






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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCamelCase_							      = logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

lowerCamelCase_							      = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCamelCase_							      = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class      UpperCamelCase_			:
	__magic_name__       =							field(
	    default='''cifar10'''							,		metadata={'''help''': '''Name of a dataset from the datasets package'''}	)
	__magic_name__       =							field(
	    default=__A							,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
	__magic_name__       =							field(
	    default=__A							,		metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}							,		)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''A folder containing the training data.'''}	)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''A folder containing the validation data.'''}	)
	__magic_name__       =							field(
	    default=0.15							,		metadata={'''help''': '''Percent to split off of train for validation.'''}	)
	__magic_name__       =							field(default=32							,		metadata={'''help''': '''The size of the square patches to use for masking.'''}	)
	__magic_name__       =							field(
	    default=0.6							,		metadata={'''help''': '''Percentage of patches to mask.'''}							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
	            '''value if set.'''
	        )
	    }							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
	            '''value if set.'''
	        )
	    }							,		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     List[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   {}
				if self.train_dir is not None:
							UpperCAmelCase_	:					int     	=   self.train_dir
				if self.validation_dir is not None:
							UpperCAmelCase_	:					Optional[int]     	=   self.validation_dir
				UpperCAmelCase_	:					str     	=   data_files if data_files else None







@dataclass
class      UpperCamelCase_			:
	__magic_name__       =							field(
	    default=__A							,		metadata={
	        '''help''': (
	            '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
	            '''checkpoint identifier on the hub. '''
	            '''Don\'t set if you want to train a model from scratch.'''
	        )
	    }							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__A	)}							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
	__magic_name__       =							field(
	    default=__A							,		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'''
	        )
	    }							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}							,		)
	__magic_name__       =							field(
	    default='''main'''							,		metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}							,		)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''Name or path of preprocessor config.'''}	)
	__magic_name__       =							field(
	    default=__A							,		metadata={
	        '''help''': (
	            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
	            '''with private models).'''
	        )
	    }							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={
	        '''help''': (
	            '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
	        )
	    }							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={
	        '''help''': (
	            '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
	        )
	    }							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={'''help''': '''Stride to use for the encoder.'''}							,		)







class      UpperCamelCase_			:





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					Any=192      ,      lowerCAmelCase_   :					Union[str, Any]=32      ,      lowerCAmelCase_   :					Tuple=4      ,      lowerCAmelCase_   :					List[Any]=0.6		)		->     Optional[Any]:
				UpperCAmelCase_	:					Optional[int]     	=   input_size
				UpperCAmelCase_	:					str     	=   mask_patch_size
				UpperCAmelCase_	:					Optional[int]     	=   model_patch_size
				UpperCAmelCase_	:					Dict     	=   mask_ratio

				if self.input_size % self.mask_patch_size != 0:
							raise ValueError("Input size must be divisible by mask patch size"		)
				if self.mask_patch_size % self.model_patch_size != 0:
							raise ValueError("Mask patch size must be divisible by model patch size"		)

				UpperCAmelCase_	:					Dict     	=   self.input_size // self.mask_patch_size
				UpperCAmelCase_	:					Any     	=   self.mask_patch_size // self.model_patch_size

				UpperCAmelCase_	:					Any     	=   self.rand_size**2
				UpperCAmelCase_	:					Union[str, Any]     	=   int(np.ceil(self.token_count * self.mask_ratio		)		)





	def __call__( self   :					str		)		->     Union[str, Any]:
				UpperCAmelCase_	:					int     	=   np.random.permutation(self.token_count		)[: self.mask_count]
				UpperCAmelCase_	:					int     	=   np.zeros(self.token_count      ,      dtype=lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   1

				UpperCAmelCase_	:					Dict     	=   mask.reshape((self.rand_size, self.rand_size)		)
				UpperCAmelCase_	:					Dict     	=   mask.repeat(self.scale      ,      axis=0		).repeat(self.scale      ,      axis=1		)

				return torch.tensor(mask.flatten()		)





def 							snake_case (  A__		):
			UpperCAmelCase_	:					Optional[int]     	=   torch.stack([example["pixel_values"] for example in examples]		)
			UpperCAmelCase_	:					List[Any]     	=   torch.stack([example["mask"] for example in examples]		)
			return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def 							snake_case (  ):
			# 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.

			UpperCAmelCase_	:					List[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.
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
			else:
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("run_mim"      ,A__      ,A__		)

			# 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		)]      ,)

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			UpperCAmelCase_	:					Optional[Any]     	=   training_args.get_process_log_level()
			logger.setLevel(A__		)
			transformers.utils.logging.set_verbosity(A__		)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# 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}"""		)
			logger.info(F"""Training/evaluation parameters {training_args}"""		)

			# Detecting last checkpoint.
			UpperCAmelCase_	:					Optional[Any]     	=   None
			if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
						UpperCAmelCase_	:					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 and training_args.resume_from_checkpoint is 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."		)

    # Initialize our dataset.
			UpperCAmelCase_	:					List[str]     	=   load_dataset(
			    data_args.dataset_name      ,data_args.dataset_config_name      ,data_files=data_args.data_files      ,cache_dir=model_args.cache_dir      ,use_auth_token=True if model_args.use_auth_token else None      ,)

			# If we don't have a validation split, split off a percentage of train as validation.
			UpperCAmelCase_	:					Union[str, Any]     	=   None if "validation" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split      ,A__		) and data_args.train_val_split > 0.0:
						UpperCAmelCase_	:					Optional[Any]     	=   ds["train"].train_test_split(data_args.train_val_split		)
						UpperCAmelCase_	:					List[str]     	=   split["train"]
						UpperCAmelCase_	:					Union[str, Any]     	=   split["test"]

			# Create config
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			UpperCAmelCase_	:					List[str]     	=   {
			    "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_or_path:
						UpperCAmelCase_	:					Any     	=   AutoConfig.from_pretrained(model_args.config_name_or_path      ,**A__		)
			elif model_args.model_name_or_path:
						UpperCAmelCase_	:					Any     	=   AutoConfig.from_pretrained(model_args.model_name_or_path      ,**A__		)
			else:
						UpperCAmelCase_	:					Optional[int]     	=   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}"""		)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
			if hasattr(A__      ,"decoder_type"		):
						UpperCAmelCase_	:					Tuple     	=   "simmim"

			# adapt config
			UpperCAmelCase_	:					Optional[int]     	=   model_args.image_size if model_args.image_size is not None else config.image_size
			UpperCAmelCase_	:					int     	=   model_args.patch_size if model_args.patch_size is not None else config.patch_size
			UpperCAmelCase_	:					Optional[Any]     	=   (
			    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
			)

			config.update(
			    {
			        "image_size": model_args.image_size,
			        "patch_size": model_args.patch_size,
			        "encoder_stride": model_args.encoder_stride,
			    }		)

			# create image processor
			if model_args.image_processor_name:
						UpperCAmelCase_	:					Tuple     	=   AutoImageProcessor.from_pretrained(model_args.image_processor_name      ,**A__		)
			elif model_args.model_name_or_path:
						UpperCAmelCase_	:					List[str]     	=   AutoImageProcessor.from_pretrained(model_args.model_name_or_path      ,**A__		)
			else:
						UpperCAmelCase_	:					Dict     	=   {
						    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
						}
						UpperCAmelCase_	:					Tuple     	=   IMAGE_PROCESSOR_TYPES[model_args.model_type]()

			# create model
			if model_args.model_name_or_path:
						UpperCAmelCase_	:					Union[str, Any]     	=   AutoModelForMaskedImageModeling.from_pretrained(
						    model_args.model_name_or_path      ,from_tf=bool(".ckpt" in model_args.model_name_or_path		)      ,config=A__      ,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"		)
						UpperCAmelCase_	:					List[str]     	=   AutoModelForMaskedImageModeling.from_config(A__		)

			if training_args.do_train:
						UpperCAmelCase_	:					List[str]     	=   ds["train"].column_names
			else:
						UpperCAmelCase_	:					str     	=   ds["validation"].column_names

			if data_args.image_column_name is not None:
						UpperCAmelCase_	:					Any     	=   data_args.image_column_name
			elif "image" in column_names:
						UpperCAmelCase_	:					Tuple     	=   "image"
			elif "img" in column_names:
						UpperCAmelCase_	:					List[str]     	=   "img"
			else:
						UpperCAmelCase_	:					Optional[Any]     	=   column_names[0]

			# transformations as done in original SimMIM paper
			# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
			UpperCAmelCase_	:					Optional[int]     	=   Compose(
			    [
			        Lambda(lambda A__		: img.convert("RGB"		) if img.mode != "RGB" else img		),
			        RandomResizedCrop(model_args.image_size      ,scale=(0.67, 1.0)      ,ratio=(3.0 / 4.0, 4.0 / 3.0)		),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean      ,std=image_processor.image_std		),
			    ]		)

			# create mask generator
			UpperCAmelCase_	:					Optional[Any]     	=   MaskGenerator(
			    input_size=model_args.image_size      ,mask_patch_size=data_args.mask_patch_size      ,model_patch_size=model_args.patch_size      ,mask_ratio=data_args.mask_ratio      ,)

			def preprocess_images(A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   [transforms(A__		) for image in examples[image_column_name]]
						UpperCAmelCase_	:					Any     	=   [mask_generator() for i in range(len(examples[image_column_name]		)		)]

						return examples

			if training_args.do_train:
						if "train" not in ds:
									raise ValueError("--do_train requires a train dataset"		)
						if data_args.max_train_samples is not None:
									UpperCAmelCase_	:					Optional[Any]     	=   ds["train"].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
						# Set the training transforms
						ds["train"].set_transform(A__		)

			if training_args.do_eval:
						if "validation" not in ds:
									raise ValueError("--do_eval requires a validation dataset"		)
						if data_args.max_eval_samples is not None:
									UpperCAmelCase_	:					List[str]     	=   (
									    ds["validation"].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
									)
						# Set the validation transforms
						ds["validation"].set_transform(A__		)

			# Initialize our trainer
			UpperCAmelCase_	:					List[Any]     	=   Trainer(
			    model=A__      ,args=A__      ,train_dataset=ds["train"] if training_args.do_train else None      ,eval_dataset=ds["validation"] if training_args.do_eval else None      ,tokenizer=A__      ,data_collator=A__      ,)

			# Training
			if training_args.do_train:
						UpperCAmelCase_	:					List[str]     	=   None
						if training_args.resume_from_checkpoint is not None:
									UpperCAmelCase_	:					int     	=   training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									UpperCAmelCase_	:					int     	=   last_checkpoint
						UpperCAmelCase_	:					Any     	=   trainer.train(resume_from_checkpoint=A__		)
						trainer.save_model()
						trainer.log_metrics("train"      ,train_result.metrics		)
						trainer.save_metrics("train"      ,train_result.metrics		)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						UpperCAmelCase_	:					Optional[Any]     	=   trainer.evaluate()
						trainer.log_metrics("eval"      ,A__		)
						trainer.save_metrics("eval"      ,A__		)

			# Write model card and (optionally) push to hub
			UpperCAmelCase_	:					int     	=   {
			    "finetuned_from": model_args.model_name_or_path,
			    "tasks": "masked-image-modeling",
			    "dataset": data_args.dataset_name,
			    "tags": ["masked-image-modeling"],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**A__		)
			else:
						trainer.create_model_card(**A__		)


if __name__ == "__main__":
			main()











"""simple docstring"""
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.distilbert.modeling_flax_distilbert import (
			    FlaxDistilBertForMaskedLM,
			    FlaxDistilBertForMultipleChoice,
			    FlaxDistilBertForQuestionAnswering,
			    FlaxDistilBertForSequenceClassification,
			    FlaxDistilBertForTokenClassification,
			    FlaxDistilBertModel,
			)







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					int      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Union[str, Any]=13      ,      lowerCAmelCase_   :					Union[str, Any]=7      ,      lowerCAmelCase_   :					Optional[Any]=True      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					Any=True      ,      lowerCAmelCase_   :					Tuple=True      ,      lowerCAmelCase_   :					Tuple=99      ,      lowerCAmelCase_   :					str=32      ,      lowerCAmelCase_   :					Optional[int]=5      ,      lowerCAmelCase_   :					List[Any]=4      ,      lowerCAmelCase_   :					List[Any]=37      ,      lowerCAmelCase_   :					str="gelu"      ,      lowerCAmelCase_   :					Optional[Any]=0.1      ,      lowerCAmelCase_   :					Union[str, Any]=0.1      ,      lowerCAmelCase_   :					List[Any]=512      ,      lowerCAmelCase_   :					Dict=16      ,      lowerCAmelCase_   :					int=2      ,      lowerCAmelCase_   :					Union[str, Any]=0.0_2      ,      lowerCAmelCase_   :					Optional[Any]=4      ,      )		->     Tuple:
				UpperCAmelCase_	:					Optional[Any]     	=   parent
				UpperCAmelCase_	:					Tuple     	=   batch_size
				UpperCAmelCase_	:					str     	=   seq_length
				UpperCAmelCase_	:					Optional[int]     	=   is_training
				UpperCAmelCase_	:					Dict     	=   use_attention_mask
				UpperCAmelCase_	:					int     	=   use_token_type_ids
				UpperCAmelCase_	:					Tuple     	=   use_labels
				UpperCAmelCase_	:					Tuple     	=   vocab_size
				UpperCAmelCase_	:					Union[str, Any]     	=   hidden_size
				UpperCAmelCase_	:					int     	=   num_hidden_layers
				UpperCAmelCase_	:					List[str]     	=   num_attention_heads
				UpperCAmelCase_	:					str     	=   intermediate_size
				UpperCAmelCase_	:					List[str]     	=   hidden_act
				UpperCAmelCase_	:					Tuple     	=   hidden_dropout_prob
				UpperCAmelCase_	:					Optional[Any]     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					Optional[int]     	=   max_position_embeddings
				UpperCAmelCase_	:					Optional[Any]     	=   type_vocab_size
				UpperCAmelCase_	:					Dict     	=   type_sequence_label_size
				UpperCAmelCase_	:					List[Any]     	=   initializer_range
				UpperCAmelCase_	:					Any     	=   num_choices





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size		)

				UpperCAmelCase_	:					Tuple     	=   None
				if self.use_attention_mask:
							UpperCAmelCase_	:					Dict     	=   random_attention_mask([self.batch_size, self.seq_length]		)

				UpperCAmelCase_	:					Any     	=   DistilBertConfig(
				    vocab_size=self.vocab_size      ,      dim=self.hidden_size      ,      n_layers=self.num_hidden_layers      ,      n_heads=self.num_attention_heads      ,      hidden_dim=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      dropout=self.hidden_dropout_prob      ,      attention_dropout=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      initializer_range=self.initializer_range      ,      tie_weights_=lowerCAmelCase_      ,      )

				return config, input_ids, attention_mask





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Any     	=   self.prepare_config_and_inputs()
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   config_and_inputs
				UpperCAmelCase_	:					Optional[Any]     	=   {"input_ids": input_ids, "attention_mask": attention_mask}
				return config, inputs_dict







@require_flax
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   FlaxDistilBertModelTester(self		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[str]:
				for model_class_name in self.all_model_classes:
							UpperCAmelCase_	:					str     	=   model_class_name.from_pretrained("distilbert-base-uncased"		)
							UpperCAmelCase_	:					List[str]     	=   model(np.ones((1, 1)		)		)
							self.assertIsNotNone(lowerCAmelCase_		)







@require_flax
class      UpperCamelCase_			(unittest.TestCase	):





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Optional[int]:
				UpperCAmelCase_	:					Any     	=   FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"		)
				UpperCAmelCase_	:					int     	=   np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]		)
				UpperCAmelCase_	:					List[str]     	=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
				UpperCAmelCase_	:					Any     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)[0]
				UpperCAmelCase_	:					Union[str, Any]     	=   (1, 11, 768)
				self.assertEqual(output.shape      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]		)

				self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]      ,      lowerCAmelCase_      ,      atol=1e-4		)		)



