


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE :   str							      =				"scheduler_config.json"






class _lowerCamelCase(		_a     ):
       lowercase_				:			Optional[int]									=    1
       lowercase_				:			Optional[Any]									=    2
       lowercase_				:			Dict									=    3
       lowercase_				:			int									=    4
       lowercase_				:			Optional[Any]									=    5






@dataclass
class _lowerCamelCase(		_a     ):
       lowercase_				:			jnp.ndarray






class _lowerCamelCase:
       lowercase_				:			Union[str, Any]									=    SCHEDULER_CONFIG_NAME
       lowercase_				:			str									=    ["""dtype"""]
       lowercase_				:			Dict									=    []
       lowercase_				:			Dict									=    True
       @classmethod
       def     UpperCamelCase  (      cls,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase=False,   **lowerCamelCase,   ) ->     str:




           """simple docstring"""

           _lowercase	,       _lowercase :							int   	=	cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase,   subfolder=lowerCamelCase,   return_unused_kwargs=lowerCamelCase,   **lowerCamelCase,   )
           _lowercase	,       _lowercase :							Any   	=	cls.from_config(lowerCamelCase,   return_unused_kwargs=lowerCamelCase,   **lowerCamelCase)

           if hasattr(lowerCamelCase,   'create_state') and getattr(lowerCamelCase,   'has_state',   lowerCamelCase):
               _lowercase :							Any   	=	scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = False,   **lowerCamelCase) ->     Dict:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase,   push_to_hub=lowerCamelCase,   **lowerCamelCase)
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def     UpperCamelCase  (      cls) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	list(set([cls.__name__] + cls._compatibles))
           _lowercase :							List[Any]   	=	importlib.import_module(__name__.split('.')[0])
           _lowercase :							int   	=	[
               getattr(lowerCamelCase,   lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase,   lowerCamelCase)
           ]
           return compatible_classes



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		jnp.ndarray:
    assert len(lowerCamelCase_				) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_				) - x.ndim)				)							,				lowerCamelCase_				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=0.9_99							,				lowerCamelCase_=jnp.floataa				)					->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_				):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

    _lowercase :							List[str]   	=	[]
    for i in range(lowerCamelCase_				):
        _lowercase :							Union[str, Any]   	=	i / num_diffusion_timesteps
        _lowercase :							Optional[int]   	=	(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_				) / alpha_bar(lowerCamelCase_				)							,				lowerCamelCase_				)				)
    return jnp.array(lowerCamelCase_							,				dtype=lowerCamelCase_				)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_				:			jnp.ndarray
       lowercase_				:			jnp.ndarray
       lowercase_				:			jnp.ndarray
       @classmethod
       def     UpperCamelCase  (      cls,   lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	scheduler.config

           if config.trained_betas is not None:
               _lowercase :							Optional[Any]   	=	jnp.asarray(config.trained_betas,   dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase :							Optional[Any]   	=	jnp.linspace(config.beta_start,   config.beta_end,   config.num_train_timesteps,   dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase :							Optional[Any]   	=	(
                   jnp.linspace(
                       config.beta_start**0.5,   config.beta_end**0.5,   config.num_train_timesteps,   dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase :							int   	=	betas_for_alpha_bar(config.num_train_timesteps,   dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase :							int   	=	1.0 - betas

           _lowercase :							List[str]   	=	jnp.cumprod(lowerCamelCase,   axis=0)

           return cls(
               alphas=lowerCamelCase,   betas=lowerCamelCase,   alphas_cumprod=lowerCamelCase,   )



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Any:
    _lowercase :							Optional[int]   	=	state.alphas_cumprod

    _lowercase :							int   	=	alphas_cumprod[timesteps] ** 0.5
    _lowercase :							Union[str, Any]   	=	sqrt_alpha_prod.flatten()
    _lowercase :							Any   	=	broadcast_to_shape_from_left(lowerCamelCase_							,				original_samples.shape				)

    _lowercase :							Optional[Any]   	=	(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase :							Tuple   	=	sqrt_one_minus_alpha_prod.flatten()
    _lowercase :							Optional[Any]   	=	broadcast_to_shape_from_left(lowerCamelCase_							,				original_samples.shape				)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		List[Any]:
    _lowercase	,       _lowercase :							Union[str, Any]   	=	get_sqrt_alpha_prod(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)
    _lowercase :							Optional[Any]   	=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		List[str]:
    _lowercase	,       _lowercase :							int   	=	get_sqrt_alpha_prod(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)
    _lowercase :							Union[str, Any]   	=	sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity






import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     DeiTForImageClassification,
     DeiTForImageClassificationWithTeacher,
     DeiTForMaskedImageModeling,
     DeiTModel,
 )
 from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor






class _lowerCamelCase:
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=30,   lowerCamelCase=2,   lowerCamelCase=3,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=32,   lowerCamelCase=5,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=10,   lowerCamelCase=0.0_2,   lowerCamelCase=3,   lowerCamelCase=None,   lowerCamelCase=2,   ) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	parent
           _lowercase :							int   	=	batch_size
           _lowercase :							int   	=	image_size
           _lowercase :							str   	=	patch_size
           _lowercase :							int   	=	num_channels
           _lowercase :							Any   	=	is_training
           _lowercase :							Union[str, Any]   	=	use_labels
           _lowercase :							Dict   	=	hidden_size
           _lowercase :							List[str]   	=	num_hidden_layers
           _lowercase :							Optional[Any]   	=	num_attention_heads
           _lowercase :							Optional[int]   	=	intermediate_size
           _lowercase :							Tuple   	=	hidden_act
           _lowercase :							str   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							Tuple   	=	type_sequence_label_size
           _lowercase :							List[str]   	=	initializer_range
           _lowercase :							Any   	=	scope
           _lowercase :							Union[str, Any]   	=	encoder_stride

           # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
           _lowercase :							Union[str, Any]   	=	(image_size // patch_size) ** 2
           _lowercase :							Any   	=	num_patches + 2
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _lowercase :							str   	=	None
           if self.use_labels:
               _lowercase :							List[Any]   	=	ids_tensor([self.batch_size],   self.type_sequence_label_size)

           _lowercase :							str   	=	self.get_config()

           return config, pixel_values, labels
       def     UpperCamelCase  (      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=lowerCamelCase,   initializer_range=self.initializer_range,   encoder_stride=self.encoder_stride,   )
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	DeiTModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	DeiTForMaskedImageModeling(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							List[Any]   	=	model(lowerCamelCase)
           self.parent.assertEqual(
               result.reconstruction.shape,   (self.batch_size, self.num_channels, self.image_size, self.image_size))

           # test greyscale images
           _lowercase :							Any   	=	1
           _lowercase :							Optional[Any]   	=	DeiTForMaskedImageModeling(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase :							List[str]   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _lowercase :							Optional[int]   	=	model(lowerCamelCase)
           self.parent.assertEqual(result.reconstruction.shape,   (self.batch_size, 1, self.image_size, self.image_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							str   	=	self.type_sequence_label_size
           _lowercase :							Dict   	=	DeiTForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							List[Any]   	=	model(lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           _lowercase :							Optional[Any]   	=	1
           _lowercase :							Optional[Any]   	=	DeiTForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase :							List[str]   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _lowercase :							List[Any]   	=	model(lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size))




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




           """simple docstring"""

           _lowercase :							Tuple   	=	self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							List[str]   	=	config_and_inputs
           _lowercase :							List[str]   	=	{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Optional[Any]									=    (
           (
               DeiTModel,
               DeiTForImageClassification,
               DeiTForImageClassificationWithTeacher,
               DeiTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       lowercase_				:			Optional[Any]									=    (
           {
               """feature-extraction""": DeiTModel,
               """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
           }
           if is_torch_available()
           else {}
       )

       lowercase_				:			Dict									=    False
       lowercase_				:			List[str]									=    False
       lowercase_				:			Union[str, Any]									=    False
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	DeiTModelTester(self)
           _lowercase :							Optional[Any]   	=	ConfigTester(self,   config_class=lowerCamelCase,   has_text_modality=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.config_tester.run_common_tests()
       @unittest.skip(reason='DeiT does not use inputs_embeds')
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase	,       _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase :							int   	=	model_class(lowerCamelCase)
               self.assertIsInstance(model.get_input_embeddings(),   (nn.Module))
               _lowercase :							Union[str, Any]   	=	model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase,   nn.Linear))
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase	,       _lowercase :							Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase :							Any   	=	model_class(lowerCamelCase)
               _lowercase :							Optional[Any]   	=	inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _lowercase :							Union[str, Any]   	=	[*signature.parameters.keys()]

               _lowercase :							str   	=	['pixel_values']
               self.assertListEqual(arg_names[:1],   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=False) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	super()._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)

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

           return inputs_dict
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           if not self.model_tester.is_training:
               return

           _lowercase	,       _lowercase :							str   	=	self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase :							Tuple   	=	True

           for model_class in self.all_model_classes:
               # DeiTForImageClassificationWithTeacher supports inference-only
               if (
                   model_class in get_values(lowerCamelCase)
                   or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
               ):
                   continue
               _lowercase :							Optional[int]   	=	model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.train()
               _lowercase :							Optional[Any]   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)
               _lowercase :							List[str]   	=	model(**lowerCamelCase).loss
               loss.backward()
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
               return

           _lowercase :							Dict   	=	False
           _lowercase :							Optional[int]   	=	True

           for model_class in self.all_model_classes:
               if model_class in get_values(lowerCamelCase) or not model_class.supports_gradient_checkpointing:
                   continue
               # DeiTForImageClassificationWithTeacher supports inference-only
               if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   continue
               _lowercase :							str   	=	model_class(lowerCamelCase)
               model.gradient_checkpointing_enable()
               model.to(lowerCamelCase)
               model.train()
               _lowercase :							Union[str, Any]   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)
               _lowercase :							List[Any]   	=	model(**lowerCamelCase).loss
               loss.backward()
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()

           _lowercase :							int   	=	[
               {'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(lowerCamelCase),
                       *get_values(lowerCamelCase),
                   ]
                   or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
               ):
                   continue

               for problem_type in problem_types:
                   with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''):
                       _lowercase :							List[Any]   	=	problem_type['title']
                       _lowercase :							str   	=	problem_type['num_labels']

                       _lowercase :							Optional[int]   	=	model_class(lowerCamelCase)
                       model.to(lowerCamelCase)
                       model.train()

                       _lowercase :							Tuple   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)

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

                       _lowercase :							Optional[int]   	=	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=lowerCamelCase) as warning_list:
                           _lowercase :							Dict   	=	model(**lowerCamelCase).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     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							Tuple   	=	DeiTModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def        UpperCamelCase_(   )					->		List[str]:
    _lowercase :							Optional[Any]   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image






@require_torch
@require_vision
class _lowerCamelCase(		unittest.TestCase     ):
       @cached_property
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           return (
               DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
               if is_vision_available()
               else None
           )
       @slow
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224').to(
               lowerCamelCase)

           _lowercase :							List[str]   	=	self.default_image_processor
           _lowercase :							List[str]   	=	prepare_img()
           _lowercase :							Tuple   	=	image_processor(images=lowerCamelCase,   return_tensors='pt').to(lowerCamelCase)

           # forward pass
           with torch.no_grad():
               _lowercase :							int   	=	model(**lowerCamelCase)

           # verify the logits
           _lowercase :							Any   	=	torch.Size((1, 10_00))
           self.assertEqual(outputs.logits.shape,   lowerCamelCase)

           _lowercase :							Union[str, Any]   	=	torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]).to(lowerCamelCase)

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




       @slow
       @require_accelerate
       @require_torch_gpu
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	DeiTModel.from_pretrained(
               'facebook/deit-base-distilled-patch16-224',   torch_dtype=torch.floataa,   device_map='auto')
           _lowercase :							Union[str, Any]   	=	self.default_image_processor

           _lowercase :							Union[str, Any]   	=	prepare_img()
           _lowercase :							int   	=	image_processor(images=lowerCamelCase,   return_tensors='pt')
           _lowercase :							Union[str, Any]   	=	inputs.pixel_values.to(lowerCamelCase)

           # forward pass to make sure inference works in fp16
           with torch.no_grad():
               _lowercase :							Optional[int]   	=	model(lowerCamelCase)


