


'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
         TFDeiTModel,
     )
     from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import DeiTImageProcessor





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=13    ,						UpperCamelCase=30    ,						UpperCamelCase=2    ,						UpperCamelCase=3    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=32    ,						UpperCamelCase=2    ,						UpperCamelCase=4    ,						UpperCamelCase=37    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=10    ,						UpperCamelCase=0.02    ,						UpperCamelCase=3    ,						UpperCamelCase=None    ,						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_  				=						scope
   lowerCamelCase_  				=						encoder_stride

   # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
   lowerCamelCase_  				=						(image_size // patch_size) ** 2
   lowerCamelCase_  				=						num_patches + 2






  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 DeiTConfig(
       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    ,						)






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







   """simple docstring"""
   lowerCamelCase_  				=						TFDeiTModel(config=UpperCamelCase  )
   lowerCamelCase_  				=						model(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_  				=						TFDeiTForMaskedImageModeling(config=UpperCamelCase  )
   lowerCamelCase_  				=						model(UpperCamelCase  )
   self.parent.assertEqual(
       result.reconstruction.shape    ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

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

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






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







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

   # test greyscale images
   lowerCamelCase_  				=						1
   lowerCamelCase_  				=						TFDeiTForImageClassification(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						=						(
      (
          TFDeiTModel,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase						=						(
      {
          "feature-extraction": TFDeiTModel,
          "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
      }
      if is_tf_available()
      else {}
  )

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TFDeiTModelTester(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="DeiT does not use inputs_embeds"  )
  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  )
    self.assertIsInstance(model.get_input_embeddings()    ,						(tf.keras.layers.Layer)  )
    lowerCamelCase_  				=						model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(UpperCamelCase    ,						tf.keras.layers.Dense  )  )






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






  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  )






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







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

   if return_labels:
    if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call  ).parameters:
     del inputs_dict["labels"]

   return inputs_dict







  @slow
  def 						snake_case	(					self  ):







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





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 (
       DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"  )
       if is_vision_available()
       else None
   )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"  )

   lowerCamelCase_  				=						self.default_image_processor
   lowerCamelCase_  				=						prepare_img()
   lowerCamelCase_  				=						image_processor(images=UpperCamelCase    ,						return_tensors="tf"  )

   # forward pass
   lowerCamelCase_  				=						model(**UpperCamelCase  )

   # verify the logits
   lowerCamelCase_  				=						tf.TensorShape((1, 1000)  )
   self.assertEqual(outputs.logits.shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						tf.constant([-1.0_266, 0.1_912, -1.2_861]  )

   self.assertTrue(np.allclose(outputs.logits[0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )







'''simple docstring'''

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
     import torch

     from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="resnet50"    ,						UpperCamelCase=3    ,						UpperCamelCase=32    ,						UpperCamelCase=3    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						out_indices if out_indices is not None else [4]
   lowerCamelCase_  				=						stage_names
   lowerCamelCase_  				=						out_features
   lowerCamelCase_  				=						backbone
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						image_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						use_pretrained_backbone
   lowerCamelCase_  				=						is_training






  def 						snake_case	(					self  ):







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

   return config, pixel_values






  def 						snake_case	(					self  ):







   """simple docstring"""
   return TimmBackboneConfig(
       image_size=self.image_size    ,						num_channels=self.num_channels    ,						out_features=self.out_features    ,						out_indices=self.out_indices    ,						stage_names=self.stage_names    ,						use_pretrained_backbone=self.use_pretrained_backbone    ,						backbone=self.backbone    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						TimmBackbone(config=UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()
   with torch.no_grad():
    lowerCamelCase_  				=						model(UpperCamelCase  )
   self.parent.assertEqual(
       result.feature_map[-1].shape    ,						(self.batch_size, model.channels[-1], 14, 14)    ,						)







  def 						snake_case	(					self  ):







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





@require_torch
@require_timm
class   snake_case					(					lowercase    ,		lowercase    ,		lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						(TimmBackbone,) if is_torch_available() else ()
  _lowerCamelCase						=						{"feature-extraction": TimmBackbone} if is_torch_available() else {}
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TimmBackboneModelTester(self  )
   lowerCamelCase_  				=						ConfigTester(self    ,						config_class=UpperCamelCase    ,						has_text_modality=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"resnet18"
   lowerCamelCase_  				=						"microsoft/resnet-18"

   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						use_timm_backbone=UpperCamelCase  )
   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase  )

   self.assertEqual(len(timm_model.out_features  )    ,						len(transformers_model.out_features  )  )
   self.assertEqual(len(timm_model.stage_names  )    ,						len(transformers_model.stage_names  )  )
   self.assertEqual(timm_model.channels    ,						transformers_model.channels  )
   # Out indices are set to the last layer by default. For timm models, we don't know
   # the number of layers in advance, so we set it to (-1,), whereas for transformers
   # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
   self.assertEqual(timm_model.out_indices    ,						(-1,)  )
   self.assertEqual(transformers_model.out_indices    ,						[len(timm_model.stage_names  ) - 1]  )

   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						use_timm_backbone=UpperCamelCase    ,						out_indices=[1, 2, 3]  )
   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						out_indices=[1, 2, 3]  )

   self.assertEqual(timm_model.out_indices    ,						transformers_model.out_indices  )
   self.assertEqual(len(timm_model.out_features  )    ,						len(transformers_model.out_features  )  )
   self.assertEqual(timm_model.channels    ,						transformers_model.channels  )






  @unittest.skip("TimmBackbone doesn't support feed forward chunking"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone initialization is managed on the timm side"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone models doesn't have inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone models doesn't have inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("model weights aren't tied in TimmBackbone."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("model weights aren't tied in TimmBackbone."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't support output_attentions."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Safetensors is not supported by timm."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
  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.forward  )
    # 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"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_  				=						True
   lowerCamelCase_  				=						self.has_attentions

   # no need to test all models as different heads yield the same functionality
   lowerCamelCase_  				=						self.all_model_classes[0]
   lowerCamelCase_  				=						model_class(UpperCamelCase  )
   model.to(UpperCamelCase  )

   lowerCamelCase_  				=						self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase  )
   lowerCamelCase_  				=						model(**UpperCamelCase  )
   lowerCamelCase_  				=						outputs[0][-1]

   # Encoder-/Decoder-only models
   lowerCamelCase_  				=						outputs.hidden_states[0]
   hidden_states.retain_grad()

   if self.has_attentions:
    lowerCamelCase_  				=						outputs.attentions[0]
    attentions.retain_grad()

   output.flatten()[0].backward(retain_graph=UpperCamelCase  )

   self.assertIsNotNone(hidden_states.grad  )

   if self.has_attentions:
    self.assertIsNotNone(attentions.grad  )







  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  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )

    self.assertEqual(len(result.feature_maps  )    ,						len(config.out_indices  )  )
    self.assertEqual(len(model.channels  )    ,						len(config.out_indices  )  )

    # Check output of last stage is taken if out_features=None, out_indices=None
    lowerCamelCase_  				=						copy.deepcopy(UpperCamelCase  )
    lowerCamelCase_  				=						None
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )

    self.assertEqual(len(result.feature_maps  )    ,						1  )
    self.assertEqual(len(model.channels  )    ,						1  )

    # Check backbone can be initialized with fresh weights
    lowerCamelCase_  				=						copy.deepcopy(UpperCamelCase  )
    lowerCamelCase_  				=						False
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )



