
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCAmelCase :			Union[str, Any]              =	logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")


@dataclass
class     lowercase :
		__lowercase	:	Optional[str]     					=    field(
		    default="cifar10"  ,       metadata={"help": "Name of a dataset from the datasets package"}       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "The column name of the images in the files."}       )
		__lowercase	:	Optional[str]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "A folder containing the training data."}       )
		__lowercase	:	Optional[str]     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "A folder containing the validation data."}       )
		__lowercase	:	Optional[float]     					=    field(
		    default=0.1_5  ,       metadata={"help": "Percent to split off of train for validation."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={
		        "help": (
		            "For debugging purposes or quicker training, truncate the number of training examples to this "
		            "value if set."
		        )
		    }  ,       )
		__lowercase	:	Optional[int]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={
		        "help": (
		            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
		            "value if set."
		        )
		    }  ,       )



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					{}
				if self.train_dir is not None:
						UpperCamelCase          =					self.train_dir
				if self.validation_dir is not None:
						UpperCamelCase          =					self.validation_dir
				UpperCamelCase          =					data_files if data_files else None






@dataclass
class     lowercase :
		__lowercase	:	str     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={
		        "help": (
		            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
		        )
		    }  ,       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={
		        "help": (
		            "Override some existing default config settings when a model is trained from scratch. Example: "
		            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
		        )
		    }  ,       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}       )
		__lowercase	:	str     					=    field(
		    default="main"  ,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}  ,       )
		__lowercase	:	str     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Name or path of preprocessor config."}       )
		__lowercase	:	bool     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={
		        "help": (
		            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
		            "with private models)."
		        )
		    }  ,       )
		__lowercase	:	float     					=    field(
		    default=0.7_5  ,       metadata={"help": "The ratio of the number of masked tokens in the input sequence."}       )
		__lowercase	:	bool     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Whether or not to train with normalized pixel values as target."}       )






@dataclass
class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	float     					=    field(
		    default=1e-3  ,       metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}       )






def 				A	( lowercase			)       ->  Tuple:
		'''simple docstring'''

		UpperCamelCase          =					torch.stack([example['pixel_values'] for example in examples]			)
		return {"pixel_values": pixel_values}


def 				A	( )       ->  List[str]:
		'''simple docstring'''

		UpperCamelCase          =					HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)			)
		if len(sys.argv			) == 2 and sys.argv[1].endswith('.json'			):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_mae'   ,	lowercase   ,	lowercase			)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,	datefmt='%m/%d/%Y %H:%M:%S'   ,	handlers=[logging.StreamHandler(sys.stdout			)]   ,	)

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		UpperCamelCase          =					training_args.get_process_log_level()
		logger.setLevel(lowercase			)
		transformers.utils.logging.set_verbosity(lowercase			)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
		    + f'''distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'''			)
		logger.info(f'''Training/evaluation parameters {training_args}'''			)

		# Detecting last checkpoint.
		UpperCamelCase          =					None
		if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
				UpperCamelCase          =					get_last_checkpoint(training_args.output_dir			)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
						    'Use --overwrite_output_dir to overcome.'			)
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'			)

    # Initialize our dataset.
		UpperCamelCase          =					load_dataset(
		    data_args.dataset_name   ,	data_args.dataset_config_name   ,	data_files=data_args.data_files   ,	cache_dir=model_args.cache_dir   ,	use_auth_token=True if model_args.use_auth_token else None   ,	)

		# If we don't have a validation split, split off a percentage of train as validation.
		UpperCamelCase          =					None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split   ,	lowercase			) and data_args.train_val_split > 0.0:
				UpperCamelCase          =					ds['train'].train_test_split(data_args.train_val_split			)
				UpperCamelCase          =					split['train']
				UpperCamelCase          =					split['test']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		UpperCamelCase          =					{
		    'cache_dir': model_args.cache_dir,
		    'revision': model_args.model_revision,
		    'use_auth_token': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				UpperCamelCase          =					ViTMAEConfig.from_pretrained(model_args.config_name   ,	**lowercase			)
		elif model_args.model_name_or_path:
				UpperCamelCase          =					ViTMAEConfig.from_pretrained(model_args.model_name_or_path   ,	**lowercase			)
		else:
				UpperCamelCase          =					ViTMAEConfig()
				logger.warning('You are instantiating a new config instance from scratch.'			)
				if model_args.config_overrides is not None:
						logger.info(f'''Overriding config: {model_args.config_overrides}'''			)
						config.update_from_string(model_args.config_overrides			)
						logger.info(f'''New config: {config}'''			)

    # adapt config
		config.update(
		    {
		        'mask_ratio': model_args.mask_ratio,
		        'norm_pix_loss': model_args.norm_pix_loss,
		    }			)

		# create image processor
		if model_args.image_processor_name:
				UpperCamelCase          =					ViTImageProcessor.from_pretrained(model_args.image_processor_name   ,	**lowercase			)
		elif model_args.model_name_or_path:
				UpperCamelCase          =					ViTImageProcessor.from_pretrained(model_args.model_name_or_path   ,	**lowercase			)
		else:
				UpperCamelCase          =					ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				UpperCamelCase          =					ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path   ,	from_tf=bool('.ckpt' in model_args.model_name_or_path			)   ,	config=lowercase   ,	cache_dir=model_args.cache_dir   ,	revision=model_args.model_revision   ,	use_auth_token=True if model_args.use_auth_token else None   ,	)
		else:
				logger.info('Training new model from scratch'			)
				UpperCamelCase          =					ViTMAEForPreTraining(lowercase			)

		if training_args.do_train:
				UpperCamelCase          =					ds['train'].column_names
		else:
				UpperCamelCase          =					ds['validation'].column_names

		if data_args.image_column_name is not None:
				UpperCamelCase          =					data_args.image_column_name
		elif "image" in column_names:
				UpperCamelCase          =					'image'
		elif "img" in column_names:
				UpperCamelCase          =					'img'
		else:
				UpperCamelCase          =					column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				UpperCamelCase          =					image_processor.size['shortest_edge']
		else:
				UpperCamelCase          =					(image_processor.size['height'], image_processor.size['width'])
		UpperCamelCase          =					Compose(
		    [
		        Lambda(lambda lowercase			: img.convert('RGB'			) if img.mode != "RGB" else img			),
		        RandomResizedCrop(lowercase   ,	scale=(0.2, 1.0)   ,	interpolation=InterpolationMode.BICUBIC			),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean   ,	std=image_processor.image_std			),
		    ]			)

		def preprocess_images(lowercase			):
				UpperCamelCase          =					[transforms(lowercase			) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('--do_train requires a train dataset'			)
				if data_args.max_train_samples is not None:
						UpperCamelCase          =					ds['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
				# Set the training transforms
				ds["train"].set_transform(lowercase			)

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('--do_eval requires a validation dataset'			)
				if data_args.max_eval_samples is not None:
						UpperCamelCase          =					(
						    ds['validation'].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
						)
				# Set the validation transforms
				ds["validation"].set_transform(lowercase			)

		# Compute absolute learning rate
		UpperCamelCase          =					(
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				UpperCamelCase          =					training_args.base_learning_rate * total_train_batch_size / 256

		# Initialize our trainer
		UpperCamelCase          =					Trainer(
		    model=lowercase   ,	args=lowercase   ,	train_dataset=ds['train'] if training_args.do_train else None   ,	eval_dataset=ds['validation'] if training_args.do_eval else None   ,	tokenizer=lowercase   ,	data_collator=lowercase   ,	)

		# Training
		if training_args.do_train:
				UpperCamelCase          =					None
				if training_args.resume_from_checkpoint is not None:
						UpperCamelCase          =					training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						UpperCamelCase          =					last_checkpoint
				UpperCamelCase          =					trainer.train(resume_from_checkpoint=lowercase			)
				trainer.save_model()
				trainer.log_metrics('train'   ,	train_result.metrics			)
				trainer.save_metrics('train'   ,	train_result.metrics			)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				UpperCamelCase          =					trainer.evaluate()
				trainer.log_metrics('eval'   ,	lowercase			)
				trainer.save_metrics('eval'   ,	lowercase			)

		# Write model card and (optionally) push to hub
		UpperCamelCase          =					{
		    'tasks': 'masked-auto-encoding',
		    'dataset': data_args.dataset_name,
		    'tags': ['masked-auto-encoding'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**lowercase			)
		else:
				trainer.create_model_card(**lowercase			)


def 				A	( lowercase			)       ->  int:
		'''simple docstring'''

		main()


if __name__ == "__main__":
					main()


import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, 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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class     lowercase :



		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_=0.02    ,				A_=3    ,				A_=4    ,				A_=None    ,				)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					seq_length
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_input_mask
				UpperCamelCase          =					use_token_type_ids
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					max_position_embeddings
				UpperCamelCase          =					type_vocab_size
				UpperCamelCase          =					type_sequence_label_size
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					num_choices
				UpperCamelCase          =					scope



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size	)

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

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

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

				UpperCamelCase          =					self.get_config()

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



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				return LlamaConfig(
				    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    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					LlamaModel(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_	)
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					True
				UpperCamelCase          =					LlamaModel(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				)
				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				)
				UpperCamelCase          =					model(A_    ,				attention_mask=A_	)
				self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					LlamaForCausalLM(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_    ,				labels=A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					True
				UpperCamelCase          =					True
				UpperCamelCase          =					LlamaForCausalLM(config=A_	)
				model.to(A_	)
				model.eval()

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

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

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

				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				output_hidden_states=A_    ,				)['hidden_states'][0]
				UpperCamelCase          =					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          =					ids_tensor((1,)    ,				output_from_past.shape[-1]	).item()
				UpperCamelCase          =					output_from_no_past[:, -3:, random_slice_idx].detach()
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  
				)          =					config_and_inputs
				UpperCamelCase          =					{'input_ids': input_ids, 'attention_mask': input_mask}
				return config, inputs_dict






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	str     					=    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
		__lowercase	:	str     					=    (LlamaForCausalLM,) if is_torch_available() else ()
		__lowercase	:	Any     					=    (
		    {
		        "feature-extraction": LlamaModel,
		        "text-classification": LlamaForSequenceClassification,
		        "text-generation": LlamaForCausalLM,
		        "zero-shot": LlamaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__lowercase	:	int     					=    False
		__lowercase	:	Optional[int]     					=    False



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					LlamaModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						UpperCamelCase          =					type
						self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_    ,				labels=A_	)
				self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					'single_label_classification'
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_    ,				labels=A_	)
				self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					'multi_label_classification'
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]    ,				self.model_tester.type_sequence_label_size	).to(torch.float	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_    ,				labels=A_	)
				self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)	)



		@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'	)
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				pass



		@parameterized.expand([('linear',), ('dynamic',)]	)
		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					ids_tensor([1, 10]    ,				config.vocab_size	)
				UpperCamelCase          =					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          =					LlamaModel(A_	)
				original_model.to(A_	)
				original_model.eval()
				UpperCamelCase          =					original_model(A_	).last_hidden_state
				UpperCamelCase          =					original_model(A_	).last_hidden_state

				set_seed(42	)  # Fixed seed at init time so the two models get the same random weights
				UpperCamelCase          =					{'type': scaling_type, 'factor': 10.0}
				UpperCamelCase          =					LlamaModel(A_	)
				scaled_model.to(A_	)
				scaled_model.eval()
				UpperCamelCase          =					scaled_model(A_	).last_hidden_state
				UpperCamelCase          =					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	)	)






@require_torch
class     lowercase (		unittest.TestCase       ):



		@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor([input_ids]	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]	)
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]	)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30]    ,				A_    ,				atol=1e-5    ,				rtol=1e-5	)



		@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]	)
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]	)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30]    ,				A_    ,				atol=1e-5    ,				rtol=1e-5	)



		@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]	)
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]	)
				# fmt: on
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)



		@unittest.skip(
		    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)

				UpperCamelCase          =					torch.tensor(
				    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]    ,				dtype=torch.floataa	)
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# fmt: off
				UpperCamelCase          =					torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]	)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30]    ,				A_    ,				atol=1e-5    ,				rtol=1e-5	)



		@unittest.skip('Model is curently gated'	)
		@slow
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
				UpperCamelCase          =					'Simply put, the theory of relativity states that '
				UpperCamelCase          =					LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'	)
				UpperCamelCase          =					tokenizer.encode(A_    ,				return_tensors='pt'	)
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained(
				    'meta-llama/Llama-2-13b-chat-hf'    ,				device_map='sequential'    ,				use_safetensors=A_	)

				# greedy generation outputs
				UpperCamelCase          =					model.generate(A_    ,				max_new_tokens=64    ,				top_p=A_    ,				temperature=1    ,				do_sample=A_	)
				UpperCamelCase          =					tokenizer.decode(generated_ids[0]    ,				skip_special_tokens=A_	)
				self.assertEqual(A_    ,				A_	)
