

'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_snake_case     =  logging.getLogger(__name__)


@dataclass
class    a__  :
   _SCREAMING_SNAKE_CASE    :     str	       =							field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  )
   _SCREAMING_SNAKE_CASE    :     Optional[str]	       =							field(
       default=__lowerCamelCase  ,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}  )
   _SCREAMING_SNAKE_CASE    :     Optional[str]	       =							field(
       default='NER'  ,      metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}  )
   _SCREAMING_SNAKE_CASE    :     Optional[str]	       =							field(
       default=__lowerCamelCase  ,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}  )
   _SCREAMING_SNAKE_CASE    :     bool	       =							field(default=__lowerCamelCase  ,      metadata={'help': 'Set this flag to use fast tokenization.'}  )
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   _SCREAMING_SNAKE_CASE    :     Optional[str]	       =							field(
       default=__lowerCamelCase  ,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,      )




@dataclass
class    a__  :
   _SCREAMING_SNAKE_CASE    :     str	       =							field(
       metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}  )
   _SCREAMING_SNAKE_CASE    :     Optional[str]	       =							field(
       default=__lowerCamelCase  ,      metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}  ,      )
   _SCREAMING_SNAKE_CASE    :     int	       =							field(
       default=128  ,      metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }  ,      )
   _SCREAMING_SNAKE_CASE    :     bool	       =							field(
       default=__lowerCamelCase  ,      metadata={'help': 'Overwrite the cached training and evaluation sets'}  )







def 					_A		(	)			->	Dict:
   _lowercase			:      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.
      _lowercase			:      str			     =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
   else:
      _lowercase			:      List[Any]			     =   parser.parse_args_into_dataclasses()

   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."     )

   _lowercase			:      int			     =   import_module("tasks"     )
   try:
      _lowercase			:      Optional[int]			     =   getattr(__lowerCAmelCase		,							model_args.task_type     )
      _lowercase			:      TokenClassificationTask			     =   token_classification_task_clazz()
   except AttributeError:
      raise ValueError(
          F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
          F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''     )

   # 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"		,							__lowerCAmelCase     )

   # Set seed
   set_seed(training_args.seed     )

   # Prepare CONLL-2003 task
   _lowercase			:      Optional[int]			     =   token_classification_task.get_labels(data_args.labels     )
   _lowercase			:      Dict[int, str]			     =   dict(enumerate(__lowerCAmelCase     )     )
   _lowercase			:      Optional[Any]			     =   len(__lowerCAmelCase     )

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

   _lowercase			:      List[str]			     =   AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path		,							num_labels=__lowerCAmelCase		,							idalabel=__lowerCAmelCase		,							labelaid={label: i for i, label in enumerate(__lowerCAmelCase     )}		,							cache_dir=model_args.cache_dir		,							)
   _lowercase			:      List[str]			     =   AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,							cache_dir=model_args.cache_dir		,							use_fast=model_args.use_fast		,							)
   _lowercase			:      Tuple			     =   AutoModelForTokenClassification.from_pretrained(
       model_args.model_name_or_path		,							from_tf=bool(".ckpt" in model_args.model_name_or_path     )		,							config=__lowerCAmelCase		,							cache_dir=model_args.cache_dir		,							)

   # Get datasets
   _lowercase			:      Dict			     =   (
       TokenClassificationDataset(
           token_classification_task=__lowerCAmelCase		,							data_dir=data_args.data_dir		,							tokenizer=__lowerCAmelCase		,							labels=__lowerCAmelCase		,							model_type=config.model_type		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							mode=Split.train		,							)
       if training_args.do_train
       else None
   )
   _lowercase			:      Optional[Any]			     =   (
       TokenClassificationDataset(
           token_classification_task=__lowerCAmelCase		,							data_dir=data_args.data_dir		,							tokenizer=__lowerCAmelCase		,							labels=__lowerCAmelCase		,							model_type=config.model_type		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							mode=Split.dev		,							)
       if training_args.do_eval
       else None
   )

   def align_predictions(snake_case		,							snake_case     ) -> Tuple[List[int], List[int]]:
      _lowercase			:      Any			     =   np.argmax(__lowerCAmelCase		,							axis=2     )

      _lowercase			:      List[Any]			     =   preds.shape

      _lowercase			:      List[Any]			     =   [[] for _ in range(__lowerCAmelCase     )]
      _lowercase			:      int			     =   [[] for _ in range(__lowerCAmelCase     )]

      for i in range(__lowerCAmelCase     ):
         for j in range(__lowerCAmelCase     ):
            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
               out_label_list[i].append(label_map[label_ids[i][j]]     )
               preds_list[i].append(label_map[preds[i][j]]     )

      return preds_list, out_label_list

   def compute_metrics(snake_case     ) -> Dict:
      _lowercase			:      List[str]			     =   align_predictions(p.predictions		,							p.label_ids     )
      return {
          "accuracy_score": accuracy_score(__lowerCAmelCase		,							__lowerCAmelCase     ),
          "precision": precision_score(__lowerCAmelCase		,							__lowerCAmelCase     ),
          "recall": recall_score(__lowerCAmelCase		,							__lowerCAmelCase     ),
          "f1": fa_score(__lowerCAmelCase		,							__lowerCAmelCase     ),
      }

   # Data collator
   _lowercase			:      Any			     =   DataCollatorWithPadding(__lowerCAmelCase		,							pad_to_multiple_of=8     ) if training_args.fpaa else None

   # Initialize our Trainer
   _lowercase			:      Tuple			     =   Trainer(
       model=__lowerCAmelCase		,							args=__lowerCAmelCase		,							train_dataset=__lowerCAmelCase		,							eval_dataset=__lowerCAmelCase		,							compute_metrics=__lowerCAmelCase		,							data_collator=__lowerCAmelCase		,							)

   # Training
   if training_args.do_train:
      trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
      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_process_zero():
         tokenizer.save_pretrained(training_args.output_dir     )

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

      _lowercase			:      Tuple			     =   trainer.evaluate()

      _lowercase			:      Optional[Any]			     =   os.path.join(training_args.output_dir		,							"eval_results.txt"     )
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase		,							"w"     ) as writer:
            logger.info("***** Eval results *****"     )
            for key, value in result.items():
               logger.info("  %s = %s"		,							__lowerCAmelCase		,							__lowerCAmelCase     )
               writer.write("%s = %s\n" % (key, value)     )

         results.update(__lowerCAmelCase     )

    # Predict
   if training_args.do_predict:
      _lowercase			:      Optional[Any]			     =   TokenClassificationDataset(
          token_classification_task=__lowerCAmelCase		,							data_dir=data_args.data_dir		,							tokenizer=__lowerCAmelCase		,							labels=__lowerCAmelCase		,							model_type=config.model_type		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							mode=Split.test		,							)

      _lowercase			:      List[str]			     =   trainer.predict(__lowerCAmelCase     )
      _lowercase			:      int			     =   align_predictions(__lowerCAmelCase		,							__lowerCAmelCase     )

      _lowercase			:      Optional[int]			     =   os.path.join(training_args.output_dir		,							"test_results.txt"     )
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase		,							"w"     ) as writer:
            for key, value in metrics.items():
               logger.info("  %s = %s"		,							__lowerCAmelCase		,							__lowerCAmelCase     )
               writer.write("%s = %s\n" % (key, value)     )

        # Save predictions
      _lowercase			:      Any			     =   os.path.join(training_args.output_dir		,							"test_predictions.txt"     )
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase		,							"w"     ) as writer:
            with open(os.path.join(data_args.data_dir		,							"test.txt"     )		,							"r"     ) as f:
               token_classification_task.write_predictions_to_file(__lowerCAmelCase		,							__lowerCAmelCase		,							__lowerCAmelCase     )

   return results







def 					_A		(	snake_case     )			->	Union[str, Any]:
   main()


if __name__ == "__main__":
    main()











from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class       a							:



		def __init__(     self     :List[Any]   ,__lowercase     :List[Any]   ,__lowercase     :Union[str, Any]=1_3   ,__lowercase     :str=3_0   ,__lowercase     :Optional[Any]=2   ,__lowercase     :int=3   ,__lowercase     :List[Any]=True   ,__lowercase     :Tuple=True   ,__lowercase     :List[Any]=3_2   ,__lowercase     :str=2   ,__lowercase     :Union[str, Any]=4   ,__lowercase     :Dict=3_7   ,__lowercase     :List[Any]="gelu"   ,__lowercase     :Optional[int]=0.1   ,__lowercase     :str=0.1   ,__lowercase     :Union[str, Any]=1_0   ,__lowercase     :Optional[Any]=0.02   ,__lowercase     :Union[str, Any]=3   ,__lowercase     :Any=0.6   ,__lowercase     :List[str]=None   ,):
						snake_case__	:      str     		=	parent
						snake_case__	:      int     		=	batch_size
						snake_case__	:      Dict     		=	image_size
						snake_case__	:      List[str]     		=	patch_size
						snake_case__	:      str     		=	num_channels
						snake_case__	:      int     		=	is_training
						snake_case__	:      List[str]     		=	use_labels
						snake_case__	:      List[str]     		=	hidden_size
						snake_case__	:      List[Any]     		=	num_hidden_layers
						snake_case__	:      str     		=	num_attention_heads
						snake_case__	:      Any     		=	intermediate_size
						snake_case__	:      Optional[Any]     		=	hidden_act
						snake_case__	:      List[str]     		=	hidden_dropout_prob
						snake_case__	:      Union[str, Any]     		=	attention_probs_dropout_prob
						snake_case__	:      Optional[Any]     		=	type_sequence_label_size
						snake_case__	:      List[str]     		=	initializer_range
						snake_case__	:      Optional[Any]     		=	mask_ratio
						snake_case__	:      List[str]     		=	scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						snake_case__	:      str     		=	(image_size // patch_size) ** 2
						snake_case__	:      List[str]     		=	int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      Optional[Any]     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						snake_case__	:      Any     		=	None
						if self.use_labels:
										snake_case__	:      Tuple     		=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size  )

						snake_case__	:      List[Any]     		=	self.get_config()

						return config, pixel_values, labels



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						return ViTMAEConfig(
						    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   ,decoder_hidden_size=self.hidden_size   ,decoder_num_hidden_layers=self.num_hidden_layers   ,decoder_num_attention_heads=self.num_attention_heads   ,decoder_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=__lowercase   ,initializer_range=self.initializer_range   ,mask_ratio=self.mask_ratio   ,)



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :Union[str, Any]   ,__lowercase     :Dict   ,__lowercase     :List[str]  ):
						snake_case__	:      Optional[int]     		=	TFViTMAEModel(config=__lowercase  )
						snake_case__	:      int     		=	model(__lowercase   ,training=__lowercase  )
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)  )



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :List[str]   ,__lowercase     :Optional[int]   ,__lowercase     :int  ):
						snake_case__	:      Dict     		=	TFViTMAEForPreTraining(__lowercase  )
						snake_case__	:      Optional[int]     		=	model(__lowercase   ,training=__lowercase  )
						# expected sequence length = num_patches
						snake_case__	:      Optional[Any]     		=	(self.image_size // self.patch_size) ** 2
						snake_case__	:      int     		=	self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )

						# test greyscale images
						snake_case__	:      Tuple     		=	1
						snake_case__	:      List[Any]     		=	TFViTMAEForPreTraining(__lowercase  )

						snake_case__	:      Any     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
						snake_case__	:      Any     		=	model(__lowercase   ,training=__lowercase  )
						snake_case__	:      Any     		=	self.patch_size**2
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )






		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Union[str, Any]     		=	self.prepare_config_and_inputs()
						((snake_case__)		, (snake_case__)		, (snake_case__))	:      Optional[Any]     		=	config_and_inputs
						snake_case__	:      List[Any]     		=	{'''pixel_values''': pixel_values}
						return config, inputs_dict


@require_tf
class       a							(			__lowerCamelCase  ,							__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Union[str, Any]											=					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		__lowerCAmelCase	:    Union[str, Any]											=					{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

		__lowerCAmelCase	:    Tuple											=					False
		__lowerCAmelCase	:    List[Any]											=					False
		__lowerCAmelCase	:    Dict											=					False
		__lowerCAmelCase	:    List[str]											=					False



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Union[str, Any]     		=	TFViTMAEModelTester(self  )
						snake_case__	:      Dict     		=	ConfigTester(self   ,config_class=__lowercase   ,has_text_modality=__lowercase   ,hidden_size=3_7  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''  )
		def 	__lowerCamelCase			(     self     :Dict  ):
						pass



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__		, snake_case__	:      int     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      Union[str, Any]     		=	model_class(__lowercase  )
										self.assertIsInstance(model.get_input_embeddings()   ,(tf.keras.layers.Layer)  )
										snake_case__	:      List[Any]     		=	model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__lowercase   ,tf.keras.layers.Layer  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__		, snake_case__	:      str     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      str     		=	model_class(__lowercase  )
										snake_case__	:      Any     		=	inspect.signature(model.call  )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case__	:      Tuple     		=	[*signature.parameters.keys()]

										snake_case__	:      Dict     		=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Dict     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase  )



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__lowercase  )



		def 	__lowerCamelCase			(     self     :int  ):
						# make the mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      Any     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      Optional[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      List[str]     		=	model_class(__lowercase  )
										snake_case__	:      Union[str, Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

										snake_case__	:      Optional[int]     		=	copy.deepcopy(self._prepare_for_class(__lowercase   ,__lowercase  )  )
										snake_case__	:      List[Any]     		=	model(**__lowercase   ,noise=__lowercase  )
										snake_case__	:      Optional[Any]     		=	outputs_dict[0].numpy()
										snake_case__	:      Optional[Any]     		=	outputs_keywords[0].numpy()

										self.assertLess(np.sum(np.abs(output_dict - output_keywords  )  )   ,1e-6  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						# make the mask reproducible
						np.random.seed(2  )

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

						snake_case__	:      List[str]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						def prepare_numpy_arrays(__lowercase     :Dict  ):
										snake_case__	:      Any     		=	{}
										for k, v in inputs_dict.items():
														if tf.is_tensor(__lowercase  ):
																		snake_case__	:      Dict     		=	v.numpy()
														else:
																		snake_case__	:      str     		=	np.array(__lowercase  )

										return inputs_np_dict

						for model_class in self.all_model_classes:
										snake_case__	:      str     		=	model_class(__lowercase  )

										snake_case__	:      List[Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      Dict     		=	prepare_numpy_arrays(__lowercase  )

										snake_case__	:      Tuple     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      Dict     		=	model(**__lowercase   ,noise=__lowercase  )
										self.assert_outputs_same(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Optional[Any]   ,__lowercase     :List[str]  ):
						# make masks reproducible
						np.random.seed(2  )

						snake_case__	:      Union[str, Any]     		=	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.constant(__lowercase  )

						# Add `noise` argument.
						# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
						snake_case__	:      Optional[Any]     		=	tf_noise

						super().check_pt_tf_models(__lowercase   ,__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      List[str]     		=	{
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__  ),)
						    for module_member_name in dir(__lowercase  )
						    if module_member_name.endswith('''MainLayer'''  )
						    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
						    and module_member_name[: -len('''MainLayer'''  )] == model_class.__name__[: -len('''Model'''  )]
						    for module_member in (getattr(__lowercase   ,__lowercase  ),)
						    if isinstance(__lowercase   ,__lowercase  )
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(__lowercase   ,'''_keras_serializable'''   ,__lowercase  )
						}

						snake_case__	:      Optional[Any]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.convert_to_tensor(__lowercase  )
						inputs_dict.update({'''noise''': noise}  )

						for main_layer_class in tf_main_layer_classes:
										snake_case__	:      List[Any]     		=	main_layer_class(__lowercase  )

										snake_case__	:      Union[str, Any]     		=	{
										    name: tf.keras.Input(tensor.shape[1:]   ,dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
										}

										snake_case__	:      Optional[Any]     		=	tf.keras.Model(__lowercase   ,outputs=main_layer(__lowercase  )  )
										snake_case__	:      List[str]     		=	model(__lowercase  )

										with tempfile.TemporaryDirectory() as tmpdirname:
														snake_case__	:      List[str]     		=	os.path.join(__lowercase   ,'''keras_model.h5'''  )
														model.save(__lowercase  )
														snake_case__	:      List[str]     		=	tf.keras.models.load_model(
														    __lowercase   ,custom_objects={main_layer_class.__name__: main_layer_class}  )
														assert isinstance(__lowercase   ,tf.keras.Model  )
														snake_case__	:      Union[str, Any]     		=	model(__lowercase  )
														self.assert_outputs_same(__lowercase   ,__lowercase  )



		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      str     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      Tuple     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      Optional[int]     		=	model_class(__lowercase  )
										snake_case__	:      str     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      List[Any]     		=	model(__lowercase   ,noise=__lowercase  )

										if model_class.__name__ == "TFViTMAEModel":
														snake_case__	:      List[Any]     		=	outputs.last_hidden_state.numpy()
														snake_case__	:      List[Any]     		=	0
										else:
														snake_case__	:      Any     		=	outputs.logits.numpy()
														snake_case__	:      Tuple     		=	0

										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(__lowercase   ,saved_model=__lowercase  )
														snake_case__	:      Optional[Any]     		=	model_class.from_pretrained(__lowercase  )
														snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

														if model_class.__name__ == "TFViTMAEModel":
																		snake_case__	:      Dict     		=	after_outputs['''last_hidden_state'''].numpy()
																		snake_case__	:      List[Any]     		=	0
														else:
																		snake_case__	:      Any     		=	after_outputs['''logits'''].numpy()
																		snake_case__	:      Optional[Any]     		=	0

														snake_case__	:      int     		=	np.amax(np.abs(out_a - out_a  )  )
														self.assertLessEqual(__lowercase   ,1e-5  )



		def 	__lowerCamelCase			(     self     :int  ):
						# make mask reproducible
						np.random.seed(2  )

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

						snake_case__	:      Dict     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[str]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      int     		=	model_class(__lowercase  )
										snake_case__	:      int     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      List[Any]     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      int     		=	model.get_config()
										# make sure that returned config is jsonifiable, which is required by keras
										json.dumps(__lowercase  )
										snake_case__	:      Any     		=	model_class.from_config(model.get_config()  )
										# make sure it also accepts a normal config
										snake_case__	:      Optional[int]     		=	model_class.from_config(model.config  )
										snake_case__	:      Tuple     		=	new_model(__lowercase  )  # Build model
										new_model.set_weights(model.get_weights()  )
										snake_case__	:      List[Any]     		=	new_model(__lowercase   ,noise=__lowercase  )

										self.assert_outputs_same(__lowercase   ,__lowercase  )



		@unittest.skip(
		    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''  )
		def 	__lowerCamelCase			(     self     :List[Any]  ):
						pass



		@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''  )
		def 	__lowerCamelCase			(     self     :Tuple  ):
						pass






		@slow
		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      List[Any]     		=	TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''  )
						self.assertIsNotNone(__lowercase  )



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      str     		=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
				return image





@require_tf
@require_vision
class       a							(			unittest.TestCase				):



		@cached_property
		def 	__lowerCamelCase			(     self     :List[str]  ):
						return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''  ) if is_vision_available() else None






		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						# make random mask reproducible across the PT and TF model
						np.random.seed(2  )

						snake_case__	:      Tuple     		=	TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''  )

						snake_case__	:      int     		=	self.default_image_processor
						snake_case__	:      int     		=	prepare_img()
						snake_case__	:      Tuple     		=	image_processor(images=__lowercase   ,return_tensors='''tf'''  )

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						snake_case__	:      str     		=	ViTMAEConfig()
						snake_case__	:      List[str]     		=	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
						snake_case__	:      Dict     		=	np.random.uniform(size=(1, num_patches)  )

						# forward pass
						snake_case__	:      List[str]     		=	model(**__lowercase   ,noise=__lowercase  )

						# verify the logits
						snake_case__	:      Optional[Any]     		=	tf.convert_to_tensor([1, 1_9_6, 7_6_8]  )
						self.assertEqual(outputs.logits.shape   ,__lowercase  )

						snake_case__	:      Tuple     		=	tf.convert_to_tensor(
						    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

						tf.debugging.assert_near(outputs.logits[0, :3, :3]   ,__lowercase   ,atol=1e-4  )

