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     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=30   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=37   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=10   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=2   ,				)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_labels
       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      type_sequence_label_size
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      scope
       UpperCamelCase__										=      encoder_stride

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






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

       UpperCamelCase__										=      self.get_config()

       return config, pixel_values, labels






  def 		_a					(self	)		->      int:





       '''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=SCREAMING_SNAKE_CASE_   ,				initializer_range=self.initializer_range   ,				encoder_stride=self.encoder_stride   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      TFDeiTModel(config=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(
           result.reconstruction.shape   ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

       # test greyscale images
       UpperCamelCase__										=      1
       UpperCamelCase__										=      TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE_	)

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.type_sequence_label_size
       UpperCamelCase__										=      TFDeiTForImageClassification(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)	)

       # test greyscale images
       UpperCamelCase__										=      1
       UpperCamelCase__										=      TFDeiTForImageClassification(SCREAMING_SNAKE_CASE_	)

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






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





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



@require_tf
class     _A   (    __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						=(
      (
          TFDeiTModel,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=(
      {
          "feature-extraction": TFDeiTModel,
          "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
      }
      if is_tf_available()
      else {}
  )

  SCREAMING_SNAKE_CASE_	:	Any						=False
  SCREAMING_SNAKE_CASE_	:	Any						=False
  SCREAMING_SNAKE_CASE_	:	Tuple						=False
  SCREAMING_SNAKE_CASE_	:	int						=False






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      TFDeiTModelTester(self	)
       UpperCamelCase__										=      ConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_   ,				has_text_modality=SCREAMING_SNAKE_CASE_   ,				hidden_size=37	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  @unittest.skip(reason='''DeiT does not use inputs_embeds'''	)
  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            self.assertIsInstance(model.get_input_embeddings()   ,				(tf.keras.layers.Layer)	)
            UpperCamelCase__										=      model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_   ,				tf.keras.layers.Dense	)	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

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

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






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      super()._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				return_labels=SCREAMING_SNAKE_CASE_	)

       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 		_a					(self	)		->      str:





       '''simple docstring'''





       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__										=      TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image



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






  @cached_property
  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





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






  @slow
  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''tf'''	)

       # forward pass
       UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

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

       UpperCamelCase__										=      tf.constant([-1.0266, 0.1912, -1.2861]	)

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




from datetime import datetime

import matplotlib.pyplot as plt
import torch







def  __UpperCamelCase    (						A							):
     for param in module.parameters():
          UpperCamelCase__										=      False







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
     if torch.backends.mps.is_available() and torch.backends.mps.is_built():
          UpperCamelCase__										=      '''mps'''
     if device == "mps":
          print(
              '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
              ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
              ''' with generations.'''							)
     return device







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      plt.imshow(A							)
     fig.axes.get_xaxis().set_visible(A							)
     fig.axes.get_yaxis().set_visible(A							)
     plt.show()







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      datetime.now()
     UpperCamelCase__										=      current_time.strftime('''%H:%M:%S'''							)
     return timestamp



