
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
__magic_name__  							=	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''')

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




@dataclass
class        _SCREAMING_SNAKE_CASE	:
      _A    :		Optional[str]     						=  field(
          default='cifar10'							,       metadata={'help': 'Name of a dataset from the datasets package'}			)
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}							,       )
      _A    :		Optional[str]     						=  field(default=__UpperCamelCase							,       metadata={'help': 'A folder containing the training data.'}			)
      _A    :		Optional[str]     						=  field(default=__UpperCamelCase							,       metadata={'help': 'A folder containing the validation data.'}			)
      _A    :		Optional[float]     						=  field(
          default=0.1_5							,       metadata={'help': 'Percent to split off of train for validation.'}			)
      _A    :		int     						=  field(default=32							,       metadata={'help': 'The size of the square patches to use for masking.'}			)
      _A    :		float     						=  field(
          default=0.6							,       metadata={'help': 'Percentage of patches to mask.'}							,       )
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of training examples to this '
                  'value if set.'
              )
          }							,       )
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                  'value if set.'
              )
          }							,       )
      def A_      (      self			):
          snake_case__    =					{}
          if self.train_dir is not None:
              snake_case__    =					self.train_dir
          if self.validation_dir is not None:
              snake_case__    =					self.validation_dir
          snake_case__    =					data_files if data_files else None





@dataclass
class        _SCREAMING_SNAKE_CASE	:
      _A    :		str     						=  field(
          default=__UpperCamelCase							,       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.'
              )
          }							,       )
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__UpperCamelCase			)}							,       )
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
      _A    :		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'
              )
          }							,       )
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}							,       )
      _A    :		str     						=  field(
          default='main'							,       metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							,       )
      _A    :		str     						=  field(default=__UpperCamelCase							,       metadata={'help': 'Name or path of preprocessor config.'}			)
      _A    :		bool     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                  'with private models).'
              )
          }							,       )
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
              )
          }							,       )
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
              )
          }							,       )
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Stride to use for the encoder.'}							,       )





class        _SCREAMING_SNAKE_CASE	:
      def __init__(      self		,						lowerCamelCase=1_92		,						lowerCamelCase=32		,						lowerCamelCase=4		,						lowerCamelCase=0.6			):
          snake_case__    =					input_size
          snake_case__    =					mask_patch_size
          snake_case__    =					model_patch_size
          snake_case__    =					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"			)

          snake_case__    =					self.input_size // self.mask_patch_size
          snake_case__    =					self.mask_patch_size // self.model_patch_size

          snake_case__    =					self.rand_size**2
          snake_case__    =					int(np.ceil(self.token_count * self.mask_ratio			)			)



      def __call__(      self			):
          snake_case__    =					np.random.permutation(self.token_count			)[: self.mask_count]
          snake_case__    =					np.zeros(self.token_count		,						dtype=lowerCamelCase			)
          snake_case__    =					1

          snake_case__    =					mask.reshape((self.rand_size, self.rand_size)			)
          snake_case__    =					mask.repeat(self.scale		,						axis=0			).repeat(self.scale		,						axis=1			)

          return torch.tensor(mask.flatten()			)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					torch.stack([example["pixel_values"] for example in examples]				)
    snake_case__    =					torch.stack([example["mask"] for example in examples]				)
    return {"pixel_values": pixel_values, "bool_masked_pos": mask}





def 						SCREAMING_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.

    snake_case__    =					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.
        snake_case__    ,       snake_case__    ,       snake_case__    =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
    else:
        snake_case__    ,       snake_case__    ,       snake_case__    =					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"     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)

    # 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()

    snake_case__    =					training_args.get_process_log_level()
    logger.setLevel(__lowerCAmelCase				)
    transformers.utils.logging.set_verbosity(__lowerCAmelCase				)
    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.
    snake_case__    =					None
    if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
        snake_case__    =					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.
    snake_case__    =					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.
    snake_case__    =					None if "validation" in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split     ,		__lowerCAmelCase				) and data_args.train_val_split > 0.0:
        snake_case__    =					ds["train"].train_test_split(data_args.train_val_split				)
        snake_case__    =					split["train"]
        snake_case__    =					split["test"]

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    snake_case__    =					{
        "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:
        snake_case__    =					AutoConfig.from_pretrained(model_args.config_name_or_path     ,		**__lowerCAmelCase				)
    elif model_args.model_name_or_path:
        snake_case__    =					AutoConfig.from_pretrained(model_args.model_name_or_path     ,		**__lowerCAmelCase				)
    else:
        snake_case__    =					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(__lowerCAmelCase     ,		"decoder_type"				):
        snake_case__    =					"simmim"

    # adapt config
    snake_case__    =					model_args.image_size if model_args.image_size is not None else config.image_size
    snake_case__    =					model_args.patch_size if model_args.patch_size is not None else config.patch_size
    snake_case__    =					(
        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:
        snake_case__    =					AutoImageProcessor.from_pretrained(model_args.image_processor_name     ,		**__lowerCAmelCase				)
    elif model_args.model_name_or_path:
        snake_case__    =					AutoImageProcessor.from_pretrained(model_args.model_name_or_path     ,		**__lowerCAmelCase				)
    else:
        snake_case__    =					{
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        snake_case__    =					IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    # create model
    if model_args.model_name_or_path:
        snake_case__    =					AutoModelForMaskedImageModeling.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     ,		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"				)
        snake_case__    =					AutoModelForMaskedImageModeling.from_config(__lowerCAmelCase				)

    if training_args.do_train:
        snake_case__    =					ds["train"].column_names
    else:
        snake_case__    =					ds["validation"].column_names

    if data_args.image_column_name is not None:
        snake_case__    =					data_args.image_column_name
    elif "image" in column_names:
        snake_case__    =					"image"
    elif "img" in column_names:
        snake_case__    =					"img"
    else:
        snake_case__    =					column_names[0]

    # transformations as done in original SimMIM paper
    # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
    snake_case__    =					Compose(
        [
            Lambda(lambda __lowerCAmelCase				: 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
    snake_case__    =					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(__lowerCAmelCase				):
        snake_case__    =					[transforms(__lowerCAmelCase				) for image in examples[image_column_name]]
        snake_case__    =					[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:
            snake_case__    =					ds["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
        # Set the training transforms
        ds["train"].set_transform(__lowerCAmelCase				)

    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:
            snake_case__    =					(
                ds["validation"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
            )
        # Set the validation transforms
        ds["validation"].set_transform(__lowerCAmelCase				)

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

    # Training
    if training_args.do_train:
        snake_case__    =					None
        if training_args.resume_from_checkpoint is not None:
            snake_case__    =					training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            snake_case__    =					last_checkpoint
        snake_case__    =					trainer.train(resume_from_checkpoint=__lowerCAmelCase				)
        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:
        snake_case__    =					trainer.evaluate()
        trainer.log_metrics("eval"     ,		__lowerCAmelCase				)
        trainer.save_metrics("eval"     ,		__lowerCAmelCase				)

    # Write model card and (optionally) push to hub
    snake_case__    =					{
        "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(**__lowerCAmelCase				)
    else:
        trainer.create_model_card(**__lowerCAmelCase				)


if __name__ == "__main__":
     main()





import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 (
         TFEfficientFormerForImageClassification,
         TFEfficientFormerForImageClassificationWithTeacher,
         TFEfficientFormerModel,
     )
     from transformers.models.efficientformer.modeling_tf_efficientformer import (
         TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )


if is_vision_available():
     from PIL import Image

     from transformers import EfficientFormerImageProcessor




class        _SCREAMING_SNAKE_CASE	:
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase = 13		,						lowerCamelCase = 64		,						lowerCamelCase = 2		,						lowerCamelCase = 3		,						lowerCamelCase = 3		,						lowerCamelCase = True		,						lowerCamelCase = True		,						lowerCamelCase = 1_28		,						lowerCamelCase=[16, 32, 64, 1_28]		,						lowerCamelCase = 7		,						lowerCamelCase = 4		,						lowerCamelCase = 37		,						lowerCamelCase = "gelu"		,						lowerCamelCase = 0.1		,						lowerCamelCase = 0.1		,						lowerCamelCase = 10		,						lowerCamelCase = 0.0_2		,						lowerCamelCase = 2		,						lowerCamelCase = 1		,						lowerCamelCase = 1_28		,						lowerCamelCase = [2, 2, 2, 2]		,						lowerCamelCase = 2		,						lowerCamelCase = 2		,						):
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					image_size
          snake_case__    =					patch_size
          snake_case__    =					num_channels
          snake_case__    =					is_training
          snake_case__    =					use_labels
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					type_sequence_label_size
          snake_case__    =					initializer_range
          snake_case__    =					encoder_stride
          snake_case__    =					num_attention_outputs
          snake_case__    =					embed_dim
          snake_case__    =					embed_dim + 1
          snake_case__    =					resolution
          snake_case__    =					depths
          snake_case__    =					hidden_sizes
          snake_case__    =					dim
          snake_case__    =					mlp_expansion_ratio
      def A_      (      self			):
          snake_case__    =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

          snake_case__    =					self.get_config()

          return config, pixel_values, labels
      def A_      (      self			):
          return EfficientFormerConfig(
              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=lowerCamelCase		,						initializer_range=self.initializer_range		,						encoder_stride=self.encoder_stride		,						resolution=self.resolution		,						depths=self.depths		,						hidden_sizes=self.hidden_sizes		,						dim=self.dim		,						mlp_expansion_ratio=self.mlp_expansion_ratio		,						)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					TFEfficientFormerModel(config=lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase		,						training=lowerCamelCase			)
          self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					self.type_sequence_label_size
          snake_case__    =					TFEfficientFormerForImageClassification(lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase		,						labels=lowerCamelCase		,						training=lowerCamelCase			)
          self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.type_sequence_label_size)			)

          # test greyscale images
          snake_case__    =					1
          snake_case__    =					TFEfficientFormerForImageClassification(lowerCamelCase			)

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



      def A_      (      self			):
          snake_case__    =					self.prepare_config_and_inputs()
          snake_case__    ,       snake_case__    ,       snake_case__    =					config_and_inputs
          snake_case__    =					{"pixel_values": pixel_values}
          return config, inputs_dict





@require_tf
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       __UpperCamelCase							,       unittest.TestCase			):
      _A    :		str     						=  (
          (
              TFEfficientFormerModel,
              TFEfficientFormerForImageClassificationWithTeacher,
              TFEfficientFormerForImageClassification,
          )
          if is_tf_available()
          else ()
      )
      _A    :		List[str]     						=  (
          {
              'feature-extraction': TFEfficientFormerModel,
              'image-classification': (
                  TFEfficientFormerForImageClassification,
                  TFEfficientFormerForImageClassificationWithTeacher,
              ),
          }
          if is_tf_available()
          else {}
      )

      _A    :		Optional[Any]     						=  False

      _A    :		List[Any]     						=  False
      _A    :		Tuple     						=  False
      _A    :		List[Any]     						=  False
      _A    :		Any     						=  False
      def A_      (      self			):
          snake_case__    =					TFEfficientFormerModelTester(self			)
          snake_case__    =					ConfigTester(
              self		,						config_class=lowerCamelCase		,						has_text_modality=lowerCamelCase		,						hidden_size=37			)
      def A_      (      self			):
          self.config_tester.run_common_tests()
      @unittest.skip(reason="EfficientFormer does not use inputs_embeds"			)
      def A_      (      self			):
          pass
      @unittest.skip(reason="EfficientFormer does not support input and output embeddings"			)
      def A_      (      self			):
          pass
      def A_      (      self			):
          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case__    =					["pixel_values"]
              self.assertListEqual(arg_names[:1]		,						lowerCamelCase			)
      def A_      (      self			):
          def check_hidden_states_output(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
              snake_case__    =					model_class(lowerCamelCase			)

              snake_case__    =					model(**self._prepare_for_class(lowerCamelCase		,						lowerCamelCase			)		,						training=lowerCamelCase			)
              snake_case__    =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              snake_case__    =					getattr(
                  self.model_tester		,						"expected_num_hidden_layers"		,						self.model_tester.num_hidden_layers + 1			)
              self.assertEqual(len(lowerCamelCase			)		,						lowerCamelCase			)

              if hasattr(self.model_tester		,						"encoder_seq_length"			):
                  snake_case__    =					self.model_tester.encoder_seq_length
                  if hasattr(self.model_tester		,						"chunk_length"			) and self.model_tester.chunk_length > 1:
                      snake_case__    =					seq_length * self.model_tester.chunk_length
              else:
                  snake_case__    =					self.model_tester.seq_length

              self.assertListEqual(
                  list(hidden_states[-1].shape[-2:]			)		,						[seq_length, self.model_tester.hidden_size]		,						)

              if config.is_encoder_decoder:
                  snake_case__    =					outputs.decoder_hidden_states

                  self.asseretIsInstance(lowerCamelCase		,						(list, tuple)			)
                  self.assertEqual(len(lowerCamelCase			)		,						lowerCamelCase			)
                  snake_case__    =					getattr(self.model_tester		,						"seq_length"		,						lowerCamelCase			)
                  snake_case__    =					getattr(self.model_tester		,						"decoder_seq_length"		,						lowerCamelCase			)

                  self.assertListEqual(
                      list(hidden_states[-1].shape[-2:]			)		,						[decoder_seq_length, self.model_tester.hidden_size]		,						)

          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__    =					True
              check_hidden_states_output(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)

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

              check_hidden_states_output(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase=False			):
          snake_case__    =					super()._prepare_for_class(lowerCamelCase		,						lowerCamelCase		,						return_labels=lowerCamelCase			)

          if return_labels:
              if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                  del inputs_dict["labels"]

          return inputs_dict
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase			)
      @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase			)
      @slow
      def A_      (      self			):
          for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__    =					TFEfficientFormerModel.from_pretrained(lowerCamelCase			)
              self.assertIsNotNone(lowerCamelCase			)
      def A_      (      self			):
          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__    =					True

          snake_case__    =					getattr(self.model_tester		,						"seq_length"		,						lowerCamelCase			)
          snake_case__    =					getattr(self.model_tester		,						"encoder_seq_length"		,						lowerCamelCase			)
          snake_case__    =					getattr(self.model_tester		,						"key_length"		,						lowerCamelCase			)
          snake_case__    =					getattr(self.model_tester		,						"chunk_length"		,						lowerCamelCase			)

          if chunk_length is not None and hasattr(self.model_tester		,						"num_hashes"			):
              snake_case__    =					encoder_seq_length * self.model_tester.num_hashes

          for model_class in self.all_model_classes:
              snake_case__    =					True
              snake_case__    =					False
              snake_case__    =					True
              snake_case__    =					model_class(lowerCamelCase			)

              snake_case__    =					model(**self._prepare_for_class(lowerCamelCase		,						lowerCamelCase			)		,						training=lowerCamelCase			)
              snake_case__    =					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
              self.assertEqual(len(lowerCamelCase			)		,						self.model_tester.num_attention_outputs			)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              snake_case__    =					True
              snake_case__    =					model_class(lowerCamelCase			)
              snake_case__    =					model(**self._prepare_for_class(lowerCamelCase		,						lowerCamelCase			)		,						training=lowerCamelCase			)

              snake_case__    =					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
              self.assertEqual(len(lowerCamelCase			)		,						self.model_tester.num_attention_outputs			)

              if chunk_length is not None:
                  self.assertListEqual(
                      list(attentions[0].shape[-4:]			)		,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]		,						)
              else:
                  self.assertListEqual(
                      list(attentions[0].shape[-3:]			)		,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]		,						)



      def A_      (      self			):
          # We use a simplified version of this test for EfficientFormer because it requires training=False
          # and Keras refuses to let us force that during functional construction
          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              # Prepare our model
              snake_case__    =					model_class(lowerCamelCase			)
              # These are maximally general inputs for the model, with multiple None dimensions
              # Hopefully this will catch any conditionals that fail for flexible shapes
              snake_case__    =					{
                  key: tf.keras.Input(shape=val.shape[1:]		,						dtype=val.dtype		,						name=lowerCamelCase			)
                  for key, val in model.input_signature.items()
                  if key in model.dummy_inputs
              }
              snake_case__    =					model(lowerCamelCase			)
              self.assertTrue(outputs_dict is not None			)





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    return image





@require_tf
@require_vision
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      @cached_property
      def A_      (      self			):
          return (
              EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"			)
              if is_vision_available()
              else None
          )
      @slow
      def A_      (      self			):
          snake_case__    =					TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"			)
          snake_case__    =					self.default_image_processor
          snake_case__    =					prepare_img()
          snake_case__    =					image_processor(images=lowerCamelCase		,						return_tensors="tf"			)
          # forward pass
          snake_case__    =					model(**lowerCamelCase		,						training=lowerCamelCase			)
          # verify the logits
          snake_case__    =					tf.TensorShape((1, 10_00)			)
          self.assertEqual(outputs.logits.shape		,						lowerCamelCase			)
          snake_case__    =					tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]			)
          self.assertTrue(np.allclose(outputs.logits[0, :3]		,						lowerCamelCase		,						atol=1e-4			)			)



      @slow
      def A_      (      self			):
          snake_case__    =					TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
              "snap-research/efficientformer-l1-300"			)
          snake_case__    =					self.default_image_processor
          snake_case__    =					prepare_img()
          snake_case__    =					image_processor(images=lowerCamelCase		,						return_tensors="tf"			)
          # forward pass
          snake_case__    =					model(**lowerCamelCase		,						training=lowerCamelCase			)
          # verify the logits
          snake_case__    =					tf.TensorShape((1, 10_00)			)
          self.assertEqual(outputs.logits.shape		,						lowerCamelCase			)
          snake_case__    =					tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]			)
          self.assertTrue(np.allclose(outputs.logits[0, :3]		,						lowerCamelCase		,						atol=1e-4			)			)



