

import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A_							:			Tuple											= re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")





@total_ordering
@dataclass
class    lowerCamelCase						:
			lowerCamelCase__   :		Any							=							4_2
			lowerCamelCase__   :		Union[str, Any]							=							None
			lowerCamelCase__   :		Any							=							None
			lowerCamelCase__   :		str							=							None
			lowerCamelCase__   :		Dict							=							None

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Any:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      _str_to_version_tuple(self.version_str       )

			def __repr__(					self				:						Optional[int]       )   ->    str:
						return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Tuple:
						return self.major, self.minor, self.patch

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Dict:
						if isinstance(_lowercase    ,				_lowercase       ):
									return Version(_lowercase       )
						elif isinstance(_lowercase    ,				_lowercase       ):
									return other
						raise TypeError(F"""{other} (type {type(_lowercase       )}) cannot be compared to version."""       )

			def __eq__(					self				:						List[str]    ,				__UpperCAmelCase				:						int       )   ->    List[str]:
						try:
									SCREAMING_SNAKE_CASE__								=      self._validate_operand(_lowercase       )
						except (TypeError, ValueError):
									return False
						else:
									return self.tuple == other.tuple

			def __lt__(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self._validate_operand(_lowercase       )
						return self.tuple < other.tuple

			def __hash__(					self				:						Optional[int]       )   ->    Any:
						return hash(_version_tuple_to_str(self.tuple       )       )

			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						List[str]    ,				__UpperCAmelCase				:						List[str]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      {f.name for f in dataclasses.fields(cls       )}
						return cls(**{k: v for k, v in dic.items() if k in field_names}       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Any:
						return self.version_str

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      _VERSION_REG.match(UpperCamelCase__    )
			if not res:
						raise ValueError(f"""Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits."""    )
			return tuple(int(UpperCamelCase__    ) for v in [res.group("""major"""    ), res.group("""minor"""    ), res.group("""patch"""    )]    )




def 	A       (     snake_case__    ):




			'''simple docstring'''


			return ".".join(str(UpperCamelCase__    ) for v in version_tuple    )





"""simple docstring"""




import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
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 (
						    TFEfficientFormerForImageClassification,
						    TFEfficientFormerForImageClassificationWithTeacher,
						    TFEfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_tf_efficientformer import (
						    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor





class    lowerCamelCase						:

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						int = 1_3    ,				__UpperCAmelCase				:						int = 6_4    ,				__UpperCAmelCase				:						int = 2    ,				__UpperCAmelCase				:						int = 3    ,				__UpperCAmelCase				:						int = 3    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						int = 1_2_8    ,				__UpperCAmelCase				:						Optional[int]=[1_6, 3_2, 6_4, 1_2_8]    ,				__UpperCAmelCase				:						int = 7    ,				__UpperCAmelCase				:						int = 4    ,				__UpperCAmelCase				:						int = 3_7    ,				__UpperCAmelCase				:						str = "gelu"    ,				__UpperCAmelCase				:						float = 0.1    ,				__UpperCAmelCase				:						float = 0.1    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						float = 0.02    ,				__UpperCAmelCase				:						int = 2    ,				__UpperCAmelCase				:						int = 1    ,				__UpperCAmelCase				:						int = 1_2_8    ,				__UpperCAmelCase				:						List[int] = [2, 2, 2, 2]    ,				__UpperCAmelCase				:						int = 2    ,				__UpperCAmelCase				:						int = 2    ,				)   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      encoder_stride
						SCREAMING_SNAKE_CASE__								=      num_attention_outputs
						SCREAMING_SNAKE_CASE__								=      embed_dim
						SCREAMING_SNAKE_CASE__								=      embed_dim + 1
						SCREAMING_SNAKE_CASE__								=      resolution
						SCREAMING_SNAKE_CASE__								=      depths
						SCREAMING_SNAKE_CASE__								=      hidden_sizes
						SCREAMING_SNAKE_CASE__								=      dim
						SCREAMING_SNAKE_CASE__								=      mlp_expansion_ratio

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    int:
						return EfficientFormerConfig(
						    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    ,				resolution=self.resolution    ,				depths=self.depths    ,				hidden_sizes=self.hidden_sizes    ,				dim=self.dim    ,				mlp_expansion_ratio=self.mlp_expansion_ratio    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						str       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerModel(config=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				training=__UpperCAmelCase       )
						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Any       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerForImageClassification(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase    ,				training=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerForImageClassification(__UpperCAmelCase       )

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_tf
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Any							=							(
			    (
			        TFEfficientFormerModel,
			        TFEfficientFormerForImageClassificationWithTeacher,
			        TFEfficientFormerForImageClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__   :		List[Any]							=							(
			    {
			        'feature-extraction': TFEfficientFormerModel,
			        'image-classification': (
			            TFEfficientFormerForImageClassification,
			            TFEfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__   :		Dict							=							False

			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		Union[str, Any]							=							False
			lowerCamelCase__   :		Optional[int]							=							False
			lowerCamelCase__   :		List[Any]							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(
						    self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Any:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
						pass

			@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Optional[Any]:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__								=      ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    str:
						def check_hidden_states_output(__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						int       ):
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )

									SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase       )    ,				training=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									SCREAMING_SNAKE_CASE__								=      getattr(
									    self.model_tester    ,				"""expected_num_hidden_layers"""    ,				self.model_tester.num_hidden_layers + 1       )
									self.assertEqual(len(__UpperCAmelCase       )    ,				__UpperCAmelCase       )

									if hasattr(self.model_tester    ,				"""encoder_seq_length"""       ):
												SCREAMING_SNAKE_CASE__								=      self.model_tester.encoder_seq_length
												if hasattr(self.model_tester    ,				"""chunk_length"""       ) and self.model_tester.chunk_length > 1:
															SCREAMING_SNAKE_CASE__								=      seq_length * self.model_tester.chunk_length
									else:
												SCREAMING_SNAKE_CASE__								=      self.model_tester.seq_length

									self.assertListEqual(
									    list(hidden_states[-1].shape[-2:]       )    ,				[seq_length, self.model_tester.hidden_size]    ,				)

									if config.is_encoder_decoder:
												SCREAMING_SNAKE_CASE__								=      outputs.decoder_hidden_states

												self.asseretIsInstance(__UpperCAmelCase    ,				(list, tuple)       )
												self.assertEqual(len(__UpperCAmelCase       )    ,				__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""seq_length"""    ,				__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""decoder_seq_length"""    ,				__UpperCAmelCase       )

												self.assertListEqual(
												    list(hidden_states[-1].shape[-2:]       )    ,				[decoder_seq_length, self.model_tester.hidden_size]    ,				)

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE__								=      True

									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Any=False       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

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

						return inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[int]:
						for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      TFEfficientFormerModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__								=      True

						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""seq_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""encoder_seq_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""key_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""chunk_length"""    ,				__UpperCAmelCase       )

						if chunk_length is not None and hasattr(self.model_tester    ,				"""num_hashes"""       ):
									SCREAMING_SNAKE_CASE__								=      encoder_seq_length * self.model_tester.num_hashes

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      False
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )

									SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase       )    ,				training=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(__UpperCAmelCase       )    ,				self.model_tester.num_attention_outputs       )

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase       )    ,				training=__UpperCAmelCase       )

									SCREAMING_SNAKE_CASE__								=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(__UpperCAmelCase       )    ,				self.model_tester.num_attention_outputs       )

									if chunk_length is not None:
												self.assertListEqual(
												    list(attentions[0].shape[-4:]       )    ,				[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]    ,				)
									else:
												self.assertListEqual(
												    list(attentions[0].shape[-3:]       )    ,				[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]    ,				)





			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Tuple:
						# We use a simplified version of this test for EfficientFormer because it requires training=False
						# and Keras refuses to let us force that during functional construction
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									# Prepare our model
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									# These are maximally general inputs for the model, with multiple None dimensions
									# Hopefully this will catch any conditionals that fail for flexible shapes
									SCREAMING_SNAKE_CASE__								=      {
									    key: tf.keras.Input(shape=val.shape[1:]    ,				dtype=val.dtype    ,				name=__UpperCAmelCase       )
									    for key, val in model.input_signature.items()
									    if key in model.dummy_inputs
									}
									SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
									self.assertTrue(outputs_dict is not None       )

def 	A       (     ):




			'''simple docstring'''


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





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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Dict:
						return (
						    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""       )
						    if is_vision_available()
						    else None
						)

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""       )
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""tf"""       )
						# forward pass
						SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase    ,				training=__UpperCAmelCase       )
						# verify the logits
						SCREAMING_SNAKE_CASE__								=      tf.TensorShape((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      tf.constant([-0.0_555, 0.4_825, -0.0_852]       )
						self.assertTrue(np.allclose(outputs.logits[0, :3]    ,				__UpperCAmelCase    ,				atol=1e-4       )       )





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
						    """snap-research/efficientformer-l1-300"""       )
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""tf"""       )
						# forward pass
						SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase    ,				training=__UpperCAmelCase       )
						# verify the logits
						SCREAMING_SNAKE_CASE__								=      tf.TensorShape((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      tf.constant([-0.1_312, 0.4_353, -1.0_499]       )
						self.assertTrue(np.allclose(outputs.logits[0, :3]    ,				__UpperCAmelCase    ,				atol=1e-4       )       )


