
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_							=     logging.get_logger(__name__)

lowerCamelCase_							=     {
    """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 	__A(				a__ ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			"""gpt_neox"""







  def __init__(self       ,			SCREAMING_SNAKE_CASE_=5_04_32       ,			SCREAMING_SNAKE_CASE_=61_44       ,			SCREAMING_SNAKE_CASE_=44       ,			SCREAMING_SNAKE_CASE_=64       ,			SCREAMING_SNAKE_CASE_=2_45_76       ,			SCREAMING_SNAKE_CASE_="gelu"       ,			SCREAMING_SNAKE_CASE_=0.25       ,			SCREAMING_SNAKE_CASE_=1_00_00       ,			SCREAMING_SNAKE_CASE_=0.0       ,			SCREAMING_SNAKE_CASE_=0.0       ,			SCREAMING_SNAKE_CASE_=0.1       ,			SCREAMING_SNAKE_CASE_=20_48       ,			SCREAMING_SNAKE_CASE_=0.02       ,			SCREAMING_SNAKE_CASE_=1E-5       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=0       ,			SCREAMING_SNAKE_CASE_=2       ,			SCREAMING_SNAKE_CASE_=False       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=None       ,			**SCREAMING_SNAKE_CASE_       ,			):
       super().__init__(bos_token_id=__lowerCAmelCase       ,			eos_token_id=__lowerCAmelCase       ,			**__lowerCAmelCase						)
       UpperCamelCase__				=				vocab_size
       UpperCamelCase__				=				max_position_embeddings
       UpperCamelCase__				=				hidden_size
       UpperCamelCase__				=				num_hidden_layers
       UpperCamelCase__				=				num_attention_heads
       UpperCamelCase__				=				intermediate_size
       UpperCamelCase__				=				hidden_act
       UpperCamelCase__				=				rotary_pct
       UpperCamelCase__				=				rotary_emb_base
       UpperCamelCase__				=				attention_dropout
       UpperCamelCase__				=				hidden_dropout
       UpperCamelCase__				=				classifier_dropout
       UpperCamelCase__				=				initializer_range
       UpperCamelCase__				=				layer_norm_eps
       UpperCamelCase__				=				use_cache
       UpperCamelCase__				=				tie_word_embeddings
       UpperCamelCase__				=				use_parallel_residual
       UpperCamelCase__				=				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        UpperCAmelCase_					(self						):
       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__				=				self.rope_scaling.get("""type"""       ,			__lowerCAmelCase						)
       UpperCamelCase__				=				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'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

