


import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


a					  =     logging.get_logger(__name__)

a					  =     list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a					  =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default=__magic_name__							,				metadata={'help': 'Model type selected in the list: ' + ', '.join(__magic_name__   )}   )
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default=__magic_name__							,				metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}   )
					__SCREAMING_SNAKE_CASE       :  int						=   field(
					    default=128							,				metadata={
					        'help': (
					            'The maximum total input sequence length after tokenization. Sequences longer '
					            'than this will be truncated, sequences shorter will be padded.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  int						=   field(
					    default=128							,				metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}							,				)
					__SCREAMING_SNAKE_CASE       :  int						=   field(
					    default=64							,				metadata={
					        'help': (
					            'The maximum number of tokens for the question. Questions longer than this will '
					            'be truncated to this length.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  int						=   field(
					    default=30							,				metadata={
					        'help': (
					            'The maximum length of an answer that can be generated. This is needed because the start '
					            'and end predictions are not conditioned on one another.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={'help': 'Overwrite the cached training and evaluation sets'}   )
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}   )
					__SCREAMING_SNAKE_CASE       :  float						=   field(
					    default=0.0							,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}   )
					__SCREAMING_SNAKE_CASE       :  int						=   field(
					    default=20							,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}   )
					__SCREAMING_SNAKE_CASE       :  int						=   field(
					    default=0							,				metadata={
					        'help': (
					            'language id of input for language-specific xlm models (see'
					            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  int						=   field(default=1							,				metadata={'help': 'multiple threads for converting example to features'}   )







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   'train'
					__SCREAMING_SNAKE_CASE       :  Dict						=   'dev'







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  SquadDataTrainingArguments
					__SCREAMING_SNAKE_CASE       :  List[SquadFeatures]
					__SCREAMING_SNAKE_CASE       :  Split
					__SCREAMING_SNAKE_CASE       :  bool

					def __init__(     self       :   Tuple							,					UpperCamelCase__       :   SquadDataTrainingArguments							,					UpperCamelCase__       :   PreTrainedTokenizer							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Union[str, Split] = Split.train							,					UpperCamelCase__       :   Optional[bool] = False							,					UpperCamelCase__       :   Optional[str] = None							,					UpperCamelCase__       :   Optional[str] = "pt"							,					):






										'''simple docstring'''






										lowercase_  							=			args
										lowercase_  							=			is_language_sensitive
										lowercase_  							=			SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
										if isinstance(UpperCamelCase__							,					UpperCamelCase__       ):
															try:
																				lowercase_  							=			Split[mode]
															except KeyError:
																				raise KeyError("""mode is not a valid split name"""       )
										lowercase_  							=			mode
										# Load data features from cache or dataset file
										lowercase_  							=			"""v2""" if args.version_2_with_negative else """v1"""
										lowercase_  							=			os.path.join(
										    cache_dir if cache_dir is not None else args.data_dir							,					F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'''							,					)

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										lowercase_  							=			cached_features_file + """.lock"""
										with FileLock(UpperCamelCase__       ):
															if os.path.exists(UpperCamelCase__       ) and not args.overwrite_cache:
																				lowercase_  							=			time.time()
																				lowercase_  							=			torch.load(UpperCamelCase__       )

																				# Legacy cache files have only features, while new cache files
																				# will have dataset and examples also.
																				lowercase_  							=			self.old_features["""features"""]
																				lowercase_  							=			self.old_features.get("""dataset"""							,					UpperCamelCase__       )
																				lowercase_  							=			self.old_features.get("""examples"""							,					UpperCamelCase__       )
																				logger.info(
																				    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''							,					time.time() - start       )

																				if self.dataset is None or self.examples is None:
																									logger.warning(
																									    F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
																									    """ future run"""       )
															else:
																				if mode == Split.dev:
																									lowercase_  							=			self.processor.get_dev_examples(args.data_dir       )
																				else:
																									lowercase_  							=			self.processor.get_train_examples(args.data_dir       )

																				lowercase_							,					lowercase_  							=			squad_convert_examples_to_features(
																				    examples=self.examples							,					tokenizer=UpperCamelCase__							,					max_seq_length=args.max_seq_length							,					doc_stride=args.doc_stride							,					max_query_length=args.max_query_length							,					is_training=mode == Split.train							,					threads=args.threads							,					return_dataset=UpperCamelCase__							,					)

																				lowercase_  							=			time.time()
																				torch.save(
																				    {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples}							,					UpperCamelCase__							,					)
																				# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																				logger.info(
																				    F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''       )

					def __len__(     self       :   List[Any]       ):






										'''simple docstring'''






										return len(self.features       )







					def __getitem__(     self       :   int							,					UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			self.features[i]

										lowercase_  							=			torch.tensor(feature.input_ids							,					dtype=torch.long       )
										lowercase_  							=			torch.tensor(feature.attention_mask							,					dtype=torch.long       )
										lowercase_  							=			torch.tensor(feature.token_type_ids							,					dtype=torch.long       )
										lowercase_  							=			torch.tensor(feature.cls_index							,					dtype=torch.long       )
										lowercase_  							=			torch.tensor(feature.p_mask							,					dtype=torch.float       )
										lowercase_  							=			torch.tensor(feature.is_impossible							,					dtype=torch.float       )

										lowercase_  							=			{
										    """input_ids""": input_ids,
										    """attention_mask""": attention_mask,
										    """token_type_ids""": token_type_ids,
										}

										if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
															del inputs["token_type_ids"]

										if self.args.model_type in ["xlnet", "xlm"]:
															inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask}       )
															if self.args.version_2_with_negative:
																				inputs.update({"""is_impossible""": is_impossible}       )
															if self.is_language_sensitive:
																				inputs.update({"""langs""": (torch.ones(input_ids.shape							,					dtype=torch.intaa       ) * self.args.lang_id)}       )

										if self.mode == Split.train:
															lowercase_  							=			torch.tensor(feature.start_position							,					dtype=torch.long       )
															lowercase_  							=			torch.tensor(feature.end_position							,					dtype=torch.long       )
															inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}       )

										return inputs






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       :   List[str]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   List[str]=13							,					UpperCamelCase__       :   str=7							,					UpperCamelCase__       :   List[str]=True							,					UpperCamelCase__       :   List[str]=True							,					UpperCamelCase__       :   str=False							,					UpperCamelCase__       :   str=True							,					UpperCamelCase__       :   Union[str, Any]=99							,					UpperCamelCase__       :   Dict=32							,					UpperCamelCase__       :   Union[str, Any]=5							,					UpperCamelCase__       :   Tuple=4							,					UpperCamelCase__       :   Dict=37							,					UpperCamelCase__       :   Any="gelu"							,					UpperCamelCase__       :   Dict=0.1							,					UpperCamelCase__       :   Tuple=0.1							,					UpperCamelCase__       :   Tuple=512							,					UpperCamelCase__       :   Union[str, Any]=16							,					UpperCamelCase__       :   List[str]=2							,					UpperCamelCase__       :   int=0.02							,					UpperCamelCase__       :   int=3							,					UpperCamelCase__       :   Optional[Any]=4							,					UpperCamelCase__       :   int=None							,					):






										'''simple docstring'''






										lowercase_  							=			parent
										lowercase_  							=			batch_size
										lowercase_  							=			seq_length
										lowercase_  							=			is_training
										lowercase_  							=			use_input_mask
										lowercase_  							=			use_token_type_ids
										lowercase_  							=			use_labels
										lowercase_  							=			vocab_size
										lowercase_  							=			hidden_size
										lowercase_  							=			num_hidden_layers
										lowercase_  							=			num_attention_heads
										lowercase_  							=			intermediate_size
										lowercase_  							=			hidden_act
										lowercase_  							=			hidden_dropout_prob
										lowercase_  							=			attention_probs_dropout_prob
										lowercase_  							=			max_position_embeddings
										lowercase_  							=			type_vocab_size
										lowercase_  							=			type_sequence_label_size
										lowercase_  							=			initializer_range
										lowercase_  							=			num_labels
										lowercase_  							=			num_choices
										lowercase_  							=			scope

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size       )

										lowercase_  							=			None
										if self.use_input_mask:
															lowercase_  							=			random_attention_mask([self.batch_size, self.seq_length]       )

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

										lowercase_  							=			None
										lowercase_  							=			None
										lowercase_  							=			None
										if self.use_labels:
															lowercase_  							=			ids_tensor([self.batch_size]							,					self.type_sequence_label_size       )
															lowercase_  							=			ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels       )
															lowercase_  							=			ids_tensor([self.batch_size]							,					self.num_choices       )

										lowercase_  							=			self.get_config()

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

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''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=UpperCamelCase__							,					initializer_range=self.initializer_range							,					use_stable_embedding=UpperCamelCase__							,					)

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			OpenLlamaModel(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__       )
										lowercase_  							=			model(UpperCamelCase__       )
										self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   Optional[Any]							,					):






										'''simple docstring'''






										lowercase_  							=			True
										lowercase_  							=			OpenLlamaModel(UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					encoder_hidden_states=UpperCamelCase__							,					encoder_attention_mask=UpperCamelCase__							,					)
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					encoder_hidden_states=UpperCamelCase__							,					)
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__       )
										self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)       )

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   List[Any]							,					):






										'''simple docstring'''






										lowercase_  							=			OpenLlamaForCausalLM(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					labels=UpperCamelCase__       )
										self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)       )

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Tuple							,					):






										'''simple docstring'''






										lowercase_  							=			True
										lowercase_  							=			True
										lowercase_  							=			OpenLlamaForCausalLM(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()

										# first forward pass
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					encoder_hidden_states=UpperCamelCase__							,					encoder_attention_mask=UpperCamelCase__							,					use_cache=UpperCamelCase__							,					)
										lowercase_  							=			outputs.past_key_values

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

										# append to next input_ids and
										lowercase_  							=			torch.cat([input_ids, next_tokens]							,					dim=-1       )
										lowercase_  							=			torch.cat([input_mask, next_mask]							,					dim=-1       )

										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					encoder_hidden_states=UpperCamelCase__							,					encoder_attention_mask=UpperCamelCase__							,					output_hidden_states=UpperCamelCase__							,					)["""hidden_states"""][0]
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					encoder_hidden_states=UpperCamelCase__							,					encoder_attention_mask=UpperCamelCase__							,					past_key_values=UpperCamelCase__							,					output_hidden_states=UpperCamelCase__							,					)["""hidden_states"""][0]

										# select random slice
										lowercase_  							=			ids_tensor((1,)							,					output_from_past.shape[-1]       ).item()
										lowercase_  							=			output_from_no_past[:, -3:, random_slice_idx].detach()
										lowercase_  							=			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(UpperCamelCase__							,					UpperCamelCase__							,					atol=1e-3       )       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.prepare_config_and_inputs()
										(
										    (
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					
										)  							=			config_and_inputs
										lowercase_  							=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
										return config, inputs_dict







@require_torch
class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  Union[str, Any]						=   (
					    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
					)
					__SCREAMING_SNAKE_CASE       :  List[str]						=   (OpenLlamaForCausalLM,) if is_torch_available() else ()
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   (
					    {
					        'feature-extraction': OpenLlamaModel,
					        'text-classification': OpenLlamaForSequenceClassification,
					        'text-generation': OpenLlamaForCausalLM,
					        'zero-shot': OpenLlamaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__SCREAMING_SNAKE_CASE       :  Tuple						=   False
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   False

					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			OpenLlamaModelTester(self       )
										lowercase_  							=			ConfigTester(self							,					config_class=UpperCamelCase__							,					hidden_size=37       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										self.config_tester.run_common_tests()

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
															lowercase_  							=			type
															self.model_tester.create_and_check_model(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			self.model_tester.prepare_config_and_inputs_for_common()
										lowercase_  							=			3
										lowercase_  							=			input_dict["""input_ids"""]
										lowercase_  							=			input_ids.ne(1       ).to(UpperCamelCase__       )
										lowercase_  							=			ids_tensor([self.model_tester.batch_size]							,					self.model_tester.type_sequence_label_size       )
										lowercase_  							=			OpenLlamaForSequenceClassification(UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					labels=UpperCamelCase__       )
										self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels)       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			self.model_tester.prepare_config_and_inputs_for_common()
										lowercase_  							=			3
										lowercase_  							=			"""single_label_classification"""
										lowercase_  							=			input_dict["""input_ids"""]
										lowercase_  							=			input_ids.ne(1       ).to(UpperCamelCase__       )
										lowercase_  							=			ids_tensor([self.model_tester.batch_size]							,					self.model_tester.type_sequence_label_size       )
										lowercase_  							=			OpenLlamaForSequenceClassification(UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					labels=UpperCamelCase__       )
										self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels)       )

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			self.model_tester.prepare_config_and_inputs_for_common()
										lowercase_  							=			3
										lowercase_  							=			"""multi_label_classification"""
										lowercase_  							=			input_dict["""input_ids"""]
										lowercase_  							=			input_ids.ne(1       ).to(UpperCamelCase__       )
										lowercase_  							=			ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]							,					self.model_tester.type_sequence_label_size       ).to(torch.float       )
										lowercase_  							=			OpenLlamaForSequenceClassification(UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					labels=UpperCamelCase__       )
										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       :   Dict       ):






										'''simple docstring'''






										pass







					@parameterized.expand([("""linear""",), ("""dynamic""",)]       )
					def   UpperCAmelCase__       (     self       :   Tuple							,					UpperCamelCase__       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			self.model_tester.prepare_config_and_inputs_for_common()
										lowercase_  							=			ids_tensor([1, 10]							,					config.vocab_size       )
										lowercase_  							=			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
										lowercase_  							=			OpenLlamaModel(UpperCamelCase__       )
										original_model.to(UpperCamelCase__       )
										original_model.eval()
										lowercase_  							=			original_model(UpperCamelCase__       ).last_hidden_state
										lowercase_  							=			original_model(UpperCamelCase__       ).last_hidden_state

										set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
										lowercase_  							=			{"""type""": scaling_type, """factor""": 10.0}
										lowercase_  							=			OpenLlamaModel(UpperCamelCase__       )
										scaled_model.to(UpperCamelCase__       )
										scaled_model.eval()
										lowercase_  							=			scaled_model(UpperCamelCase__       ).last_hidden_state
										lowercase_  							=			scaled_model(UpperCamelCase__       ).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(UpperCamelCase__							,					UpperCamelCase__							,					atol=1e-5       )       )
										else:
															self.assertFalse(torch.allclose(UpperCamelCase__							,					UpperCamelCase__							,					atol=1e-5       )       )

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


