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 		_lowercase						(					snake_case_       ):
 lowercase				     =   'gpt_neox'

 def __init__( self	:							Dict   ,      snake_case	:							List[str]=5_0_4_3_2   ,      snake_case	:							Any=6_1_4_4   ,      snake_case	:							Any=4_4   ,      snake_case	:							Any=6_4   ,      snake_case	:							List[str]=2_4_5_7_6   ,      snake_case	:							List[str]="gelu"   ,      snake_case	:							Any=0.25   ,      snake_case	:							Any=1_0_0_0_0   ,      snake_case	:							Any=0.0   ,      snake_case	:							Any=0.0   ,      snake_case	:							Dict=0.1   ,      snake_case	:							Union[str, Any]=2_0_4_8   ,      snake_case	:							Optional[Any]=0.02   ,      snake_case	:							List[str]=1e-5   ,      snake_case	:							Optional[int]=True   ,      snake_case	:							Optional[Any]=0   ,      snake_case	:							str=2   ,      snake_case	:							int=False   ,      snake_case	:							Optional[Any]=True   ,      snake_case	:							Tuple=None   ,      **snake_case	:							Optional[int]   ,      )   ->						Any:

  """simple docstring"""





  super().__init__(bos_token_id=snake_case   ,      eos_token_id=snake_case   ,      **snake_case			)
  UpperCamelCase_					:						Tuple =					vocab_size
  UpperCamelCase_					:						Dict =					max_position_embeddings
  UpperCamelCase_					:						str =					hidden_size
  UpperCamelCase_					:						Dict =					num_hidden_layers
  UpperCamelCase_					:						str =					num_attention_heads
  UpperCamelCase_					:						List[str] =					intermediate_size
  UpperCamelCase_					:						List[str] =					hidden_act
  UpperCamelCase_					:						Tuple =					rotary_pct
  UpperCamelCase_					:						int =					rotary_emb_base
  UpperCamelCase_					:						Any =					attention_dropout
  UpperCamelCase_					:						Any =					hidden_dropout
  UpperCamelCase_					:						List[str] =					classifier_dropout
  UpperCamelCase_					:						Union[str, Any] =					initializer_range
  UpperCamelCase_					:						Union[str, Any] =					layer_norm_eps
  UpperCamelCase_					:						Tuple =					use_cache
  UpperCamelCase_					:						Optional[int] =					tie_word_embeddings
  UpperCamelCase_					:						Optional[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 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  if self.rope_scaling is None:
   return

  if not isinstance(self.rope_scaling   ,      snake_case			) 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[int] =					self.rope_scaling.get('type'   ,      snake_case			)
  UpperCamelCase_					:						Optional[Any] =					self.rope_scaling.get('factor'   ,      snake_case			)
  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(snake_case   ,      snake_case			) or rope_scaling_factor <= 1.0:
   raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"			)




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   XLMTokenizer
 lowercase				     =   False

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

  """simple docstring"""





  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  UpperCamelCase_					:						List[Any] =					[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  UpperCamelCase_					:						str =					dict(zip(snake_case   ,      range(len(snake_case			)			)			)			)
  UpperCamelCase_					:						Tuple =					['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  UpperCamelCase_					:						Tuple =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file']			)
  UpperCamelCase_					:						Any =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file']			)
  with open(self.vocab_file   ,      'w'			) as fp:
   fp.write(json.dumps(snake_case			)			)
  with open(self.merges_file   ,      'w'			) as fp:
   fp.write('\n'.join(snake_case			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'lower newer'
  UpperCamelCase_					:						Optional[int] =					'lower newer'
  return input_text, output_text

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer(self.vocab_file   ,      self.merges_file			)

  UpperCamelCase_					:						List[str] =					'lower'
  UpperCamelCase_					:						Optional[int] =					['low', 'er</w>']
  UpperCamelCase_					:						Optional[Any] =					tokenizer.tokenize(snake_case			)
  self.assertListEqual(snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					tokens + ['<unk>']
  UpperCamelCase_					:						int =					[1_4, 1_5, 2_0]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case			)   ,      snake_case			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer.from_pretrained('xlm-mlm-en-2048'			)

  UpperCamelCase_					:						int =					tokenizer.encode('sequence builders'   ,      add_special_tokens=snake_case			)
  UpperCamelCase_					:						Optional[int] =					tokenizer.encode('multi-sequence build'   ,      add_special_tokens=snake_case			)

  UpperCamelCase_					:						Tuple =					tokenizer.build_inputs_with_special_tokens(snake_case			)
  UpperCamelCase_					:						Any =					tokenizer.build_inputs_with_special_tokens(snake_case   ,      snake_case			)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]



