmelCase =						tokenizer(["Hello this is a long string", "Hey"] ,						return_tensors="np" ,						padding=a_ ,						truncation=a_   )
  
  								_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(a_ ,						skip_special_tokens=a_   )
  
  								_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(a_ ,						a_   )
  
  
  
  							@is_pt_flax_cross_test
  							def       _a   (      self   )							->							Optional[Any]:
  								_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(a_ ,						a_   )
  										_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(a_ ,						a_   )
  
  										_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(a_   ):
  											_UpperCAmelCase =						0
  											_UpperCAmelCase =						1
  											_UpperCAmelCase =						0
  											_UpperCAmelCase =						1
  										_UpperCAmelCase =						pt_model_class(a_   ).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(a_ ,						dtype=jnp.floataa   )
  										_UpperCAmelCase =						convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,						a_   )
  										_UpperCAmelCase =						fx_state
  
  										with torch.no_grad():
  											_UpperCAmelCase =						pt_model(**a_   ).to_tuple()
  
  										_UpperCAmelCase =						fx_model(**a_   ).to_tuple()
  										self.assertEqual(len(a_   ) ,						len(a_   ) ,						"Output lengths differ between Flax and PyTorch"   )
  										for fx_output, pt_output in zip(a_ ,						a_   ):
  											self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4e-2   )
  
  										with tempfile.TemporaryDirectory() as tmpdirname:
  											pt_model.save_pretrained(a_   )
  											_UpperCAmelCase =						model_class.from_pretrained(a_ ,						from_pt=a_   )
  
  										_UpperCAmelCase =						fx_model_loaded(**a_   ).to_tuple()
  										self.assertEqual(
  										    len(a_   ) ,						len(a_   ) ,						"Output lengths differ between Flax and PyTorch"   )
  										for fx_output_loaded, pt_output in zip(a_ ,						a_   ):
  											self.assert_almost_equals(fx_output_loaded[:, -1] ,						pt_output[:, -1].numpy() ,						4e-2   )
  
  
  
  							@is_pt_flax_cross_test
  							def       _a   (      self   )							->							Tuple:
  								_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(a_ ,						a_   )
  										_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(a_ ,						a_   )
  
  										_UpperCAmelCase =						pt_model_class(a_   ).eval()
  										_UpperCAmelCase =						False
  										_UpperCAmelCase =						model_class(a_ ,						dtype=jnp.floataa   )
  
  										_UpperCAmelCase =						load_flax_weights_in_pytorch_model(a_ ,						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(a_   ):
  											_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(**a_   ).to_tuple()
  
  										_UpperCAmelCase =						fx_model(**a_   ).to_tuple()
  										self.assertEqual(len(a_   ) ,						len(a_   ) ,						"Output lengths differ between Flax and PyTorch"   )
  										for fx_output, pt_output in zip(a_ ,						a_   ):
  											self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4e-2   )
  
  										with tempfile.TemporaryDirectory() as tmpdirname:
  											fx_model.save_pretrained(a_   )
  											_UpperCAmelCase =						pt_model_class.from_pretrained(a_ ,						from_flax=a_   )
  
  										with torch.no_grad():
  											_UpperCAmelCase =						pt_model_loaded(**a_   ).to_tuple()
  
  										self.assertEqual(
  										    len(a_   ) ,						len(a_   ) ,						"Output lengths differ between Flax and PyTorch"   )
  										for fx_output, pt_output in zip(a_ ,						a_   ):
  											self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4e-2   )
  
  
  
  							@slow
  							def       _a   (      self   )							->							Union[str, Any]:
  								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(a_   )
  
  
  
  
  
style_context: 
  """simple docstring"""
  from .stack import Stack
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						Stack()
  	_UpperCAmelCase =						{"(": ")", "[": "]", "{": "}"}
  	for bracket in parentheses:
  		if bracket in bracket_pairs:
  			stack.push(UpperCamelCase__      )
  		elif bracket in (")", "]", "}"):
  			if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
  				return False
  	return stack.is_empty()
  
  
  if __name__ == "__main__":
  			from doctest import testmod
  
  			testmod()
  
  			__magic_name__		       =				['''((()))''', '''((())''', '''(()))''']
  			print('''Balanced parentheses demonstration:\n''')
  			for example in examples:
  						__magic_name__		       =				'''''' if balanced_parentheses(example) else '''not '''
  						print(f'''{example} is {not_str}balanced''')
  
  
  
  
  