


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE       :      Optional[int]												= 16
SCREAMING_SNAKE_CASE       :      List[Any]												= 32



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ = 16 )    ->						Dict:
    _lowercase       :							Any      		=		AutoTokenizer.from_pretrained('bert-base-cased' )
    _lowercase       :							int      		=		load_dataset('glue'							, 'mrpc' )

    def tokenize_function(lowerCamelCase_ ):
        # max_length=None => use the model max length (it's actually the default)
        _lowercase       :							Any      		=		tokenizer(examples['sentence1']							, examples['sentence2']							, truncation=lowerCamelCase_							, max_length=lowerCamelCase_ )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        _lowercase       :							int      		=		datasets.map(
            lowerCamelCase_							, batched=lowerCamelCase_							, remove_columns=['idx', 'sentence1', 'sentence2']							, )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    _lowercase       :							Any      		=		tokenized_datasets.rename_column('label'							, 'labels' )

    def collate_fn(lowerCamelCase_ ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        _lowercase       :							Optional[Any]      		=		128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            _lowercase       :							Tuple      		=		16
        elif accelerator.mixed_precision != "no":
            _lowercase       :							Tuple      		=		8
        else:
            _lowercase       :							Optional[int]      		=		None

        return tokenizer.pad(
            lowerCamelCase_							, padding='longest'							, max_length=lowerCamelCase_							, pad_to_multiple_of=lowerCamelCase_							, return_tensors='pt'							, )

    # Instantiate dataloaders.
    _lowercase       :							Dict      		=		DataLoader(
        tokenized_datasets['train']							, shuffle=lowerCamelCase_							, collate_fn=lowerCamelCase_							, batch_size=lowerCamelCase_ )
    _lowercase       :							Optional[Any]      		=		DataLoader(
        tokenized_datasets['validation']							, shuffle=lowerCamelCase_							, collate_fn=lowerCamelCase_							, batch_size=lowerCamelCase_ )

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 SCREAMING_SNAKE_CASE       :      Tuple												= mocked_dataloaders  # noqa: F811



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'							, lowerCamelCase_ ) == "1":
        _lowercase       :							int      		=		2
    # Initialize accelerator
    _lowercase       :							List[Any]      		=		Accelerator(cpu=args.cpu							, mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowercase       :							Dict      		=		config['lr']
    _lowercase       :							Optional[Any]      		=		int(config['num_epochs'] )
    _lowercase       :							Optional[Any]      		=		int(config['seed'] )
    _lowercase       :							List[str]      		=		int(config['batch_size'] )

    _lowercase       :							int      		=		evaluate.load('glue'							, 'mrpc' )

    # If the batch size is too big we use gradient accumulation
    _lowercase       :							Optional[int]      		=		1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        _lowercase       :							Any      		=		batch_size // MAX_GPU_BATCH_SIZE
        _lowercase       :							List[str]      		=		MAX_GPU_BATCH_SIZE

    set_seed(lowerCamelCase_ )
    _lowercase		,				_lowercase       :							Any      		=		get_dataloaders(lowerCamelCase_							, lowerCamelCase_ )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase       :							Union[str, Any]      		=		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'							, return_dict=lowerCamelCase_ )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    _lowercase       :							List[str]      		=		model.to(accelerator.device )

    # Instantiate optimizer
    _lowercase       :							int      		=		AdamW(params=model.parameters()							, lr=lowerCamelCase_ )

    # Instantiate scheduler
    _lowercase       :							Union[str, Any]      		=		get_linear_schedule_with_warmup(
        optimizer=lowerCamelCase_							, num_warmup_steps=100							, num_training_steps=(len(lowerCamelCase_ ) * num_epochs) // gradient_accumulation_steps							, )

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    _lowercase		,				_lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							int      		=		accelerator.prepare(
        lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

    # Now we train the model
    for epoch in range(lowerCamelCase_ ):
        model.train()
        for step, batch in enumerate(lowerCamelCase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            _lowercase       :							List[Any]      		=		model(**lowerCamelCase_ )
            _lowercase       :							Tuple      		=		outputs.loss
            _lowercase       :							int      		=		loss / gradient_accumulation_steps
            accelerator.backward(lowerCamelCase_ )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        _lowercase       :							Tuple      		=		0
        for step, batch in enumerate(lowerCamelCase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            with torch.no_grad():
                _lowercase       :							List[str]      		=		model(**lowerCamelCase_ )
            _lowercase       :							Union[str, Any]      		=		outputs.logits.argmax(dim=-1 )
            _lowercase		,				_lowercase       :							Optional[Any]      		=		accelerator.gather((predictions, batch['labels']) )
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(lowerCamelCase_ ) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    _lowercase       :							List[Any]      		=		predictions[: len(eval_dataloader.dataset ) - samples_seen]
                    _lowercase       :							Dict      		=		references[: len(eval_dataloader.dataset ) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=lowerCamelCase_							, references=lowerCamelCase_							, )

        _lowercase       :							List[str]      		=		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''							, lowerCamelCase_ )



def 				UpperCamelCase_(      )    ->						Union[str, Any]:
    _lowercase       :							List[Any]      		=		argparse.ArgumentParser(description='Simple example of training script.' )
    parser.add_argument(
        '--mixed_precision'							, type=lowerCamelCase_							, default=lowerCamelCase_							, choices=['no', 'fp16', 'bf16', 'fp8']							, help='Whether to use mixed precision. Choose'
        'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
        'and an Nvidia Ampere GPU.'							, )
    parser.add_argument('--cpu'							, action='store_true'							, help='If passed, will train on the CPU.' )
    _lowercase       :							str      		=		parser.parse_args()
    _lowercase       :							Dict      		=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(lowerCamelCase_							, lowerCamelCase_ )


if __name__ == "__main__":
 main()






'''simple docstring'''

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      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase = 13			,  UpperCamelCase = 64			,  UpperCamelCase = 2			,  UpperCamelCase = 3			,  UpperCamelCase = 3			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = 128			,  UpperCamelCase=[16, 32, 64, 128]			,  UpperCamelCase = 7			,  UpperCamelCase = 4			,  UpperCamelCase = 37			,  UpperCamelCase = "gelu"			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 10			,  UpperCamelCase = 0.02			,  UpperCamelCase = 2			,  UpperCamelCase = 1			,  UpperCamelCase = 128			,  UpperCamelCase = [2, 2, 2, 2]			,  UpperCamelCase = 2			,  UpperCamelCase = 2			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  image_size
   lowerCamelCase_      =  patch_size
   lowerCamelCase_      =  num_channels
   lowerCamelCase_      =  is_training
   lowerCamelCase_      =  use_labels
   lowerCamelCase_      =  hidden_size
   lowerCamelCase_      =  num_hidden_layers
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  intermediate_size
   lowerCamelCase_      =  hidden_act
   lowerCamelCase_      =  hidden_dropout_prob
   lowerCamelCase_      =  attention_probs_dropout_prob
   lowerCamelCase_      =  type_sequence_label_size
   lowerCamelCase_      =  initializer_range
   lowerCamelCase_      =  encoder_stride
   lowerCamelCase_      =  num_attention_outputs
   lowerCamelCase_      =  embed_dim
   lowerCamelCase_      =  embed_dim + 1
   lowerCamelCase_      =  resolution
   lowerCamelCase_      =  depths
   lowerCamelCase_      =  hidden_sizes
   lowerCamelCase_      =  dim
   lowerCamelCase_      =  mlp_expansion_ratio






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

   lowerCamelCase_      =  self.get_config()

   return config, pixel_values, labels






  def   snake_case		(    self     ):







   """simple docstring"""
   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=UpperCamelCase			,  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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerModel(config=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  training=UpperCamelCase     )
   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.type_sequence_label_size
   lowerCamelCase_      =  TFEfficientFormerForImageClassification(UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  labels=UpperCamelCase			,  training=UpperCamelCase     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.type_sequence_label_size)     )

   # test greyscale images
   lowerCamelCase_      =  1
   lowerCamelCase_      =  TFEfficientFormerForImageClassification(UpperCamelCase     )

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







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  config_and_inputs
   lowerCamelCase_      =  {"pixel_values": pixel_values}
   return config, inputs_dict





@require_tf
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (
      (
          TFEfficientFormerModel,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerForImageClassification,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase		    =  (
      {
          "feature-extraction": TFEfficientFormerModel,
          "image-classification": (
              TFEfficientFormerForImageClassification,
              TFEfficientFormerForImageClassificationWithTeacher,
          ),
      }
      if is_tf_available()
      else {}
  )

  _lowerCamelCase		    =  False

  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerModelTester(self     )
   lowerCamelCase_      =  ConfigTester(
       self			,  config_class=UpperCamelCase			,  has_text_modality=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  @unittest.skip(reason="EfficientFormer does not use inputs_embeds"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @unittest.skip(reason="EfficientFormer does not support input and output embeddings"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_      =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   def check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  model_class(UpperCamelCase     )

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

    lowerCamelCase_      =  getattr(
        self.model_tester			,  "expected_num_hidden_layers"			,  self.model_tester.num_hidden_layers + 1     )
    self.assertEqual(len(UpperCamelCase     )			,  UpperCamelCase     )

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

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

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

   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  True
    check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

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

    check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=False     ):







   """simple docstring"""
   lowerCamelCase_      =  super()._prepare_for_class(UpperCamelCase			,  UpperCamelCase			,  return_labels=UpperCamelCase     )

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

   return inputs_dict






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase     )






  @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCamelCase     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFEfficientFormerModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_      =  True

   lowerCamelCase_      =  getattr(self.model_tester			,  "seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "encoder_seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "key_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "chunk_length"			,  UpperCamelCase     )

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

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  True
    lowerCamelCase_      =  False
    lowerCamelCase_      =  True
    lowerCamelCase_      =  model_class(UpperCamelCase     )

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

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

    lowerCamelCase_      =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
    self.assertEqual(len(UpperCamelCase     )			,  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   snake_case		(    self     ):







   """simple docstring"""
   # 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
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

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





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







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





  """simple docstring"""




  @cached_property
  def   snake_case		(    self     ):







   """simple docstring"""
   return (
       EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"     )
       if is_vision_available()
       else None
   )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"     )
   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  image_processor(images=UpperCamelCase			,  return_tensors="tf"     )
   # forward pass
   lowerCamelCase_      =  model(**UpperCamelCase			,  training=UpperCamelCase     )
   # verify the logits
   lowerCamelCase_      =  tf.TensorShape((1, 1000)     )
   self.assertEqual(outputs.logits.shape			,  UpperCamelCase     )
   lowerCamelCase_      =  tf.constant([-0.0_555, 0.4_825, -0.0_852]     )
   self.assertTrue(np.allclose(outputs.logits[0, :3]			,  UpperCamelCase			,  atol=1e-4     )     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
       "snap-research/efficientformer-l1-300"     )
   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  image_processor(images=UpperCamelCase			,  return_tensors="tf"     )
   # forward pass
   lowerCamelCase_      =  model(**UpperCamelCase			,  training=UpperCamelCase     )
   # verify the logits
   lowerCamelCase_      =  tf.TensorShape((1, 1000)     )
   self.assertEqual(outputs.logits.shape			,  UpperCamelCase     )
   lowerCamelCase_      =  tf.constant([-0.1_312, 0.4_353, -1.0_499]     )
   self.assertTrue(np.allclose(outputs.logits[0, :3]			,  UpperCamelCase			,  atol=1e-4     )     )



