'''simple docstring'''


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


snake_case__					:      List[Any]				      =  '''scheduler_config.json'''






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:str						   =		1
  lowerCamelCase_		:Union[str, Any]						   =		2
  lowerCamelCase_		:List[str]						   =		3
  lowerCamelCase_		:Optional[int]						   =		4
  lowerCamelCase_		:str						   =		5







@dataclass
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:jnp.ndarray







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:Any						   =		SCHEDULER_CONFIG_NAME
  lowerCamelCase_		:List[str]						   =		['''dtype''']
  lowerCamelCase_		:List[str]						   =		[]
  lowerCamelCase_		:Optional[Any]						   =		True


  @classmethod
  def _UpperCamelCase					(			cls				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_=False				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				cls.load_config(
           pretrained_model_name_or_path=snake_case_				,		subfolder=snake_case_				,		return_unused_kwargs=snake_case_				,		**snake_case_				,		)
       UpperCAmelCase_       ,      UpperCAmelCase_							:       Union[str, Any]       =				cls.from_config(snake_case_				,		return_unused_kwargs=snake_case_				,		**snake_case_						)

       if hasattr(snake_case_				,		'create_state'						) and getattr(snake_case_				,		'has_state'				,		snake_case_						):
            UpperCAmelCase_							:       List[Any]       =				scheduler.create_state()

       if return_unused_kwargs:
            return scheduler, state, unused_kwargs

       return scheduler, state


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = False				,		**snake_case_						):




       '''simple docstring'''

       self.save_config(save_directory=snake_case_				,		push_to_hub=snake_case_				,		**snake_case_						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._get_compatibles()






  @classmethod
  def _UpperCamelCase					(			cls						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				list(set([cls.__name__] + cls._compatibles						)						)
       UpperCAmelCase_							:       Optional[int]       =				importlib.import_module(__name__.split('.'						)[0]						)
       UpperCAmelCase_							:       Any       =				[
           getattr(snake_case_				,		snake_case_						) for c in compatible_classes_str if hasattr(snake_case_				,		snake_case_						)
       ]
       return compatible_classes







def  _lowerCamelCase   (				lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							Tuple[int]       ):



     """simple docstring"""





     assert len(lowerCamelCase_       ) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_       ) - x.ndim)       )     ,					lowerCamelCase_       )


def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							Optional[Any]=0.999     ,					lowerCamelCase_ :							List[str]=jnp.floataa       ):



     """simple docstring"""





     def alpha_bar(lowerCamelCase_ :							Optional[Any]       ):
          return math.cos((time_step + 0.008) / 1.008 * math.pi / 2       ) ** 2

     UpperCAmelCase_							:       int       =				[]
     for i in range(lowerCamelCase_       ):
          UpperCAmelCase_							:       Optional[int]       =				i / num_diffusion_timesteps
          UpperCAmelCase_							:       Any       =				(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(lowerCamelCase_       ) / alpha_bar(lowerCamelCase_       )     ,					lowerCamelCase_       )       )
     return jnp.array(lowerCamelCase_     ,					dtype=lowerCamelCase_       )






@flax.struct.dataclass
class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:jnp.ndarray
  lowerCamelCase_		:jnp.ndarray
  lowerCamelCase_		:jnp.ndarray


  @classmethod
  def _UpperCamelCase					(			cls				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				scheduler.config

       if config.trained_betas is not None:
            UpperCAmelCase_							:       List[Any]       =				jnp.asarray(config.trained_betas				,		dtype=scheduler.dtype						)
       elif config.beta_schedule == "linear":
            UpperCAmelCase_							:       Tuple       =				jnp.linspace(config.beta_start				,		config.beta_end				,		config.num_train_timesteps				,		dtype=scheduler.dtype						)
       elif config.beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            UpperCAmelCase_							:       Optional[Any]       =				(
                jnp.linspace(
                    config.beta_start**0.5				,		config.beta_end**0.5				,		config.num_train_timesteps				,		dtype=scheduler.dtype						)
                ** 2
            )
       elif config.beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            UpperCAmelCase_							:       Any       =				betas_for_alpha_bar(config.num_train_timesteps				,		dtype=scheduler.dtype						)
       else:
            raise NotImplementedError(
                F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''						)

       UpperCAmelCase_							:       Union[str, Any]       =				1.0 - betas

       UpperCAmelCase_							:       Optional[int]       =				jnp.cumprod(snake_case_				,		axis=0						)

       return cls(
           alphas=snake_case_				,		betas=snake_case_				,		alphas_cumprod=snake_case_				,		)







def  _lowerCamelCase   (				lowerCamelCase_ :							CommonSchedulerState     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				state.alphas_cumprod

     UpperCAmelCase_							:       int       =				alphas_cumprod[timesteps] ** 0.5
     UpperCAmelCase_							:       int       =				sqrt_alpha_prod.flatten()
     UpperCAmelCase_							:       Any       =				broadcast_to_shape_from_left(lowerCamelCase_     ,					original_samples.shape       )

     UpperCAmelCase_							:       Dict       =				(1 - alphas_cumprod[timesteps]) ** 0.5
     UpperCAmelCase_							:       int       =				sqrt_one_minus_alpha_prod.flatten()
     UpperCAmelCase_							:       Dict       =				broadcast_to_shape_from_left(lowerCamelCase_     ,					original_samples.shape       )

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def  _lowerCamelCase   (				lowerCamelCase_ :							CommonSchedulerState     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray       ):



     """simple docstring"""





     UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				get_sqrt_alpha_prod(lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       Any       =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples





def  _lowerCamelCase   (				lowerCamelCase_ :							CommonSchedulerState     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray       ):



     """simple docstring"""





     UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				get_sqrt_alpha_prod(lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       int       =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity







'''simple docstring'''


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=1_3				,		snake_case_=7				,		snake_case_=True				,		snake_case_=True				,		snake_case_=False				,		snake_case_=True				,		snake_case_=9_9				,		snake_case_=3_2				,		snake_case_=5				,		snake_case_=4				,		snake_case_=3_7				,		snake_case_="gelu"				,		snake_case_=0.1				,		snake_case_=0.1				,		snake_case_=5_1_2				,		snake_case_=1_6				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=3				,		snake_case_=4				,		snake_case_=None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				parent
       UpperCAmelCase_							:       List[str]       =				batch_size
       UpperCAmelCase_							:       Dict       =				seq_length
       UpperCAmelCase_							:       Any       =				is_training
       UpperCAmelCase_							:       List[Any]       =				use_input_mask
       UpperCAmelCase_							:       Tuple       =				use_token_type_ids
       UpperCAmelCase_							:       Tuple       =				use_labels
       UpperCAmelCase_							:       Dict       =				vocab_size
       UpperCAmelCase_							:       Union[str, Any]       =				hidden_size
       UpperCAmelCase_							:       Optional[Any]       =				num_hidden_layers
       UpperCAmelCase_							:       Union[str, Any]       =				num_attention_heads
       UpperCAmelCase_							:       Optional[int]       =				intermediate_size
       UpperCAmelCase_							:       Dict       =				hidden_act
       UpperCAmelCase_							:       Any       =				hidden_dropout_prob
       UpperCAmelCase_							:       Optional[Any]       =				attention_probs_dropout_prob
       UpperCAmelCase_							:       Optional[int]       =				max_position_embeddings
       UpperCAmelCase_							:       List[Any]       =				type_vocab_size
       UpperCAmelCase_							:       Optional[Any]       =				type_sequence_label_size
       UpperCAmelCase_							:       List[Any]       =				initializer_range
       UpperCAmelCase_							:       List[Any]       =				num_labels
       UpperCAmelCase_							:       Any       =				num_choices
       UpperCAmelCase_							:       List[str]       =				scope


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size						)

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

       UpperCAmelCase_							:       int       =				None
       if self.use_token_type_ids:
            UpperCAmelCase_							:       Dict       =				ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size						)

       UpperCAmelCase_							:       Tuple       =				None
       UpperCAmelCase_							:       Any       =				None
       UpperCAmelCase_							:       str       =				None
       if self.use_labels:
            UpperCAmelCase_							:       str       =				ids_tensor([self.batch_size]				,		self.type_sequence_label_size						)
            UpperCAmelCase_							:       Optional[int]       =				ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels						)
            UpperCAmelCase_							:       Any       =				ids_tensor([self.batch_size]				,		self.num_choices						)

       UpperCAmelCase_							:       int       =				self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return OpenLlamaConfig(
           vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=snake_case_				,		initializer_range=self.initializer_range				,		use_stable_embedding=snake_case_				,		)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				OpenLlamaModel(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       str       =				model(snake_case_				,		attention_mask=snake_case_						)
       UpperCAmelCase_							:       Dict       =				model(snake_case_						)
       self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				True
       UpperCAmelCase_							:       Dict       =				OpenLlamaModel(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		encoder_attention_mask=snake_case_				,		)
       UpperCAmelCase_							:       Any       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		)
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		attention_mask=snake_case_						)
       self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				OpenLlamaForCausalLM(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Union[str, Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				True
       UpperCAmelCase_							:       List[Any]       =				True
       UpperCAmelCase_							:       Tuple       =				OpenLlamaForCausalLM(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()

       # first forward pass
       UpperCAmelCase_							:       Tuple       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		encoder_attention_mask=snake_case_				,		use_cache=snake_case_				,		)
       UpperCAmelCase_							:       int       =				outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       UpperCAmelCase_							:       Optional[int]       =				ids_tensor((self.batch_size, 3)				,		config.vocab_size						)
       UpperCAmelCase_							:       Optional[int]       =				ids_tensor((self.batch_size, 3)				,		vocab_size=2						)

       # append to next input_ids and
       UpperCAmelCase_							:       Union[str, Any]       =				torch.cat([input_ids, next_tokens]				,		dim=-1						)
       UpperCAmelCase_							:       Tuple       =				torch.cat([input_mask, next_mask]				,		dim=-1						)

       UpperCAmelCase_							:       List[Any]       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		encoder_attention_mask=snake_case_				,		output_hidden_states=snake_case_				,		)['hidden_states'][0]
       UpperCAmelCase_							:       List[str]       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		encoder_attention_mask=snake_case_				,		past_key_values=snake_case_				,		output_hidden_states=snake_case_				,		)['hidden_states'][0]

       # select random slice
       UpperCAmelCase_							:       Union[str, Any]       =				ids_tensor((1,)				,		output_from_past.shape[-1]						).item()
       UpperCAmelCase_							:       List[Any]       =				output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCAmelCase_							:       Optional[Any]       =				output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-3						)						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      
       )							:       Tuple       =				config_and_inputs
       UpperCAmelCase_							:       List[Any]       =				{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		(
      (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
  )
  lowerCamelCase_		:Tuple						   =		(OpenLlamaForCausalLM,) if is_torch_available() else ()
  lowerCamelCase_		:Union[str, Any]						   =		(
      {
          '''feature-extraction''': OpenLlamaModel,
          '''text-classification''': OpenLlamaForSequenceClassification,
          '''text-generation''': OpenLlamaForCausalLM,
          '''zero-shot''': OpenLlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase_		:str						   =		False
  lowerCamelCase_		:Optional[int]						   =		False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				OpenLlamaModelTester(self						)
       UpperCAmelCase_							:       List[Any]       =				ConfigTester(self				,		config_class=snake_case_				,		hidden_size=3_7						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            UpperCAmelCase_							:       int       =				type
            self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       Optional[Any]       =				3
       UpperCAmelCase_							:       Union[str, Any]       =				input_dict['input_ids']
       UpperCAmelCase_							:       int       =				input_ids.ne(1						).to(snake_case_						)
       UpperCAmelCase_							:       str       =				ids_tensor([self.model_tester.batch_size]				,		self.model_tester.type_sequence_label_size						)
       UpperCAmelCase_							:       Any       =				OpenLlamaForSequenceClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       List[str]       =				3
       UpperCAmelCase_							:       str       =				'single_label_classification'
       UpperCAmelCase_							:       List[str]       =				input_dict['input_ids']
       UpperCAmelCase_							:       Optional[Any]       =				input_ids.ne(1						).to(snake_case_						)
       UpperCAmelCase_							:       List[str]       =				ids_tensor([self.model_tester.batch_size]				,		self.model_tester.type_sequence_label_size						)
       UpperCAmelCase_							:       Optional[int]       =				OpenLlamaForSequenceClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       List[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       List[str]       =				3
       UpperCAmelCase_							:       int       =				'multi_label_classification'
       UpperCAmelCase_							:       Dict       =				input_dict['input_ids']
       UpperCAmelCase_							:       int       =				input_ids.ne(1						).to(snake_case_						)
       UpperCAmelCase_							:       str       =				ids_tensor(
           [self.model_tester.batch_size, config.num_labels]				,		self.model_tester.type_sequence_label_size						).to(torch.float						)
       UpperCAmelCase_							:       Union[str, Any]       =				OpenLlamaForSequenceClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels)						)


  @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass






  @parameterized.expand([('linear',), ('dynamic',)]						)
  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       int       =				ids_tensor([1, 1_0]				,		config.vocab_size						)
       UpperCAmelCase_							:       Tuple       =				ids_tensor([1, int(config.max_position_embeddings * 1.5						)]				,		config.vocab_size						)

       set_seed(4_2						)  # Fixed seed at init time so the two models get the same random weights
       UpperCAmelCase_							:       Tuple       =				OpenLlamaModel(snake_case_						)
       original_model.to(snake_case_						)
       original_model.eval()
       UpperCAmelCase_							:       List[str]       =				original_model(snake_case_						).last_hidden_state
       UpperCAmelCase_							:       Any       =				original_model(snake_case_						).last_hidden_state

       set_seed(4_2						)  # Fixed seed at init time so the two models get the same random weights
       UpperCAmelCase_							:       Tuple       =				{'type': scaling_type, 'factor': 10.0}
       UpperCAmelCase_							:       List[str]       =				OpenLlamaModel(snake_case_						)
       scaled_model.to(snake_case_						)
       scaled_model.eval()
       UpperCAmelCase_							:       Tuple       =				scaled_model(snake_case_						).last_hidden_state
       UpperCAmelCase_							:       Optional[int]       =				scaled_model(snake_case_						).last_hidden_state

       # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
       # maximum sequence length, so the outputs for the short input should match.
       if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-5						)						)
       else:
            self.assertFalse(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-5						)						)

       # The output should be different for long inputs
       self.assertFalse(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-5						)						)






