



'''simple docstring'''





import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE__      					=     logging.getLogger(__name__)


SCREAMING_SNAKE_CASE__      					=     list(MODEL_WITH_LM_HEAD_MAPPING.keys())
SCREAMING_SNAKE_CASE__      					=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class 				snake_case :
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={
               "help": (
                   "The model checkpoint for weights initialization. Leave None if you want to train a model from"
                   " scratch."
               )
           }		,      )
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCamelCase   )}		,      )
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}		,      )





@dataclass
class 				snake_case :
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={"help": "The input training data file (a text file)."}   )
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={
               "help": (
                   "The input training data files (multiple files in glob format). "
                   "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
               )
           }		,      )
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}		,      )
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}		,      )
       lowerCAmelCase__  :Optional[str]						   =					field(
           default=UpperCamelCase		,      metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}		,      )
       lowerCAmelCase__  :bool						   =					field(
           default=UpperCamelCase		,      metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}		,      )

       lowerCAmelCase__  :bool						   =					field(
           default=UpperCamelCase		,      metadata={"help": "Train with masked-language modeling loss instead of language modeling."}   )
       lowerCAmelCase__  :bool						   =					field(default=UpperCamelCase		,      metadata={"help": "Whether ot not to use whole word mask."}   )
       lowerCAmelCase__  :float						   =					field(
           default=0.15		,      metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}   )
       lowerCAmelCase__  :float						   =					field(
           default=1 / 6		,      metadata={
               "help": (
                   "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
                   " modeling."
               )
           }		,      )
       lowerCAmelCase__  :int						   =					field(
           default=5		,      metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}   )

       lowerCAmelCase__  :int						   =					field(
           default=-1		,      metadata={
               "help": (
                   "Optional input sequence length after tokenization."
                   "The training dataset will be truncated in block of this size for training."
                   "Default to the model max input length for single sentence inputs (take into account special tokens)."
               )
           }		,      )
       lowerCAmelCase__  :bool						   =					field(
           default=UpperCamelCase		,      metadata={"help": "Overwrite the cached training and evaluation sets"}   )





def        lowerCamelCase  (				_snake_case  :						DataTrainingArguments     ,_snake_case  :						PreTrainedTokenizer     ,_snake_case  :						bool = False     ,_snake_case  :						Optional[str] = None     ,):





    '''simple docstring'''


    def _dataset(_snake_case  :						List[Any]     ,_snake_case  :						int=None       ):
        if args.line_by_line:
            if ref_path is not None:
                if not args.whole_word_mask or not args.mlm:
                    raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask"       )
                return LineByLineWithRefDataset(
                    tokenizer=_snake_case     ,file_path=_snake_case     ,block_size=args.block_size     ,ref_path=_snake_case     ,)

            return LineByLineTextDataset(tokenizer=_snake_case     ,file_path=_snake_case     ,block_size=args.block_size       )
        else:
            return TextDataset(
                tokenizer=_snake_case     ,file_path=_snake_case     ,block_size=args.block_size     ,overwrite_cache=args.overwrite_cache     ,cache_dir=_snake_case     ,)

    if evaluate:
        return _dataset(args.eval_data_file     ,args.eval_ref_file       )
    elif args.train_data_files:
        return ConcatDataset([_dataset(_snake_case       ) for f in glob(args.train_data_files       )]       )
    else:
        return _dataset(args.train_data_file     ,args.train_ref_file       )




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
    lowercase__       ,       lowercase__       ,       lowercase__		       =						parser.parse_args_into_dataclasses()

    if data_args.eval_data_file is None and training_args.do_eval:
        raise ValueError(
            "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
            "or remove the --do_eval argument."       )
    if (
        os.path.exists(training_args.output_dir       )
        and os.listdir(training_args.output_dir       )
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
            " --overwrite_output_dir to overcome."       )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,datefmt="%m/%d/%Y %H:%M:%S"     ,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     ,)
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"     ,training_args.local_rank     ,training_args.device     ,training_args.n_gpu     ,bool(training_args.local_rank != -1       )     ,training_args.fpaa     ,)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank       ):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"     ,_snake_case       )

    # Set seed
    set_seed(training_args.seed       )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    if model_args.config_name:
        lowercase__		       =						AutoConfig.from_pretrained(model_args.config_name     ,cache_dir=model_args.cache_dir       )
    elif model_args.model_name_or_path:
        lowercase__		       =						AutoConfig.from_pretrained(model_args.model_name_or_path     ,cache_dir=model_args.cache_dir       )
    else:
        lowercase__		       =						CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch."       )

    if model_args.tokenizer_name:
        lowercase__		       =						AutoTokenizer.from_pretrained(model_args.tokenizer_name     ,cache_dir=model_args.cache_dir       )
    elif model_args.model_name_or_path:
        lowercase__		       =						AutoTokenizer.from_pretrained(model_args.model_name_or_path     ,cache_dir=model_args.cache_dir       )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"
            " script, save it,and load it from here, using --tokenizer_name"       )

    if model_args.model_name_or_path:
        lowercase__		       =						AutoModelWithLMHead.from_pretrained(
            model_args.model_name_or_path     ,from_tf=bool(".ckpt" in model_args.model_name_or_path       )     ,config=_snake_case     ,cache_dir=model_args.cache_dir     ,)
    else:
        logger.info("Training new model from scratch"       )
        lowercase__		       =						AutoModelWithLMHead.from_config(_snake_case       )

    model.resize_token_embeddings(len(_snake_case       )       )

    if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
        raise ValueError(
            "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
            "--mlm flag (masked language modeling)."       )

    if data_args.block_size <= 0:
        lowercase__		       =						tokenizer.max_len
        # Our input block size will be the max possible for the model
    else:
        lowercase__		       =						min(data_args.block_size     ,tokenizer.max_len       )

    # Get datasets

    lowercase__		       =						(
        get_dataset(_snake_case     ,tokenizer=_snake_case     ,cache_dir=model_args.cache_dir       ) if training_args.do_train else None
    )
    lowercase__		       =						(
        get_dataset(_snake_case     ,tokenizer=_snake_case     ,evaluate=_snake_case     ,cache_dir=model_args.cache_dir       )
        if training_args.do_eval
        else None
    )
    if config.model_type == "xlnet":
        lowercase__		       =						DataCollatorForPermutationLanguageModeling(
            tokenizer=_snake_case     ,plm_probability=data_args.plm_probability     ,max_span_length=data_args.max_span_length     ,)
    else:
        if data_args.mlm and data_args.whole_word_mask:
            lowercase__		       =						DataCollatorForWholeWordMask(
                tokenizer=_snake_case     ,mlm_probability=data_args.mlm_probability       )
        else:
            lowercase__		       =						DataCollatorForLanguageModeling(
                tokenizer=_snake_case     ,mlm=data_args.mlm     ,mlm_probability=data_args.mlm_probability       )

    # Initialize our Trainer
    lowercase__		       =						Trainer(
        model=_snake_case     ,args=_snake_case     ,data_collator=_snake_case     ,train_dataset=_snake_case     ,eval_dataset=_snake_case     ,prediction_loss_only=_snake_case     ,)

    # Training
    if training_args.do_train:
        lowercase__		       =						(
            model_args.model_name_or_path
            if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path       )
            else None
        )
        trainer.train(model_path=_snake_case       )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
    lowercase__		       =						{}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"       )

        lowercase__		       =						trainer.evaluate()

        lowercase__		       =						math.exp(eval_output["eval_loss"]       )
        lowercase__		       =						{"perplexity": perplexity}

        lowercase__		       =						os.path.join(training_args.output_dir     ,"eval_results_lm.txt"       )
        if trainer.is_world_master():
            with open(_snake_case     ,"w"       ) as writer:
                logger.info("***** Eval results *****"       )
                for key in sorted(result.keys()       ):
                    logger.info("  %s = %s"     ,_snake_case     ,str(result[key]       )       )
                    writer.write("%s = %s\n" % (key, str(result[key]       ))       )

        results.update(_snake_case       )

    return results




def        lowerCamelCase  (				_snake_case  :						str       ):





    '''simple docstring'''


    main()


if __name__ == "__main__":
 main()









'''simple docstring'''





import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     BeitForImageClassification,
     BeitForMaskedImageModeling,
     BeitForSemanticSegmentation,
     BeitModel,
 )
 from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 import PIL
 from PIL import Image

 from transformers import BeitImageProcessor


class 				snake_case :



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=100     ,UpperCAmelCase_=13     ,UpperCAmelCase_=30     ,UpperCAmelCase_=2     ,UpperCAmelCase_=3     ,UpperCAmelCase_=True     ,UpperCAmelCase_=True     ,UpperCAmelCase_=32     ,UpperCAmelCase_=4     ,UpperCAmelCase_=4     ,UpperCAmelCase_=37     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=10     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=3     ,UpperCAmelCase_=None     ,UpperCAmelCase_=[0, 1, 2, 3]     ,)  ->							Any:
           lowercase__		       =						parent
           lowercase__		       =						100
           lowercase__		       =						batch_size
           lowercase__		       =						image_size
           lowercase__		       =						patch_size
           lowercase__		       =						num_channels
           lowercase__		       =						is_training
           lowercase__		       =						use_labels
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						type_sequence_label_size
           lowercase__		       =						initializer_range
           lowercase__		       =						scope
           lowercase__		       =						out_indices
           lowercase__		       =						num_labels

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowercase__		       =						(image_size // patch_size) ** 2
           lowercase__		       =						num_patches + 1



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           lowercase__		       =						None
           lowercase__		       =						None
           if self.use_labels:
               lowercase__		       =						ids_tensor([self.batch_size]     ,self.type_sequence_label_size      )
               lowercase__		       =						ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels      )

           lowercase__		       =						self.get_config()

           return config, pixel_values, labels, pixel_labels



       def 							_a      (				self      )  ->							List[str]:
           return BeitConfig(
               vocab_size=self.vocab_size     ,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=UpperCAmelCase_     ,initializer_range=self.initializer_range     ,out_indices=self.out_indices     ,)



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						BeitModel(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						BeitForMaskedImageModeling(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length - 1, self.vocab_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						BeitForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						BeitForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

           lowercase__		       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						self.num_labels
           lowercase__		       =						BeitForSemanticSegmentation(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(
               result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(
               result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )







       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.prepare_config_and_inputs()
           lowercase__       ,       lowercase__       ,       lowercase__       ,       lowercase__		       =						config_and_inputs
           lowercase__		       =						{"pixel_values": pixel_values}
           return config, inputs_dict





@require_torch
class 				snake_case (UpperCamelCase		,      UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Optional[Any]						   =					(
           (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__  :Optional[int]						   =					(
           {
               "feature-extraction": BeitModel,
               "image-classification": BeitForImageClassification,
               "image-segmentation": BeitForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase__  :Any						   =					False
       lowerCAmelCase__  :Optional[Any]						   =					False
       lowerCAmelCase__  :Optional[Any]						   =					False



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,has_text_modality=UpperCAmelCase_     ,hidden_size=37      )



       def 							_a      (				self      )  ->							Tuple:
           self.config_tester.run_common_tests()



       @unittest.skip(reason="BEiT does not use inputs_embeds"      )
       def 							_a      (				self      )  ->							Dict:
           pass



       @require_torch_multi_gpu
       @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"      )
       def 							_a      (				self      )  ->							int:
           pass



       def 							_a      (				self      )  ->							Any:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

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



       def 							_a      (				self      )  ->							Tuple:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__		       =						["pixel_values"]
               self.assertListEqual(arg_names[:1]     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           if not self.model_tester.is_training:
               return

           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()
           lowercase__		       =						True

           for model_class in self.all_model_classes:
               # we don't test BeitForMaskedImageModeling
               if model_class in [*get_values(UpperCAmelCase_      ), BeitForMaskedImageModeling]:
                   continue

               lowercase__		       =						model_class(UpperCAmelCase_      )
               model.to(UpperCAmelCase_      )
               model.train()
               lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_     ,return_labels=UpperCAmelCase_      )
               lowercase__		       =						model(**UpperCAmelCase_      ).loss
               loss.backward()



       def 							_a      (				self      )  ->							str:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
               return

           lowercase__		       =						False
           lowercase__		       =						True

           for model_class in self.all_model_classes:
               # we don't test BeitForMaskedImageModeling
               if (
                   model_class in [*get_values(UpperCAmelCase_      ), BeitForMaskedImageModeling]
                   or not model_class.supports_gradient_checkpointing
               ):
                   continue

               lowercase__		       =						model_class(UpperCAmelCase_      )
               model.gradient_checkpointing_enable()
               model.to(UpperCAmelCase_      )
               model.train()
               lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_     ,return_labels=UpperCAmelCase_      )
               lowercase__		       =						model(**UpperCAmelCase_      ).loss
               loss.backward()



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           lowercase__		       =						_config_zero_init(UpperCAmelCase_      )
           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(config=UpperCAmelCase_      )
               for name, param in model.named_parameters():
                   # we skip lambda parameters as these require special initial values
                   # determined by config.layer_scale_init_value
                   if "lambda" in name:
                       continue
                   if param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1E9).round() / 1E9).item()     ,[0.0, 1.0]     ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,)







       @slow
       def 							_a      (				self      )  ->							Optional[Any]:
           for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__		       =						BeitModel.from_pretrained(UpperCAmelCase_      )
               self.assertIsNotNone(UpperCAmelCase_      )





def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image



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



       @cached_property
       def 							_a      (				self      )  ->							Any:
           return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None



       @slow
       def 							_a      (				self      )  ->							int:
           lowercase__		       =						BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      ).to(UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).pixel_values.to(UpperCAmelCase_      )

           # prepare bool_masked_pos
           lowercase__		       =						torch.ones((1, 196)     ,dtype=torch.bool      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(pixel_values=UpperCAmelCase_     ,bool_masked_pos=UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 196, 8_192)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor(
               [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,UpperCAmelCase_     ,atol=1E-2      )      )



       @slow
       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      ).to(UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 1_000)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([-1.23_85, -1.09_87, -1.01_08]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						281
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      ).to(
               UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 21_841)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([1.68_81, -0.27_87, 0.59_01]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						2_396
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
           lowercase__		       =						model.to(UpperCAmelCase_      )

           lowercase__		       =						BeitImageProcessor(do_resize=UpperCAmelCase_     ,size=640     ,do_center_crop=UpperCAmelCase_      )

           lowercase__		       =						load_dataset("hf-internal-testing/fixtures_ade20k"     ,split="test"      )
           lowercase__		       =						Image.open(ds[0]["file"]      )
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 150, 160, 160)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						version.parse(PIL.__version__      ) < version.parse("9.0.0"      )

           if is_pillow_less_than_a:
               lowercase__		       =						torch.tensor(
                   [
                       [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                       [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                       [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
                   ]     ,device=UpperCAmelCase_     ,)
           else:
               lowercase__		       =						torch.tensor(
                   [
                       [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
                       [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
                       [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
                   ]     ,device=UpperCAmelCase_     ,)

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )







       @slow
       def 							_a      (				self      )  ->							str:
           lowercase__		       =						BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
           lowercase__		       =						model.to(UpperCAmelCase_      )

           lowercase__		       =						BeitImageProcessor(do_resize=UpperCAmelCase_     ,size=640     ,do_center_crop=UpperCAmelCase_      )

           lowercase__		       =						load_dataset("hf-internal-testing/fixtures_ade20k"     ,split="test"      )
           lowercase__		       =						Image.open(ds[0]["file"]      )
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )

           lowercase__		       =						outputs.logits.detach().cpu()

           lowercase__		       =						image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_     ,target_sizes=[(500, 300)]      )
           lowercase__		       =						torch.Size((500, 300)      )
           self.assertEqual(segmentation[0].shape     ,UpperCAmelCase_      )

           lowercase__		       =						image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_      )
           lowercase__		       =						torch.Size((160, 160)      )
           self.assertEqual(segmentation[0].shape     ,UpperCAmelCase_      )




