
"""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_   )






"""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''')




