

'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=None    ,__UpperCamelCase=None	)       ->			Optional[Any]:
					if attention_mask is None:
										lowerCamelCase_  				=      tf.cast(tf.math.not_equal(__UpperCamelCase    ,config.pad_token_id	)    ,tf.inta	)
					return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class  UpperCAmelCase :

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					OPTConfig
		SCREAMING_SNAKE_CASE_           =					{}
		SCREAMING_SNAKE_CASE_           =					'gelu'
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=13   ,			SCREAMING_SNAKE_CASE_=7   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=99   ,			SCREAMING_SNAKE_CASE_=16   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=4   ,			SCREAMING_SNAKE_CASE_=4   ,			SCREAMING_SNAKE_CASE_="gelu"   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=20   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=1   ,			SCREAMING_SNAKE_CASE_=0   ,			SCREAMING_SNAKE_CASE_=16   ,			SCREAMING_SNAKE_CASE_=16   ,			)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      parent
							lowerCamelCase_  				=      batch_size
							lowerCamelCase_  				=      seq_length
							lowerCamelCase_  				=      is_training
							lowerCamelCase_  				=      use_labels
							lowerCamelCase_  				=      vocab_size
							lowerCamelCase_  				=      hidden_size
							lowerCamelCase_  				=      num_hidden_layers
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      intermediate_size
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      hidden_dropout_prob
							lowerCamelCase_  				=      attention_probs_dropout_prob
							lowerCamelCase_  				=      max_position_embeddings
							lowerCamelCase_  				=      eos_token_id
							lowerCamelCase_  				=      pad_token_id
							lowerCamelCase_  				=      bos_token_id
							lowerCamelCase_  				=      embed_dim
							lowerCamelCase_  				=      word_embed_proj_dim
							lowerCamelCase_  				=      False
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      ids_tensor([self.batch_size, self.seq_length - 1]   ,			self.vocab_size		)
							lowerCamelCase_  				=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)   ,			1		)
							lowerCamelCase_  				=      tf.concat([input_ids, eos_tensor]   ,			axis=1		)

							lowerCamelCase_  				=      self.config_cls(
							    vocab_size=self.vocab_size   ,			hidden_size=self.hidden_size   ,			num_hidden_layers=self.num_hidden_layers   ,			num_attention_heads=self.num_attention_heads   ,			ffn_dim=self.intermediate_size   ,			dropout=self.hidden_dropout_prob   ,			attention_dropout=self.attention_probs_dropout_prob   ,			max_position_embeddings=self.max_position_embeddings   ,			eos_token_id=self.eos_token_id   ,			bos_token_id=self.bos_token_id   ,			pad_token_id=self.pad_token_id   ,			embed_dim=self.embed_dim   ,			word_embed_proj_dim=self.word_embed_proj_dim   ,			is_encoder_decoder=SCREAMING_SNAKE_CASE_   ,			**self.config_updates   ,			)
							lowerCamelCase_  				=      prepare_opt_inputs_dict(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							return config, inputs_dict






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      TFOPTModel(config=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      inputs_dict['input_ids']

							lowerCamelCase_  				=      input_ids[:1, :]
							lowerCamelCase_  				=      inputs_dict['attention_mask'][:1, :]
							lowerCamelCase_  				=      1

							# first forward pass
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			attention_mask=SCREAMING_SNAKE_CASE_   ,			use_cache=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_				,lowerCamelCase_  				=      outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCamelCase_  				=      ids_tensor((self.batch_size, 3)   ,			config.vocab_size		)
							lowerCamelCase_  				=      tf.cast(ids_tensor((self.batch_size, 3)   ,			2		)   ,			tf.inta		)

							# append to next input_ids and
							lowerCamelCase_  				=      tf.concat([input_ids, next_tokens]   ,			axis=-1		)
							lowerCamelCase_  				=      tf.concat([attention_mask, next_attn_mask]   ,			axis=-1		)

							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			attention_mask=SCREAMING_SNAKE_CASE_		)[0]
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			attention_mask=SCREAMING_SNAKE_CASE_   ,			past_key_values=SCREAMING_SNAKE_CASE_		)[0]

							self.parent.assertEqual(next_tokens.shape[1]   ,			output_from_past.shape[1]		)

							# select random slice
							lowerCamelCase_  				=      int(ids_tensor((1,)   ,			output_from_past.shape[-1]		)		)
							lowerCamelCase_  				=      output_from_no_past[:, -3:, random_slice_idx]
							lowerCamelCase_  				=      output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			rtol=1E-3		)


@require_tf
class  UpperCAmelCase (							UpperCAmelCase__  , UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
		SCREAMING_SNAKE_CASE_           =					(TFOPTForCausalLM,) if is_tf_available() else ()
		SCREAMING_SNAKE_CASE_           =					(
		    {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
		)
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					10
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      TFOPTModelTester(self		)
							lowerCamelCase_  				=      ConfigTester(self   ,			config_class=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							self.config_tester.run_common_tests()
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_		)






		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		):
												if hasattr(SCREAMING_SNAKE_CASE_   ,			'weight'		):
																	return embedding_layer.weight
												else:
																	# Here we build the word embeddings weights if not exists.
																	# And then we retry to get the attribute once built.
																	model.build()
																	if hasattr(SCREAMING_SNAKE_CASE_   ,			'weight'		):
																						return embedding_layer.weight
																	else:
																						return None

							for model_class in self.all_model_classes:
												for size in [config.vocab_size - 10, config.vocab_size + 10]:
																	# build the embeddings
																	lowerCamelCase_  				=      model_class(config=SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      _get_word_embedding_weight(SCREAMING_SNAKE_CASE_   ,			model.get_input_embeddings()		)
																	lowerCamelCase_  				=      _get_word_embedding_weight(SCREAMING_SNAKE_CASE_   ,			model.get_output_embeddings()		)

																	# reshape the embeddings
																	model.resize_token_embeddings(SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      _get_word_embedding_weight(SCREAMING_SNAKE_CASE_   ,			model.get_input_embeddings()		)
																	lowerCamelCase_  				=      _get_word_embedding_weight(SCREAMING_SNAKE_CASE_   ,			model.get_output_embeddings()		)

																	# check that the resized embeddings size matches the desired size.
																	lowerCamelCase_  				=      size if size is not None else config.vocab_size

																	self.assertEqual(new_input_embeddings.shape[0]   ,			SCREAMING_SNAKE_CASE_		)

																	# check that weights remain the same after resizing
																	lowerCamelCase_  				=      True
																	for pa, pa in zip(old_input_embeddings.value()   ,			new_input_embeddings.value()		):
																						if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
																											lowerCamelCase_  				=      False
																	self.assertTrue(SCREAMING_SNAKE_CASE_		)

																	if old_output_embeddings is not None and new_output_embeddings is not None:
																						self.assertEqual(new_output_embeddings.shape[0]   ,			SCREAMING_SNAKE_CASE_		)

																						lowerCamelCase_  				=      True
																						for pa, pa in zip(old_output_embeddings.value()   ,			new_output_embeddings.value()		):
																											if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
																																lowerCamelCase_  				=      False
																						self.assertTrue(SCREAMING_SNAKE_CASE_		)







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			int:
					return tf.constant(__UpperCamelCase    ,dtype=tf.intaa	)


@require_tf
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					99
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      tf.ones((4, 1)   ,			dtype=tf.intaa		) * 2
							lowerCamelCase_  				=      tf.concat([ids_tensor((4, 6)   ,			self.vocab_size - 3		) + 3, eos_column_vector]   ,			axis=1		)
							lowerCamelCase_  				=      input_ids.shape[0]
							lowerCamelCase_  				=      OPTConfig(
							    vocab_size=self.vocab_size   ,			hidden_size=24   ,			num_hidden_layers=2   ,			num_attention_heads=2   ,			ffn_dim=32   ,			max_position_embeddings=48   ,			eos_token_id=2   ,			pad_token_id=1   ,			bos_token_id=0   ,			)
							return config, input_ids, batch_size


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

		'''simple docstring'''






		@slow
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      TFOPTModel.from_pretrained('facebook/opt-350m'		)
							lowerCamelCase_  				=      _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
							lowerCamelCase_  				=      tf.not_equal(SCREAMING_SNAKE_CASE_   ,			model.config.pad_token_id		)
							with tf.GradientTape():
												lowerCamelCase_  				=      model(input_ids=SCREAMING_SNAKE_CASE_   ,			attention_mask=SCREAMING_SNAKE_CASE_		).last_hidden_state
							lowerCamelCase_  				=      (1, 11, 512)
							self.assertEqual(output.shape   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      tf.constant(
							    [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]		)
							self.assertTrue(np.allclose(output[:, :3, :3]   ,			SCREAMING_SNAKE_CASE_   ,			atol=4E-3		)		)

							lowerCamelCase_  				=      tf.function(SCREAMING_SNAKE_CASE_   ,			jit_compile=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      xla_generate(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)[0]
							self.assertTrue(np.allclose(output[:, :3, :3]   ,			SCREAMING_SNAKE_CASE_   ,			atol=4E-2		)		)


@require_tf
@slow
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							super().setUp()
							lowerCamelCase_  				=      'facebook/opt-350m'






		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      TFOPTForCausalLM.from_pretrained(self.path_model		)
							lowerCamelCase_  				=      GPTaTokenizer.from_pretrained(self.path_model		)

							lowerCamelCase_  				=      [
							    'Today is a beautiful day and I want to',
							    'In the city of',
							    'Paris is the capital of France and',
							    'Computers and mobile phones have taken',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			return_tensors='tf'   ,			padding=SCREAMING_SNAKE_CASE_   ,			add_special_tokens=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      tf.math.reduce_mean(model(inputs.input_ids   ,			attention_mask=inputs.attention_mask		)[0]   ,			axis=-1		)
							lowerCamelCase_  				=      tf.constant(
							    [
							        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
							        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
							        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
							        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
							    ]		)
							self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			atol=1E-4		)		)

							lowerCamelCase_  				=      tf.function(SCREAMING_SNAKE_CASE_   ,			jit_compile=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      tf.math.reduce_mean(xla_generate(inputs.input_ids   ,			attention_mask=inputs.attention_mask		)[0]   ,			axis=-1		)
							self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			atol=1E-4		)		)


@require_tf
@slow
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		@property
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      'facebook/opt-125m'

							lowerCamelCase_  				=      [
							    'Today is a beautiful day and I want to',
							    'In the city of New York, the city',
							    'Paris is the capital of France and the capital',
							    'Computers and mobile phones have taken over the',
							]

							lowerCamelCase_  				=      []
							lowerCamelCase_  				=      GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_		)

							for prompt in self.prompts:
												lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			return_tensors='tf'		).input_ids

												lowerCamelCase_  				=      model.generate(SCREAMING_SNAKE_CASE_   ,			max_length=10		)

												lowerCamelCase_  				=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_   ,			skip_special_tokens=SCREAMING_SNAKE_CASE_		)
												predicted_outputs += generated_string

							self.assertListEqual(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      'facebook/opt-350m'

							lowerCamelCase_  				=      GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      'left'

							# use different length sentences to test batching
							lowerCamelCase_  				=      [
							    'Hello, my dog is a little',
							    'Today, I',
							]

							lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			return_tensors='tf'   ,			padding=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      inputs['input_ids']

							lowerCamelCase_  				=      model.generate(input_ids=SCREAMING_SNAKE_CASE_   ,			attention_mask=inputs['attention_mask']		)

							lowerCamelCase_  				=      tokenizer(sentences[0]   ,			return_tensors='tf'		).input_ids
							lowerCamelCase_  				=      model.generate(input_ids=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs['attention_mask'][-1]   ,			tf.intaa		)		)
							lowerCamelCase_  				=      tokenizer(sentences[1]   ,			return_tensors='tf'		).input_ids
							lowerCamelCase_  				=      model.generate(input_ids=SCREAMING_SNAKE_CASE_   ,			max_length=model.config.max_length - num_paddings		)

							lowerCamelCase_  				=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_   ,			skip_special_tokens=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      tokenizer.decode(output_non_padded[0]   ,			skip_special_tokens=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      tokenizer.decode(output_padded[0]   ,			skip_special_tokens=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      [
							    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
							    'Today, I was in the middle of a conversation with a friend about the',
							]
							self.assertListEqual(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							self.assertListEqual(SCREAMING_SNAKE_CASE_   ,			[non_padded_sentence, padded_sentence]		)






		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      'facebook/opt-350m'

							lowerCamelCase_  				=      [
							    'Today is a beautiful day and I want to',
							    'In the city of San Francisco, the city',
							    'Paris is the capital of France and the capital',
							    'Computers and mobile phones have taken over the',
							]

							lowerCamelCase_  				=      []
							lowerCamelCase_  				=      GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_		)

							for prompt in self.prompts:
												lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			return_tensors='tf'		).input_ids

												lowerCamelCase_  				=      model.generate(SCREAMING_SNAKE_CASE_   ,			max_length=10		)

												lowerCamelCase_  				=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_   ,			skip_special_tokens=SCREAMING_SNAKE_CASE_		)
												predicted_outputs += generated_string

							self.assertListEqual(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)






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


__A			:					Optional[int]						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{
    '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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "gpt_neox"





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			List[str]=50432     , __lowerCamelCase					:			int=6144     , __lowerCamelCase					:			Optional[Any]=44     , __lowerCamelCase					:			Tuple=64     , __lowerCamelCase					:			Optional[int]=24576     , __lowerCamelCase					:			List[str]="gelu"     , __lowerCamelCase					:			Any=0.25     , __lowerCamelCase					:			List[Any]=10000     , __lowerCamelCase					:			List[Any]=0.0     , __lowerCamelCase					:			int=0.0     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[Any]=2048     , __lowerCamelCase					:			Tuple=0.02     , __lowerCamelCase					:			Tuple=1e-5     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			int=0     , __lowerCamelCase					:			Optional[Any]=2     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			Optional[int]=None     , **__lowerCamelCase					:			str     , ):
       super().__init__(bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   rotary_pct
       SCREAMING_SNAKE_CASE										=   rotary_emb_base
       SCREAMING_SNAKE_CASE										=   attention_dropout
       SCREAMING_SNAKE_CASE										=   hidden_dropout
       SCREAMING_SNAKE_CASE										=   classifier_dropout
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   use_cache
       SCREAMING_SNAKE_CASE										=   tie_word_embeddings
       SCREAMING_SNAKE_CASE										=   use_parallel_residual
       SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Union[str, Any]			):
       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}"			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("type"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   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}"			)