



"""simple docstring"""







from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
     from transformers import (
         TFAutoModel,
         TFAutoModelForCausalLM,
         TFAutoModelForMaskedLM,
         TFAutoModelForPreTraining,
         TFAutoModelForQuestionAnswering,
         TFAutoModelForSeqaSeqLM,
         TFAutoModelForSequenceClassification,
         TFAutoModelForTableQuestionAnswering,
         TFAutoModelForTokenClassification,
         TFAutoModelWithLMHead,
         TFBertForMaskedLM,
         TFBertForPreTraining,
         TFBertForQuestionAnswering,
         TFBertForSequenceClassification,
         TFBertModel,
         TFFunnelBaseModel,
         TFFunnelModel,
         TFGPTaLMHeadModel,
         TFRobertaForMaskedLM,
         TFTaForConditionalGeneration,
         TFTapasForQuestionAnswering,
     )
     from transformers.models.auto.modeling_tf_auto import (
         TF_MODEL_FOR_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_MASKED_LM_MAPPING,
         TF_MODEL_FOR_PRETRAINING_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         TF_MODEL_MAPPING,
     )
     from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""new-model"""


if is_tf_available():







     class 				UpperCamelCase__       (     lowercase_  ):




           """simple docstring"""




           _SCREAMING_SNAKE_CASE      =					NewModelConfig







@require_tf
class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    Tuple								=			'bert-base-cased'
        lowerCAmelCase_       :    List[str]								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        lowerCAmelCase_       :    Optional[Any]								=			'bert-base-cased'
        lowerCAmelCase_       :    Union[str, Any]								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Tuple								=			TFAutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE_	)
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_       :    int								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    List[str]								=			TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_						,lowerCAmelCase_       :    str								=			TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_   ,      output_loading_info=SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_       :    Optional[Any]								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    str								=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_       :    str								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    str								=			TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_						,lowerCAmelCase_       :    Tuple								=			TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE_   ,      output_loading_info=SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_       :    Dict								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    str								=			TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_						,lowerCAmelCase_       :    Dict								=			TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_   ,      output_loading_info=SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        for model_name in ["bert-base-uncased"]:
          lowerCAmelCase_       :    Tuple								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    Any								=			TFAutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        for model_name in ["bert-base-uncased"]:
          lowerCAmelCase_       :    str								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    Optional[Any]								=			TFAutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      @slow
      @require_tensorflow_probability
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
          lowerCAmelCase_       :    Optional[Any]								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    List[str]								=			TFAutoModelForTableQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_						,lowerCAmelCase_       :    str								=			TFAutoModelForTableQuestionAnswering.from_pretrained(
              SCREAMING_SNAKE_CASE_   ,      output_loading_info=SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Union[str, Any]								=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_	)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        self.assertEqual(model.num_parameters()   ,      1_4_4_1_0	)
        self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE_	)   ,      1_4_4_1_0	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    Optional[Any]								=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_	)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        self.assertEqual(model.num_parameters()   ,      1_4_4_1_0	)
        self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE_	)   ,      1_4_4_1_0	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
        lowerCAmelCase_       :    Optional[int]								=			TFAutoModel.from_pretrained('sgugger/funnel-random-tiny'	)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Tuple								=			copy.deepcopy(model.config	)
        lowerCAmelCase_       :    Dict								=			['FunnelBaseModel']
        lowerCAmelCase_       :    Any								=			TFAutoModel.from_config(SCREAMING_SNAKE_CASE_	)
        self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

        with tempfile.TemporaryDirectory() as tmp_dir:
          model.save_pretrained(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    List[str]								=			TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        try:
          AutoConfig.register('new-model'   ,      SCREAMING_SNAKE_CASE_	)

          lowerCAmelCase_       :    Dict								=			[
              TFAutoModel,
              TFAutoModelForCausalLM,
              TFAutoModelForMaskedLM,
              TFAutoModelForPreTraining,
              TFAutoModelForQuestionAnswering,
              TFAutoModelForSequenceClassification,
              TFAutoModelForTokenClassification,
          ]

          for auto_class in auto_classes:
            with self.subTest(auto_class.__name__	):
              # Wrong config class will raise an error
              with self.assertRaises(SCREAMING_SNAKE_CASE_	):
                auto_class.register(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
              auto_class.register(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(SCREAMING_SNAKE_CASE_	):
                auto_class.register(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

              # Now that the config is registered, it can be used as any other config with the auto-API
              lowerCAmelCase_       :    str								=			BertModelTester(self	).get_config()
              lowerCAmelCase_       :    Optional[int]								=			NewModelConfig(**tiny_config.to_dict()	)
              lowerCAmelCase_       :    List[str]								=			auto_class.from_config(SCREAMING_SNAKE_CASE_	)
              self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

              with tempfile.TemporaryDirectory() as tmp_dir:
                model.save_pretrained(SCREAMING_SNAKE_CASE_	)
                lowerCAmelCase_       :    Optional[Any]								=			auto_class.from_pretrained(SCREAMING_SNAKE_CASE_	)
                self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

        finally:
          if "new-model" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["new-model"]
          for mapping in (
              TF_MODEL_MAPPING,
              TF_MODEL_FOR_PRETRAINING_MAPPING,
              TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
              TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_CAUSAL_LM_MAPPING,
              TF_MODEL_FOR_MASKED_LM_MAPPING,
          ):
            if NewModelConfig in mapping._extra_content:
              del mapping._extra_content[NewModelConfig]



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        with self.assertRaisesRegex(
            SCREAMING_SNAKE_CASE_   ,      'bert-base is not a local folder and is not a valid model identifier'	):
          lowerCAmelCase_       :    Optional[Any]								=			TFAutoModel.from_pretrained('bert-base'	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        with self.assertRaisesRegex(
            SCREAMING_SNAKE_CASE_   ,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'	):
          lowerCAmelCase_       :    Optional[int]								=			TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_   ,      revision='aaaaaa'	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        with self.assertRaisesRegex(
            SCREAMING_SNAKE_CASE_   ,      'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'   ,      ):
          lowerCAmelCase_       :    Optional[Any]								=			TFAutoModel.from_pretrained('hf-internal-testing/config-no-model'	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_   ,      'Use `from_pt=True` to load this model'	):
          lowerCAmelCase_       :    Dict								=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        # Make sure we have cached the model.
        lowerCAmelCase_       :    int								=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'	)
        with RequestCounter() as counter:
          lowerCAmelCase_       :    Dict								=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'	)
          self.assertEqual(counter.get_request_count   ,      0	)
          self.assertEqual(counter.head_request_count   ,      1	)
          self.assertEqual(counter.other_request_count   ,      0	)

        # With a sharded checkpoint
        lowerCAmelCase_       :    Any								=			TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'	)
        with RequestCounter() as counter:
          lowerCAmelCase_       :    Optional[int]								=			TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'	)
          self.assertEqual(counter.get_request_count   ,      0	)
          self.assertEqual(counter.head_request_count   ,      1	)
          self.assertEqual(counter.other_request_count   ,      0	)










"""simple docstring"""







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


lowercase__		:    List[Any]				   =     logging.get_logger(__name__)

lowercase__		:    List[str]				   =     {
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""open-llama"""



      def __init__(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_0_0_0_0_0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=4_0_9_6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_1_0_0_8   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=3_2   ,      SCREAMING_SNAKE_CASE_    :				str="silu"   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2_0_4_8   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				int=1E-6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=True   ,      SCREAMING_SNAKE_CASE_    :				Dict=0   ,      SCREAMING_SNAKE_CASE_    :				Dict=1   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2   ,      SCREAMING_SNAKE_CASE_    :				str=False   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=True   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				Dict=True   ,      SCREAMING_SNAKE_CASE_    :				Any=True   ,      SCREAMING_SNAKE_CASE_    :				Tuple=None   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      ):
        lowerCAmelCase_       :    Union[str, Any]								=			vocab_size
        lowerCAmelCase_       :    int								=			max_position_embeddings
        lowerCAmelCase_       :    Union[str, Any]								=			hidden_size
        lowerCAmelCase_       :    Optional[Any]								=			intermediate_size
        lowerCAmelCase_       :    List[Any]								=			num_hidden_layers
        lowerCAmelCase_       :    Optional[int]								=			num_attention_heads
        lowerCAmelCase_       :    List[Any]								=			hidden_act
        lowerCAmelCase_       :    List[Any]								=			initializer_range
        lowerCAmelCase_       :    List[str]								=			rms_norm_eps
        lowerCAmelCase_       :    List[Any]								=			use_cache
        lowerCAmelCase_       :    Optional[int]								=			kwargs.pop(
            'use_memorry_efficient_attention'   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    List[str]								=			attention_dropout_prob
        lowerCAmelCase_       :    Tuple								=			use_stable_embedding
        lowerCAmelCase_       :    Optional[Any]								=			shared_input_output_embedding
        lowerCAmelCase_       :    Optional[Any]								=			rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=SCREAMING_SNAKE_CASE_   ,      bos_token_id=SCREAMING_SNAKE_CASE_   ,      eos_token_id=SCREAMING_SNAKE_CASE_   ,      tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_   ,      )





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        if self.rope_scaling is None:
          return

        if not isinstance(self.rope_scaling   ,      SCREAMING_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}"	)
        lowerCAmelCase_       :    int								=			self.rope_scaling.get('type'   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			self.rope_scaling.get('factor'   ,      SCREAMING_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(SCREAMING_SNAKE_CASE_   ,      SCREAMING_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}"	)





