



"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_UpperCAmelCase     =			logging.getLogger(__name__)
_UpperCAmelCase     =			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_UpperCAmelCase     =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
          )
      }	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase__       )}	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'Override some existing default config settings when a model is trained from scratch. Example: '
              'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
          )
      }	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,      )
  UpperCamelCase	:				bool     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}	,      )
  UpperCamelCase	:				str     						=					field(
      default='main'	,      metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,      )
  UpperCamelCase	:				bool     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
              'with private models).'
          )
      }	,      )



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


     '''simple docstring'''




     if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
        raise ValueError(
            """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""		)







@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'The name of the dataset to use (via the datasets library).'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
  UpperCamelCase	:				Optional[str]     						=					field(default=UpperCAmelCase__	,      metadata={'help': 'The input training data file (a text file).'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}	,      )
  UpperCamelCase	:				bool     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=5	,      metadata={
          'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
      }	,      )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated. Default to the max input length of the model.'
          )
      }	,      )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'The number of processes to use for the preprocessing.'}	,      )
  UpperCamelCase	:				float     						=					field(
      default=0.15	,      metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}       )
  UpperCamelCase	:				bool     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'Whether to pad all samples to `max_seq_length`. '
              'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
          )
      }	,      )



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Optional[Any]:


     '''simple docstring'''




     if self.train_file is not None:
        SCREAMING_SNAKE_CASE_: Any     			=self.train_file.split("""."""		)[-1]
        assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
     if self.validation_file is not None:
        SCREAMING_SNAKE_CASE_: List[str]     			=self.validation_file.split("""."""		)[-1]
        assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def      __magic_name__				(							lowercase							,			lowercase ):
   with open(lowercase							,			"""r"""							,			encoding="""utf-8""" ) as f:
      SCREAMING_SNAKE_CASE_: int     			=[json.loads(lowercase ) for line in f.read().splitlines() if (len(lowercase ) > 0 and not line.isspace())]
   assert len(lowercase ) == len(lowercase )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			={c: dataset[c] for c in dataset.column_names}
   SCREAMING_SNAKE_CASE_: List[str]     			=refs
   return Dataset.from_dict(lowercase )





def      __magic_name__				(							):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

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

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

    # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,			datefmt="""%m/%d/%Y %H:%M:%S"""							,			handlers=[logging.StreamHandler(sys.stdout )]							,			)
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )

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

   # Set seed before initializing model.
   set_seed(training_args.seed )

   # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
   # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
   # (the dataset will be downloaded automatically from the datasets Hub).
   #
   # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
   # 'text' is found. You can easily tweak this behavior (see below).
   #
   # In distributed training, the load_dataset function guarantee that only one local process can concurrently
   # download the dataset.
   if data_args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      SCREAMING_SNAKE_CASE_: List[str]     			=load_dataset(data_args.dataset_name							,			data_args.dataset_config_name )
      if "validation" not in datasets.keys():
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_dataset(
             data_args.dataset_name							,			data_args.dataset_config_name							,			split=f'''train[:{data_args.validation_split_percentage}%]'''							,			)
         SCREAMING_SNAKE_CASE_: Optional[int]     			=load_dataset(
             data_args.dataset_name							,			data_args.dataset_config_name							,			split=f'''train[{data_args.validation_split_percentage}%:]'''							,			)
   else:
      SCREAMING_SNAKE_CASE_: List[Any]     			={}
      if data_args.train_file is not None:
         SCREAMING_SNAKE_CASE_: List[str]     			=data_args.train_file
      if data_args.validation_file is not None:
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=data_args.validation_file
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=data_args.train_file.split(""".""" )[-1]
      if extension == "txt":
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""text"""
      SCREAMING_SNAKE_CASE_: Optional[int]     			=load_dataset(lowercase							,			data_files=lowercase )
   # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
   # https://huggingface.co/docs/datasets/loading_datasets.html.

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   SCREAMING_SNAKE_CASE_: Any     			={
       """cache_dir""": model_args.cache_dir,
       """revision""": model_args.model_revision,
       """use_auth_token""": True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      SCREAMING_SNAKE_CASE_: int     			=AutoConfig.from_pretrained(model_args.config_name							,			**lowercase )
   elif model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoConfig.from_pretrained(model_args.model_name_or_path							,			**lowercase )
   else:
      SCREAMING_SNAKE_CASE_: List[str]     			=CONFIG_MAPPING[model_args.model_type]()
      logger.warning("""You are instantiating a new config instance from scratch.""" )
      if model_args.config_overrides is not None:
         logger.info(f'''Overriding config: {model_args.config_overrides}''' )
         config.update_from_string(model_args.config_overrides )
         logger.info(f'''New config: {config}''' )

   SCREAMING_SNAKE_CASE_: List[Any]     			={
       """cache_dir""": model_args.cache_dir,
       """use_fast""": model_args.use_fast_tokenizer,
       """revision""": model_args.model_revision,
       """use_auth_token""": True if model_args.use_auth_token else None,
   }
   if model_args.tokenizer_name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained(model_args.tokenizer_name							,			**lowercase )
   elif model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained(model_args.model_name_or_path							,			**lowercase )
   else:
      raise ValueError(
          """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
          """You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )

   if model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: Dict     			=AutoModelForMaskedLM.from_pretrained(
          model_args.model_name_or_path							,			from_tf=bool(""".ckpt""" in model_args.model_name_or_path )							,			config=lowercase							,			cache_dir=model_args.cache_dir							,			revision=model_args.model_revision							,			use_auth_token=True if model_args.use_auth_token else None							,			)
   else:
      logger.info("""Training new model from scratch""" )
      SCREAMING_SNAKE_CASE_: str     			=AutoModelForMaskedLM.from_config(lowercase )

   model.resize_token_embeddings(len(lowercase ) )

   # Preprocessing the datasets.
   # First we tokenize all the texts.
   if training_args.do_train:
      SCREAMING_SNAKE_CASE_: str     			=datasets["""train"""].column_names
   else:
      SCREAMING_SNAKE_CASE_: List[Any]     			=datasets["""validation"""].column_names
   SCREAMING_SNAKE_CASE_: Dict     			="""text""" if """text""" in column_names else column_names[0]

   SCREAMING_SNAKE_CASE_: List[str]     			="""max_length""" if data_args.pad_to_max_length else False

   def tokenize_function(lowercase ):
      # Remove empty lines
      SCREAMING_SNAKE_CASE_: List[str]     			=[line for line in examples["""text"""] if len(lowercase ) > 0 and not line.isspace()]
      return tokenizer(examples["""text"""]							,			padding=lowercase							,			truncation=lowercase							,			max_length=data_args.max_seq_length )

   SCREAMING_SNAKE_CASE_: int     			=datasets.map(
       lowercase							,			batched=lowercase							,			num_proc=data_args.preprocessing_num_workers							,			remove_columns=[text_column_name]							,			load_from_cache_file=not data_args.overwrite_cache							,			)

   # Add the chinese references if provided
   if data_args.train_ref_file is not None:
      SCREAMING_SNAKE_CASE_: Dict     			=add_chinese_references(tokenized_datasets["""train"""]							,			data_args.train_ref_file )
   if data_args.validation_ref_file is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=add_chinese_references(
          tokenized_datasets["""validation"""]							,			data_args.validation_ref_file )
   # If we have ref files, need to avoid it removed by trainer
   SCREAMING_SNAKE_CASE_: Dict     			=data_args.train_ref_file or data_args.validation_ref_file
   if has_ref:
      SCREAMING_SNAKE_CASE_: Tuple     			=False

   # Data collator
   # This one will take care of randomly masking the tokens.
   SCREAMING_SNAKE_CASE_: Tuple     			=DataCollatorForWholeWordMask(tokenizer=lowercase							,			mlm_probability=data_args.mlm_probability )

   # Initialize our Trainer
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=Trainer(
       model=lowercase							,			args=lowercase							,			train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None							,			eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None							,			tokenizer=lowercase							,			data_collator=lowercase							,			)

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
         SCREAMING_SNAKE_CASE_: Dict     			=model_args.model_name_or_path
      else:
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=None
      SCREAMING_SNAKE_CASE_: str     			=trainer.train(resume_from_checkpoint=lowercase )
      trainer.save_model()  # Saves the tokenizer too for easy upload

      SCREAMING_SNAKE_CASE_: Any     			=os.path.join(training_args.output_dir							,			"""train_results.txt""" )
      if trainer.is_world_process_zero():
         with open(lowercase							,			"""w""" ) as writer:
            logger.info("""***** Train results *****""" )
            for key, value in sorted(train_result.metrics.items() ):
               logger.info(f'''  {key} = {value}''' )
               writer.write(f'''{key} = {value}\n''' )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir							,			"""trainer_state.json""" ) )

    # Evaluation
   SCREAMING_SNAKE_CASE_: List[str]     			={}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***""" )

      SCREAMING_SNAKE_CASE_: List[Any]     			=trainer.evaluate()

      SCREAMING_SNAKE_CASE_: Dict     			=math.exp(eval_output["""eval_loss"""] )
      SCREAMING_SNAKE_CASE_: Tuple     			=perplexity

      SCREAMING_SNAKE_CASE_: List[str]     			=os.path.join(training_args.output_dir							,			"""eval_results_mlm_wwm.txt""" )
      if trainer.is_world_process_zero():
         with open(lowercase							,			"""w""" ) as writer:
            logger.info("""***** Eval results *****""" )
            for key, value in sorted(results.items() ):
               logger.info(f'''  {key} = {value}''' )
               writer.write(f'''{key} = {value}\n''' )

   return results





def      __magic_name__				(							lowercase ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()








"""simple docstring"""
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 					a	:



  def __init__(						self :					Any       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					List[str]=13       ,		lowerCAmelCase :					Dict=3       ,		lowerCAmelCase :					Optional[Any]=True       ,		lowerCAmelCase :					Dict=True       ,		lowerCAmelCase :					Dict=0.1       ,		lowerCAmelCase :					str=0.1       ,		lowerCAmelCase :					List[str]=224       ,		lowerCAmelCase :					List[str]=1000       ,		lowerCAmelCase :					Optional[Any]=[3, 3, 6, 4]       ,		lowerCAmelCase :					int=[48, 56, 112, 220]       ,		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=parent
     SCREAMING_SNAKE_CASE_: Any     			=batch_size
     SCREAMING_SNAKE_CASE_: Tuple     			=num_channels
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=is_training
     SCREAMING_SNAKE_CASE_: Tuple     			=use_labels
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Tuple     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_labels
     SCREAMING_SNAKE_CASE_: int     			=image_size
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=layer_depths
     SCREAMING_SNAKE_CASE_: List[Any]     			=embed_dims



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

     SCREAMING_SNAKE_CASE_: Tuple     			=self.get_config()

     return config, pixel_values, labels



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Union[str, Any]:


     '''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 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					Optional[int]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=SwiftFormerModel(config=lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()
     SCREAMING_SNAKE_CASE_: Any     			=model(lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.embed_dims[-1], 7, 7)		)



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					Union[str, Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.num_labels
     SCREAMING_SNAKE_CASE_: Dict     			=SwiftFormerForImageClassification(lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()
     SCREAMING_SNAKE_CASE_: Dict     			=model(lowerCAmelCase       ,		labels=lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)		)

     SCREAMING_SNAKE_CASE_: int     			=SwiftFormerForImageClassification(lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()

     SCREAMING_SNAKE_CASE_: Tuple     			=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
     SCREAMING_SNAKE_CASE_: Dict     			=model(lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)		)





  def 	lowerCamelCase__       (						self :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     ((SCREAMING_SNAKE_CASE_)			,		(SCREAMING_SNAKE_CASE_)			,		(SCREAMING_SNAKE_CASE_)): str     			=self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE_: Tuple     			={"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase       ):
  UpperCamelCase	:				Optional[int]     						=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
  UpperCamelCase	:				Tuple     						=					(
      {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCamelCase	:				Any     						=					False
  UpperCamelCase	:				Optional[int]     						=					False
  UpperCamelCase	:				Optional[Any]     						=					False
  UpperCamelCase	:				Dict     						=					False
  UpperCamelCase	:				List[str]     						=					False



  def 	lowerCamelCase__       (						self :					Dict		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=SwiftFormerModelTester(self		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ConfigTester(
         self       ,		config_class=lowerCAmelCase       ,		has_text_modality=lowerCAmelCase       ,		hidden_size=37       ,		num_attention_heads=12       ,		num_hidden_layers=12       ,		)



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					int:


     '''simple docstring'''




     self.config_tester.run_common_tests()



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


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_: Any     			=model_class(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[Any]     			=model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCAmelCase       ,		nn.Linear		)		)



  def 	lowerCamelCase__       (						self :					str		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=self.model_tester.prepare_config_and_inputs_for_common()

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

        SCREAMING_SNAKE_CASE_: Tuple     			=["""pixel_values"""]
        self.assertListEqual(arg_names[:1]       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					int		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase		)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase		)



  @slow
  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[str]:


     '''simple docstring'''




     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=SwiftFormerModel.from_pretrained(lowerCAmelCase		)
        self.assertIsNotNone(lowerCAmelCase		)



  @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					str:


     '''simple docstring'''




     pass



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


     '''simple docstring'''




     def check_hidden_states_output(lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]		):
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=model_class(lowerCAmelCase		)
        model.to(lowerCAmelCase		)
        model.eval()

        with torch.no_grad():
           SCREAMING_SNAKE_CASE_: List[str]     			=model(**self._prepare_for_class(lowerCAmelCase       ,		lowerCAmelCase		)		)

        SCREAMING_SNAKE_CASE_: Dict     			=outputs.hidden_states

        SCREAMING_SNAKE_CASE_: List[Any]     			=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),
                   ]		)       ,		)

     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_: Dict     			=True
        check_hidden_states_output(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        SCREAMING_SNAKE_CASE_: Any     			=True

        check_hidden_states_output(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Optional[int]:


     '''simple docstring'''




     def _config_zero_init(lowerCAmelCase :					str		):
        SCREAMING_SNAKE_CASE_: Dict     			=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-10		)
           if isinstance(getattr(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)       ,		lowerCAmelCase		):
              SCREAMING_SNAKE_CASE_: Tuple     			=_config_zero_init(getattr(lowerCAmelCase       ,		lowerCAmelCase		)		)
              setattr(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
        return configs_no_init

     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE_: List[Any]     			=_config_zero_init(lowerCAmelCase		)
     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_: Any     			=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 	lowerCamelCase__       (						self :					List[str]		)     ->					List[str]:


     '''simple docstring'''




     pass







def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: List[Any]     			=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image
@require_torch
@require_vision
class 					a	(  unittest.TestCase       ):



  @cached_property
  def 	lowerCamelCase__       (						self :					str		)     ->					str:


     '''simple docstring'''




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





  @slow
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: str     			=self.default_image_processor
     SCREAMING_SNAKE_CASE_: int     			=prepare_img()
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image_processor(images=lowerCAmelCase       ,		return_tensors="""pt"""		).to(lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
        SCREAMING_SNAKE_CASE_: Dict     			=model(**lowerCAmelCase		)

     # verify the logits
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]		).to(lowerCAmelCase		)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,		lowerCAmelCase       ,		atol=1E-4		)		)



