


'''simple docstring'''

import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_  :				Tuple						= 16
a_  :				Tuple						= 32





def __snake_case					(      UpperCAmelCase_						:						Accelerator					,				UpperCAmelCase_						:						int = 16					,				UpperCAmelCase_						:						str = "bert-base-cased"		):
 lowerCamelCase_      =  AutoTokenizer.from_pretrained(UpperCAmelCase_		)
 lowerCamelCase_      =  load_dataset("glue"					,				"mrpc"		)

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

 # Apply the method we just defined to all the examples in all the splits of the dataset
 lowerCamelCase_      =  datasets.map(
     UpperCAmelCase_					,				batched=UpperCAmelCase_					,				remove_columns=["idx", "sentence1", "sentence2"]					,				load_from_cache_file=UpperCAmelCase_		)

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

 def collate_fn(UpperCAmelCase_						:						str		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(UpperCAmelCase_					,				padding="max_length"					,				max_length=128					,				return_tensors="pt"		)
  return tokenizer.pad(UpperCAmelCase_					,				padding="longest"					,				return_tensors="pt"		)

 # Instantiate dataloaders.
 lowerCamelCase_      =  DataLoader(
     tokenized_datasets["train"]					,				shuffle=UpperCAmelCase_					,				collate_fn=UpperCAmelCase_					,				batch_size=UpperCAmelCase_		)
 lowerCamelCase_      =  DataLoader(
     tokenized_datasets["validation"]					,				shuffle=UpperCAmelCase_					,				collate_fn=UpperCAmelCase_					,				batch_size=UpperCAmelCase_		)

 return train_dataloader, eval_dataloader





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Any		):
 # Initialize accelerator
 lowerCamelCase_      =  Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCamelCase_      =  config["lr"]
 lowerCamelCase_      =  int(config["num_epochs"]		)
 lowerCamelCase_      =  int(config["seed"]		)
 lowerCamelCase_      =  int(config["batch_size"]		)
 lowerCamelCase_      =  args.model_name_or_path

 set_seed(UpperCAmelCase_		)
 lowerCamelCase_ ,lowerCamelCase_      =  get_dataloaders(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowerCamelCase_      =  AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_					,				return_dict=UpperCAmelCase_		)

 # Instantiate optimizer
 lowerCamelCase_      =  (
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 lowerCamelCase_      =  optimizer_cls(params=model.parameters()					,				lr=UpperCAmelCase_		)

 if accelerator.state.deepspeed_plugin is not None:
  lowerCamelCase_      =  accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  lowerCamelCase_      =  1
 lowerCamelCase_      =  (len(UpperCAmelCase_		) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  lowerCamelCase_      =  get_linear_schedule_with_warmup(
      optimizer=UpperCAmelCase_					,				num_warmup_steps=0					,				num_training_steps=UpperCAmelCase_					,				)
 else:
  lowerCamelCase_      =  DummyScheduler(UpperCAmelCase_					,				total_num_steps=UpperCAmelCase_					,				warmup_num_steps=0		)

 # 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.
 lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  accelerator.prepare(
     UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 # We need to keep track of how many total steps we have iterated over
 lowerCamelCase_      =  0
 # We also need to keep track of the stating epoch so files are named properly
 lowerCamelCase_      =  0

 # Now we train the model
 lowerCamelCase_      =  evaluate.load("glue"					,				"mrpc"		)
 lowerCamelCase_      =  0
 lowerCamelCase_      =  {}
 for epoch in range(UpperCAmelCase_					,				UpperCAmelCase_		):
  model.train()
  for step, batch in enumerate(UpperCAmelCase_		):
   lowerCamelCase_      =  model(**UpperCAmelCase_		)
   lowerCamelCase_      =  outputs.loss
   lowerCamelCase_      =  loss / gradient_accumulation_steps
   accelerator.backward(UpperCAmelCase_		)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

  model.eval()
  lowerCamelCase_      =  0
  for step, batch in enumerate(UpperCAmelCase_		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device		)
   with torch.no_grad():
    lowerCamelCase_      =  model(**UpperCAmelCase_		)
   lowerCamelCase_      =  outputs.logits.argmax(dim=-1		)
   # It is slightly faster to call this once, than multiple times
   lowerCamelCase_ ,lowerCamelCase_      =  accelerator.gather(
       (predictions, batch["labels"])		)  # If we are in a multiprocess environment, the last batch has duplicates
   if accelerator.use_distributed:
    if step == len(UpperCAmelCase_		) - 1:
     lowerCamelCase_      =  predictions[: len(eval_dataloader.dataset		) - samples_seen]
     lowerCamelCase_      =  references[: len(eval_dataloader.dataset		) - samples_seen]
    else:
     samples_seen += references.shape[0]
   metric.add_batch(
       predictions=UpperCAmelCase_					,				references=UpperCAmelCase_					,				)

  lowerCamelCase_      =  metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'''epoch {epoch}:'''					,				UpperCAmelCase_		)
  lowerCamelCase_      =  eval_metric["accuracy"]

  if best_performance < eval_metric["accuracy"]:
   lowerCamelCase_      =  eval_metric["accuracy"]

 if args.performance_lower_bound is not None:
  assert (
      args.performance_lower_bound <= best_performance
  ), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

 accelerator.wait_for_everyone()
 if accelerator.is_main_process:
  with open(os.path.join(args.output_dir					,				"all_results.json"		)					,				"w"		) as f:
   json.dump(UpperCAmelCase_					,				UpperCAmelCase_		)





def __snake_case					(      ):
 lowerCamelCase_      =  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."		)
 parser.add_argument(
     "--model_name_or_path"					,				type=UpperCAmelCase_					,				default="bert-base-cased"					,				help="Path to pretrained model or model identifier from huggingface.co/models."					,				required=UpperCAmelCase_					,				)
 parser.add_argument(
     "--output_dir"					,				type=UpperCAmelCase_					,				default="."					,				help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,				)
 parser.add_argument(
     "--performance_lower_bound"					,				type=UpperCAmelCase_					,				default=UpperCAmelCase_					,				help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."					,				)
 parser.add_argument(
     "--num_epochs"					,				type=UpperCAmelCase_					,				default=3					,				help="Number of train epochs."					,				)
 lowerCamelCase_      =  parser.parse_args()
 lowerCamelCase_      =  {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
 training_function(UpperCAmelCase_					,				UpperCAmelCase_		)


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     )     )



