





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




              """simple docstring"""


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

       def lowercase       (						self:       Union[str, Any]						)				->       Optional[Any]:




              """simple docstring"""


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

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

              UpperCamelCase_      				=     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=_SCREAMING_SNAKE_CASE    ,      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[str]						)				->       Tuple:




              """simple docstring"""


              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 lowercase       (						self:       Optional[Any]    ,      _SCREAMING_SNAKE_CASE:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       Optional[Any]    ,      _SCREAMING_SNAKE_CASE:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       List[Any]						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_      				=     20
              UpperCamelCase_      				=     model_class_name(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     model.init_cache(input_ids.shape[0]    ,      _SCREAMING_SNAKE_CASE						)
              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=_SCREAMING_SNAKE_CASE    ,      past_key_values=_SCREAMING_SNAKE_CASE    ,      position_ids=_SCREAMING_SNAKE_CASE    ,      )

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

              UpperCamelCase_      				=     model(_SCREAMING_SNAKE_CASE						)

              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 lowercase       (						self:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       Dict    ,      _SCREAMING_SNAKE_CASE:       Dict    ,      _SCREAMING_SNAKE_CASE:       List[Any]						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     20
              UpperCamelCase_      				=     model_class_name(_SCREAMING_SNAKE_CASE						)

              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]    ,      _SCREAMING_SNAKE_CASE						)
              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=_SCREAMING_SNAKE_CASE    ,      past_key_values=_SCREAMING_SNAKE_CASE    ,      position_ids=_SCREAMING_SNAKE_CASE    ,      )
              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=_SCREAMING_SNAKE_CASE    ,      position_ids=_SCREAMING_SNAKE_CASE    ,      )

              UpperCamelCase_      				=     model(_SCREAMING_SNAKE_CASE    ,      attention_mask=_SCREAMING_SNAKE_CASE						)

              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_flax
class 							_UpperCamelCase    (		lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase   ):
       _UpperCamelCase :    Union[str, Any]   			=      (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
       _UpperCamelCase :    List[Any]   			=      (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

       def lowercase       (						self:       Dict						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_      				=     FlaxGPTNeoModelTester(self						)

       def lowercase       (						self:       Dict						)				->       List[Any]:




              """simple docstring"""


              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(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       List[Any]						)				->       Any:




              """simple docstring"""


              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(
                         _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

       @slow
       def lowercase       (						self:       Union[str, Any]						)				->       List[str]:




              """simple docstring"""


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

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

              UpperCamelCase_      				=     jax.jit(model.generate						)

              UpperCamelCase_      				=     jit_generate(
                  inputs["input_ids"]    ,      attention_mask=inputs["attention_mask"]    ,      pad_token_id=tokenizer.pad_token_id						).sequences

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

              UpperCamelCase_      				=     [
                  "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(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

       @is_pt_flax_cross_test
       def lowercase       (						self:       Dict						)				->       Optional[Any]:




              """simple docstring"""


              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(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
                            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(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                            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(_SCREAMING_SNAKE_CASE						):
                                   UpperCamelCase_      				=     0
                                   UpperCamelCase_      				=     1
                                   UpperCamelCase_      				=     0
                                   UpperCamelCase_      				=     1
                            UpperCamelCase_      				=     pt_model_class(_SCREAMING_SNAKE_CASE						).eval()
                            UpperCamelCase_      				=     model_class(_SCREAMING_SNAKE_CASE    ,      dtype=jnp.floataa						)

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

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

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

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

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

       @is_pt_flax_cross_test
       def lowercase       (						self:       List[str]						)				->       Tuple:




              """simple docstring"""


              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(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
                            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(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

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

                            UpperCamelCase_      				=     load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE    ,      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(_SCREAMING_SNAKE_CASE						):
                                   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(**_SCREAMING_SNAKE_CASE						).to_tuple()

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

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

                            with torch.no_grad():
                                   UpperCamelCase_      				=     pt_model_loaded(**_SCREAMING_SNAKE_CASE						).to_tuple()

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



       @slow
       def lowercase       (						self:       Dict						)				->       List[str]:




              """simple docstring"""


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








import math




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5    ,						int(math.sqrt(UpperCamelCase_     ) + 1     )    ,						6     ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True




def        lowerCAmelCase_       (							UpperCamelCase_ = 600851475143     )   ->						int:

       try:
              UpperCamelCase_      				=     int(UpperCamelCase_     )
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."     )
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."     )
       UpperCamelCase_      				=     0
       if is_prime(UpperCamelCase_     ):
              return n
       while n % 2 == 0:
              n //= 2
       if is_prime(UpperCamelCase_     ):
              return n
       for i in range(3    ,						int(math.sqrt(UpperCamelCase_     )     ) + 1    ,						2     ):
              if n % i == 0:
                     if is_prime(n // i     ):
                            UpperCamelCase_      				=     n // i
                            break
                     elif is_prime(UpperCamelCase_     ):
                            UpperCamelCase_      				=     i
       return max_number


if __name__ == "__main__":
     print(f'''{solution() = }''')

