






'''simple docstring'''
from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
		from transformers import (
		    TFBertModel,
		    TFCLIPVisionModel,
		    TFDeiTModel,
		    TFRobertaModel,
		    TFVisionTextDualEncoderModel,
		    TFViTModel,
		    VisionTextDualEncoderConfig,
		)

if is_vision_available():
		from PIL import Image

		from transformers import VisionTextDualEncoderProcessor





def  _a(  UpperCamelCase__				:						List[str]							):




						'''simple docstring'''






						if isinstance(UpperCamelCase__,	collections.abc.Iterable							):
												return x
						return (x, x)






@require_tf
class      __SCREAMING_SNAKE_CASE      :




	def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Tuple					,      __lowercase	:							Tuple      )	->						Optional[Any]:
							pass




	def      __magic_name__	(      self	:							List[Any]      )	->						Any:
							pass




	def      __magic_name__	(      self	:							Optional[Any]      )	->						Union[str, Any]:
							pass




	def      __magic_name__	(      self	:							int					,      __lowercase	:							List[str]					,      __lowercase	:							Optional[int]					,      __lowercase	:							Any					,      __lowercase	:							Dict					,      __lowercase	:							Optional[int]=None					,      **__lowercase	:							List[str]      )	->						int:
							SCREAMING_SNAKE_CASE__     :				Tuple									=VisionTextDualEncoderConfig.from_vision_text_configs(__lowercase					,      __lowercase      )

							SCREAMING_SNAKE_CASE__     :				List[Any]									=TFVisionTextDualEncoderModel(__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(input_ids=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase      )

							self.assertEqual(output['''text_embeds'''].shape					,      (input_ids.shape[0], config.projection_dim)      )
							self.assertEqual(output['''image_embeds'''].shape					,      (pixel_values.shape[0], config.projection_dim)      )




	def      __magic_name__	(      self	:							int					,      __lowercase	:							str					,      __lowercase	:							int					,      __lowercase	:							int					,      __lowercase	:							int					,      __lowercase	:							str=None					,      **__lowercase	:							Optional[int]      )	->						Dict:
							SCREAMING_SNAKE_CASE__     :				Tuple									=self.get_vision_text_model(__lowercase					,      __lowercase      )
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFVisionTextDualEncoderModel(vision_model=__lowercase					,      text_model=__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Dict									=model(input_ids=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase      )

							self.assertEqual(output['''text_embeds'''].shape					,      (input_ids.shape[0], model.config.projection_dim)      )
							self.assertEqual(output['''image_embeds'''].shape					,      (pixel_values.shape[0], model.config.projection_dim)      )




	def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							int					,      __lowercase	:							Any					,      __lowercase	:							int					,      __lowercase	:							Tuple					,      __lowercase	:							Union[str, Any]=None					,      **__lowercase	:							Optional[Any]      )	->						Optional[int]:
							SCREAMING_SNAKE_CASE__     :				int									=self.get_vision_text_model(__lowercase					,      __lowercase      )
							SCREAMING_SNAKE_CASE__     :				List[str]									={'''vision_model''': vision_model, '''text_model''': text_model}
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowercase      )

							SCREAMING_SNAKE_CASE__     :				List[Any]									=model(input_ids=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase      )

							self.assertEqual(output['''text_embeds'''].shape					,      (input_ids.shape[0], model.config.projection_dim)      )
							self.assertEqual(output['''image_embeds'''].shape					,      (pixel_values.shape[0], model.config.projection_dim)      )




	def      __magic_name__	(      self	:							str					,      __lowercase	:							Optional[int]					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							Any					,      __lowercase	:							Any					,      __lowercase	:							str=None					,      **__lowercase	:							str      )	->						str:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.get_vision_text_model(__lowercase					,      __lowercase      )
							SCREAMING_SNAKE_CASE__     :				Dict									=TFVisionTextDualEncoderModel(vision_model=__lowercase					,      text_model=__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(input_ids=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Any									=output[0].numpy()

							with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(__lowercase      )
													SCREAMING_SNAKE_CASE__     :				str									=TFVisionTextDualEncoderModel.from_pretrained(__lowercase      )

													SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(input_ids=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase      )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=after_output[0].numpy()
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=np.amax(np.abs(out_a - out_a      )      )
													self.assertLessEqual(__lowercase					,      1e-5      )




	def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							List[Any]					,      __lowercase	:							List[Any]					,      __lowercase	:							List[Any]=None					,      **__lowercase	:							Tuple      )	->						int:
							SCREAMING_SNAKE_CASE__     :				List[Any]									=self.get_vision_text_model(__lowercase					,      __lowercase      )
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=TFVisionTextDualEncoderModel(vision_model=__lowercase					,      text_model=__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(
							    input_ids=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase					,      output_attentions=__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Dict									=output.vision_model_output.attentions
							self.assertEqual(len(__lowercase      )					,      vision_config.num_hidden_layers      )

							# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
							SCREAMING_SNAKE_CASE__     :				int									=to_atuple(vision_model.config.image_size      )
							SCREAMING_SNAKE_CASE__     :				Dict									=to_atuple(vision_model.config.patch_size      )
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=num_patches + 1
							self.assertEqual(vision_attentions[0].shape[-3:]					,      (vision_config.num_attention_heads, seq_len, seq_len)      )

							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=output.text_model_output.attentions
							self.assertEqual(len(__lowercase      )					,      text_config.num_hidden_layers      )

							self.assertEqual(
							    text_attentions[0].shape[-3:]					,      (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,      )




	def      __magic_name__	(      self	:							int					,      __lowercase	:							np.ndarray					,      __lowercase	:							np.ndarray					,      __lowercase	:							float      )	->						List[Any]:
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=np.abs((a - b)      ).max()
							self.assertLessEqual(__lowercase					,      __lowercase					,      F"Difference between torch and flax is {diff} (>= {tol})."      )




	def      __magic_name__	(      self	:							str      )	->						Optional[Any]:
							SCREAMING_SNAKE_CASE__     :				Tuple									=self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_model(**__lowercase      )




	def      __magic_name__	(      self	:							Any      )	->						str:
							SCREAMING_SNAKE_CASE__     :				Tuple									=self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**__lowercase      )




	def      __magic_name__	(      self	:							List[str]      )	->						Optional[Any]:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**__lowercase      )




	def      __magic_name__	(      self	:							List[str]      )	->						List[Any]:
							SCREAMING_SNAKE_CASE__     :				List[str]									=self.prepare_config_and_inputs()
							self.check_save_load(**__lowercase      )




	def      __magic_name__	(      self	:							int      )	->						Optional[int]:
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**__lowercase      )




	@slow
	def      __magic_name__	(      self	:							Optional[int]      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.get_pretrained_model_and_inputs()

							SCREAMING_SNAKE_CASE__     :				List[Any]									=model_a(**__lowercase      )
							SCREAMING_SNAKE_CASE__     :				List[Any]									=outputs[0].numpy()

							with tempfile.TemporaryDirectory() as tmp_dirname:
													model_a.save_pretrained(__lowercase      )
													SCREAMING_SNAKE_CASE__     :				str									=TFVisionTextDualEncoderModel.from_pretrained(__lowercase      )

													SCREAMING_SNAKE_CASE__     :				int									=model_a(**__lowercase      )
													SCREAMING_SNAKE_CASE__     :				str									=after_outputs[0].numpy()
													SCREAMING_SNAKE_CASE__     :				Optional[int]									=np.amax(np.abs(out_a - out_a      )      )
													self.assertLessEqual(__lowercase					,      1e-5      )






@require_tf
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):




	def      __magic_name__	(      self	:							Tuple      )	->						List[Any]:
							SCREAMING_SNAKE_CASE__     :				Tuple									=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
							    '''hf-internal-testing/tiny-random-vit'''					,      '''hf-internal-testing/tiny-random-bert'''      )
							SCREAMING_SNAKE_CASE__     :				List[str]									=13
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=floats_tensor(
							    [
							        batch_size,
							        model.vision_model.config.num_channels,
							        model.vision_model.config.image_size,
							        model.vision_model.config.image_size,
							    ]      )
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=ids_tensor([batch_size, 4]					,      model.text_model.config.vocab_size      )
							SCREAMING_SNAKE_CASE__     :				List[Any]									=random_attention_mask([batch_size, 4]      )
							SCREAMING_SNAKE_CASE__     :				Dict									={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

							return model, inputs




	def      __magic_name__	(      self	:							Dict					,      __lowercase	:							str					,      __lowercase	:							List[str]      )	->						str:
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFViTModel(__lowercase					,      name='''vision_model'''      )
							SCREAMING_SNAKE_CASE__     :				Dict									=TFBertModel(__lowercase					,      name='''text_model'''      )
							return vision_model, text_model




	def      __magic_name__	(      self	:							Tuple      )	->						Dict:
							SCREAMING_SNAKE_CASE__     :				int									=TFViTModelTester(self      )
							SCREAMING_SNAKE_CASE__     :				Any									=TFBertModelTester(self      )
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=vit_model_tester.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE__     :				Any									=bert_model_tester.prepare_config_and_inputs()

							SCREAMING_SNAKE_CASE__     :				List[Any]									=vision_config_and_inputs

							(
							    SCREAMING_SNAKE_CASE__	
							)     :				Tuple									=text_config_and_inputs

							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": input_mask,
							    "input_ids": input_ids,
							    "text_token_type_ids": token_type_ids,
							    "text_sequence_labels": sequence_labels,
							    "text_token_labels": token_labels,
							    "text_choice_labels": choice_labels,
							}






@require_tf
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):




	def      __magic_name__	(      self	:							List[str]      )	->						int:
							# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
							# just reinitialize it.
							SCREAMING_SNAKE_CASE__     :				List[str]									=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
							    '''Rocketknight1/tiny-random-deit-tf'''					,      '''hf-internal-testing/tiny-random-roberta'''      )
							SCREAMING_SNAKE_CASE__     :				Any									=13
							SCREAMING_SNAKE_CASE__     :				Tuple									=floats_tensor(
							    [
							        batch_size,
							        model.vision_model.config.num_channels,
							        model.vision_model.config.image_size,
							        model.vision_model.config.image_size,
							    ]      )
							SCREAMING_SNAKE_CASE__     :				List[Any]									=ids_tensor([batch_size, 4]					,      model.text_model.config.vocab_size      )
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=random_attention_mask([batch_size, 4]      )
							SCREAMING_SNAKE_CASE__     :				Optional[int]									={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

							return model, inputs




	def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							Optional[int]					,      __lowercase	:							Optional[int]					,      __lowercase	:							Tuple					,      __lowercase	:							Optional[Any]					,      __lowercase	:							str=None					,      **__lowercase	:							Tuple      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.get_vision_text_model(__lowercase					,      __lowercase      )
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFVisionTextDualEncoderModel(vision_model=__lowercase					,      text_model=__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Dict									=model(
							    input_ids=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase					,      output_attentions=__lowercase      )

							SCREAMING_SNAKE_CASE__     :				List[str]									=output.vision_model_output.attentions
							self.assertEqual(len(__lowercase      )					,      vision_config.num_hidden_layers      )

							# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=to_atuple(vision_model.config.image_size      )
							SCREAMING_SNAKE_CASE__     :				int									=to_atuple(vision_model.config.patch_size      )
							SCREAMING_SNAKE_CASE__     :				str									=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							SCREAMING_SNAKE_CASE__     :				Tuple									=num_patches + 2
							self.assertEqual(vision_attentions[0].shape[-3:]					,      (vision_config.num_attention_heads, seq_len, seq_len)      )

							SCREAMING_SNAKE_CASE__     :				Any									=output.text_model_output.attentions
							self.assertEqual(len(__lowercase      )					,      text_config.num_hidden_layers      )

							self.assertEqual(
							    text_attentions[0].shape[-3:]					,      (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,      )




	def      __magic_name__	(      self	:							str					,      __lowercase	:							List[str]					,      __lowercase	:							List[Any]      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				Dict									=TFDeiTModel(__lowercase					,      name='''vision_model'''      )
							SCREAMING_SNAKE_CASE__     :				List[Any]									=TFRobertaModel(__lowercase					,      name='''text_model'''      )
							return vision_model, text_model




	def      __magic_name__	(      self	:							Dict      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=TFDeiTModelTester(self      )
							SCREAMING_SNAKE_CASE__     :				str									=TFRobertaModelTester(self      )
							SCREAMING_SNAKE_CASE__     :				str									=vit_model_tester.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE__     :				Any									=bert_model_tester.prepare_config_and_inputs()

							SCREAMING_SNAKE_CASE__     :				int									=vision_config_and_inputs

							(
							    SCREAMING_SNAKE_CASE__	
							)     :				Dict									=text_config_and_inputs

							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": input_mask,
							    "input_ids": input_ids,
							    "text_token_type_ids": token_type_ids,
							    "text_sequence_labels": sequence_labels,
							    "text_token_labels": token_labels,
							    "text_choice_labels": choice_labels,
							}






@require_tf
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):




	def      __magic_name__	(      self	:							str      )	->						List[Any]:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
							    '''Rocketknight1/tiny-random-clip-tf'''					,      '''hf-internal-testing/tiny-random-bert'''      )
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=13
							SCREAMING_SNAKE_CASE__     :				Any									=floats_tensor(
							    [
							        batch_size,
							        model.vision_model.config.num_channels,
							        model.vision_model.config.image_size,
							        model.vision_model.config.image_size,
							    ]      )
							SCREAMING_SNAKE_CASE__     :				Dict									=ids_tensor([batch_size, 4]					,      model.text_model.config.vocab_size      )
							SCREAMING_SNAKE_CASE__     :				Dict									=random_attention_mask([batch_size, 4]      )
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

							return model, inputs




	def      __magic_name__	(      self	:							int					,      __lowercase	:							Optional[int]					,      __lowercase	:							str      )	->						Union[str, Any]:
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=TFCLIPVisionModel(__lowercase					,      name='''vision_model'''      )
							SCREAMING_SNAKE_CASE__     :				Any									=TFBertModel(__lowercase					,      name='''text_model'''      )
							return vision_model, text_model




	def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=TFCLIPVisionModelTester(self      )
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=TFBertModelTester(self      )
							SCREAMING_SNAKE_CASE__     :				List[str]									=clip_model_tester.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE__     :				List[str]									=bert_model_tester.prepare_config_and_inputs()

							SCREAMING_SNAKE_CASE__     :				Dict									=vision_config_and_inputs

							(
							    SCREAMING_SNAKE_CASE__	
							)     :				Any									=text_config_and_inputs

							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": input_mask,
							    "input_ids": input_ids,
							    "text_token_type_ids": token_type_ids,
							    "text_sequence_labels": sequence_labels,
							    "text_token_labels": token_labels,
							    "text_choice_labels": choice_labels,
							}






@require_vision
@require_tf
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




	@slow
	def      __magic_name__	(      self	:							Any      )	->						Dict:
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=TFVisionTextDualEncoderModel.from_pretrained(
							    '''clip-italian/clip-italian'''					,      logit_scale_init_value=1.0					,      from_pt=__lowercase      )
							SCREAMING_SNAKE_CASE__     :				str									=VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''      )

							SCREAMING_SNAKE_CASE__     :				Dict									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=processor(
							    text=['''una foto di un gatto''', '''una foto di un cane''']					,      images=__lowercase					,      padding=__lowercase					,      return_tensors='''np'''      )

							SCREAMING_SNAKE_CASE__     :				Dict									=model(**__lowercase      )

							# verify the logits
							self.assertEqual(outputs.logits_per_image.shape					,      (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])      )
							self.assertEqual(
							    outputs.logits_per_text.shape					,      (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])					,      )

							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=np.array([[1.2284727, 0.3104122]]      )

							self.assertTrue(np.allclose(outputs.logits_per_image.numpy()					,      __lowercase					,      atol=1e-3      )      )







'''simple docstring'''
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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""gpt_neox"""




 def __init__(      self	:							List[Any]					,      __lowercase	:							Union[str, Any]=5_04_32					,      __lowercase	:							int=61_44					,      __lowercase	:							Tuple=44					,      __lowercase	:							List[str]=64					,      __lowercase	:							str=2_45_76					,      __lowercase	:							Dict="gelu"					,      __lowercase	:							Tuple=0.25					,      __lowercase	:							Tuple=1_00_00					,      __lowercase	:							Tuple=0.0					,      __lowercase	:							str=0.0					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Dict=20_48					,      __lowercase	:							Any=0.02					,      __lowercase	:							Dict=1e-5					,      __lowercase	:							List[Any]=True					,      __lowercase	:							str=0					,      __lowercase	:							Optional[Any]=2					,      __lowercase	:							Tuple=False					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Optional[Any]=None					,      **__lowercase	:							Any					,      )	->						Dict:
       super().__init__(bos_token_id=__lowercase					,      eos_token_id=__lowercase					,      **__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				Any									=hidden_size
       SCREAMING_SNAKE_CASE__     :				str									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Any									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				Dict									=hidden_act
       SCREAMING_SNAKE_CASE__     :				str									=rotary_pct
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=rotary_emb_base
       SCREAMING_SNAKE_CASE__     :				List[Any]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_dropout
       SCREAMING_SNAKE_CASE__     :				str									=classifier_dropout
       SCREAMING_SNAKE_CASE__     :				Any									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=layer_norm_eps
       SCREAMING_SNAKE_CASE__     :				Any									=use_cache
       SCREAMING_SNAKE_CASE__     :				Tuple									=tie_word_embeddings
       SCREAMING_SNAKE_CASE__     :				Tuple									=use_parallel_residual
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=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      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       if self.rope_scaling is None:
             return

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