



'''simple docstring'''




import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a     :    str      						=					logging.get_logger(__name__)

a     :    Tuple      						=					{"""vocab_file""": """spiece.model"""}

a     :    Optional[Any]      						=					{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

a     :    Dict      						=					{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}

class     UpperCamelCase_				(							__magic_name__    ):
 lowercase    =				VOCAB_FILES_NAMES
 lowercase    =				PRETRAINED_VOCAB_FILES_MAP
 lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase    =				['input_ids', 'attention_mask']
 lowercase    =				[]





 def __init__(						self	,	A	,	A="<unk>"	,	A="<s>"	,	A="</s>"	,	A="<pad>"	,	A="[SEP]"	,	A="[MASK]"	,	A="[CLS]"	,	A = None	,	**A	,	)    ->    None:
        UpperCAmelCase  :	int							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else bos_token
        UpperCAmelCase  :	Dict							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else eos_token
        UpperCAmelCase  :	Optional[Any]							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else unk_token
        UpperCAmelCase  :	Union[str, Any]							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else pad_token
        UpperCAmelCase  :	Any							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else cls_token
        UpperCAmelCase  :	Tuple							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        UpperCAmelCase  :	Union[str, Any]							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else mask_token

        UpperCAmelCase  :	int							  =	{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=A	,	eos_token=A	,	unk_token=A	,	pad_token=A	,	sep_token=A	,	mask_token=A	,	cls_token=A	,	sp_model_kwargs=self.sp_model_kwargs	,	**A	,	)

        UpperCAmelCase  :	Union[str, Any]							  =	vocab_file

        UpperCAmelCase  :	int							  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(A					)





 @property
 def 				_lowercase(						self					)    ->    Optional[int]:
        return self.sp_model.get_piece_size()





 def 				_lowercase(						self					)    ->    List[str]:
        UpperCAmelCase  :	int							  =	{self.convert_ids_to_tokens(A					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab





 def __getstate__(						self					)    ->    Optional[Any]:
        UpperCAmelCase  :	int							  =	self.__dict__.copy()
        UpperCAmelCase  :	Union[str, Any]							  =	None
        return state





 def __setstate__(						self	,	A					)    ->    int:
        UpperCAmelCase  :	Optional[Any]							  =	d

        # for backward compatibility
        if not hasattr(self	,	"""sp_model_kwargs"""					):
               UpperCAmelCase  :	int							  =	{}

        UpperCAmelCase  :	Tuple							  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(self.vocab_file					)





 def 				_lowercase(						self	,	A					)    ->    List[str]:
        return self.sp_model.encode(A	,	out_type=A					)





 def 				_lowercase(						self	,	A					)    ->    Union[str, Any]:
        return self.sp_model.piece_to_id(A					)





 def 				_lowercase(						self	,	A					)    ->    int:
        UpperCAmelCase  :	Dict							  =	self.sp_model.IdToPiece(A					)
        return token





 def 				_lowercase(						self	,	A					)    ->    List[Any]:
        UpperCAmelCase  :	int							  =	[]
        UpperCAmelCase  :	int							  =	""""""
        UpperCAmelCase  :	Dict							  =	False
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      if not prev_is_special:
                             out_string += " "
                      out_string += self.sp_model.decode(A					) + token
                      UpperCAmelCase  :	List[Any]							  =	True
                      UpperCAmelCase  :	Optional[int]							  =	[]
               else:
                      current_sub_tokens.append(A					)
                      UpperCAmelCase  :	Dict							  =	False
        out_string += self.sp_model.decode(A					)
        return out_string.strip()





 def 				_lowercase(						self	,	A	,	A = False	,	A = None	,	A = True	,	**A	,	)    ->    str:
        UpperCAmelCase  :	Tuple							  =	kwargs.pop("""use_source_tokenizer"""	,	A					)

        UpperCAmelCase  :	str							  =	self.convert_ids_to_tokens(A	,	skip_special_tokens=A					)

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        UpperCAmelCase  :	Tuple							  =	[]
        UpperCAmelCase  :	Dict							  =	[]
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(A					)					)
                             UpperCAmelCase  :	int							  =	[]
                      sub_texts.append(A					)
               else:
                      current_sub_text.append(A					)
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(A					)					)

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
               UpperCAmelCase  :	List[Any]							  =	re.sub(r""" (\[(MASK|SEP)\])"""	,	r"""\1"""	,	""" """.join(A					)					)
        else:
               UpperCAmelCase  :	str							  =	"""""".join(A					)

        UpperCAmelCase  :	List[str]							  =	(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               UpperCAmelCase  :	List[Any]							  =	self.clean_up_tokenization(A					)
               return clean_text
        else:
               return text





 def 				_lowercase(						self	,	A	,	A = None					)    ->    Tuple[str]:
        if not os.path.isdir(A					):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
               return
        UpperCAmelCase  :	Any							  =	os.path.join(
            A	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

        if os.path.abspath(self.vocab_file					) != os.path.abspath(A					) and os.path.isfile(self.vocab_file					):
               copyfile(self.vocab_file	,	A					)
        elif not os.path.isfile(self.vocab_file					):
               with open(A	,	"""wb"""					) as fi:
                      UpperCAmelCase  :	Tuple							  =	self.sp_model.serialized_model_proto()
                      fi.write(A					)

        return (out_vocab_file,)





 def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        UpperCAmelCase  :	int							  =	[self.cls_token_id]
        UpperCAmelCase  :	List[Any]							  =	[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep





 def 				_lowercase(						self	,	A	,	A = None	,	A = False					)    ->    List[int]:
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=A	,	token_ids_a=A	,	already_has_special_tokens=A					)

        if token_ids_a is None:
               return [1] + ([0] * len(A					)) + [1]
        return [1] + ([0] * len(A					)) + [1] + ([0] * len(A					)) + [1]






 def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
        UpperCAmelCase  :	int							  =	[self.sep_token_id]
        UpperCAmelCase  :	Any							  =	[self.cls_token_id]
        if token_ids_a is None:
               return len(cls + token_ids_a + sep					) * [0]
        return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






'''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     UpperCamelCase_				:





	def __init__(						self	,	A	,	A=13	,	A=7	,	A=True	,	A=True	,	A=False	,	A=True	,	A=99	,	A=32	,	A=5	,	A=4	,	A=37	,	A="gelu"	,	A=0.1	,	A=0.1	,	A=512	,	A=16	,	A=2	,	A=0.0_2	,	A=3	,	A=4	,	A=None	,	)    ->    Any:
								UpperCAmelCase  :	Optional[int]							  =	parent
								UpperCAmelCase  :	List[Any]							  =	batch_size
								UpperCAmelCase  :	Union[str, Any]							  =	seq_length
								UpperCAmelCase  :	Optional[int]							  =	is_training
								UpperCAmelCase  :	str							  =	use_input_mask
								UpperCAmelCase  :	Optional[int]							  =	use_token_type_ids
								UpperCAmelCase  :	Dict							  =	use_labels
								UpperCAmelCase  :	str							  =	vocab_size
								UpperCAmelCase  :	Optional[int]							  =	hidden_size
								UpperCAmelCase  :	str							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	num_attention_heads
								UpperCAmelCase  :	Union[str, Any]							  =	intermediate_size
								UpperCAmelCase  :	str							  =	hidden_act
								UpperCAmelCase  :	Any							  =	hidden_dropout_prob
								UpperCAmelCase  :	str							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	Tuple							  =	max_position_embeddings
								UpperCAmelCase  :	Optional[Any]							  =	type_vocab_size
								UpperCAmelCase  :	Optional[Any]							  =	type_sequence_label_size
								UpperCAmelCase  :	str							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	num_labels
								UpperCAmelCase  :	Dict							  =	num_choices
								UpperCAmelCase  :	Tuple							  =	scope





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	Dict							  =	ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size					)

								UpperCAmelCase  :	Optional[Any]							  =	None
								if self.use_input_mask:
															UpperCAmelCase  :	Optional[int]							  =	random_attention_mask([self.batch_size, self.seq_length]					)

								UpperCAmelCase  :	Optional[Any]							  =	None
								if self.use_token_type_ids:
															UpperCAmelCase  :	Optional[Any]							  =	ids_tensor([self.batch_size, self.seq_length]	,	self.type_vocab_size					)

								UpperCAmelCase  :	Union[str, Any]							  =	None
								UpperCAmelCase  :	Dict							  =	None
								UpperCAmelCase  :	Union[str, Any]							  =	None
								if self.use_labels:
															UpperCAmelCase  :	Optional[Any]							  =	ids_tensor([self.batch_size]	,	self.type_sequence_label_size					)
															UpperCAmelCase  :	List[Any]							  =	ids_tensor([self.batch_size, self.seq_length]	,	self.num_labels					)
															UpperCAmelCase  :	Optional[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 				_lowercase(						self					)    ->    Dict:
								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=A	,	initializer_range=self.initializer_range	,	use_stable_embedding=A	,	)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	A					)    ->    str:
								UpperCAmelCase  :	Union[str, Any]							  =	OpenLlamaModel(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Dict							  =	model(A	,	attention_mask=A					)
								UpperCAmelCase  :	Optional[int]							  =	model(A					)
								self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size)					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	)    ->    List[Any]:
								UpperCAmelCase  :	Optional[int]							  =	True
								UpperCAmelCase  :	Union[str, Any]							  =	OpenLlamaModel(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	List[Any]							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	)
								UpperCAmelCase  :	str							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	)
								UpperCAmelCase  :	List[Any]							  =	model(A	,	attention_mask=A					)
								self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size)					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	)    ->    int:
								UpperCAmelCase  :	Optional[int]							  =	OpenLlamaForCausalLM(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Tuple							  =	model(A	,	attention_mask=A	,	labels=A					)
								self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size)					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	)    ->    int:
								UpperCAmelCase  :	Dict							  =	True
								UpperCAmelCase  :	Tuple							  =	True
								UpperCAmelCase  :	str							  =	OpenLlamaForCausalLM(config=A					)
								model.to(A					)
								model.eval()

								# first forward pass
								UpperCAmelCase  :	Union[str, Any]							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	use_cache=A	,	)
								UpperCAmelCase  :	str							  =	outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase  :	Tuple							  =	ids_tensor((self.batch_size, 3)	,	config.vocab_size					)
								UpperCAmelCase  :	Tuple							  =	ids_tensor((self.batch_size, 3)	,	vocab_size=2					)

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

								UpperCAmelCase  :	List[Any]							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	output_hidden_states=A	,	)["""hidden_states"""][0]
								UpperCAmelCase  :	Optional[Any]							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	past_key_values=A	,	output_hidden_states=A	,	)["""hidden_states"""][0]

								# select random slice
								UpperCAmelCase  :	str							  =	ids_tensor((1,)	,	output_from_past.shape[-1]					).item()
								UpperCAmelCase  :	Any							  =	output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase  :	Dict							  =	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(A	,	A	,	atol=1e-3					)					)






	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Union[str, Any]							  =	self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  
								)  :	Tuple							  =	config_and_inputs
								UpperCAmelCase  :	str							  =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class     UpperCamelCase_				(							__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  unittest.TestCase    ):
	lowercase    =				(
	    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
	)
	lowercase    =				(OpenLlamaForCausalLM,) if is_torch_available() else ()
	lowercase    =				(
	    {
	        'feature-extraction': OpenLlamaModel,
	        'text-classification': OpenLlamaForSequenceClassification,
	        'text-generation': OpenLlamaForCausalLM,
	        'zero-shot': OpenLlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowercase    =				False
	lowercase    =				False





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	Dict							  =	OpenLlamaModelTester(self					)
								UpperCAmelCase  :	List[str]							  =	ConfigTester(self	,	config_class=A	,	hidden_size=37					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								self.config_tester.run_common_tests()





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Tuple							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A					)





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Any							  =	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(*A					)





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  ,  UpperCAmelCase  :	List[str]							  =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase  :	List[str]							  =	3
								UpperCAmelCase  :	Optional[Any]							  =	input_dict["""input_ids"""]
								UpperCAmelCase  :	str							  =	input_ids.ne(1					).to(A					)
								UpperCAmelCase  :	Tuple							  =	ids_tensor([self.model_tester.batch_size]	,	self.model_tester.type_sequence_label_size					)
								UpperCAmelCase  :	Optional[Any]							  =	OpenLlamaForSequenceClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	List[Any]							  =	model(A	,	attention_mask=A	,	labels=A					)
								self.assertEqual(result.logits.shape	,	(self.model_tester.batch_size, self.model_tester.num_labels)					)





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  ,  UpperCAmelCase  :	Union[str, Any]							  =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase  :	Dict							  =	3
								UpperCAmelCase  :	Any							  =	"""single_label_classification"""
								UpperCAmelCase  :	Dict							  =	input_dict["""input_ids"""]
								UpperCAmelCase  :	Optional[Any]							  =	input_ids.ne(1					).to(A					)
								UpperCAmelCase  :	str							  =	ids_tensor([self.model_tester.batch_size]	,	self.model_tester.type_sequence_label_size					)
								UpperCAmelCase  :	Optional[Any]							  =	OpenLlamaForSequenceClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Tuple							  =	model(A	,	attention_mask=A	,	labels=A					)
								self.assertEqual(result.logits.shape	,	(self.model_tester.batch_size, self.model_tester.num_labels)					)





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  ,  UpperCAmelCase  :	Dict							  =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase  :	Tuple							  =	3
								UpperCAmelCase  :	Optional[Any]							  =	"""multi_label_classification"""
								UpperCAmelCase  :	Dict							  =	input_dict["""input_ids"""]
								UpperCAmelCase  :	int							  =	input_ids.ne(1					).to(A					)
								UpperCAmelCase  :	int							  =	ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]	,	self.model_tester.type_sequence_label_size					).to(torch.float					)
								UpperCAmelCase  :	Any							  =	OpenLlamaForSequenceClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Dict							  =	model(A	,	attention_mask=A	,	labels=A					)
								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 				_lowercase(						self					)    ->    Dict:
								pass






	@parameterized.expand([("""linear""",), ("""dynamic""",)]					)
	def 				_lowercase(						self	,	A					)    ->    str:
								UpperCAmelCase  ,  UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase  :	Dict							  =	ids_tensor([1, 10]	,	config.vocab_size					)
								UpperCAmelCase  :	List[str]							  =	ids_tensor([1, int(config.max_position_embeddings * 1.5					)]	,	config.vocab_size					)

								set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase  :	Any							  =	OpenLlamaModel(A					)
								original_model.to(A					)
								original_model.eval()
								UpperCAmelCase  :	List[str]							  =	original_model(A					).last_hidden_state
								UpperCAmelCase  :	List[Any]							  =	original_model(A					).last_hidden_state

								set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase  :	Union[str, Any]							  =	{"""type""": scaling_type, """factor""": 1_0.0}
								UpperCAmelCase  :	str							  =	OpenLlamaModel(A					)
								scaled_model.to(A					)
								scaled_model.eval()
								UpperCAmelCase  :	List[str]							  =	scaled_model(A					).last_hidden_state
								UpperCAmelCase  :	Optional[int]							  =	scaled_model(A					).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(A	,	A	,	atol=1e-5					)					)
								else:
															self.assertFalse(torch.allclose(A	,	A	,	atol=1e-5					)					)

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

