




'''simple docstring'''






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  A		:




    '''simple docstring'''





    def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase=1_4			,  _UpperCAmelCase=7			,  _UpperCAmelCase=True			,  _UpperCAmelCase=True			,  _UpperCAmelCase=True			,  _UpperCAmelCase=9_9			,  _UpperCAmelCase=3_2			,  _UpperCAmelCase=5			,  _UpperCAmelCase=4			,  _UpperCAmelCase=3_7			,  _UpperCAmelCase="gelu"			,  _UpperCAmelCase=0.1			,  _UpperCAmelCase=0.1			,  _UpperCAmelCase=5_1_2			,  _UpperCAmelCase=0.02			,  _UpperCAmelCase=None			,  )     ->	int:
       __UpperCamelCase				:      Union[str, Any]									=       parent
       __UpperCamelCase				:      Dict									=       batch_size
       __UpperCamelCase				:      List[str]									=       seq_length
       __UpperCamelCase				:      List[Any]									=       is_training
       __UpperCamelCase				:      Optional[int]									=       use_input_mask
       __UpperCamelCase				:      Optional[Any]									=       use_labels
       __UpperCamelCase				:      Tuple									=       vocab_size
       __UpperCamelCase				:      Optional[Any]									=       d_model
       __UpperCamelCase				:      List[Any]									=       num_hidden_layers
       __UpperCamelCase				:      Optional[Any]									=       num_attention_heads
       __UpperCamelCase				:      Any									=       ffn_dim
       __UpperCamelCase				:      List[Any]									=       activation_function
       __UpperCamelCase				:      Dict									=       activation_dropout
       __UpperCamelCase				:      Tuple									=       attention_dropout
       __UpperCamelCase				:      Dict									=       max_position_embeddings
       __UpperCamelCase				:      int									=       initializer_range
       __UpperCamelCase				:      str									=       None
       __UpperCamelCase				:      Dict									=       0
       __UpperCamelCase				:      Optional[int]									=       2
       __UpperCamelCase				:      Optional[int]									=       1
    def 	a_	(self      )     ->	Tuple:
       __UpperCamelCase				:      List[str]									=       np.clip(ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size      )			,  3			,  self.vocab_size      )

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

       __UpperCamelCase				:      Tuple									=       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=__SCREAMING_SNAKE_CASE			,  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 	a_	(self      )     ->	str:
       __UpperCamelCase				:      Tuple									=       self.prepare_config_and_inputs()
       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      Any									=       config_and_inputs
       __UpperCamelCase				:      Union[str, Any]									=       {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict
    def 	a_	(self      )     ->	Dict:
       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      List[Any]									=       self.prepare_config_and_inputs()

       __UpperCamelCase				:      Union[str, Any]									=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
       __UpperCamelCase				:      str									=       ids_tensor([self.batch_size, self.seq_length]			,  vocab_size=2      )

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )
    def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Dict:
       __UpperCamelCase				:      int									=       2_0
       __UpperCamelCase				:      int									=       model_class_name(__SCREAMING_SNAKE_CASE      )

       __UpperCamelCase				:      Union[str, Any]									=       model.init_cache(input_ids.shape[0]			,  __SCREAMING_SNAKE_CASE      )
       __UpperCamelCase				:      Union[str, Any]									=       jnp.ones((input_ids.shape[0], max_decoder_length)			,  dtype="i4"      )

       __UpperCamelCase				:      Tuple									=       jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1      )[None, :]			,  (input_ids.shape[0], input_ids.shape[-1] - 1)      )
       __UpperCamelCase				:      List[Any]									=       model(
           input_ids[:, :-1]			,  attention_mask=__SCREAMING_SNAKE_CASE			,  past_key_values=__SCREAMING_SNAKE_CASE			,  position_ids=__SCREAMING_SNAKE_CASE			,  )

       __UpperCamelCase				:      List[str]									=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,  dtype="i4"      )
       __UpperCamelCase				:      Union[str, Any]									=       model(
           input_ids[:, -1:]			,  attention_mask=__SCREAMING_SNAKE_CASE			,  past_key_values=outputs_cache.past_key_values			,  position_ids=__SCREAMING_SNAKE_CASE			,  )

       __UpperCamelCase				:      int									=       model(__SCREAMING_SNAKE_CASE      )

       __UpperCamelCase				:      List[str]									=       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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	int:
       __UpperCamelCase				:      str									=       2_0
       __UpperCamelCase				:      List[str]									=       model_class_name(__SCREAMING_SNAKE_CASE      )

       __UpperCamelCase				:      List[str]									=       jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]			,  axis=-1			,  )

       __UpperCamelCase				:      List[str]									=       model.init_cache(input_ids.shape[0]			,  __SCREAMING_SNAKE_CASE      )
       __UpperCamelCase				:      Any									=       jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1      )[None, :]			,  (input_ids.shape[0], input_ids.shape[-1] - 1)      )

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

       __UpperCamelCase				:      List[Any]									=       model(__SCREAMING_SNAKE_CASE			,  attention_mask=__SCREAMING_SNAKE_CASE      )
       __UpperCamelCase				:      Dict									=       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  A		(			__lowerCAmelCase      ,							__lowerCAmelCase      ,							unittest.TestCase		):




    '''simple docstring'''





    A				     =    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
    A				     =    (FlaxXGLMForCausalLM,) if is_flax_available() else ()
    def 	a_	(self      )     ->	int:
       __UpperCamelCase				:      Union[str, Any]									=       FlaxXGLMModelTester(self      )
    def 	a_	(self      )     ->	Optional[int]:




       for model_class_name in self.all_model_classes:
          __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      Dict									=       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 	a_	(self      )     ->	Any:




       for model_class_name in self.all_model_classes:
          __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      Tuple									=       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 	a_	(self      )     ->	Optional[Any]:
       __UpperCamelCase				:      Dict									=       XGLMTokenizer.from_pretrained("XGLM"			,  padding_side="left"      )
       __UpperCamelCase				:      Optional[int]									=       tokenizer(["Hello this is a long string", "Hey"]			,  return_tensors="np"			,  padding=__SCREAMING_SNAKE_CASE			,  truncation=__SCREAMING_SNAKE_CASE      )

       __UpperCamelCase				:      List[Any]									=       FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"      )
       __UpperCamelCase				:      Tuple									=       1
       __UpperCamelCase				:      List[Any]									=       False

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

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

       __UpperCamelCase				:      List[str]									=       tokenizer.batch_decode(__SCREAMING_SNAKE_CASE			,  skip_special_tokens=__SCREAMING_SNAKE_CASE      )

       __UpperCamelCase				:      List[str]									=       [
           "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(__SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE      )
    @is_pt_flax_cross_test
    def 	a_	(self      )     ->	str:
       __UpperCamelCase		, __UpperCamelCase				:      str									=       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				:      Optional[Any]									=       self._prepare_for_class(__SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE      )
             __UpperCamelCase				:      Tuple									=       {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

             __UpperCamelCase		, __UpperCamelCase				:      Optional[int]									=       pt_inputs["input_ids"].shape
             __UpperCamelCase				:      Dict									=       np.random.randint(0			,  seq_length - 1			,  size=(batch_size,)      )
             for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE      ):
                __UpperCamelCase				:      List[str]									=       0
                __UpperCamelCase				:      Union[str, Any]									=       1
                __UpperCamelCase				:      str									=       0
                __UpperCamelCase				:      str									=       1
             __UpperCamelCase				:      List[str]									=       pt_model_class(__SCREAMING_SNAKE_CASE      ).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.
             __UpperCamelCase				:      Optional[int]									=       False
             __UpperCamelCase				:      List[Any]									=       model_class(__SCREAMING_SNAKE_CASE			,  dtype=jnp.floataa      )
             __UpperCamelCase				:      List[str]									=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,  __SCREAMING_SNAKE_CASE      )
             __UpperCamelCase				:      Dict									=       fx_state

             with torch.no_grad():
                __UpperCamelCase				:      Dict									=       pt_model(**__SCREAMING_SNAKE_CASE      ).to_tuple()

             __UpperCamelCase				:      List[str]									=       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				:      Optional[int]									=       model_class.from_pretrained(__SCREAMING_SNAKE_CASE			,  from_pt=__SCREAMING_SNAKE_CASE      )

             __UpperCamelCase				:      Union[str, Any]									=       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 	a_	(self      )     ->	int:
       __UpperCamelCase		, __UpperCamelCase				:      Tuple									=       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				:      Optional[Any]									=       self._prepare_for_class(__SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE      )
             __UpperCamelCase				:      Any									=       {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             __UpperCamelCase				:      str									=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
             __UpperCamelCase				:      Union[str, Any]									=       getattr(__SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE      )

             __UpperCamelCase				:      Optional[int]									=       pt_model_class(__SCREAMING_SNAKE_CASE      ).eval()
             __UpperCamelCase				:      Optional[int]									=       False
             __UpperCamelCase				:      Optional[Any]									=       model_class(__SCREAMING_SNAKE_CASE			,  dtype=jnp.floataa      )

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

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

             with torch.no_grad():
                __UpperCamelCase				:      Optional[Any]									=       pt_model(**__SCREAMING_SNAKE_CASE      ).to_tuple()

             __UpperCamelCase				:      str									=       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				:      Dict									=       pt_model_class.from_pretrained(__SCREAMING_SNAKE_CASE			,  from_flax=__SCREAMING_SNAKE_CASE      )

             with torch.no_grad():
                __UpperCamelCase				:      Optional[int]									=       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 	a_	(self      )     ->	List[Any]:




       for model_class_name in self.all_model_classes:
          __UpperCamelCase				:      List[str]									=       model_class_name.from_pretrained("facebook/xglm-564M"      )
          __UpperCamelCase				:      Dict									=       model(np.ones((1, 1)      )      )
          self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )









'''simple docstring'''






def       __lowerCAmelCase (   snake_case__ = 4_000_000						):
			__UpperCamelCase				:      Tuple									=       1
			__UpperCamelCase				:      int									=       2
			__UpperCamelCase				:      str									=       0
			while j <= n:
						if j % 2 == 0:
									total += j
						__UpperCamelCase		, __UpperCamelCase				:      List[Any]									=       j, i + j

			return total


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



