


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   A__							:




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[Any]=14 ,			_a    :					Optional[Any]=7 ,			_a    :					List[Any]=True ,			_a    :					Union[str, Any]=True ,			_a    :					int=False ,			_a    :					Any=True ,			_a    :					Union[str, Any]=99 ,			_a    :					Dict=32 ,			_a    :					Tuple=4 ,			_a    :					Optional[int]=4 ,			_a    :					Optional[int]=[[["global", "local"], 2]] ,			_a    :					List[str]=37 ,			_a    :					List[str]="gelu" ,			_a    :					List[str]=0.1 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Any=512 ,			_a    :					Optional[int]=7 ,			_a    :					str=0.02 ,			)							->   List[str]:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					str     )							->   List[str]:


										"""simple docstring"""





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

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

										_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				self    :					Tuple     )							->   List[Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					str ,			_a    :					Union[str, Any] ,			_a    :					Tuple ,			_a    :					Dict ,			_a    :					Any     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =20
										_SCREAMING_SNAKE_CASE	     =model_class_name(_a     )

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

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

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

										_SCREAMING_SNAKE_CASE	     =model(_a     )

										_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Union[str, Any] ,			_a    :					Any ,			_a    :					Union[str, Any] ,			_a    :					List[str]     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =20
										_SCREAMING_SNAKE_CASE	     =model_class_name(_a     )

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

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

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

										_SCREAMING_SNAKE_CASE	     =model(_a ,			attention_mask=_a     )

										_SCREAMING_SNAKE_CASE	     =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   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
						UpperCAmelCase									=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




						def 							__UpperCamelCase (				self    :					Dict     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =FlaxGPTNeoModelTester(self     )




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   List[str]:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Tuple:


										"""simple docstring"""





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




						@slow
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Dict:


										"""simple docstring"""





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

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

										_SCREAMING_SNAKE_CASE	     =jax.jit(model.generate     )

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

										_SCREAMING_SNAKE_CASE	     =tokenizer.batch_decode(_a ,			skip_special_tokens=_a     )

										_SCREAMING_SNAKE_CASE	     =[
										    '''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 							__UpperCamelCase (				self    :					str     )							->   Optional[int]:


										"""simple docstring"""





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

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

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

																		_SCREAMING_SNAKE_CASE	     =convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,			_a     )
																		_SCREAMING_SNAKE_CASE	     =fx_state

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE	     =pt_model(**_a     ).to_tuple()

																		_SCREAMING_SNAKE_CASE	     =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     )
																						_SCREAMING_SNAKE_CASE	     =model_class.from_pretrained(_a ,			from_pt=_a     )

																		_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				self    :					Tuple     )							->   Tuple:


										"""simple docstring"""





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

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

																		_SCREAMING_SNAKE_CASE	     =pt_model_class(_a     ).eval()
																		_SCREAMING_SNAKE_CASE	     =model_class(_a ,			dtype=jnp.floataa     )

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

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

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE	     =pt_model(**_a     ).to_tuple()

																		_SCREAMING_SNAKE_CASE	     =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     )
																						_SCREAMING_SNAKE_CASE	     =pt_model_class.from_pretrained(_a ,			from_flax=_a     )

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				self    :					Tuple     )							->   Union[str, Any]:


										"""simple docstring"""





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







import math
from collections.abc import Generator


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    # only need to check for factors up to sqrt(i)
    SCREAMING_SNAKE_CASE				:					str		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1)  ,   2 ))
  # It's useless to test even numbers as they will not be prime
  if max_n > 2:
    yield 2  # Because 2 will not be tested, it's necessary to yield it now
  for i in (n for n in numbers if n > 1):
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(3  ,   lowercase  ,   2 ):
      # As we removed the even numbers, we don't need them now
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   ):

  """simple docstring"""






  from timeit import timeit

  SCREAMING_SNAKE_CASE				:					Tuple		  =			"from __main__ import slow_primes, primes, fast_primes"
  print(timeit("slow_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("fast_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )


if __name__ == "__main__":
   snake_case			 =						int(input("""Calculate primes up to:\n>> """).strip())
   for ret in primes(number):
      print(ret)
   benchmark()






