





import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__UpperCamelCase:	str = """sshleifer/mar_enro_6_3_student"""


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Dict			):
			super().setUp()

			lowercase__					:      List[str]      =		cached_path(
			    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz',					extract_compressed_file=lowerCamelCase_,					)
			lowercase__					:      Dict      =		F"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""

	@slow
	@require_torch_gpu
	def    snake_case__(						self:						str			):
			MarianMTModel.from_pretrained(lowerCamelCase_			)





	@slow
	@require_torch_gpu
	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      Any      =		{
			    '$MAX_LEN': 64,
			    '$BS': 64,
			    '$GAS': 1,
			    '$ENRO_DIR': self.data_dir,
			    'facebook/mbart-large-cc25': MARIAN_MODEL,
			    # "val_check_interval=0.25": "val_check_interval=1.0",
			    '--learning_rate=3e-5': '--learning_rate 3e-4',
			    '--num_train_epochs 6': '--num_train_epochs 1',
			}

			# Clean up bash script
			lowercase__					:      Optional[Any]      =		(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'			)[1].strip()
			lowercase__					:      Dict      =		bash_script.replace('\\\n',					''			).strip().replace('"$@"',					''			)
			for k, v in env_vars_to_replace.items():
					lowercase__					:      Union[str, Any]      =		bash_script.replace(lowerCamelCase_,					str(lowerCamelCase_			)			)
			lowercase__					:      int      =		self.get_auto_remove_tmp_dir()

			# bash_script = bash_script.replace("--fp16 ", "")
			lowercase__					:      str      =		F"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
			# XXX: args.gpus > 1 : handle multi_gpu in the future

			lowercase__					:      List[str]      =		['finetune.py'] + bash_script.split() + args
			with patch.object(lowerCamelCase_,					'argv',					lowerCamelCase_			):
					lowercase__					:      Dict      =		argparse.ArgumentParser()
					lowercase__					:      Any      =		pl.Trainer.add_argparse_args(lowerCamelCase_			)
					lowercase__					:      int      =		SummarizationModule.add_model_specific_args(lowerCamelCase_,					os.getcwd()			)
					lowercase__					:      Dict      =		parser.parse_args()
					lowercase__					:      Union[str, Any]      =		main(lowerCamelCase_			)

			# Check metrics
			lowercase__					:      Dict      =		load_json(model.metrics_save_path			)
			lowercase__					:      int      =		metrics['val'][0]
			lowercase__					:      Optional[int]      =		metrics['val'][-1]
			self.assertEqual(len(metrics['val']			),					(args.max_epochs / args.val_check_interval)			)
			assert isinstance(last_step_stats[F"""val_avg_{model.val_metric}"""],					lowerCamelCase_			)

			self.assertGreater(last_step_stats['val_avg_gen_time'],					0.0_1			)
			# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
			self.assertLessEqual(last_step_stats['val_avg_gen_time'],					1.0			)

			# test learning requirements:

			# 1. BLEU improves over the course of training by more than 2 pts
			self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'],					2			)

			# 2. BLEU finishes above 17
			self.assertGreater(last_step_stats['val_avg_bleu'],					17			)

			# 3. test BLEU and val BLEU within ~1.1 pt.
			self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']			),					1.1			)

			# check lightning ckpt can be loaded and has a reasonable statedict
			lowercase__					:      List[str]      =		os.listdir(lowerCamelCase_			)
			lowercase__					:      List[str]      =		[x for x in contents if x.endswith('.ckpt'			)][0]
			lowercase__					:      Tuple      =		os.path.join(args.output_dir,					lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		torch.load(lowerCamelCase_,					map_location='cpu'			)
			lowercase__					:      int      =		'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
			assert expected_key in ckpt["state_dict"]
			assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

			# TODO: turn on args.do_predict when PL bug fixed.
			if args.do_predict:
					lowercase__					:      Dict      =		{os.path.basename(lowerCamelCase_			) for p in contents}
					assert "test_generations.txt" in contents
					assert "test_results.txt" in contents
					# assert len(metrics["val"]) ==  desired_n_evals
					assert len(metrics['test']			) == 1


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	@timeout_decorator.timeout(600			)
	@slow
	@require_torch_gpu
	def    snake_case__(						self:						int			):
			lowercase__					:      Dict      =		F"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
			lowercase__					:      Optional[Any]      =		{
			    '--fp16_opt_level=O1': '',
			    '$MAX_LEN': 128,
			    '$BS': 16,
			    '$GAS': 1,
			    '$ENRO_DIR': data_dir,
			    '$m': 'sshleifer/student_marian_en_ro_6_1',
			    'val_check_interval=0.25': 'val_check_interval=1.0',
			}

			# Clean up bash script
			lowercase__					:      Dict      =		(
			    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'			)[1].strip()
			)
			lowercase__					:      List[str]      =		bash_script.replace('\\\n',					''			).strip().replace('"$@"',					''			)
			lowercase__					:      Optional[int]      =		bash_script.replace('--fp16 ',					' '			)

			for k, v in env_vars_to_replace.items():
					lowercase__					:      List[str]      =		bash_script.replace(lowerCamelCase_,					str(lowerCamelCase_			)			)
			lowercase__					:      Union[str, Any]      =		self.get_auto_remove_tmp_dir()
			lowercase__					:      Tuple      =		bash_script.replace('--fp16',					''			)
			lowercase__					:      Optional[int]      =		6
			lowercase__					:      List[Any]      =		(
			    ['distillation.py']
			    + bash_script.split()
			    + [
			        F"""--output_dir={output_dir}""",
			        '--gpus=1',
			        '--learning_rate=1e-3',
			        F"""--num_train_epochs={epochs}""",
			        '--warmup_steps=10',
			        '--val_check_interval=1.0',
			        '--do_predict',
			    ]
			)
			with patch.object(lowerCamelCase_,					'argv',					lowerCamelCase_			):
					lowercase__					:      Optional[int]      =		argparse.ArgumentParser()
					lowercase__					:      str      =		pl.Trainer.add_argparse_args(lowerCamelCase_			)
					lowercase__					:      int      =		SummarizationDistiller.add_model_specific_args(lowerCamelCase_,					os.getcwd()			)
					lowercase__					:      Union[str, Any]      =		parser.parse_args()
					# assert args.gpus == gpus THIS BREAKS for multi_gpu

					lowercase__					:      Union[str, Any]      =		distill_main(lowerCamelCase_			)

			# Check metrics
			lowercase__					:      int      =		load_json(model.metrics_save_path			)
			lowercase__					:      Dict      =		metrics['val'][0]
			lowercase__					:      Dict      =		metrics['val'][-1]
			assert len(metrics['val']			) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

			assert last_step_stats["val_avg_gen_time"] >= 0.0_1

			assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
			assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
			assert isinstance(last_step_stats[F"""val_avg_{model.val_metric}"""],					lowerCamelCase_			)

			# check lightning ckpt can be loaded and has a reasonable statedict
			lowercase__					:      List[Any]      =		os.listdir(lowerCamelCase_			)
			lowercase__					:      List[Any]      =		[x for x in contents if x.endswith('.ckpt'			)][0]
			lowercase__					:      Optional[Any]      =		os.path.join(args.output_dir,					lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.load(lowerCamelCase_,					map_location='cpu'			)
			lowercase__					:      Dict      =		'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
			assert expected_key in ckpt["state_dict"]
			assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

			# TODO: turn on args.do_predict when PL bug fixed.
			if args.do_predict:
					lowercase__					:      Union[str, Any]      =		{os.path.basename(lowerCamelCase_			) for p in contents}
					assert "test_generations.txt" in contents
					assert "test_results.txt" in contents
					# assert len(metrics["val"]) ==  desired_n_evals
					assert len(metrics['test']			) == 1









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       __lowerCAmelCase					:


	'''simple docstring'''





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

	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      Any      =		ids_tensor([self.batch_size, self.seq_length],					self.vocab_size			)

			lowercase__					:      List[Any]      =		None
			if self.use_input_mask:
					lowercase__					:      List[str]      =		random_attention_mask([self.batch_size, self.seq_length]			)

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

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

			lowercase__					:      Tuple      =		self.get_config()

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

	def    snake_case__(						self:						Tuple			):
			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=lowerCamelCase_,					initializer_range=self.initializer_range,					use_stable_embedding=lowerCamelCase_,					)

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						str			):
			lowercase__					:      Union[str, Any]      =		OpenLlamaModel(config=lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      Union[str, Any]      =		model(lowerCamelCase_,					attention_mask=lowerCamelCase_			)
			lowercase__					:      List[Any]      =		model(lowerCamelCase_			)
			self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)			)

	def    snake_case__(						self:						str,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						str,					lowerCamelCase_:						str,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Union[str, Any],					):
			lowercase__					:      Tuple      =		True
			lowercase__					:      int      =		OpenLlamaModel(lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      List[Any]      =		model(
			    lowerCamelCase_,					attention_mask=lowerCamelCase_,					encoder_hidden_states=lowerCamelCase_,					encoder_attention_mask=lowerCamelCase_,					)
			lowercase__					:      Optional[int]      =		model(
			    lowerCamelCase_,					attention_mask=lowerCamelCase_,					encoder_hidden_states=lowerCamelCase_,					)
			lowercase__					:      str      =		model(lowerCamelCase_,					attention_mask=lowerCamelCase_			)
			self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)			)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Any,					lowerCamelCase_:						str,					lowerCamelCase_:						List[str],					lowerCamelCase_:						Any,					lowerCamelCase_:						Dict,					lowerCamelCase_:						int,					lowerCamelCase_:						Any,					lowerCamelCase_:						str,					):
			lowercase__					:      Optional[Any]      =		OpenLlamaForCausalLM(config=lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      List[Any]      =		model(lowerCamelCase_,					attention_mask=lowerCamelCase_,					labels=lowerCamelCase_			)
			self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)			)

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						str,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						str,					lowerCamelCase_:						List[Any],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						str,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Union[str, Any],					):
			lowercase__					:      Optional[int]      =		True
			lowercase__					:      Optional[int]      =		True
			lowercase__					:      Tuple      =		OpenLlamaForCausalLM(config=lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()

			# first forward pass
			lowercase__					:      Any      =		model(
			    lowerCamelCase_,					attention_mask=lowerCamelCase_,					encoder_hidden_states=lowerCamelCase_,					encoder_attention_mask=lowerCamelCase_,					use_cache=lowerCamelCase_,					)
			lowercase__					:      str      =		outputs.past_key_values

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

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

			lowercase__					:      int      =		model(
			    lowerCamelCase_,					attention_mask=lowerCamelCase_,					encoder_hidden_states=lowerCamelCase_,					encoder_attention_mask=lowerCamelCase_,					output_hidden_states=lowerCamelCase_,					)['hidden_states'][0]
			lowercase__					:      int      =		model(
			    lowerCamelCase_,					attention_mask=lowerCamelCase_,					encoder_hidden_states=lowerCamelCase_,					encoder_attention_mask=lowerCamelCase_,					past_key_values=lowerCamelCase_,					output_hidden_states=lowerCamelCase_,					)['hidden_states'][0]

			# select random slice
			lowercase__					:      Tuple      =		ids_tensor((1,),					output_from_past.shape[-1]			).item()
			lowercase__					:      Union[str, Any]      =		output_from_no_past[:, -3:, random_slice_idx].detach()
			lowercase__					:      Tuple      =		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(lowerCamelCase_,					lowerCamelCase_,					atol=1E-3			)			)





	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      Dict      =		self.prepare_config_and_inputs()
			(
			    (
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				
			)					:      List[str]      =		config_and_inputs
			lowercase__					:      Any      =		{'input_ids': input_ids, 'attention_mask': input_mask}
			return config, inputs_dict


@require_torch
class       __lowerCAmelCase					(       _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				(
	    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
	)
	_A													=				(OpenLlamaForCausalLM,) if is_torch_available() else ()
	_A													=				(
	    {
	        "feature-extraction": OpenLlamaModel,
	        "text-classification": OpenLlamaForSequenceClassification,
	        "text-generation": OpenLlamaForCausalLM,
	        "zero-shot": OpenLlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_A													=				False
	_A													=				False

	def    snake_case__(						self:						Any			):
			lowercase__					:      List[Any]      =		OpenLlamaModelTester(self			)
			lowercase__					:      Dict      =		ConfigTester(self,					config_class=lowerCamelCase_,					hidden_size=37			)

	def    snake_case__(						self:						Any			):
			self.config_tester.run_common_tests()

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Any      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			lowercase__					:      Any      =		self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					lowercase__					:      Dict      =		type
					self.model_tester.create_and_check_model(*lowerCamelCase_			)

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

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

	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__   ,				lowercase__					:      Optional[int]      =		self.model_tester.prepare_config_and_inputs_for_common()
			lowercase__					:      Dict      =		3
			lowercase__					:      List[Any]      =		'multi_label_classification'
			lowercase__					:      Any      =		input_dict['input_ids']
			lowercase__					:      int      =		input_ids.ne(1			).to(lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		ids_tensor(
			    [self.model_tester.batch_size, config.num_labels],					self.model_tester.type_sequence_label_size			).to(torch.float			)
			lowercase__					:      Optional[int]      =		OpenLlamaForSequenceClassification(lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      List[Any]      =		model(lowerCamelCase_,					attention_mask=lowerCamelCase_,					labels=lowerCamelCase_			)
			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    snake_case__(						self:						List[str]			):
			pass





	@parameterized.expand([('linear',), ('dynamic',)]			)
	def    snake_case__(						self:						Any,					lowerCamelCase_:						List[Any]			):
			lowercase__   ,				lowercase__					:      Optional[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
			lowercase__					:      Optional[int]      =		ids_tensor([1, 10],					config.vocab_size			)
			lowercase__					:      Optional[Any]      =		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__					:      str      =		OpenLlamaModel(lowerCamelCase_			)
			original_model.to(lowerCamelCase_			)
			original_model.eval()
			lowercase__					:      Union[str, Any]      =		original_model(lowerCamelCase_			).last_hidden_state
			lowercase__					:      List[str]      =		original_model(lowerCamelCase_			).last_hidden_state

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

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


