

'''simple docstring'''

def 			__UpperCamelCase							(		lowercase__  :					float	):






     '''simple docstring'''





     if num < 0:
          return -1
     return 1 if num else 0

def 			__UpperCamelCase							(		):






     '''simple docstring'''





     assert signum(5	) == 1
     assert signum(-5	) == -1
     assert signum(0	) == 0


if __name__ == "__main__":
       print(signum(12))
       print(signum(-12))
       print(signum(0))





'''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 					lowerCAmelCase       :







       def __init__(							self     :	Tuple  , __lowercase     :	Optional[int]  , __lowercase     :	Union[str, Any]=14  , __lowercase     :	Any=7  , __lowercase     :	List[str]=True  , __lowercase     :	Dict=True  , __lowercase     :	Tuple=False  , __lowercase     :	Tuple=True  , __lowercase     :	Optional[Any]=99  , __lowercase     :	List[Any]=32  , __lowercase     :	Any=4  , __lowercase     :	str=4  , __lowercase     :	Tuple=[[["global", "local"], 2]]  , __lowercase     :	List[str]=37  , __lowercase     :	Tuple="gelu"  , __lowercase     :	Optional[int]=0.1  , __lowercase     :	List[Any]=0.1  , __lowercase     :	int=512  , __lowercase     :	str=7  , __lowercase     :	int=0.0_2  , ):







            """simple docstring"""







            __lowercase     						=parent
            __lowercase     						=batch_size
            __lowercase     						=seq_length
            __lowercase     						=is_training
            __lowercase     						=use_input_mask
            __lowercase     						=use_token_type_ids
            __lowercase     						=use_labels
            __lowercase     						=vocab_size
            __lowercase     						=hidden_size
            __lowercase     						=num_hidden_layers
            __lowercase     						=num_attention_heads
            __lowercase     						=attention_types
            __lowercase     						=intermediate_size
            __lowercase     						=hidden_act
            __lowercase     						=hidden_dropout_prob
            __lowercase     						=attention_probs_dropout_prob
            __lowercase     						=max_position_embeddings
            __lowercase     						=window_size
            __lowercase     						=initializer_range
            __lowercase     						=None
            __lowercase     						=vocab_size - 1
            __lowercase     						=vocab_size - 1
            __lowercase     						=vocab_size - 1







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size )

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

            __lowercase     						=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=__lowercase  , 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      snake_case				(							self     :	Tuple ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Tuple  , __lowercase     :	str  , __lowercase     :	Union[str, Any]  , __lowercase     :	Dict  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=20
            __lowercase     						=model_class_name(__lowercase )

            __lowercase     						=model.init_cache(input_ids.shape[0]  , __lowercase )
            __lowercase     						=jnp.ones((input_ids.shape[0], max_decoder_length)  , dtype='i4' )

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

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

            __lowercase     						=model(__lowercase )

            __lowercase     						=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      snake_case				(							self     :	Any  , __lowercase     :	List[str]  , __lowercase     :	Dict  , __lowercase     :	Dict  , __lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=20
            __lowercase     						=model_class_name(__lowercase )

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

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

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

            __lowercase     						=model(__lowercase  , attention_mask=__lowercase )

            __lowercase     						=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 					lowerCAmelCase       ( A				,     A				,     unittest.TestCase				):
       lowerCAmelCase_				=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
       lowerCAmelCase_				=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=FlaxGPTNeoModelTester(self )







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







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







       @slow
       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=GPTaTokenizer.from_pretrained('gpt2'  , pad_token='<|endoftext|>'  , padding_side='left' )
            __lowercase     						=tokenizer(['Hello this is a long string', 'Hey']  , return_tensors='np'  , padding=__lowercase  , truncation=__lowercase )

            __lowercase     						=FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M' )
            __lowercase     						=False
            __lowercase     						=model.config.eos_token_id

            __lowercase     						=jax.jit(model.generate )

            __lowercase     						=jit_generate(
                inputs['input_ids']  , attention_mask=inputs['attention_mask']  , pad_token_id=tokenizer.pad_token_id ).sequences

            __lowercase     						=tokenizer.batch_decode(__lowercase  , skip_special_tokens=__lowercase )

            __lowercase     						=[
                '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(__lowercase  , __lowercase )







       @is_pt_flax_cross_test
       def      snake_case				(							self     :	int ):







            """simple docstring"""







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

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

                      __lowercase						,       __lowercase     						=pt_inputs['input_ids'].shape
                      __lowercase     						=np.random.randint(0  , seq_length - 1  , size=(batch_size,) )
                      for batch_idx, start_index in enumerate(__lowercase ):
                           __lowercase     						=0
                           __lowercase     						=1
                           __lowercase     						=0
                           __lowercase     						=1
                      __lowercase     						=pt_model_class(__lowercase ).eval()
                      __lowercase     						=model_class(__lowercase  , dtype=jnp.floataa )

                      __lowercase     						=convert_pytorch_state_dict_to_flax(pt_model.state_dict()  , __lowercase )
                      __lowercase     						=fx_state

                      with torch.no_grad():
                           __lowercase     						=pt_model(**__lowercase ).to_tuple()

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

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

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







       @is_pt_flax_cross_test
       def      snake_case				(							self     :	str ):







            """simple docstring"""







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

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

                      __lowercase     						=pt_model_class(__lowercase ).eval()
                      __lowercase     						=model_class(__lowercase  , dtype=jnp.floataa )

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

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

                      with torch.no_grad():
                           __lowercase     						=pt_model(**__lowercase ).to_tuple()

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

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

                      with torch.no_grad():
                           __lowercase     						=pt_model_loaded(**__lowercase ).to_tuple()

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







       @slow
       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            for model_class_name in self.all_model_classes:
                 __lowercase     						=model_class_name.from_pretrained('EleutherAI/gpt-neo-125M' )
                 __lowercase     						=model(np.ones((1, 1) ) )
                 self.assertIsNotNone(__lowercase )


