

import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           EfficientFormerForImageClassification,
           EfficientFormerForImageClassificationWithTeacher,
           EfficientFormerModel,
       )
       from transformers.models.efficientformer.modeling_efficientformer import (
           EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


if is_vision_available():
       from PIL import Image

       from transformers import EfficientFormerImageProcessor



class    __snake_case	:

  def __init__(       self    ,		_A    ,		_A = 13    ,		_A = 64    ,		_A = 2    ,		_A = 3    ,		_A = 3    ,		_A = True    ,		_A = True    ,		_A = 128    ,		_A=[16, 32, 64, 128]    ,		_A = 7    ,		_A = 4    ,		_A = 37    ,		_A = "gelu"    ,		_A = 0.1    ,		_A = 0.1    ,		_A = 10    ,		_A = 0.0_2    ,		_A = 2    ,		_A = 1    ,		_A = 128    ,		_A = [2, 2, 2, 2]    ,		_A = 2    ,		_A = 2    ,		):
      SCREAMING_SNAKE_CASE_						=    parent
      SCREAMING_SNAKE_CASE_						=    batch_size
      SCREAMING_SNAKE_CASE_						=    image_size
      SCREAMING_SNAKE_CASE_						=    patch_size
      SCREAMING_SNAKE_CASE_						=    num_channels
      SCREAMING_SNAKE_CASE_						=    is_training
      SCREAMING_SNAKE_CASE_						=    use_labels
      SCREAMING_SNAKE_CASE_						=    hidden_size
      SCREAMING_SNAKE_CASE_						=    num_hidden_layers
      SCREAMING_SNAKE_CASE_						=    num_attention_heads
      SCREAMING_SNAKE_CASE_						=    intermediate_size
      SCREAMING_SNAKE_CASE_						=    hidden_act
      SCREAMING_SNAKE_CASE_						=    hidden_dropout_prob
      SCREAMING_SNAKE_CASE_						=    attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_						=    type_sequence_label_size
      SCREAMING_SNAKE_CASE_						=    initializer_range
      SCREAMING_SNAKE_CASE_						=    encoder_stride
      SCREAMING_SNAKE_CASE_						=    num_attention_outputs
      SCREAMING_SNAKE_CASE_						=    embed_dim
      SCREAMING_SNAKE_CASE_						=    embed_dim + 1
      SCREAMING_SNAKE_CASE_						=    resolution
      SCREAMING_SNAKE_CASE_						=    depths
      SCREAMING_SNAKE_CASE_						=    hidden_sizes
      SCREAMING_SNAKE_CASE_						=    dim
      SCREAMING_SNAKE_CASE_						=    mlp_expansion_ratio

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

      SCREAMING_SNAKE_CASE_						=    self.get_config()
      return config, pixel_values, labels

  def     lowerCAmelCase__  (       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=_A    ,		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     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A):
      SCREAMING_SNAKE_CASE_						=    EfficientFormerModel(config=_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE_						=    model(_A)
      self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size))

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A):
      SCREAMING_SNAKE_CASE_						=    self.type_sequence_label_size
      SCREAMING_SNAKE_CASE_						=    EfficientFormerForImageClassification(_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE_						=    model(_A    ,		labels=_A)
      self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size))

      # test greyscale images
      SCREAMING_SNAKE_CASE_						=    1
      SCREAMING_SNAKE_CASE_						=    EfficientFormerForImageClassification(_A)
      model.to(_A)
      model.eval()

      SCREAMING_SNAKE_CASE_						=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      SCREAMING_SNAKE_CASE_						=    model(_A)
      self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size))


  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.prepare_config_and_inputs()
      (
          (
          SCREAMING_SNAKE_CASE_
      )				,      (
          SCREAMING_SNAKE_CASE_
      )				,      (
          SCREAMING_SNAKE_CASE_
      )				,      
      )						=    config_and_inputs
      SCREAMING_SNAKE_CASE_						=    {'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class    __snake_case	(     lowerCAmelCase__  ,			lowerCAmelCase__  ,			unittest.TestCase							):
  __lowerCAmelCase  :						List[Any]    			=	(
      (
          EfficientFormerModel,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerForImageClassification,
      )
      if is_torch_available()
      else ()
  )
  __lowerCAmelCase  :						Optional[Any]    			=	(
      {
          'feature-extraction': EfficientFormerModel,
          'image-classification': (
              EfficientFormerForImageClassification,
              EfficientFormerForImageClassificationWithTeacher,
          ),
      }
      if is_torch_available()
      else {}
  )
  __lowerCAmelCase  :						str    			=	False

  __lowerCAmelCase  :						str    			=	False
  __lowerCAmelCase  :						int    			=	False
  __lowerCAmelCase  :						Dict    			=	False

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    EfficientFormerModelTester(self)
      SCREAMING_SNAKE_CASE_						=    ConfigTester(
          self    ,		config_class=_A    ,		has_text_modality=_A    ,		hidden_size=37)

  def     lowerCAmelCase__  (       self):
      self.config_tester.run_common_tests()

  @unittest.skip(reason='EfficientFormer does not use inputs_embeds')
  def     lowerCAmelCase__  (       self):
      pass

  @unittest.skip(reason='EfficientFormer does not support input and output embeddings')
  def     lowerCAmelCase__  (       self):
      pass

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    self.model_tester.prepare_config_and_inputs_for_common()

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

          SCREAMING_SNAKE_CASE_						=    ['pixel_values']
          self.assertListEqual(arg_names[:1]    ,		_A)

  def     lowerCAmelCase__  (       self):
      def check_hidden_states_output(_A    ,		_A    ,		_A):
          SCREAMING_SNAKE_CASE_						=    model_class(_A)
          model.to(_A)
          model.eval()

          with torch.no_grad():
              SCREAMING_SNAKE_CASE_						=    model(**self._prepare_for_class(_A    ,		_A))

          SCREAMING_SNAKE_CASE_						=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          SCREAMING_SNAKE_CASE_						=    getattr(
              self.model_tester    ,		'expected_num_hidden_layers'    ,		self.model_tester.num_hidden_layers + 1)
          self.assertEqual(len(_A)    ,		_A)

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

              self.assertIsInstance(_A    ,		(list, tuple))
              self.assertEqual(len(_A)    ,		_A)
              SCREAMING_SNAKE_CASE_						=    getattr(self.model_tester    ,		'seq_length'    ,		_A)
              SCREAMING_SNAKE_CASE_						=    getattr(self.model_tester    ,		'decoder_seq_length'    ,		_A)

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

      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_						=    True
          check_hidden_states_output(_A    ,		_A    ,		_A)

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

          check_hidden_states_output(_A    ,		_A    ,		_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A=False):
      SCREAMING_SNAKE_CASE_						=    super()._prepare_for_class(_A    ,		_A    ,		return_labels=_A)

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

      return inputs_dict

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A)

  @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet')
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*_A)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A)

  def     lowerCAmelCase__  (       self):
      if not self.model_tester.is_training:
          return

      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_						=    True

      for model_class in self.all_model_classes:
          # EfficientFormerForImageClassificationWithTeacher supports inference-only
          if (
              model_class in get_values(_A)
              or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
          ):
              continue
          SCREAMING_SNAKE_CASE_						=    model_class(_A)
          model.to(_A)
          model.train()
          SCREAMING_SNAKE_CASE_						=    self._prepare_for_class(_A    ,		_A    ,		return_labels=_A)
          SCREAMING_SNAKE_CASE_						=    model(**_A).loss
          loss.backward()

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    self.model_tester.prepare_config_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_						=    [
          {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
          {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
          {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
      ]

      for model_class in self.all_model_classes:
          if (
              model_class
              not in [
                  *get_values(_A),
              ]
              or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
          ):
              continue

          for problem_type in problem_types:
              with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""):
                  SCREAMING_SNAKE_CASE_						=    problem_type['title']
                  SCREAMING_SNAKE_CASE_						=    problem_type['num_labels']

                  SCREAMING_SNAKE_CASE_						=    model_class(_A)
                  model.to(_A)
                  model.train()

                  SCREAMING_SNAKE_CASE_						=    self._prepare_for_class(_A    ,		_A    ,		return_labels=_A)

                  if problem_type["num_labels"] > 1:
                      SCREAMING_SNAKE_CASE_						=    inputs['labels'].unsqueeze(1).repeat(1    ,		problem_type['num_labels'])

                  SCREAMING_SNAKE_CASE_						=    inputs['labels'].to(problem_type['dtype'])

                  # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                  # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                  # they have the same size." which is a symptom something in wrong for the regression problem.
                  # See https://github.com/huggingface/transformers/issues/11780
                  with warnings.catch_warnings(record=_A) as warning_list:
                      SCREAMING_SNAKE_CASE_						=    model(**_A).loss
                  for w in warning_list:
                      if "Using a target size that is different to the input size" in str(w.message):
                          raise ValueError(
                              f"""Something is going wrong in the regression problem: intercepted {w.message}""")

                  loss.backward()

  @slow
  def     lowerCAmelCase__  (       self):
      for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          SCREAMING_SNAKE_CASE_						=    EfficientFormerModel.from_pretrained(_A)
          self.assertIsNotNone(_A)


  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_						=    True

      SCREAMING_SNAKE_CASE_						=    getattr(self.model_tester    ,		'seq_length'    ,		_A)
      SCREAMING_SNAKE_CASE_						=    getattr(self.model_tester    ,		'encoder_seq_length'    ,		_A)
      SCREAMING_SNAKE_CASE_						=    getattr(self.model_tester    ,		'key_length'    ,		_A)
      SCREAMING_SNAKE_CASE_						=    getattr(self.model_tester    ,		'chunk_length'    ,		_A)

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

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_						=    True
          SCREAMING_SNAKE_CASE_						=    False
          SCREAMING_SNAKE_CASE_						=    True
          SCREAMING_SNAKE_CASE_						=    model_class(_A)
          model.to(_A)
          model.eval()
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_						=    model(**self._prepare_for_class(_A    ,		_A))
          SCREAMING_SNAKE_CASE_						=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(_A)    ,		self.model_tester.num_attention_outputs)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          SCREAMING_SNAKE_CASE_						=    True
          SCREAMING_SNAKE_CASE_						=    model_class(_A)
          model.to(_A)
          model.eval()
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_						=    model(**self._prepare_for_class(_A    ,		_A))
          SCREAMING_SNAKE_CASE_						=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(_A)    ,		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        _UpperCAmelCase							(	):





    """simple docstring"""


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





@require_torch
@require_vision
class    __snake_case	(     unittest.TestCase							):

  @cached_property
  def     lowerCAmelCase__  (       self):
      return (
          EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300')
          if is_vision_available()
          else None
      )

  @slow
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300').to(
          _A)

      SCREAMING_SNAKE_CASE_						=    self.default_image_processor
      SCREAMING_SNAKE_CASE_						=    prepare_img()
      SCREAMING_SNAKE_CASE_						=    image_processor(images=_A    ,		return_tensors='pt').to(_A)

      # forward pass
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_						=    model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_						=    (1, 1000)
      self.assertEqual(outputs.logits.shape    ,		_A)

      SCREAMING_SNAKE_CASE_						=    torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]).to(_A)
      self.assertTrue(torch.allclose(outputs.logits[0][:3]    ,		_A    ,		atol=1E-4))


  @slow
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    EfficientFormerForImageClassificationWithTeacher.from_pretrained(
          'snap-research/efficientformer-l1-300').to(_A)

      SCREAMING_SNAKE_CASE_						=    self.default_image_processor
      SCREAMING_SNAKE_CASE_						=    prepare_img()
      SCREAMING_SNAKE_CASE_						=    image_processor(images=_A    ,		return_tensors='pt').to(_A)

      # forward pass
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_						=    model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_						=    (1, 1000)
      self.assertEqual(outputs.logits.shape    ,		_A)

      SCREAMING_SNAKE_CASE_						=    torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]).to(_A)
      self.assertTrue(torch.allclose(outputs.logits[0][:3]    ,		_A    ,		atol=1E-4))





import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


UpperCamelCase__	:   Optional[Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):

  def __init__(       self    ,		*_A    ,		**_A):
      warnings.warn(
          'The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use ViltImageProcessor instead.'    ,		_A    ,		)
      super().__init__(*_A    ,		**_A)


