





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


lowerCAmelCase_        =							logging.getLogger(__name__)



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	int			     = '''summarization'''
			lowerCamelCase_	:	int			     = ['''loss''']
			lowerCamelCase_	:	List[str]			     = ROUGE_KEYS
			lowerCamelCase_	:	Tuple			     = '''rouge2'''






			def __init__(self   ,				__magic_name__   ,				**__magic_name__       )	->  Dict:


							'''simple docstring'''



							if hparams.sortish_sampler and hparams.gpus > 1:
											snake_case_		:       List[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__(__magic_name__   ,				num_labels=__magic_name__   ,				mode=self.mode   ,				**__magic_name__       )
							use_task_specific_params(self.model   ,				'''summarization'''       )
							save_git_info(self.hparams.output_dir       )
							snake_case_		:       Union[str, Any]           =    Path(self.output_dir       ) / '''metrics.json'''
							snake_case_		:       Union[str, Any]           =    Path(self.output_dir       ) / '''hparams.pkl'''
							pickle_save(self.hparams   ,				self.hparams_save_path       )
							snake_case_		:       Any           =    0
							snake_case_		:       Any           =    defaultdict(__magic_name__       )
							snake_case_		:       List[Any]           =    self.config.model_type
							snake_case_		:       str           =    self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

							snake_case_		:       Dict           =    {
							    '''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()       )

							snake_case_		:       Tuple           =    get_git_info()['''repo_sha''']
							snake_case_		:       str           =    hparams.num_workers
							snake_case_		:       List[Any]           =    None  # default to config
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer   ,				__magic_name__       ):
											snake_case_		:       str           =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]
											snake_case_		:       Any           =    self.decoder_start_token_id
							snake_case_		:       Dict           =    (
							    SeqaSeqDataset if hasattr(self.tokenizer   ,				'''prepare_seq2seq_batch'''       ) else LegacySeqaSeqDataset
							)
							snake_case_		:       str           =    False
							snake_case_		:       List[Any]           =    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:
											snake_case_		:       Any           =    self.hparams.eval_max_gen_length
							else:
											snake_case_		:       Optional[Any]           =    self.model.config.max_length
							snake_case_		:       Union[str, Any]           =    self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict[str, List[str]]:


							'''simple docstring'''



							snake_case_		:       Tuple           =    {
							    k: self.tokenizer.batch_decode(v.tolist()       ) if '''mask''' not in k else v.shape for k, v in batch.items()
							}
							save_json(__magic_name__   ,				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'''       )

							snake_case_		:       int           =    True
							return readable_batch






			def      lowerCamelCase    (self   ,				__magic_name__   ,				**__magic_name__       )	->  Tuple:


							'''simple docstring'''



							return self.model(__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Any:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    self.tokenizer.batch_decode(
							    __magic_name__   ,				skip_special_tokens=__magic_name__   ,				clean_up_tokenization_spaces=__magic_name__       )
							return lmap(str.strip   ,				__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       int           =    self.tokenizer.pad_token_id
							snake_case_     ,				snake_case_		:       Tuple           =    batch['''input_ids'''], batch['''attention_mask''']
							snake_case_		:       Dict           =    batch['''labels''']
							if isinstance(self.model   ,				__magic_name__       ):
											snake_case_		:       Tuple           =    self.model._shift_right(__magic_name__       )
							else:
											snake_case_		:       Any           =    shift_tokens_right(__magic_name__   ,				__magic_name__       )
							if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
											snake_case_		:       Union[str, Any]           =    decoder_input_ids
											self.save_readable_batch(__magic_name__       )

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

											assert lm_logits.shape[-1] == self.vocab_size
											snake_case_		:       List[str]           =    ce_loss_fct(lm_logits.view(-1   ,				lm_logits.shape[-1]       )   ,				tgt_ids.view(-1       )       )
							else:
											snake_case_		:       Union[str, Any]           =    nn.functional.log_softmax(__magic_name__   ,				dim=-1       )
											snake_case_     ,				snake_case_		:       List[Any]           =    label_smoothed_nll_loss(
											    __magic_name__   ,				__magic_name__   ,				self.hparams.label_smoothing   ,				ignore_index=__magic_name__       )
							return (loss,)






			@property
			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							return self.tokenizer.pad_token_id






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Any           =    self._step(__magic_name__       )

							snake_case_		:       Union[str, Any]           =    dict(zip(self.loss_names   ,				__magic_name__       )       )
							# tokens per batch
							snake_case_		:       Union[str, Any]           =    batch['''input_ids'''].ne(self.pad       ).sum() + batch['''labels'''].ne(self.pad       ).sum()
							snake_case_		:       Union[str, Any]           =    batch['''input_ids'''].shape[0]
							snake_case_		:       Any           =    batch['''input_ids'''].eq(self.pad       ).sum()
							snake_case_		:       Optional[int]           =    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      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							return self._generative_step(__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__="val"       )	->  Dict:


							'''simple docstring'''



							self.step_count += 1
							snake_case_		:       str           =    {k: torch.stack([x[k] for x in outputs]       ).mean() for k in self.loss_names}
							snake_case_		:       Dict           =    losses['''loss''']
							snake_case_		:       int           =    {
							    k: np.array([x[k] for x in outputs]       ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
							}
							snake_case_		:       Tuple           =    (
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							snake_case_		:       torch.FloatTensor           =    torch.tensor(__magic_name__       ).type_as(__magic_name__       )
							generative_metrics.update({k: v.item() for k, v in losses.items()}       )
							losses.update(__magic_name__       )
							snake_case_		:       int           =    {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
							snake_case_		:       Union[str, Any]           =    self.step_count
							self.metrics[prefix].append(__magic_name__       )  # callback writes this to self.metrics_save_path
							snake_case_		:       Union[str, Any]           =    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      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							return calculate_rouge(__magic_name__   ,				__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  dict:


							'''simple docstring'''



							snake_case_		:       int           =    time.time()

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






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  Optional[int]:


							'''simple docstring'''



							return self._generative_step(__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							return self.validation_epoch_end(__magic_name__   ,				prefix='''test'''       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  SeqaSeqDataset:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.n_obs[type_path]
							snake_case_		:       Tuple           =    self.target_lens[type_path]
							snake_case_		:       Tuple           =    self.dataset_class(
							    self.tokenizer   ,				type_path=__magic_name__   ,				n_obs=__magic_name__   ,				max_target_length=__magic_name__   ,				**self.dataset_kwargs   ,				)
							return dataset






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = False       )	->  DataLoader:


							'''simple docstring'''



							snake_case_		:       Tuple           =    self.get_dataset(__magic_name__       )

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
											snake_case_		:       Union[str, Any]           =    dataset.make_sortish_sampler(__magic_name__   ,				distributed=self.hparams.gpus > 1       )
											return DataLoader(
											    __magic_name__   ,				batch_size=__magic_name__   ,				collate_fn=dataset.collate_fn   ,				shuffle=__magic_name__   ,				num_workers=self.num_workers   ,				sampler=__magic_name__   ,				)

							elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
											snake_case_		:       Tuple           =    dataset.make_dynamic_sampler(
											    self.hparams.max_tokens_per_batch   ,				distributed=self.hparams.gpus > 1       )
											return DataLoader(
											    __magic_name__   ,				batch_sampler=__magic_name__   ,				collate_fn=dataset.collate_fn   ,				num_workers=self.num_workers   ,				)
							else:
											return DataLoader(
											    __magic_name__   ,				batch_size=__magic_name__   ,				collate_fn=dataset.collate_fn   ,				shuffle=__magic_name__   ,				num_workers=self.num_workers   ,				sampler=__magic_name__   ,				)






			def      lowerCamelCase    (self       )	->  DataLoader:


							'''simple docstring'''



							snake_case_		:       List[str]           =    self.get_dataloader('''train'''   ,				batch_size=self.hparams.train_batch_size   ,				shuffle=__magic_name__       )
							return dataloader






			def      lowerCamelCase    (self       )	->  DataLoader:


							'''simple docstring'''



							return self.get_dataloader('''val'''   ,				batch_size=self.hparams.eval_batch_size       )






			def      lowerCamelCase    (self       )	->  DataLoader:


							'''simple docstring'''



							return self.get_dataloader('''test'''   ,				batch_size=self.hparams.eval_batch_size       )






			@staticmethod
			def      lowerCamelCase    (__magic_name__   ,				__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							BaseTransformer.add_model_specific_args(__magic_name__   ,				__magic_name__       )
							add_generic_args(__magic_name__   ,				__magic_name__       )
							parser.add_argument(
							    '''--max_source_length'''   ,				default=1024   ,				type=__magic_name__   ,				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=__magic_name__   ,				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=142   ,				type=__magic_name__   ,				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=142   ,				type=__magic_name__   ,				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=__magic_name__       )
							parser.add_argument('''--overwrite_output_dir'''   ,				action='''store_true'''   ,				default=__magic_name__       )
							parser.add_argument('''--max_tokens_per_batch'''   ,				type=__magic_name__   ,				default=__magic_name__       )
							parser.add_argument('''--logger_name'''   ,				type=__magic_name__   ,				choices=['''default''', '''wandb''', '''wandb_shared''']   ,				default='''default'''       )
							parser.add_argument('''--n_train'''   ,				type=__magic_name__   ,				default=-1   ,				required=__magic_name__   ,				help='''# examples. -1 means use all.'''       )
							parser.add_argument('''--n_val'''   ,				type=__magic_name__   ,				default=500   ,				required=__magic_name__   ,				help='''# examples. -1 means use all.'''       )
							parser.add_argument('''--n_test'''   ,				type=__magic_name__   ,				default=-1   ,				required=__magic_name__   ,				help='''# examples. -1 means use all.'''       )
							parser.add_argument(
							    '''--task'''   ,				type=__magic_name__   ,				default='''summarization'''   ,				required=__magic_name__   ,				help='''# examples. -1 means use all.'''       )
							parser.add_argument('''--label_smoothing'''   ,				type=__magic_name__   ,				default=0.0   ,				required=__magic_name__       )
							parser.add_argument('''--src_lang'''   ,				type=__magic_name__   ,				default=''''''   ,				required=__magic_name__       )
							parser.add_argument('''--tgt_lang'''   ,				type=__magic_name__   ,				default=''''''   ,				required=__magic_name__       )
							parser.add_argument('''--eval_beams'''   ,				type=__magic_name__   ,				default=__magic_name__   ,				required=__magic_name__       )
							parser.add_argument(
							    '''--val_metric'''   ,				type=__magic_name__   ,				default=__magic_name__   ,				required=__magic_name__   ,				choices=['''bleu''', '''rouge2''', '''loss''', None]       )
							parser.add_argument('''--eval_max_gen_length'''   ,				type=__magic_name__   ,				default=__magic_name__   ,				help='''never generate more than n tokens'''       )
							parser.add_argument('''--save_top_k'''   ,				type=__magic_name__   ,				default=1   ,				required=__magic_name__   ,				help='''How many checkpoints to save'''       )
							parser.add_argument(
							    '''--early_stopping_patience'''   ,				type=__magic_name__   ,				default=-1   ,				required=__magic_name__   ,				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	):
			lowerCamelCase_	:	Dict			     = '''translation'''
			lowerCamelCase_	:	List[str]			     = ['''loss''']
			lowerCamelCase_	:	Any			     = ['''bleu''']
			lowerCamelCase_	:	Dict			     = '''bleu'''






			def __init__(self   ,				__magic_name__   ,				**__magic_name__       )	->  List[str]:


							'''simple docstring'''



							super().__init__(__magic_name__   ,				**__magic_name__       )
							snake_case_		:       Union[str, Any]           =    hparams.src_lang
							snake_case_		:       Optional[int]           =    hparams.tgt_lang






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  dict:


							'''simple docstring'''



							return calculate_bleu(__magic_name__   ,				__magic_name__       )






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=None					)						->		SummarizationModule:






				"""simple docstring"""


				Path(args.output_dir					).mkdir(exist_ok=_UpperCamelCase					)
				check_output_dir(_UpperCamelCase					,							expected_items=3					)

				if model is None:
								if "summarization" in args.task:
												snake_case_		:       SummarizationModule           =    SummarizationModule(_UpperCamelCase					)
								else:
												snake_case_		:       SummarizationModule           =    TranslationModule(_UpperCamelCase					)
				snake_case_		:       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'''					)
				):
								snake_case_		:       List[str]           =    True  # don't pollute wandb logs unnecessarily
				elif args.logger_name == "wandb":
								from pytorch_lightning.loggers import WandbLogger

								snake_case_		:       Dict           =    os.environ.get('''WANDB_PROJECT'''					,							_UpperCamelCase					)
								snake_case_		:       str           =    WandbLogger(name=model.output_dir.name					,							project=_UpperCamelCase					)

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

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

				if args.early_stopping_patience >= 0:
								snake_case_		:       List[str]           =    get_early_stopping_callback(model.val_metric					,							args.early_stopping_patience					)
				else:
								snake_case_		:       Tuple           =    False

				snake_case_		:       List[str]           =    args.val_metric == '''loss'''
				snake_case_		:       pl.Trainer           =    generic_train(
				    _UpperCamelCase					,							_UpperCamelCase					,							logging_callback=SeqaSeqLoggingCallback()					,							checkpoint_callback=get_checkpoint_callback(
				        args.output_dir					,							model.val_metric					,							args.save_top_k					,							_UpperCamelCase					)					,							early_stopping_callback=_UpperCamelCase					,							logger=_UpperCamelCase					,							)
				pickle_save(model.hparams					,							model.output_dir / '''hparams.pkl'''					)
				if not args.do_predict:
								return model

				snake_case_		:       List[Any]           =    ''''''
				snake_case_		:       Union[str, Any]           =    sorted(glob.glob(os.path.join(args.output_dir					,							'''*.ckpt'''					)					,							recursive=_UpperCamelCase					)					)
				if checkpoints:
								snake_case_		:       int           =    checkpoints[-1]
								snake_case_		:       Tuple           =    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__":
	lowerCAmelCase_        =							argparse.ArgumentParser()
	lowerCAmelCase_        =							pl.Trainer.add_argparse_args(parser)
	lowerCAmelCase_        =							SummarizationModule.add_model_specific_args(parser, os.getcwd())

	lowerCAmelCase_        =							parser.parse_args()

	main(args)




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    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 SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor






class 	__lowercase					:





      def __init__(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       int=1_3       ,							__lowerCamelCase    :       List[str]=3       ,							__lowerCamelCase    :       List[str]=True       ,							__lowerCamelCase    :       Any=True       ,							__lowerCamelCase    :       Optional[int]=0.1       ,							__lowerCamelCase    :       Optional[Any]=0.1       ,							__lowerCamelCase    :       Tuple=2_2_4       ,							__lowerCamelCase    :       int=1_0_0_0       ,							__lowerCamelCase    :       int=[3, 3, 6, 4]       ,							__lowerCamelCase    :       Dict=[4_8, 5_6, 1_1_2, 2_2_0]       ,							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  is_training
         UpperCAmelCase      					=  use_labels
         UpperCAmelCase      					=  hidden_dropout_prob
         UpperCAmelCase      					=  attention_probs_dropout_prob
         UpperCAmelCase      					=  num_labels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  layer_depths
         UpperCAmelCase      					=  embed_dims





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCAmelCase      					=  None
         if self.use_labels:
            UpperCAmelCase      					=  ids_tensor([self.batch_size]       ,							self.num_labels							)

         UpperCAmelCase      					=  self.get_config()

         return config, pixel_values, labels





      def 							_lowercase					(		self    :       Optional[Any]							)     -> str:






         """simple docstring"""







         return SwiftFormerConfig(
             depths=self.layer_depths       ,							embed_dims=self.embed_dims       ,							mlp_ratio=4       ,							downsamples=[True, True, True, True]       ,							hidden_act="""gelu"""       ,							num_labels=self.num_labels       ,							down_patch_size=3       ,							down_stride=2       ,							down_pad=1       ,							drop_rate=0.0       ,							drop_path_rate=0.0       ,							use_layer_scale=__lowerCamelCase       ,							layer_scale_init_value=1e-5       ,							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Optional[int]							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  SwiftFormerModel(config=__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.embed_dims[-1], 7, 7)							)





      def 							_lowercase					(		self    :       List[str]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Any							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  self.num_labels
         UpperCAmelCase      					=  SwiftFormerForImageClassification(__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()
         UpperCAmelCase      					=  model(__lowerCamelCase       ,							labels=__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)

         UpperCAmelCase      					=  SwiftFormerForImageClassification(__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()

         UpperCAmelCase      					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)





      def 							_lowercase					(		self    :       Tuple							)     -> Optional[Any]:






         """simple docstring"""







         ((UpperCAmelCase)					,	(UpperCAmelCase)					,	(UpperCAmelCase))      					=  self.prepare_config_and_inputs()
         UpperCAmelCase      					=  {"""pixel_values""": pixel_values}
         return config, inputs_dict






@require_torch
class 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      UpperCamelCase										=							(
          {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False





      def 							_lowercase					(		self    :       str							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  SwiftFormerModelTester(self							)
         UpperCAmelCase      					=  ConfigTester(
             self       ,							config_class=__lowerCamelCase       ,							has_text_modality=__lowerCamelCase       ,							hidden_size=3_7       ,							num_attention_heads=1_2       ,							num_hidden_layers=1_2       ,							)





      def 							_lowercase					(		self    :       Dict							)     -> Union[str, Any]:






         """simple docstring"""







         self.config_tester.run_common_tests()





      @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       str							)     -> str:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__lowerCamelCase       ,							nn.Linear							)							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  inspect.signature(model.forward							)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase      					=  [*signature.parameters.keys()]

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





      def 							_lowercase					(		self    :       List[str]							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase							)





      @slow
      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[Any]:






         """simple docstring"""







         for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase      					=  SwiftFormerModel.from_pretrained(__lowerCamelCase							)
            self.assertIsNotNone(__lowerCamelCase							)





      @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
      def 							_lowercase					(		self    :       List[str]							)     -> Union[str, Any]:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       Dict							)     -> List[Any]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Optional[Any]							):
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()

            with torch.no_grad():
               UpperCAmelCase      					=  model(**self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase							)							)

            UpperCAmelCase      					=  outputs.hidden_states

            UpperCAmelCase      					=  8
            self.assertEqual(len(__lowerCamelCase							)       ,							__lowerCamelCase							)  # TODO

            # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
            # with the width and height being successively divided by 2, after every 2 blocks
            for i in range(len(__lowerCamelCase							)							):
               self.assertEqual(
                   hidden_states[i].shape       ,							torch.Size(
                       [
                           self.model_tester.batch_size,
                           self.model_tester.embed_dims[i // 2],
                           (self.model_tester.image_size // 4) // 2 ** (i // 2),
                           (self.model_tester.image_size // 4) // 2 ** (i // 2),
                       ]							)       ,							)

         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  True
            check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            UpperCAmelCase      					=  True

            check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Dict							)     -> Union[str, Any]:






         """simple docstring"""







         def _config_zero_init(__lowerCamelCase    :       Any							):
            UpperCAmelCase      					=  copy.deepcopy(__lowerCamelCase							)
            for key in configs_no_init.__dict__.keys():
               if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                  setattr(__lowerCamelCase       ,							__lowerCamelCase       ,							1e-1_0							)
               if isinstance(getattr(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)       ,							__lowerCamelCase							):
                  UpperCAmelCase      					=  _config_zero_init(getattr(__lowerCamelCase       ,							__lowerCamelCase							)							)
                  setattr(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)
            return configs_no_init

         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase      					=  _config_zero_init(__lowerCamelCase							)
         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(config=__lowerCamelCase							)
            for name, param in model.named_parameters():
               if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1e9) / 1e9).round().item()       ,							[0.0, 1.0]       ,							msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,							)





      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         pass

def 	_UpperCamelCase					(    )    ->Dict:
   UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





      @cached_property
      def 							_lowercase					(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None





      @slow
      def 							_lowercase					(		self    :       Optional[int]							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(__lowerCamelCase							)

         UpperCAmelCase      					=  self.default_image_processor
         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""pt"""							).to(__lowerCamelCase							)

         # forward pass
         with torch.no_grad():
            UpperCAmelCase      					=  model(**__lowerCamelCase							)

         # verify the logits
         UpperCAmelCase      					=  torch.Size((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]							).to(__lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,							__lowerCamelCase       ,							atol=1e-4							)							)



