

"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A							=  logging.get_logger(__name__)

_A							=  {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  'gpt_neox'



  def __init__(self							,       _lowerCamelCase=50432							,       _lowerCamelCase=6144							,       _lowerCamelCase=44							,       _lowerCamelCase=64							,       _lowerCamelCase=24576							,       _lowerCamelCase="gelu"							,       _lowerCamelCase=0.25							,       _lowerCamelCase=10000							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.1							,       _lowerCamelCase=2048							,       _lowerCamelCase=0.02							,       _lowerCamelCase=1e-5							,       _lowerCamelCase=True							,       _lowerCamelCase=0							,       _lowerCamelCase=2							,       _lowerCamelCase=False							,       _lowerCamelCase=True							,       _lowerCamelCase=None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        super().__init__(bos_token_id=_lowerCamelCase							,       eos_token_id=_lowerCamelCase							,       **_lowerCamelCase	)
        UpperCAmelCase__			:							str							=    vocab_size
        UpperCAmelCase__			:							Union[str, Any]							=    max_position_embeddings
        UpperCAmelCase__			:							Dict							=    hidden_size
        UpperCAmelCase__			:							Optional[Any]							=    num_hidden_layers
        UpperCAmelCase__			:							List[str]							=    num_attention_heads
        UpperCAmelCase__			:							List[Any]							=    intermediate_size
        UpperCAmelCase__			:							str							=    hidden_act
        UpperCAmelCase__			:							List[Any]							=    rotary_pct
        UpperCAmelCase__			:							Tuple							=    rotary_emb_base
        UpperCAmelCase__			:							Dict							=    attention_dropout
        UpperCAmelCase__			:							List[Any]							=    hidden_dropout
        UpperCAmelCase__			:							List[Any]							=    classifier_dropout
        UpperCAmelCase__			:							str							=    initializer_range
        UpperCAmelCase__			:							Optional[int]							=    layer_norm_eps
        UpperCAmelCase__			:							List[Any]							=    use_cache
        UpperCAmelCase__			:							str							=    tie_word_embeddings
        UpperCAmelCase__			:							int							=    use_parallel_residual
        UpperCAmelCase__			:							Optional[int]							=    rope_scaling
        self._rope_scaling_validation()

        if self.hidden_size % self.num_attention_heads != 0:
              raise ValueError(
                  """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""	)




  def  _a      (self	):


        """simple docstring"""

        if self.rope_scaling is None:
              return

        if not isinstance(self.rope_scaling							,       _lowerCamelCase	) or len(self.rope_scaling	) != 2:
              raise ValueError(
                  """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                  F"""got {self.rope_scaling}"""	)
        UpperCAmelCase__			:							Optional[Any]							=    self.rope_scaling.get("""type"""							,       _lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    self.rope_scaling.get("""factor"""							,       _lowerCamelCase	)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
              raise ValueError(
                  F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""	)
        if rope_scaling_factor is None or not isinstance(_lowerCamelCase							,       _lowerCamelCase	) or rope_scaling_factor <= 1.0:
              raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""	)




"""simple docstring"""
import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad

class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    10



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    [1, 2, 3, 4]
        UpperCAmelCase__			:							Tuple							=    [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
        self.assertEqual(truncate_or_pad(_lowerCamelCase							,       self.block_size							,       0	)							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        UpperCAmelCase__			:							Tuple							=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        self.assertEqual(truncate_or_pad(_lowerCamelCase							,       self.block_size							,       0	)							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
        UpperCAmelCase__			:							Dict							=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        self.assertEqual(truncate_or_pad(_lowerCamelCase							,       self.block_size							,       0	)							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    """It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
        UpperCAmelCase__					,		UpperCAmelCase__			:							Any							=    process_story(_lowerCamelCase	)
        self.assertEqual(_lowerCamelCase							,       []	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    """"""
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[Any]							=    process_story(_lowerCamelCase	)
        self.assertEqual(_lowerCamelCase							,       []	)
        self.assertEqual(_lowerCamelCase							,       []	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    (
            """It was the year of Our Lord one thousand seven hundred and """
            """seventy-five\n\nSpiritual revelations were conceded to England """
            """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
        )
        UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[Any]							=    process_story(_lowerCamelCase	)

        UpperCAmelCase__			:							List[Any]							=    [
            """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
            """Spiritual revelations were conceded to England at that favoured period, as at this.""",
        ]
        self.assertEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							List[Any]							=    ["""It was the best of times."""]
        self.assertEqual(_lowerCamelCase							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    torch.tensor([1, 2, 3, 4]	)
        UpperCAmelCase__			:							str							=    torch.tensor([1, 1, 1, 1]	)
        np.testing.assert_array_equal(build_mask(_lowerCamelCase							,       0	).numpy()							,       expected.numpy()	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    torch.tensor([1, 2, 3, 4, 23, 23, 23]	)
        UpperCAmelCase__			:							Union[str, Any]							=    torch.tensor([1, 1, 1, 1, 0, 0, 0]	)
        np.testing.assert_array_equal(build_mask(_lowerCamelCase							,       23	).numpy()							,       expected.numpy()	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    torch.tensor([8, 2, 3, 4, 1, 1, 1]	)
        UpperCAmelCase__			:							Any							=    torch.tensor([1, 1, 1, 1, 0, 0, 0]	)
        np.testing.assert_array_equal(build_mask(_lowerCamelCase							,       1	).numpy()							,       expected.numpy()	)




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    101
        UpperCAmelCase__			:							Dict							=    torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]	)
        UpperCAmelCase__			:							Optional[Any]							=    torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]	)

        UpperCAmelCase__			:							int							=    compute_token_type_ids(_lowerCamelCase							,       _lowerCamelCase	)
        np.testing.assert_array_equal(_lowerCamelCase							,       _lowerCamelCase	)

