"""simple docstring"""







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


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

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
	import torch

class 						UpperCamelCase     :



				def __init__(self							: List[Any]      ,    _A							: Union[str, Any]      ,    _A							: List[Any]=14      ,    _A							: Dict=7      ,    _A							: Tuple=True      ,    _A							: List[Any]=True      ,    _A							: Dict=False      ,    _A							: Tuple=True      ,    _A							: Tuple=99      ,    _A							: Any=32      ,    _A							: Optional[int]=4      ,    _A							: Dict=4      ,    _A							: List[Any]=[[["global", "local"], 2]]      ,    _A							: List[str]=37      ,    _A							: Union[str, Any]="gelu"      ,    _A							: Tuple=0.1      ,    _A							: List[Any]=0.1      ,    _A							: List[str]=5_12      ,    _A							: List[Any]=7      ,    _A							: str=0.02      ,    )       ->      List[str]:
					__snake_case     :				str 						=					parent
					__snake_case     :				Tuple 						=					batch_size
					__snake_case     :				Dict 						=					seq_length
					__snake_case     :				Tuple 						=					is_training
					__snake_case     :				Optional[Any] 						=					use_input_mask
					__snake_case     :				Union[str, Any] 						=					use_token_type_ids
					__snake_case     :				Optional[Any] 						=					use_labels
					__snake_case     :				Optional[Any] 						=					vocab_size
					__snake_case     :				Optional[int] 						=					hidden_size
					__snake_case     :				Union[str, Any] 						=					num_hidden_layers
					__snake_case     :				Any 						=					num_attention_heads
					__snake_case     :				Dict 						=					attention_types
					__snake_case     :				Optional[Any] 						=					intermediate_size
					__snake_case     :				List[Any] 						=					hidden_act
					__snake_case     :				List[Any] 						=					hidden_dropout_prob
					__snake_case     :				Any 						=					attention_probs_dropout_prob
					__snake_case     :				Optional[Any] 						=					max_position_embeddings
					__snake_case     :				List[str] 						=					window_size
					__snake_case     :				Optional[int] 						=					initializer_range
					__snake_case     :				Any 						=					None
					__snake_case     :				Union[str, Any] 						=					vocab_size - 1
					__snake_case     :				Optional[int] 						=					vocab_size - 1
					__snake_case     :				int 						=					vocab_size - 1



				def    _lowercase	(self							: str)       ->      Any:
					__snake_case     :				List[Any] 						=					ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size)

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

					__snake_case     :				str 						=					GPTNeoConfig(
					    vocab_size=self.vocab_size      ,    hidden_size=self.hidden_size      ,    num_layers=self.num_hidden_layers      ,    num_heads=self.num_attention_heads      ,    max_position_embeddings=self.max_position_embeddings      ,    use_cache=_A      ,    bos_token_id=self.bos_token_id      ,    eos_token_id=self.eos_token_id      ,    pad_token_id=self.pad_token_id      ,    window_size=self.window_size      ,    attention_types=self.attention_types      ,    )

					return (config, input_ids, input_mask)



				def    _lowercase	(self							: List[Any])       ->      str:
					__snake_case     :				List[Any] 						=					self.prepare_config_and_inputs()
					__snake_case						,							__snake_case						,							__snake_case     :				Tuple 						=					config_and_inputs
					__snake_case     :				Optional[int] 						=					{'input_ids': input_ids, 'attention_mask': attention_mask}
					return config, inputs_dict



				def    _lowercase	(self							: str      ,    _A							: Optional[Any]      ,    _A							: List[Any]      ,    _A							: Dict      ,    _A							: Optional[int])       ->      Dict:
					__snake_case     :				Dict 						=					20
					__snake_case     :				List[str] 						=					model_class_name(_A)

					__snake_case     :				Union[str, Any] 						=					model.init_cache(input_ids.shape[0]      ,    _A)
					__snake_case     :				List[str] 						=					jnp.ones((input_ids.shape[0], max_decoder_length)      ,    dtype='i4')

					__snake_case     :				str 						=					jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]      ,    (input_ids.shape[0], input_ids.shape[-1] - 1))
					__snake_case     :				Dict 						=					model(
					    input_ids[:, :-1]      ,    attention_mask=_A      ,    past_key_values=_A      ,    position_ids=_A      ,    )

					__snake_case     :				List[str] 						=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,    dtype='i4')
					__snake_case     :				List[str] 						=					model(
					    input_ids[:, -1:]      ,    attention_mask=_A      ,    past_key_values=outputs_cache.past_key_values      ,    position_ids=_A      ,    )

					__snake_case     :				Dict 						=					model(_A)

					__snake_case     :				List[str] 						=					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    _lowercase	(self							: List[Any]      ,    _A							: Union[str, Any]      ,    _A							: List[str]      ,    _A							: int      ,    _A							: Union[str, Any])       ->      Tuple:
					__snake_case     :				Union[str, Any] 						=					20
					__snake_case     :				str 						=					model_class_name(_A)

					__snake_case     :				Optional[Any] 						=					jnp.concatenate(
					    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]      ,    axis=-1      ,    )

					__snake_case     :				Dict 						=					model.init_cache(input_ids.shape[0]      ,    _A)
					__snake_case     :				Any 						=					jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]      ,    (input_ids.shape[0], input_ids.shape[-1] - 1))

					__snake_case     :				Union[str, Any] 						=					model(
					    input_ids[:, :-1]      ,    attention_mask=_A      ,    past_key_values=_A      ,    position_ids=_A      ,    )
					__snake_case     :				Tuple 						=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,    dtype='i4')
					__snake_case     :				List[str] 						=					model(
					    input_ids[:, -1:]      ,    past_key_values=outputs_cache.past_key_values      ,    attention_mask=_A      ,    position_ids=_A      ,    )

					__snake_case     :				str 						=					model(_A      ,    attention_mask=_A)

					__snake_case     :				List[str] 						=					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_flax
class 						UpperCamelCase     (       lowercase ,      lowercase ,      unittest.TestCase		):
				UpperCAmelCase							: Any       =						(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
				UpperCAmelCase							: Dict       =						(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



				def    _lowercase	(self							: Optional[Any])       ->      Optional[int]:
					__snake_case     :				str 						=					FlaxGPTNeoModelTester(self)



				def    _lowercase	(self							: Tuple)       ->      Union[str, Any]:
					for model_class_name in self.all_model_classes:
						__snake_case						,							__snake_case						,							__snake_case     :				Union[str, Any] 						=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward(_A      ,    _A      ,    _A      ,    _A)



				def    _lowercase	(self							: Optional[Any])       ->      Union[str, Any]:
					for model_class_name in self.all_model_classes:
						__snake_case						,							__snake_case						,							__snake_case     :				List[str] 						=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward_with_attn_mask(
						    _A      ,    _A      ,    _A      ,    _A)



				@slow
				def    _lowercase	(self							: Union[str, Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					GPTaTokenizer.from_pretrained('gpt2'      ,    pad_token='<|endoftext|>'      ,    padding_side='left')
					__snake_case     :				str 						=					tokenizer(['Hello this is a long string', 'Hey']      ,    return_tensors='np'      ,    padding=_A      ,    truncation=_A)

					__snake_case     :				List[str] 						=					FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M')
					__snake_case     :				List[Any] 						=					False
					__snake_case     :				Union[str, Any] 						=					model.config.eos_token_id

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

					__snake_case     :				List[Any] 						=					jit_generate(
					    inputs['input_ids']      ,    attention_mask=inputs['attention_mask']      ,    pad_token_id=tokenizer.pad_token_id).sequences

					__snake_case     :				Union[str, Any] 						=					tokenizer.batch_decode(_A      ,    skip_special_tokens=_A)

					__snake_case     :				Union[str, Any] 						=					[
					    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
					    'Hey, I\'m a little late to the party. I\'m going to',
					]

					self.assertListEqual(_A      ,    _A)



				@is_pt_flax_cross_test
				def    _lowercase	(self							: Optional[Any])       ->      List[Any]:
					__snake_case						,							__snake_case     :				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(_A      ,    _A)
							__snake_case     :				int 						=					{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     :				List[Any] 						=					getattr(_A      ,    _A)

							__snake_case						,							__snake_case     :				List[str] 						=					pt_inputs['input_ids'].shape
							__snake_case     :				int 						=					np.random.randint(0      ,    seq_length - 1      ,    size=(batch_size,))
							for batch_idx, start_index in enumerate(_A):
								__snake_case     :				List[Any] 						=					0
								__snake_case     :				Optional[Any] 						=					1
								__snake_case     :				Any 						=					0
								__snake_case     :				Optional[int] 						=					1
							__snake_case     :				Tuple 						=					pt_model_class(_A).eval()
							__snake_case     :				List[Any] 						=					model_class(_A      ,    dtype=jnp.floataa)

							__snake_case     :				Optional[Any] 						=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,    _A)
							__snake_case     :				Tuple 						=					fx_state

							with torch.no_grad():
								__snake_case     :				Optional[Any] 						=					pt_model(**_A).to_tuple()

							__snake_case     :				str 						=					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)
								__snake_case     :				List[str] 						=					model_class.from_pretrained(_A      ,    from_pt=_A)

							__snake_case     :				Union[str, Any] 						=					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    _lowercase	(self							: int)       ->      Union[str, Any]:
					__snake_case						,							__snake_case     :				Optional[int] 						=					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     :				Optional[int] 						=					self._prepare_for_class(_A      ,    _A)
							__snake_case     :				Dict 						=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

							# load corresponding PyTorch class
							__snake_case     :				List[Any] 						=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
							__snake_case     :				Tuple 						=					getattr(_A      ,    _A)

							__snake_case     :				str 						=					pt_model_class(_A).eval()
							__snake_case     :				List[Any] 						=					model_class(_A      ,    dtype=jnp.floataa)

							__snake_case     :				Optional[Any] 						=					load_flax_weights_in_pytorch_model(_A      ,    fx_model.params)
							__snake_case						,							__snake_case     :				Union[str, Any] 						=					pt_inputs['input_ids'].shape
							__snake_case     :				Dict 						=					np.random.randint(0      ,    seq_length - 1      ,    size=(batch_size,))
							for batch_idx, start_index in enumerate(_A):
								__snake_case     :				int 						=					0
								__snake_case     :				List[str] 						=					1
								__snake_case     :				Optional[int] 						=					0
								__snake_case     :				Tuple 						=					1

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

							with torch.no_grad():
								__snake_case     :				Optional[Any] 						=					pt_model(**_A).to_tuple()

							__snake_case     :				Dict 						=					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)
								__snake_case     :				str 						=					pt_model_class.from_pretrained(_A      ,    from_flax=_A)

							with torch.no_grad():
								__snake_case     :				List[str] 						=					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    _lowercase	(self							: Dict)       ->      Tuple:
					for model_class_name in self.all_model_classes:
						__snake_case     :				List[str] 						=					model_class_name.from_pretrained('EleutherAI/gpt-neo-125M')
						__snake_case     :				Union[str, Any] 						=					model(np.ones((1, 1)))
						self.assertIsNotNone(_A)






"""simple docstring"""







_a	:	Optional[Any]=					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]    )				->     List[Any]:

	'''simple docstring'''





	return c.isdigit()
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	__snake_case     :				Dict 						=					[]

	# iterate over the string in reverse order
	for c in expression.split()[::-1]:
		# push operand to stack
		if is_operand(UpperCAmelCase_    ):
			stack.append(int(UpperCAmelCase_    )    )

		else:
			# pop values from stack can calculate the result
			# push the result onto the stack again
			__snake_case     :				Optional[Any] 						=					stack.pop()
			__snake_case     :				int 						=					stack.pop()
			stack.append(calc[c](UpperCAmelCase_     ,				UpperCAmelCase_    )    )

	return stack.pop()


# Driver code
if __name__ == "__main__":
	_a	:	Union[str, Any]=					"+ 9 * 2 6"
	print(evaluate(test_expression))

	_a	:	List[Any]=					"/ * 10 2 + 4 1 "
	print(evaluate(test_expression))





