
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					:     str   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[Any]=1_3   ,					__UpperCamelCase					:     str=3_0   ,					__UpperCamelCase					:     str=2   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     List[Any]=True   ,					__UpperCamelCase					:     Optional[Any]=True   ,					__UpperCamelCase					:     Tuple=3_2   ,					__UpperCamelCase					:     str=2   ,					__UpperCamelCase					:     Dict=4   ,					__UpperCamelCase					:     List[Any]=3_7   ,					__UpperCamelCase					:     str="gelu"   ,					__UpperCamelCase					:     int=0.1   ,					__UpperCamelCase					:     Optional[int]=0.1   ,					__UpperCamelCase					:     Any=1_0   ,					__UpperCamelCase					:     Dict=0.02   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     Union[str, Any]=None   ,					__UpperCamelCase					:     Dict=2   ,					):
            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    lowercase__      (	self					:     Any   ):
            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    lowercase__      (	self					:     Optional[int]   ):
            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=__a   ,					initializer_range=self.initializer_range   ,					encoder_stride=self.encoder_stride   ,					)

     def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     int   ):
            lowerCamelCase_							  =					TFDeiTModel(config=__a   )
            lowerCamelCase_							  =					model(__a   )
            self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )

     def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Tuple   ):
            lowerCamelCase_							  =					TFDeiTForMaskedImageModeling(config=__a   )
            lowerCamelCase_							  =					model(__a   )
            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(__a   )

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

     def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
            lowerCamelCase_							  =					self.type_sequence_label_size
            lowerCamelCase_							  =					TFDeiTForImageClassification(__a   )
            lowerCamelCase_							  =					model(__a   ,					labels=__a   )
            self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.type_sequence_label_size)   )

            # test greyscale images
            lowerCamelCase_							  =					1
            lowerCamelCase_							  =					TFDeiTForImageClassification(__a   )

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



     def    lowercase__      (	self					:     Tuple   ):
            lowerCamelCase_							  =					self.prepare_config_and_inputs()
            lowerCamelCase_							  =					config_and_inputs
            lowerCamelCase_							  =					{'pixel_values': pixel_values}
            return config, inputs_dict





@require_tf
class       __A(				UpperCamelCase_     ,		UpperCamelCase_     ,		unittest.TestCase       ):
     SCREAMING_SNAKE_CASE			    =    (
         (
             TFDeiTModel,
             TFDeiTForImageClassification,
             TFDeiTForImageClassificationWithTeacher,
             TFDeiTForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE			    =    (
         {
             '''feature-extraction''': TFDeiTModel,
             '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
         }
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE			    =    False
     SCREAMING_SNAKE_CASE			    =    False
     SCREAMING_SNAKE_CASE			    =    False
     SCREAMING_SNAKE_CASE			    =    False

     def    lowercase__      (	self					:     Optional[int]   ):
            lowerCamelCase_							  =					TFDeiTModelTester(self   )
            lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__a   ,					has_text_modality=__a   ,					hidden_size=3_7   )

     def    lowercase__      (	self					:     Optional[Any]   ):
            self.config_tester.run_common_tests()

     @unittest.skip(reason="""DeiT does not use inputs_embeds"""   )
     def    lowercase__      (	self					:     Tuple   ):
            pass

     def    lowercase__      (	self					:     List[str]   ):
            lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

     def    lowercase__      (	self					:     Any   ):
            lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   lowerCamelCase_							  =					model_class(__a   )
                   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]   ,					__a   )

     def    lowercase__      (	self					:     int   ):
            lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__a   )

     def    lowercase__      (	self					:     List[Any]   ):
            lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*__a   )

     def    lowercase__      (	self					:     Any   ):
            lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__a   )

     def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     int=False   ):
            lowerCamelCase_							  =					super()._prepare_for_class(__a   ,					__a   ,					return_labels=__a   )

            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    lowercase__      (	self					:     Dict   ):






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



def      __lowerCAmelCase						(							)  ->    Any:
       lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
       return image





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

     @cached_property
     def    lowercase__      (	self					:     Tuple   ):
            return (
                DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""   )
                if is_vision_available()
                else None
            )



     @slow
     def    lowercase__      (	self					:     Union[str, Any]   ):
            lowerCamelCase_							  =					TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""   )

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

            # forward pass
            lowerCamelCase_							  =					model(**__a   )

            # verify the logits
            lowerCamelCase_							  =					tf.TensorShape((1, 1_0_0_0)   )
            self.assertEqual(outputs.logits.shape   ,					__a   )

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

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







def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float(moles / volume       ) * nfactor       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((moles * 0.0_8_2_1 * temperature) / (volume)       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((pressure * volume) / (0.0_8_2_1 * moles)       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





