



"""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  UpperCamelCase__  :




      """simple docstring"""




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



      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]			):
        lowerCAmelCase_  :							Union[str, Any]								=					np.clip(ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size			)       ,     3       ,     self.vocab_size			)

        lowerCAmelCase_  :							List[str]								=					None
        if self.use_input_mask:
          lowerCAmelCase_  :							Optional[Any]								=					random_attention_mask([self.batch_size, self.seq_length]			)

        lowerCAmelCase_  :							Union[str, Any]								=					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 	SCREAMING_SNAKE_CASE__					( self							: str			):
        lowerCAmelCase_  :							Optional[int]								=					self.prepare_config_and_inputs()
        lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_  :							Optional[Any]								=					config_and_inputs
        lowerCAmelCase_  :							Optional[Any]								=					{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					self.prepare_config_and_inputs()

        lowerCAmelCase_  :							int								=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
        lowerCAmelCase_  :							Optional[int]								=					ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2			)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )



      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Dict       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: List[str]       ,     SCREAMING_SNAKE_CASE_							: Dict			):
        lowerCAmelCase_  :							Dict								=					2_0
        lowerCAmelCase_  :							List[str]								=					model_class_name(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							str								=					model.init_cache(input_ids.shape[0]       ,     SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[int]								=					jnp.ones((input_ids.shape[0], max_decoder_length)       ,     dtype='i4'			)

        lowerCAmelCase_  :							Any								=					jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1			)[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)			)
        lowerCAmelCase_  :							str								=					model(
            input_ids[:, :-1]       ,     attention_mask=SCREAMING_SNAKE_CASE_       ,     past_key_values=SCREAMING_SNAKE_CASE_       ,     position_ids=SCREAMING_SNAKE_CASE_       ,     )

        lowerCAmelCase_  :							Optional[int]								=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype='i4'			)
        lowerCAmelCase_  :							Any								=					model(
            input_ids[:, -1:]       ,     attention_mask=SCREAMING_SNAKE_CASE_       ,     past_key_values=outputs_cache.past_key_values       ,     position_ids=SCREAMING_SNAKE_CASE_       ,     )

        lowerCAmelCase_  :							str								=					model(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							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 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: str       ,     SCREAMING_SNAKE_CASE_							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Dict       ,     SCREAMING_SNAKE_CASE_							: str			):
        lowerCAmelCase_  :							Dict								=					2_0
        lowerCAmelCase_  :							Optional[Any]								=					model_class_name(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							int								=					jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]       ,     axis=-1       ,     )

        lowerCAmelCase_  :							Optional[Any]								=					model.init_cache(input_ids.shape[0]       ,     SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[int]								=					jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1			)[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)			)

        lowerCAmelCase_  :							Tuple								=					model(
            input_ids[:, :-1]       ,     attention_mask=SCREAMING_SNAKE_CASE_       ,     past_key_values=SCREAMING_SNAKE_CASE_       ,     position_ids=SCREAMING_SNAKE_CASE_       ,     )
        lowerCAmelCase_  :							List[Any]								=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype='i4'			)
        lowerCAmelCase_  :							str								=					model(
            input_ids[:, -1:]       ,     past_key_values=outputs_cache.past_key_values       ,     attention_mask=SCREAMING_SNAKE_CASE_       ,     position_ids=SCREAMING_SNAKE_CASE_       ,     )

        lowerCAmelCase_  :							str								=					model(SCREAMING_SNAKE_CASE_       ,     attention_mask=SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							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}"			)







@require_sentencepiece
@require_flax
class  UpperCamelCase__  (      lowercase_,   lowercase_,   unittest.TestCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
      _SCREAMING_SNAKE_CASE    							=	(FlaxXGLMForCausalLM,) if is_flax_available() else ()



      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        lowerCAmelCase_  :							List[str]								=					FlaxXGLMModelTester(self			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        for model_class_name in self.all_model_classes:
          lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_  :							int								=					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 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        for model_class_name in self.all_model_classes:
          lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_  :							str								=					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 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        lowerCAmelCase_  :							Any								=					XGLMTokenizer.from_pretrained('XGLM'       ,     padding_side='left'			)
        lowerCAmelCase_  :							Any								=					tokenizer(['Hello this is a long string', 'Hey']       ,     return_tensors='np'       ,     padding=SCREAMING_SNAKE_CASE_       ,     truncation=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'			)
        lowerCAmelCase_  :							Dict								=					1
        lowerCAmelCase_  :							Optional[Any]								=					False

        lowerCAmelCase_  :							Optional[Any]								=					jax.jit(model.generate			)

        lowerCAmelCase_  :							List[str]								=					jit_generate(inputs['input_ids']       ,     attention_mask=inputs['attention_mask']			).sequences

        lowerCAmelCase_  :							List[Any]								=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,     skip_special_tokens=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Any								=					[
            '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 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        lowerCAmelCase_		,lowerCAmelCase_  :							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
            lowerCAmelCase_  :							Any								=					self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
            lowerCAmelCase_  :							Optional[Any]								=					{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

            # load corresponding PyTorch class
            lowerCAmelCase_  :							str								=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
            lowerCAmelCase_  :							int								=					getattr(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)

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

            with torch.no_grad():
              lowerCAmelCase_  :							int								=					pt_model(**SCREAMING_SNAKE_CASE_			).to_tuple()

            lowerCAmelCase_  :							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_			)
              lowerCAmelCase_  :							Optional[Any]								=					model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,     from_pt=SCREAMING_SNAKE_CASE_			)

            lowerCAmelCase_  :							str								=					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 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Dict								=					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
            lowerCAmelCase_  :							List[str]								=					self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
            lowerCAmelCase_  :							Union[str, Any]								=					{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

            lowerCAmelCase_  :							List[Any]								=					pt_model_class(SCREAMING_SNAKE_CASE_			).eval()
            lowerCAmelCase_  :							Tuple								=					False
            lowerCAmelCase_  :							Union[str, Any]								=					model_class(SCREAMING_SNAKE_CASE_       ,     dtype=jnp.floataa			)

            lowerCAmelCase_  :							Dict								=					load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_       ,     fx_model.params			)
            lowerCAmelCase_		,lowerCAmelCase_  :							List[Any]								=					pt_inputs['input_ids'].shape
            lowerCAmelCase_  :							Any								=					np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)			)
            for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_			):
              lowerCAmelCase_  :							List[Any]								=					0
              lowerCAmelCase_  :							Tuple								=					1
              lowerCAmelCase_  :							Dict								=					0
              lowerCAmelCase_  :							Optional[Any]								=					1

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

            with torch.no_grad():
              lowerCAmelCase_  :							List[str]								=					pt_model(**SCREAMING_SNAKE_CASE_			).to_tuple()

            lowerCAmelCase_  :							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:
              fx_model.save_pretrained(SCREAMING_SNAKE_CASE_			)
              lowerCAmelCase_  :							Optional[Any]								=					pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,     from_flax=SCREAMING_SNAKE_CASE_			)

            with torch.no_grad():
              lowerCAmelCase_  :							List[str]								=					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 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        for model_class_name in self.all_model_classes:
          lowerCAmelCase_  :							List[Any]								=					model_class_name.from_pretrained('facebook/xglm-564M'			)
          lowerCAmelCase_  :							List[Any]								=					model(np.ones((1, 1)			)			)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)










"""simple docstring"""







import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax







@require_flax
class  UpperCamelCase__  :




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							List[str]								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Tuple								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							str								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Union[str, Any]								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							Tuple								=					1_6
        lowerCAmelCase_  :							Dict								=					(1_6, 3_2)

        lowerCAmelCase_  :							Any								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Dict								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_  :							List[str]								=					'kwargs' in inspect.signature(self.model_class.__init__			).parameters
        lowerCAmelCase_  :							Optional[Any]								=					len(self.model_class._deprecated_kwargs			) > 0

        if has_kwarg_in_model_class and not has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
              ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
              ' [<deprecated_argument>]`'			)

        if not has_kwarg_in_model_class and has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
              F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
              ' from `_deprecated_kwargs = [<deprecated_argument>]`'			)





