



import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__SCREAMING_SNAKE_CASE           =  logging.getLogger(__name__)

class 	lowerCamelCase_    ( a__						):






						'''simple docstring'''
						a__   =  """summarization"""
						a__   =  ["""loss"""]
						a__   =  ROUGE_KEYS
						a__   =  """rouge2"""







						def __init__(	self      :			Any	,	__lowerCamelCase      :			List[Any]	,	**__lowerCamelCase      :			Dict )      ->				Tuple:
									if hparams.sortish_sampler and hparams.gpus > 1:
												A							:					Union[str, Any]              =						False
									elif hparams.max_tokens_per_batch is not None:
												if hparams.gpus > 1:
															raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training" )
												if hparams.sortish_sampler:
															raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously" )

									super().__init__(lowerCAmelCase__	,	num_labels=lowerCAmelCase__	,	mode=self.mode	,	**lowerCAmelCase__ )
									use_task_specific_params(self.model	,	"summarization" )
									save_git_info(self.hparams.output_dir )
									A							:					Optional[Any]              =						Path(self.output_dir ) / "metrics.json"
									A							:					int              =						Path(self.output_dir ) / "hparams.pkl"
									pickle_save(self.hparams	,	self.hparams_save_path )
									A							:					List[Any]              =						0
									A							:					Union[str, Any]              =						defaultdict(lowerCAmelCase__ )
									A							:					Any              =						self.config.model_type
									A							:					str              =						self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

									A							:					dict              =						{
									    "data_dir": self.hparams.data_dir,
									    "max_source_length": self.hparams.max_source_length,
									    "prefix": self.model.config.prefix or "",
									}
									A							:					Tuple              =						{
									    "train": self.hparams.n_train,
									    "val": self.hparams.n_val,
									    "test": self.hparams.n_test,
									}
									A							:					Optional[Any]              =						{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

									A							:					str              =						{
									    "train": self.hparams.max_target_length,
									    "val": self.hparams.val_max_target_length,
									    "test": self.hparams.test_max_target_length,
									}
									assert self.target_lens["train"] <= self.target_lens["val"], F"""target_lens: {self.target_lens}"""
									assert self.target_lens["train"] <= self.target_lens["test"], F"""target_lens: {self.target_lens}"""
									if self.hparams.freeze_embeds:
												freeze_embeds(self.model )
									if self.hparams.freeze_encoder:
												freeze_params(self.model.get_encoder() )
												assert_all_frozen(self.model.get_encoder() )

									A							:					Optional[Any]              =						get_git_info()["repo_sha"]
									A							:					List[Any]              =						hparams.num_workers
									A							:					Union[str, Any]              =						None  # default to config
									if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer	,	lowerCAmelCase__ ):
												A							:					Optional[int]              =						self.tokenizer.lang_code_to_id[hparams.tgt_lang]
												A							:					Optional[Any]              =						self.decoder_start_token_id
									A							:					Union[str, Any]              =						(
									    SeqaSeqDataset if hasattr(self.tokenizer	,	"prepare_seq2seq_batch" ) else LegacySeqaSeqDataset
									)
									A							:					Optional[Any]              =						False
									A							:					int              =						self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
									if self.hparams.eval_max_gen_length is not None:
												A							:					Any              =						self.hparams.eval_max_gen_length
									else:
												A							:					Optional[int]              =						self.model.config.max_length
									A							:					Optional[Any]              =						self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Any )      ->				Dict[str, List[str]]:
									A							:					str              =						{
									    k: self.tokenizer.batch_decode(v.tolist() ) if "mask" not in k else v.shape for k, v in batch.items()
									}
									save_json(lowerCAmelCase__	,	Path(self.output_dir ) / "text_batch.json" )
									save_json({k: v.tolist() for k, v in batch.items()}	,	Path(self.output_dir ) / "tok_batch.json" )

									A							:					Any              =						True
									return readable_batch







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Any	,	**__lowerCamelCase      :			List[str] )      ->				int:
									return self.model(lowerCAmelCase__	,	**lowerCAmelCase__ )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Dict )      ->				int:
									A							:					str              =						self.tokenizer.batch_decode(
									    lowerCAmelCase__	,	skip_special_tokens=lowerCAmelCase__	,	clean_up_tokenization_spaces=lowerCAmelCase__ )
									return lmap(str.strip	,	lowerCAmelCase__ )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Optional[Any] )      ->				Tuple:
									A							:					List[Any]              =						self.tokenizer.pad_token_id
									A							:					Optional[int]              =						batch["input_ids"], batch["attention_mask"]
									A							:					Tuple              =						batch["labels"]
									if isinstance(self.model	,	lowerCAmelCase__ ):
												A							:					Optional[int]              =						self.model._shift_right(lowerCAmelCase__ )
									else:
												A							:					Optional[int]              =						shift_tokens_right(lowerCAmelCase__	,	lowerCAmelCase__ )
									if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
												A							:					List[str]              =						decoder_input_ids
												self.save_readable_batch(lowerCAmelCase__ )

									A							:					Any              =						self(lowerCAmelCase__	,	attention_mask=lowerCAmelCase__	,	decoder_input_ids=lowerCAmelCase__	,	use_cache=lowerCAmelCase__ )
									A							:					Any              =						outputs["logits"]
									if self.hparams.label_smoothing == 0:
												# Same behavior as modeling_bart.py, besides ignoring pad_token_id
												A							:					List[str]              =						nn.CrossEntropyLoss(ignore_index=lowerCAmelCase__ )

												assert lm_logits.shape[-1] == self.vocab_size
												A							:					Optional[int]              =						ce_loss_fct(lm_logits.view(-1	,	lm_logits.shape[-1] )	,	tgt_ids.view(-1 ) )
									else:
												A							:					Tuple              =						nn.functional.log_softmax(lowerCAmelCase__	,	dim=-1 )
												A							:					Any              =						label_smoothed_nll_loss(
												    lowerCAmelCase__	,	lowerCAmelCase__	,	self.hparams.label_smoothing	,	ignore_index=lowerCAmelCase__ )
									return (loss,)







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				int:
									return self.tokenizer.pad_token_id







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			List[str] )      ->				Dict:
									A							:					Optional[int]              =						self._step(lowerCAmelCase__ )

									A							:					Dict              =						dict(zip(self.loss_names	,	lowerCAmelCase__ ) )
									# tokens per batch
									A							:					List[Any]              =						batch["input_ids"].ne(self.pad ).sum() + batch["labels"].ne(self.pad ).sum()
									A							:					List[Any]              =						batch["input_ids"].shape[0]
									A							:					Any              =						batch["input_ids"].eq(self.pad ).sum()
									A							:					str              =						batch["input_ids"].eq(self.pad ).float().mean()
									# TODO(SS): make a wandb summary metric for this
									return {"loss": loss_tensors[0], "log": logs}







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Any )      ->				Dict:
									return self._generative_step(lowerCAmelCase__ )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Optional[int]="val" )      ->				Dict:
									self.step_count += 1
									A							:					List[str]              =						{k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
									A							:					int              =						losses["loss"]
									A							:					Any              =						{
									    k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
									}
									A							:					Optional[Any]              =						(
									    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
									)
									A							:					torch.FloatTensor              =						torch.tensor(lowerCAmelCase__ ).type_as(lowerCAmelCase__ )
									generative_metrics.update({k: v.item() for k, v in losses.items()} )
									losses.update(lowerCAmelCase__ )
									A							:					Optional[int]              =						{F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
									A							:					List[Any]              =						self.step_count
									self.metrics[prefix].append(lowerCAmelCase__ )  # callback writes this to self.metrics_save_path
									A							:					Optional[int]              =						flatten_list([x["preds"] for x in outputs] )
									return {
									    "log": all_metrics,
									    "preds": preds,
									    F"""{prefix}_loss""": loss,
									    F"""{prefix}_{self.val_metric}""": metric_tensor,
									}







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Dict )      ->				Dict:
									return calculate_rouge(lowerCAmelCase__	,	lowerCAmelCase__ )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			int )      ->				dict:
									A							:					Any              =						time.time()

									# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
									A							:					Optional[int]              =						self.model.generate(
									    batch["input_ids"]	,	attention_mask=batch["attention_mask"]	,	use_cache=lowerCAmelCase__	,	decoder_start_token_id=self.decoder_start_token_id	,	num_beams=self.eval_beams	,	max_length=self.eval_max_length	,	)
									A							:					str              =						(time.time() - ta) / batch["input_ids"].shape[0]
									A							:					List[str]              =						self.ids_to_clean_text(lowerCAmelCase__ )
									A							:					List[str]              =						self.ids_to_clean_text(batch["labels"] )
									A							:					int              =						self._step(lowerCAmelCase__ )
									A							:					int              =						dict(zip(self.loss_names	,	lowerCAmelCase__ ) )
									A							:					Dict              =						self.calc_generative_metrics(lowerCAmelCase__	,	lowerCAmelCase__ )
									A							:					Optional[int]              =						np.mean(lmap(lowerCAmelCase__	,	lowerCAmelCase__ ) )
									base_metrics.update(gen_time=lowerCAmelCase__	,	gen_len=lowerCAmelCase__	,	preds=lowerCAmelCase__	,	target=lowerCAmelCase__	,	**lowerCAmelCase__ )
									return base_metrics







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				List[Any]:
									return self._generative_step(lowerCAmelCase__ )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			Dict )      ->				Optional[Any]:
									return self.validation_epoch_end(lowerCAmelCase__	,	prefix="test" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[Any] )      ->				SeqaSeqDataset:
									A							:					Union[str, Any]              =						self.n_obs[type_path]
									A							:					List[str]              =						self.target_lens[type_path]
									A							:					Any              =						self.dataset_class(
									    self.tokenizer	,	type_path=lowerCAmelCase__	,	n_obs=lowerCAmelCase__	,	max_target_length=lowerCAmelCase__	,	**self.dataset_kwargs	,	)
									return dataset







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			str = False )      ->				DataLoader:
									A							:					Tuple              =						self.get_dataset(lowerCAmelCase__ )

									if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
												A							:					int              =						dataset.make_sortish_sampler(lowerCAmelCase__	,	distributed=self.hparams.gpus > 1 )
												return DataLoader(
												    lowerCAmelCase__	,	batch_size=lowerCAmelCase__	,	collate_fn=dataset.collate_fn	,	shuffle=lowerCAmelCase__	,	num_workers=self.num_workers	,	sampler=lowerCAmelCase__	,	)

									elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
												A							:					Union[str, Any]              =						dataset.make_dynamic_sampler(
												    self.hparams.max_tokens_per_batch	,	distributed=self.hparams.gpus > 1 )
												return DataLoader(
												    lowerCAmelCase__	,	batch_sampler=lowerCAmelCase__	,	collate_fn=dataset.collate_fn	,	num_workers=self.num_workers	,	)
									else:
												return DataLoader(
												    lowerCAmelCase__	,	batch_size=lowerCAmelCase__	,	collate_fn=dataset.collate_fn	,	shuffle=lowerCAmelCase__	,	num_workers=self.num_workers	,	sampler=lowerCAmelCase__	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				DataLoader:
									A							:					List[str]              =						self.get_dataloader("train"	,	batch_size=self.hparams.train_batch_size	,	shuffle=lowerCAmelCase__ )
									return dataloader







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				DataLoader:
									return self.get_dataloader("val"	,	batch_size=self.hparams.eval_batch_size )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				DataLoader:
									return self.get_dataloader("test"	,	batch_size=self.hparams.eval_batch_size )







						@staticmethod
						def 			SCREAMING_SNAKE_CASE__		(	__lowerCamelCase      :			str	,	__lowerCamelCase      :			List[str] )      ->				Optional[int]:
									BaseTransformer.add_model_specific_args(lowerCAmelCase__	,	lowerCAmelCase__ )
									add_generic_args(lowerCAmelCase__	,	lowerCAmelCase__ )
									parser.add_argument(
									    "--max_source_length"	,	default=10_24	,	type=lowerCAmelCase__	,	help=(
									        "The maximum total input sequence length after tokenization. Sequences longer "
									        "than this will be truncated, sequences shorter will be padded."
									    )	,	)
									parser.add_argument(
									    "--max_target_length"	,	default=56	,	type=lowerCAmelCase__	,	help=(
									        "The maximum total input sequence length after tokenization. Sequences longer "
									        "than this will be truncated, sequences shorter will be padded."
									    )	,	)
									parser.add_argument(
									    "--val_max_target_length"	,	default=1_42	,	type=lowerCAmelCase__	,	help=(
									        "The maximum total input sequence length after tokenization. Sequences longer "
									        "than this will be truncated, sequences shorter will be padded."
									    )	,	)
									parser.add_argument(
									    "--test_max_target_length"	,	default=1_42	,	type=lowerCAmelCase__	,	help=(
									        "The maximum total input sequence length after tokenization. Sequences longer "
									        "than this will be truncated, sequences shorter will be padded."
									    )	,	)
									parser.add_argument("--freeze_encoder"	,	action="store_true" )
									parser.add_argument("--freeze_embeds"	,	action="store_true" )
									parser.add_argument("--sortish_sampler"	,	action="store_true"	,	default=lowerCAmelCase__ )
									parser.add_argument("--overwrite_output_dir"	,	action="store_true"	,	default=lowerCAmelCase__ )
									parser.add_argument("--max_tokens_per_batch"	,	type=lowerCAmelCase__	,	default=lowerCAmelCase__ )
									parser.add_argument("--logger_name"	,	type=lowerCAmelCase__	,	choices=["default", "wandb", "wandb_shared"]	,	default="default" )
									parser.add_argument("--n_train"	,	type=lowerCAmelCase__	,	default=-1	,	required=lowerCAmelCase__	,	help="# examples. -1 means use all." )
									parser.add_argument("--n_val"	,	type=lowerCAmelCase__	,	default=5_00	,	required=lowerCAmelCase__	,	help="# examples. -1 means use all." )
									parser.add_argument("--n_test"	,	type=lowerCAmelCase__	,	default=-1	,	required=lowerCAmelCase__	,	help="# examples. -1 means use all." )
									parser.add_argument(
									    "--task"	,	type=lowerCAmelCase__	,	default="summarization"	,	required=lowerCAmelCase__	,	help="# examples. -1 means use all." )
									parser.add_argument("--label_smoothing"	,	type=lowerCAmelCase__	,	default=0.0	,	required=lowerCAmelCase__ )
									parser.add_argument("--src_lang"	,	type=lowerCAmelCase__	,	default=""	,	required=lowerCAmelCase__ )
									parser.add_argument("--tgt_lang"	,	type=lowerCAmelCase__	,	default=""	,	required=lowerCAmelCase__ )
									parser.add_argument("--eval_beams"	,	type=lowerCAmelCase__	,	default=lowerCAmelCase__	,	required=lowerCAmelCase__ )
									parser.add_argument(
									    "--val_metric"	,	type=lowerCAmelCase__	,	default=lowerCAmelCase__	,	required=lowerCAmelCase__	,	choices=["bleu", "rouge2", "loss", None] )
									parser.add_argument("--eval_max_gen_length"	,	type=lowerCAmelCase__	,	default=lowerCAmelCase__	,	help="never generate more than n tokens" )
									parser.add_argument("--save_top_k"	,	type=lowerCAmelCase__	,	default=1	,	required=lowerCAmelCase__	,	help="How many checkpoints to save" )
									parser.add_argument(
									    "--early_stopping_patience"	,	type=lowerCAmelCase__	,	default=-1	,	required=lowerCAmelCase__	,	help=(
									        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
									        " val_check_interval will effect it."
									    )	,	)
									return parser







class 	lowerCamelCase_    ( a__						):






						'''simple docstring'''
						a__   =  """translation"""
						a__   =  ["""loss"""]
						a__   =  ["""bleu"""]
						a__   =  """bleu"""







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			Any	,	**__lowerCamelCase      :			int )      ->				Optional[Any]:
									super().__init__(lowerCAmelCase__	,	**lowerCAmelCase__ )
									A							:					Optional[Any]              =						hparams.src_lang
									A							:					Union[str, Any]              =						hparams.tgt_lang







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			List[str] )      ->				dict:
									return calculate_bleu(lowerCAmelCase__	,	lowerCAmelCase__ )







def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=None    ):
			Path(args.output_dir    ).mkdir(exist_ok=_lowercase    )
			check_output_dir(_lowercase ,    expected_items=3    )

			if model is None:
						if "summarization" in args.task:
									A							:					SummarizationModule              =						SummarizationModule(_lowercase    )
						else:
									A							:					SummarizationModule              =						TranslationModule(_lowercase    )
			A							:					int              =						Path(args.data_dir    ).name
			if (
			    args.logger_name == "default"
			    or args.fast_dev_run
			    or str(args.output_dir    ).startswith("/tmp"    )
			    or str(args.output_dir    ).startswith("/var"    )
			):
						A							:					Tuple              =						True  # don't pollute wandb logs unnecessarily
			elif args.logger_name == "wandb":
						from pytorch_lightning.loggers import WandbLogger

						A							:					Optional[int]              =						os.environ.get("WANDB_PROJECT" ,    _lowercase    )
						A							:					Dict              =						WandbLogger(name=model.output_dir.name ,    project=_lowercase    )

			elif args.logger_name == "wandb_shared":
						from pytorch_lightning.loggers import WandbLogger

						A							:					str              =						WandbLogger(name=model.output_dir.name ,    project=f"""hf_{dataset}"""    )

			if args.early_stopping_patience >= 0:
						A							:					Tuple              =						get_early_stopping_callback(model.val_metric ,    args.early_stopping_patience    )
			else:
						A							:					Optional[int]              =						False

			A							:					Optional[Any]              =						args.val_metric == "loss"
			A							:					pl.Trainer              =						generic_train(
			    _lowercase ,    _lowercase ,    logging_callback=SeqaSeqLoggingCallback() ,    checkpoint_callback=get_checkpoint_callback(
			        args.output_dir ,    model.val_metric ,    args.save_top_k ,    _lowercase    ) ,    early_stopping_callback=_lowercase ,    logger=_lowercase ,    )
			pickle_save(model.hparams ,    model.output_dir / "hparams.pkl"    )
			if not args.do_predict:
						return model

			A							:					Optional[Any]              =						""
			A							:					Dict              =						sorted(glob.glob(os.path.join(args.output_dir ,    "*.ckpt"    ) ,    recursive=_lowercase    )    )
			if checkpoints:
						A							:					Optional[int]              =						checkpoints[-1]
						A							:					int              =						checkpoints[-1]
			trainer.logger.log_hyperparams(model.hparams    )

			# test() without a model tests using the best checkpoint automatically
			trainer.test()
			return model


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							__SCREAMING_SNAKE_CASE           =  pl.Trainer.add_argparse_args(parser)
							__SCREAMING_SNAKE_CASE           =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

							__SCREAMING_SNAKE_CASE           =  parser.parse_args()

							main(args)




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    DeiTForImageClassification,
							    DeiTForImageClassificationWithTeacher,
							    DeiTForMaskedImageModeling,
							    DeiTModel,
							)
							from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			Tuple	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Tuple=13	,	__lowerCamelCase      :			List[str]=30	,	__lowerCamelCase      :			Tuple=2	,	__lowerCamelCase      :			Any=3	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			Optional[int]=True	,	__lowerCamelCase      :			int=32	,	__lowerCamelCase      :			List[str]=5	,	__lowerCamelCase      :			Any=4	,	__lowerCamelCase      :			Optional[int]=37	,	__lowerCamelCase      :			List[Any]="gelu"	,	__lowerCamelCase      :			int=0.1	,	__lowerCamelCase      :			Union[str, Any]=0.1	,	__lowerCamelCase      :			Optional[Any]=10	,	__lowerCamelCase      :			Dict=0.02	,	__lowerCamelCase      :			Tuple=3	,	__lowerCamelCase      :			Union[str, Any]=None	,	__lowerCamelCase      :			List[Any]=2	,	)      ->				str:
									A							:					List[Any]              =						parent
									A							:					Optional[int]              =						batch_size
									A							:					Any              =						image_size
									A							:					Optional[Any]              =						patch_size
									A							:					Optional[Any]              =						num_channels
									A							:					Tuple              =						is_training
									A							:					Optional[Any]              =						use_labels
									A							:					Union[str, Any]              =						hidden_size
									A							:					Tuple              =						num_hidden_layers
									A							:					Union[str, Any]              =						num_attention_heads
									A							:					Union[str, Any]              =						intermediate_size
									A							:					Any              =						hidden_act
									A							:					Tuple              =						hidden_dropout_prob
									A							:					Dict              =						attention_probs_dropout_prob
									A							:					Any              =						type_sequence_label_size
									A							:					Tuple              =						initializer_range
									A							:					List[Any]              =						scope
									A							:					Optional[int]              =						encoder_stride

									# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
									A							:					List[str]              =						(image_size // patch_size) ** 2
									A							:					List[str]              =						num_patches + 2







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				int:
									A							:					Union[str, Any]              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									A							:					List[Any]              =						None
									if self.use_labels:
												A							:					List[str]              =						ids_tensor([self.batch_size]	,	self.type_sequence_label_size )

									A							:					Dict              =						self.get_config()

									return config, pixel_values, labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Dict:
									return DeiTConfig(
									    image_size=self.image_size	,	patch_size=self.patch_size	,	num_channels=self.num_channels	,	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	,	is_decoder=__lowerCamelCase	,	initializer_range=self.initializer_range	,	encoder_stride=self.encoder_stride	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			List[str] )      ->				int:
									A							:					Optional[int]              =						DeiTModel(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					List[Any]              =						model(__lowerCamelCase )
									self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Any )      ->				Any:
									A							:					List[Any]              =						DeiTForMaskedImageModeling(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Optional[int]              =						model(__lowerCamelCase )
									self.parent.assertEqual(
									    result.reconstruction.shape	,	(self.batch_size, self.num_channels, self.image_size, self.image_size) )

									# test greyscale images
									A							:					List[str]              =						1
									A							:					Optional[int]              =						DeiTForMaskedImageModeling(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()

									A							:					Dict              =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
									A							:					Optional[int]              =						model(__lowerCamelCase )
									self.parent.assertEqual(result.reconstruction.shape	,	(self.batch_size, 1, self.image_size, self.image_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			int )      ->				Dict:
									A							:					str              =						self.type_sequence_label_size
									A							:					List[str]              =						DeiTForImageClassification(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Tuple              =						model(__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.type_sequence_label_size) )

									# test greyscale images
									A							:					Any              =						1
									A							:					str              =						DeiTForImageClassification(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()

									A							:					Tuple              =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
									A							:					Tuple              =						model(__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.type_sequence_label_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[int]:
									A							:					Dict              =						self.prepare_config_and_inputs()
									(
									    (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , 
									)							:					Tuple              =						config_and_inputs
									A							:					int              =						{"pixel_values": pixel_values}
									return config, inputs_dict







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






						'''simple docstring'''
						a__   =  (
						    (
						        DeiTModel,
						        DeiTForImageClassification,
						        DeiTForImageClassificationWithTeacher,
						        DeiTForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						a__   =  (
						    {
						        "feature-extraction": DeiTModel,
						        "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[int]:
									A							:					str              =						DeiTModelTester(self )
									A							:					Optional[int]              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	has_text_modality=__lowerCamelCase	,	hidden_size=37 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Dict:
									self.config_tester.run_common_tests()







						@unittest.skip(reason="DeiT does not use inputs_embeds" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				int:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									A       , A							:					Dict              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Dict              =						model_class(__lowerCamelCase )
												self.assertIsInstance(model.get_input_embeddings()	,	(nn.Module) )
												A							:					Any              =						model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__lowerCamelCase	,	nn.Linear ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Union[str, Any]              =						model_class(__lowerCamelCase )
												A							:					Optional[int]              =						inspect.signature(model.forward )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A							:					Any              =						[*signature.parameters.keys()]

												A							:					Any              =						["pixel_values"]
												self.assertListEqual(arg_names[:1]	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									A							:					Any              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Dict:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			List[Any]=False )      ->				str:
									A							:					Union[str, Any]              =						super()._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )

									if return_labels:
												if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
															del inputs_dict["labels"]

									return inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[int]:
									if not self.model_tester.is_training:
												return

									A       , A							:					Optional[int]              =						self.model_tester.prepare_config_and_inputs_for_common()
									A							:					Dict              =						True

									for model_class in self.all_model_classes:
												# DeiTForImageClassificationWithTeacher supports inference-only
												if (
												    model_class in get_values(__lowerCamelCase )
												    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
												):
															continue
												A							:					Union[str, Any]              =						model_class(__lowerCamelCase )
												model.to(__lowerCamelCase )
												model.train()
												A							:					Union[str, Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Dict              =						model(**__lowerCamelCase ).loss
												loss.backward()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
												return

									A							:					Tuple              =						False
									A							:					Any              =						True

									for model_class in self.all_model_classes:
												if model_class in get_values(__lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
															continue
												# DeiTForImageClassificationWithTeacher supports inference-only
												if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
															continue
												A							:					List[str]              =						model_class(__lowerCamelCase )
												model.gradient_checkpointing_enable()
												model.to(__lowerCamelCase )
												model.train()
												A							:					Dict              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Tuple              =						model(**__lowerCamelCase ).loss
												loss.backward()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()

									A							:					int              =						[
									    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
									    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
									    {"title": "regression", "num_labels": 1, "dtype": torch.float},
									]

									for model_class in self.all_model_classes:
												if (
												    model_class
												    not in [
												        *get_values(__lowerCamelCase ),
												        *get_values(__lowerCamelCase ),
												    ]
												    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
												):
															continue

												for problem_type in problem_types:
															with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}""" ):
																		A							:					Tuple              =						problem_type["title"]
																		A							:					Optional[Any]              =						problem_type["num_labels"]

																		A							:					List[str]              =						model_class(__lowerCamelCase )
																		model.to(__lowerCamelCase )
																		model.train()

																		A							:					List[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )

																		if problem_type["num_labels"] > 1:
																					A							:					List[str]              =						inputs["labels"].unsqueeze(1 ).repeat(1	,	problem_type["num_labels"] )

																		A							:					int              =						inputs["labels"].to(problem_type["dtype"] )

																		# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																		# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																		# they have the same size." which is a symptom something in wrong for the regression problem.
																		# See https://github.com/huggingface/transformers/issues/11780
																		with warnings.catch_warnings(record=__lowerCamelCase ) as warning_list:
																					A							:					Optional[Any]              =						model(**__lowerCamelCase ).loss
																		for w in warning_list:
																					if "Using a target size that is different to the input size" in str(w.message ):
																								raise ValueError(
																								    F"""Something is going wrong in the regression problem: intercepted {w.message}""" )

																		loss.backward()







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Optional[Any]              =						DeiTModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







def 	UpperCAmelCase   (						):
			A							:					Optional[int]              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
			return image






@require_torch
@require_vision
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@cached_property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									return (
									    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
									    if is_vision_available()
									    else None
									)







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[int]:
									A							:					Optional[int]              =						DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
									    __lowerCamelCase )

									A							:					List[Any]              =						self.default_image_processor
									A							:					List[Any]              =						prepare_img()
									A							:					Optional[Any]              =						image_processor(images=__lowerCamelCase	,	return_tensors="pt" ).to(__lowerCamelCase )

									# forward pass
									with torch.no_grad():
												A							:					List[str]              =						model(**__lowerCamelCase )

									# verify the logits
									A							:					str              =						torch.Size((1, 10_00) )
									self.assertEqual(outputs.logits.shape	,	__lowerCamelCase )

									A							:					List[str]              =						torch.tensor([-1.0266, 0.1912, -1.2861] ).to(__lowerCamelCase )

									self.assertTrue(torch.allclose(outputs.logits[0, :3]	,	__lowerCamelCase	,	atol=1e-4 ) )







						@slow
						@require_accelerate
						@require_torch_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					str              =						DeiTModel.from_pretrained(
									    "facebook/deit-base-distilled-patch16-224"	,	torch_dtype=torch.floataa	,	device_map="auto" )
									A							:					Dict              =						self.default_image_processor

									A							:					Optional[int]              =						prepare_img()
									A							:					Union[str, Any]              =						image_processor(images=__lowerCamelCase	,	return_tensors="pt" )
									A							:					Union[str, Any]              =						inputs.pixel_values.to(__lowerCamelCase )

									# forward pass to make sure inference works in fp16
									with torch.no_grad():
												A							:					List[str]              =						model(__lowerCamelCase )