import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
     from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
     import torch







@require_flax
class        UpperCAmelCase_					:




    def __init__(       self	: Optional[int]   ,   lowerCAmelCase	: List[Any]   ,   lowerCAmelCase	: Tuple=14   ,   lowerCAmelCase	: str=7   ,   lowerCAmelCase	: Optional[int]=True   ,   lowerCAmelCase	: List[str]=True   ,   lowerCAmelCase	: Union[str, Any]=True   ,   lowerCAmelCase	: List[Any]=99   ,   lowerCAmelCase	: Optional[int]=32   ,   lowerCAmelCase	: Tuple=5   ,   lowerCAmelCase	: Optional[Any]=4   ,   lowerCAmelCase	: Dict=37   ,   lowerCAmelCase	: Dict="gelu"   ,   lowerCAmelCase	: List[str]=0.1   ,   lowerCAmelCase	: Union[str, Any]=0.1   ,   lowerCAmelCase	: Union[str, Any]=512   ,   lowerCAmelCase	: str=0.02   ,   lowerCAmelCase	: List[Any]=None   ,   )						->			Optional[Any]:
      a__  		=  parent
      a__  		=  batch_size
      a__  		=  seq_length
      a__  		=  is_training
      a__  		=  use_input_mask
      a__  		=  use_labels
      a__  		=  vocab_size
      a__  		=  d_model
      a__  		=  num_hidden_layers
      a__  		=  num_attention_heads
      a__  		=  ffn_dim
      a__  		=  activation_function
      a__  		=  activation_dropout
      a__  		=  attention_dropout
      a__  		=  max_position_embeddings
      a__  		=  initializer_range
      a__  		=  None
      a__  		=  0
      a__  		=  2
      a__  		=  1




    def     __lowerCamelCase			(       self	: Optional[int] )						->			Union[str, Any]:
      a__  		=  np.clip(ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size )   ,   3   ,   self.vocab_size )

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

      a__  		=  XGLMConfig(
          vocab_size=self.vocab_size   ,   d_model=self.hidden_size   ,   num_layers=self.num_hidden_layers   ,   attention_heads=self.num_attention_heads   ,   ffn_dim=self.ffn_dim   ,   activation_function=self.activation_function   ,   activation_dropout=self.activation_dropout   ,   attention_dropout=self.attention_dropout   ,   max_position_embeddings=self.max_position_embeddings   ,   initializer_range=self.initializer_range   ,   use_cache=lowerCAmelCase   ,   bos_token_id=self.bos_token_id   ,   eos_token_id=self.eos_token_id   ,   pad_token_id=self.pad_token_id   ,   )

      return (config, input_ids, input_mask)




    def     __lowerCamelCase			(       self	: Any )						->			Optional[int]:
      a__  		=  self.prepare_config_and_inputs()
      a__		,a__		,a__  		=  config_and_inputs
      a__  		=  {"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict




    def     __lowerCamelCase			(       self	: str )						->			List[Any]:
      a__		,a__		,a__  		=  self.prepare_config_and_inputs()

      a__  		=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
      a__  		=  ids_tensor([self.batch_size, self.seq_length]   ,   vocab_size=2 )

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )




    def     __lowerCamelCase			(       self	: Union[str, Any]   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: Union[str, Any]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: Optional[int] )						->			List[Any]:
      a__  		=  20
      a__  		=  model_class_name(lowerCAmelCase )

      a__  		=  model.init_cache(input_ids.shape[0]   ,   lowerCAmelCase )
      a__  		=  jnp.ones((input_ids.shape[0], max_decoder_length)   ,   dtype="i4" )

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

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

      a__  		=  model(lowerCAmelCase )

      a__  		=  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     __lowerCamelCase			(       self	: Union[str, Any]   ,   lowerCAmelCase	: List[Any]   ,   lowerCAmelCase	: Union[str, Any]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: List[str] )						->			Optional[Any]:
      a__  		=  20
      a__  		=  model_class_name(lowerCAmelCase )

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

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

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

      a__  		=  model(lowerCAmelCase   ,   attention_mask=lowerCAmelCase )
      a__  		=  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_sentencepiece
@require_flax
class        UpperCAmelCase_					(				_a  ,  _a  ,  unittest.TestCase     ):
    UpperCAmelCase_      :							Union[str, Any]    			=				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
    UpperCAmelCase_      :							Dict    			=				(FlaxXGLMForCausalLM,) if is_flax_available() else ()




    def     __lowerCamelCase			(       self	: Dict )						->			Optional[Any]:
      a__  		=  FlaxXGLMModelTester(self )




    def     __lowerCamelCase			(       self	: List[Any] )						->			Optional[int]:
      for model_class_name in self.all_model_classes:
        a__		,a__		,a__  		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Dict )						->			str:
      for model_class_name in self.all_model_classes:
        a__		,a__		,a__  		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )




    @slow
    def     __lowerCamelCase			(       self	: List[Any] )						->			List[Any]:
      a__  		=  XGLMTokenizer.from_pretrained("XGLM"   ,   padding_side="left" )
      a__  		=  tokenizer(["Hello this is a long string", "Hey"]   ,   return_tensors="np"   ,   padding=lowerCAmelCase   ,   truncation=lowerCAmelCase )

      a__  		=  FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
      a__  		=  1
      a__  		=  False

      a__  		=  jax.jit(model.generate )

      a__  		=  jit_generate(inputs["input_ids"]   ,   attention_mask=inputs["attention_mask"] ).sequences

      a__  		=  tokenizer.batch_decode(lowerCAmelCase   ,   skip_special_tokens=lowerCAmelCase )

      a__  		=  [
          "Hello this is a long string of questions, but I'm not sure if I'm",
          "Hey, I'm a newbie to the forum and I'",
      ]

      self.assertListEqual(lowerCAmelCase   ,   lowerCAmelCase )




    @is_pt_flax_cross_test
    def     __lowerCamelCase			(       self	: Any )						->			Union[str, Any]:
      a__		,a__  		=  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
          a__  		=  self._prepare_for_class(lowerCAmelCase   ,   lowerCAmelCase )
          a__  		=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

          a__		,a__  		=  pt_inputs["input_ids"].shape
          a__  		=  np.random.randint(0   ,   seq_length - 1   ,   size=(batch_size,) )
          for batch_idx, start_index in enumerate(lowerCAmelCase ):
            a__  		=  0
            a__  		=  1
            a__  		=  0
            a__  		=  1
          a__  		=  pt_model_class(lowerCAmelCase ).eval()
          # Flax models don't use the `use_cache` option and cache is not returned as a default.
          # So we disable `use_cache` here for PyTorch model.
          a__  		=  False
          a__  		=  model_class(lowerCAmelCase   ,   dtype=jnp.floataa )
          a__  		=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,   lowerCAmelCase )
          a__  		=  fx_state

          with torch.no_grad():
            a__  		=  pt_model(**lowerCAmelCase ).to_tuple()

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

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

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




    @is_pt_flax_cross_test
    def     __lowerCamelCase			(       self	: List[Any] )						->			Any:
      a__		,a__  		=  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
          a__  		=  self._prepare_for_class(lowerCAmelCase   ,   lowerCAmelCase )
          a__  		=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

          a__  		=  pt_model_class(lowerCAmelCase ).eval()
          a__  		=  False
          a__  		=  model_class(lowerCAmelCase   ,   dtype=jnp.floataa )

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

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

          with torch.no_grad():
            a__  		=  pt_model(**lowerCAmelCase ).to_tuple()

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

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

          with torch.no_grad():
            a__  		=  pt_model_loaded(**lowerCAmelCase ).to_tuple()

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




    @slow
    def     __lowerCamelCase			(       self	: int )						->			int:
      for model_class_name in self.all_model_classes:
        a__  		=  model_class_name.from_pretrained("facebook/xglm-564M" )
        a__  		=  model(np.ones((1, 1) ) )
        self.assertIsNotNone(lowerCAmelCase )


import math
def 					__lowercase						(    lowerCamelCase__       )					->bool:

  '''simple docstring'''




  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(lowerCamelCase__       ) + 1       )			,					6       ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True
def 					__lowercase						(    lowerCamelCase__ = 2_00_00_00       )					->int:

  '''simple docstring'''




  return sum(num for num in range(3			,					lowerCamelCase__			,					2       ) if is_prime(lowerCamelCase__       )       ) + 2 if n > 2 else 0


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

