'''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 				a__:


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=13       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=32       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=4       ,    __lowerCAmelCase=37       ,    __lowerCAmelCase="gelu"       ,    __lowerCAmelCase=0.1       ,    __lowerCAmelCase=0.1       ,    __lowerCAmelCase=10       ,    __lowerCAmelCase=0.02       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=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 							a_       (   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 							a_       (   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=__lowerCAmelCase       ,    initializer_range=self.initializer_range       ,    encoder_stride=self.encoder_stride       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  TFDeiTForMaskedImageModeling(config=__lowerCAmelCase)
					lowerCAmelCase   					=  model(__lowerCAmelCase)
					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(__lowerCAmelCase)

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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


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

					# test greyscale images
					lowerCAmelCase   					=  1
					lowerCAmelCase   					=  TFDeiTForImageClassification(__lowerCAmelCase)

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







			def 							a_       (   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 				a__(					lowerCAmelCase__ ,						lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       (
			    (
			        TFDeiTModel,
			        TFDeiTForImageClassification,
			        TFDeiTForImageClassificationWithTeacher,
			        TFDeiTForMaskedImageModeling,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCAmelCase_			:     Any      =       (
			    {
			        '''feature-extraction''': TFDeiTModel,
			        '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
			    }
			    if is_tf_available()
			    else {}
			)

			UpperCAmelCase_			:     Union[str, Any]      =       False
			UpperCAmelCase_			:     Tuple      =       False
			UpperCAmelCase_			:     Optional[Any]      =       False
			UpperCAmelCase_			:     Optional[int]      =       False



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  TFDeiTModelTester(self)
					lowerCAmelCase   					=  ConfigTester(self       ,    config_class=__lowerCAmelCase       ,    has_text_modality=__lowerCAmelCase       ,    hidden_size=37)



			def 							a_       (   self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



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




					"""simple docstring"""


					pass



			def 							a_       (   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(__lowerCAmelCase)
							self.assertIsInstance(model.get_input_embeddings()       ,    (tf.keras.layers.Layer))
							lowerCAmelCase   					=  model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__lowerCAmelCase       ,    tf.keras.layers.Dense))



			def 							a_       (   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(__lowerCAmelCase)
							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]       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase=False):




					"""simple docstring"""


					lowerCAmelCase   					=  super()._prepare_for_class(__lowerCAmelCase       ,    __lowerCAmelCase       ,    return_labels=__lowerCAmelCase)

					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):




					"""simple docstring"""


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







def 				snake_case__							(			)    ->	List[Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
		return image






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


			'''simple docstring'''






			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


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







			@slow
			def 							a_       (   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=__lowerCAmelCase       ,    return_tensors="""tf""")

					# forward pass
					lowerCAmelCase   					=  model(**__lowerCAmelCase)

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

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

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





'''simple docstring'''




import math

import flax.linen as nn
import jax.numpy as jnp






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''simple docstring'''


		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
		lowerCAmelCase   					=  float(embedding_dim // 2 )
		lowerCAmelCase   					=  math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
		lowerCAmelCase   					=  min_timescale * jnp.exp(jnp.arange(_A      ,   dtype=jnp.floataa ) * -log_timescale_increment )
		lowerCAmelCase   					=  jnp.expand_dims(_A      ,   1 ) * jnp.expand_dims(_A      ,   0 )

		# scale embeddings
		lowerCAmelCase   					=  scale * emb

		if flip_sin_to_cos:
				lowerCAmelCase   					=  jnp.concatenate([jnp.cos(_A ), jnp.sin(_A )]      ,   axis=1 )
		else:
				lowerCAmelCase   					=  jnp.concatenate([jnp.sin(_A ), jnp.cos(_A )]      ,   axis=1 )
		lowerCAmelCase   					=  jnp.reshape(_A      ,   [jnp.shape(_A )[0], embedding_dim] )
		return signal






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return get_sinusoidal_embeddings(
					    __lowerCAmelCase       ,    embedding_dim=self.dim       ,    flip_sin_to_cos=self.flip_sin_to_cos       ,    freq_shift=self.freq_shift)




