
"""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 		a__						:
   def __init__(      self     ,   _a     ,   _a=14     ,   _a=7     ,   _a=True     ,   _a=True     ,   _a=False     ,   _a=True     ,   _a=99     ,   _a=32     ,   _a=4     ,   _a=4     ,   _a=[[["global", "local"], 2]]     ,   _a=37     ,   _a="gelu"     ,   _a=0.1     ,   _a=0.1     ,   _a=512     ,   _a=7     ,   _a=0.0_2     ,   ):
          lowercase   :    str			=							parent
          lowercase   :    Optional[int]			=							batch_size
          lowercase   :    List[Any]			=							seq_length
          lowercase   :    str			=							is_training
          lowercase   :    Optional[int]			=							use_input_mask
          lowercase   :    Any			=							use_token_type_ids
          lowercase   :    Tuple			=							use_labels
          lowercase   :    int			=							vocab_size
          lowercase   :    Optional[int]			=							hidden_size
          lowercase   :    Tuple			=							num_hidden_layers
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    Optional[Any]			=							attention_types
          lowercase   :    Union[str, Any]			=							intermediate_size
          lowercase   :    str			=							hidden_act
          lowercase   :    Any			=							hidden_dropout_prob
          lowercase   :    List[Any]			=							attention_probs_dropout_prob
          lowercase   :    Tuple			=							max_position_embeddings
          lowercase   :    List[str]			=							window_size
          lowercase   :    Optional[int]			=							initializer_range
          lowercase   :    List[Any]			=							None
          lowercase   :    List[Any]			=							vocab_size - 1
          lowercase   :    Dict			=							vocab_size - 1
          lowercase   :    Optional[Any]			=							vocab_size - 1
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size    )

          lowercase   :    Tuple			=							None
          if self.use_input_mask:
                 lowercase   :    str			=							random_attention_mask([self.batch_size, self.seq_length]    )

          lowercase   :    Tuple			=							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       __magic_name__      (      self    ):
          lowercase   :    int			=							self.prepare_config_and_inputs()
          lowercase		,   lowercase		,   lowercase   :    List[str]			=							config_and_inputs
          lowercase   :    Optional[Any]			=							{"input_ids": input_ids, "attention_mask": attention_mask}
          return config, inputs_dict
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    Optional[int]			=							20
          lowercase   :    Tuple			=							model_class_name(_a    )

          lowercase   :    Optional[int]			=							model.init_cache(input_ids.shape[0]     ,   _a    )
          lowercase   :    Any			=							jnp.ones((input_ids.shape[0], max_decoder_length)     ,   dtype="i4"    )

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

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

          lowercase   :    Dict			=							model(_a    )

          lowercase   :    Any			=							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       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    Optional[Any]			=							20
          lowercase   :    Optional[int]			=							model_class_name(_a    )

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

          lowercase   :    Tuple			=							model.init_cache(input_ids.shape[0]     ,   _a    )
          lowercase   :    Union[str, Any]			=							jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1    )[None, :]     ,   (input_ids.shape[0], input_ids.shape[-1] - 1)    )

          lowercase   :    List[Any]			=							model(
              input_ids[:, :-1]     ,   attention_mask=_a     ,   past_key_values=_a     ,   position_ids=_a     ,   )
          lowercase   :    Tuple			=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,   dtype="i4"    )
          lowercase   :    List[Any]			=							model(
              input_ids[:, -1:]     ,   past_key_values=outputs_cache.past_key_values     ,   attention_mask=_a     ,   position_ids=_a     ,   )

          lowercase   :    List[str]			=							model(_a     ,   attention_mask=_a    )

          lowercase   :    Union[str, Any]			=							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__						(				a_,      a_,      unittest.TestCase						):
   __lowerCAmelCase						=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   __lowerCAmelCase						=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							FlaxGPTNeoModelTester(self    )
   def       __magic_name__      (      self    ):
          for model_class_name in self.all_model_classes:
                 lowercase		,   lowercase		,   lowercase   :    Dict			=							self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(_a     ,   _a     ,   _a     ,   _a    )
   def       __magic_name__      (      self    ):
          for model_class_name in self.all_model_classes:
                 lowercase		,   lowercase		,   lowercase   :    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       __magic_name__      (      self    ):
          lowercase   :    str			=							GPTaTokenizer.from_pretrained("gpt2"     ,   pad_token="<|endoftext|>"     ,   padding_side="left"    )
          lowercase   :    List[Any]			=							tokenizer(["Hello this is a long string", "Hey"]     ,   return_tensors="np"     ,   padding=_a     ,   truncation=_a    )

          lowercase   :    List[str]			=							FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"    )
          lowercase   :    List[str]			=							False
          lowercase   :    Tuple			=							model.config.eos_token_id

          lowercase   :    int			=							jax.jit(model.generate    )

          lowercase   :    Optional[Any]			=							jit_generate(
              inputs["input_ids"]     ,   attention_mask=inputs["attention_mask"]     ,   pad_token_id=tokenizer.pad_token_id    ).sequences

          lowercase   :    Tuple			=							tokenizer.batch_decode(_a     ,   skip_special_tokens=_a    )

          lowercase   :    Optional[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       __magic_name__      (      self    ):
          lowercase		,   lowercase   :    List[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
                        lowercase   :    str			=							self._prepare_for_class(_a     ,   _a    )
                        lowercase   :    Optional[int]			=							{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

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

                        lowercase   :    Optional[int]			=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,   _a    )
                        lowercase   :    Optional[int]			=							fx_state

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

                        lowercase   :    int			=							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    )
                               lowercase   :    Union[str, Any]			=							model_class.from_pretrained(_a     ,   from_pt=_a    )

                        lowercase   :    Optional[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       __magic_name__      (      self    ):
          lowercase		,   lowercase   :    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
                        lowercase   :    int			=							self._prepare_for_class(_a     ,   _a    )
                        lowercase   :    int			=							{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

                        lowercase   :    Tuple			=							pt_model_class(_a    ).eval()
                        lowercase   :    Optional[int]			=							model_class(_a     ,   dtype=jnp.floataa    )

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

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

                        with torch.no_grad():
                               lowercase   :    Optional[Any]			=							pt_model(**_a    ).to_tuple()

                        lowercase   :    Optional[Any]			=							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    )
                               lowercase   :    Dict			=							pt_model_class.from_pretrained(_a     ,   from_flax=_a    )

                        with torch.no_grad():
                               lowercase   :    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       __magic_name__      (      self    ):
          for model_class_name in self.all_model_classes:
                 lowercase   :    str			=							model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"    )
                 lowercase   :    Dict			=							model(np.ones((1, 1)    )    )
                 self.assertIsNotNone(_a    )




"""simple docstring"""




import math
from collections.abc import Generator


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    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					,			__snake_case  ):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    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)
              lowercase   :    Dict			=							int(math.sqrt(__snake_case  )  ) + 1
              for j in range(2					,			__snake_case  ):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    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):
              lowercase   :    int			=							int(math.sqrt(__snake_case  )  ) + 1
              for j in range(3					,			__snake_case					,			2  ):
                     # As we removed the even numbers, we don't need them now
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				)			->    Tuple:
       from timeit import timeit

       lowercase   :    List[str]			=							"from __main__ import slow_primes, primes, fast_primes"
       print(timeit("slow_primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )
       print(timeit("primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )
       print(timeit("fast_primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )


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


