"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy
		import tensorflow as tf

		from transformers.models.esm.modeling_tf_esm import (
		    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFEsmForMaskedLM,
		    TFEsmForSequenceClassification,
		    TFEsmForTokenClassification,
		    TFEsmModel,
		)







class      UpperCAmelCase_       :






							def __init__(		self :      List[str]     ,						__UpperCamelCase :      Dict     ,						)       ->       Optional[Any]:
												_UpperCamelCase      						=	parent
												_UpperCamelCase      						=	13
												_UpperCamelCase      						=	7
												_UpperCamelCase      						=	True
												_UpperCamelCase      						=	True
												_UpperCamelCase      						=	True
												_UpperCamelCase      						=	99
												_UpperCamelCase      						=	32
												_UpperCamelCase      						=	2
												_UpperCamelCase      						=	4
												_UpperCamelCase      						=	37
												_UpperCamelCase      						=	'''gelu'''
												_UpperCamelCase      						=	0.1
												_UpperCamelCase      						=	0.1
												_UpperCamelCase      						=	512
												_UpperCamelCase      						=	16
												_UpperCamelCase      						=	2
												_UpperCamelCase      						=	0.0_2
												_UpperCamelCase      						=	3
												_UpperCamelCase      						=	4
												_UpperCamelCase      						=	None






							def       _UpperCamelCase			(		self :      int  )       ->       Dict:
												_UpperCamelCase      						=	ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size  )

												_UpperCamelCase      						=	None
												if self.use_input_mask:
																	_UpperCamelCase      						=	random_attention_mask([self.batch_size, self.seq_length]  )

												_UpperCamelCase      						=	None
												_UpperCamelCase      						=	None
												_UpperCamelCase      						=	None
												if self.use_labels:
																	_UpperCamelCase      						=	ids_tensor([self.batch_size]     ,						self.type_sequence_label_size  )
																	_UpperCamelCase      						=	ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels  )
																	_UpperCamelCase      						=	ids_tensor([self.batch_size]     ,						self.num_choices  )

												_UpperCamelCase      						=	EsmConfig(
												    vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						num_hidden_layers=self.num_hidden_layers     ,						pad_token_id=1     ,						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     ,						max_position_embeddings=self.max_position_embeddings     ,						type_vocab_size=self.type_vocab_size     ,						initializer_range=self.initializer_range     ,						)

												return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






							def       _UpperCamelCase			(		self :      Tuple  )       ->       int:
												(
												    (
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							
												)      						=	self.prepare_config_and_inputs()

												_UpperCamelCase      						=	True
												_UpperCamelCase      						=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
												_UpperCamelCase      						=	ids_tensor([self.batch_size, self.seq_length]     ,						vocab_size=2  )

												return (
												    config,
												    input_ids,
												    input_mask,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)






							def       _UpperCamelCase			(		self :      str     ,						__UpperCamelCase :      int     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      List[Any]  )       ->       Optional[int]:
												_UpperCamelCase      						=	TFEsmModel(config=__UpperCamelCase  )
												_UpperCamelCase      						=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												_UpperCamelCase      						=	model(__UpperCamelCase  )

												_UpperCamelCase      						=	[input_ids, input_mask]
												_UpperCamelCase      						=	model(__UpperCamelCase  )

												_UpperCamelCase      						=	model(__UpperCamelCase  )

												self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)  )






							def       _UpperCamelCase			(		self :      str     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      Tuple     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      int     ,						__UpperCamelCase :      int     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      Union[str, Any]     ,						)       ->       List[Any]:
												_UpperCamelCase      						=	True

												_UpperCamelCase      						=	TFEsmModel(config=__UpperCamelCase  )
												_UpperCamelCase      						=	{
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''encoder_hidden_states''': encoder_hidden_states,
												    '''encoder_attention_mask''': encoder_attention_mask,
												}
												_UpperCamelCase      						=	model(__UpperCamelCase  )

												_UpperCamelCase      						=	[input_ids, input_mask]
												_UpperCamelCase      						=	model(__UpperCamelCase     ,						encoder_hidden_states=__UpperCamelCase  )

												# Also check the case where encoder outputs are not passed
												_UpperCamelCase      						=	model(__UpperCamelCase     ,						attention_mask=__UpperCamelCase  )

												self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)  )






							def       _UpperCamelCase			(		self :      str     ,						__UpperCamelCase :      List[str]     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      Any     ,						__UpperCamelCase :      Any     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      Tuple  )       ->       Optional[int]:
												_UpperCamelCase      						=	TFEsmForMaskedLM(config=__UpperCamelCase  )
												_UpperCamelCase      						=	model([input_ids, input_mask]  )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)  )






							def       _UpperCamelCase			(		self :      Optional[int]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      int     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      List[str]     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      Any  )       ->       Union[str, Any]:
												_UpperCamelCase      						=	self.num_labels
												_UpperCamelCase      						=	TFEsmForTokenClassification(config=__UpperCamelCase  )
												_UpperCamelCase      						=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												_UpperCamelCase      						=	model(__UpperCamelCase  )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)  )






							def       _UpperCamelCase			(		self :      Dict  )       ->       Optional[Any]:
												_UpperCamelCase      						=	self.prepare_config_and_inputs()
												(
												    (
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							(
												    _UpperCamelCase
												)						,							
												)      						=	config_and_inputs
												_UpperCamelCase      						=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







@require_tf
class      UpperCAmelCase_       (						_lowercase			,							_lowercase			,							unittest.TestCase):
							snake_case__        =      (
							    (
							        TFEsmModel,
							        TFEsmForMaskedLM,
							        TFEsmForSequenceClassification,
							        TFEsmForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							snake_case__        =      (
							    {
							        '''feature-extraction''': TFEsmModel,
							        '''fill-mask''': TFEsmForMaskedLM,
							        '''text-classification''': TFEsmForSequenceClassification,
							        '''token-classification''': TFEsmForTokenClassification,
							        '''zero-shot''': TFEsmForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							snake_case__        =      False
							snake_case__        =      False






							def       _UpperCamelCase			(		self :      List[str]  )       ->       List[Any]:
												_UpperCamelCase      						=	TFEsmModelTester(self  )
												_UpperCamelCase      						=	ConfigTester(self     ,						config_class=__UpperCamelCase     ,						hidden_size=37  )






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






							def       _UpperCamelCase			(		self :      Optional[Any]  )       ->       Dict:
												_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase  )






							def       _UpperCamelCase			(		self :      List[Any]  )       ->       Dict:
												_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(*__UpperCamelCase  )






							def       _UpperCamelCase			(		self :      str  )       ->       Union[str, Any]:
												_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase  )






							def       _UpperCamelCase			(		self :      Optional[int]  )       ->       Optional[Any]:
												_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase  )






							@slow
							def       _UpperCamelCase			(		self :      int  )       ->       List[Any]:
												for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_UpperCamelCase      						=	TFEsmModel.from_pretrained(__UpperCamelCase  )
																	self.assertIsNotNone(__UpperCamelCase  )






							@unittest.skip('''Protein models do not support embedding resizing.'''  )
							def       _UpperCamelCase			(		self :      str  )       ->       List[Any]:
												pass






							@unittest.skip('''Protein models do not support embedding resizing.'''  )
							def       _UpperCamelCase			(		self :      Union[str, Any]  )       ->       int:
												pass






							def       _UpperCamelCase			(		self :      Union[str, Any]  )       ->       Any:
												_UpperCamelCase						,							_UpperCamelCase      						=	self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_UpperCamelCase      						=	model_class(__UpperCamelCase  )
																	assert isinstance(model.get_input_embeddings()     ,						tf.keras.layers.Layer  )
																	if model_class is TFEsmForMaskedLM:
																						# Output embedding test differs from the main test because they're a matrix, not a layer
																						_UpperCamelCase      						=	model.get_bias()
																						assert isinstance(__UpperCamelCase     ,						__UpperCamelCase  )
																						for k, v in name.items():
																											assert isinstance(__UpperCamelCase     ,						tf.Variable  )
																	else:
																						_UpperCamelCase      						=	model.get_output_embeddings()
																						assert x is None
																						_UpperCamelCase      						=	model.get_bias()
																						assert name is None







@require_tf
class      UpperCAmelCase_       (						unittest.TestCase):






							@slow
							def       _UpperCamelCase			(		self :      List[Any]  )       ->       Any:
												_UpperCamelCase      						=	TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''  )

												_UpperCamelCase      						=	tf.constant([[0, 1, 2, 3, 4, 5]]  )
												_UpperCamelCase      						=	model(__UpperCamelCase  )[0]
												_UpperCamelCase      						=	[1, 6, 33]
												self.assertEqual(list(output.numpy().shape  )     ,						__UpperCamelCase  )
												# compare the actual values for a slice.
												_UpperCamelCase      						=	tf.constant(
												    [
												        [
												            [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
												            [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
												            [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
												        ]
												    ]  )
												self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()     ,						expected_slice.numpy()     ,						atol=1E-2  )  )






							@slow
							def       _UpperCamelCase			(		self :      List[str]  )       ->       List[Any]:
												_UpperCamelCase      						=	TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''  )

												_UpperCamelCase      						=	tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]  )
												_UpperCamelCase      						=	model(__UpperCamelCase  )[0]
												# compare the actual values for a slice.
												_UpperCamelCase      						=	tf.constant(
												    [
												        [
												            [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
												            [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
												            [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
												        ]
												    ]  )
												self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()     ,						expected_slice.numpy()     ,						atol=1E-4  )  )




'''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      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase = 13			,  UpperCamelCase = 64			,  UpperCamelCase = 2			,  UpperCamelCase = 3			,  UpperCamelCase = 3			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = 128			,  UpperCamelCase=[16, 32, 64, 128]			,  UpperCamelCase = 7			,  UpperCamelCase = 4			,  UpperCamelCase = 37			,  UpperCamelCase = "gelu"			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 10			,  UpperCamelCase = 0.02			,  UpperCamelCase = 2			,  UpperCamelCase = 1			,  UpperCamelCase = 128			,  UpperCamelCase = [2, 2, 2, 2]			,  UpperCamelCase = 2			,  UpperCamelCase = 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_      =  encoder_stride
   lowerCamelCase_      =  num_attention_outputs
   lowerCamelCase_      =  embed_dim
   lowerCamelCase_      =  embed_dim + 1
   lowerCamelCase_      =  resolution
   lowerCamelCase_      =  depths
   lowerCamelCase_      =  hidden_sizes
   lowerCamelCase_      =  dim
   lowerCamelCase_      =  mlp_expansion_ratio






  def   snake_case		(    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   snake_case		(    self     ):







   """simple docstring"""
   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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerModel(config=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  training=UpperCamelCase     )
   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.type_sequence_label_size
   lowerCamelCase_      =  TFEfficientFormerForImageClassification(UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  labels=UpperCamelCase			,  training=UpperCamelCase     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.type_sequence_label_size)     )

   # test greyscale images
   lowerCamelCase_      =  1
   lowerCamelCase_      =  TFEfficientFormerForImageClassification(UpperCamelCase     )

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







  def   snake_case		(    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      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (
      (
          TFEfficientFormerModel,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerForImageClassification,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase		    =  (
      {
          "feature-extraction": TFEfficientFormerModel,
          "image-classification": (
              TFEfficientFormerForImageClassification,
              TFEfficientFormerForImageClassificationWithTeacher,
          ),
      }
      if is_tf_available()
      else {}
  )

  _lowerCamelCase		    =  False

  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerModelTester(self     )
   lowerCamelCase_      =  ConfigTester(
       self			,  config_class=UpperCamelCase			,  has_text_modality=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()






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







   """simple docstring"""
   pass






  @unittest.skip(reason="EfficientFormer does not support input and output embeddings"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    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(UpperCamelCase     )
    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]			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   def check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  model_class(UpperCamelCase     )

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

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

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

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

   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  True
    check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

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

    check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=False     ):







   """simple docstring"""
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCamelCase     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFEfficientFormerModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_      =  True

   lowerCamelCase_      =  getattr(self.model_tester			,  "seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "encoder_seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "key_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "chunk_length"			,  UpperCamelCase     )

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

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  True
    lowerCamelCase_      =  False
    lowerCamelCase_      =  True
    lowerCamelCase_      =  model_class(UpperCamelCase     )

    lowerCamelCase_      =  model(**self._prepare_for_class(UpperCamelCase			,  UpperCamelCase     )			,  training=UpperCamelCase     )
    lowerCamelCase_      =  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"]
    lowerCamelCase_      =  True
    lowerCamelCase_      =  model_class(UpperCamelCase     )
    lowerCamelCase_      =  model(**self._prepare_for_class(UpperCamelCase			,  UpperCamelCase     )			,  training=UpperCamelCase     )

    lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   # 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
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    # Prepare our model
    lowerCamelCase_      =  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
    lowerCamelCase_      =  {
        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
    }
    lowerCamelCase_      =  model(UpperCamelCase     )
    self.assertTrue(outputs_dict is not None     )





def __snake_case					(      ):
 lowerCamelCase_      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
 return image







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





  """simple docstring"""




  @cached_property
  def   snake_case		(    self     ):







   """simple docstring"""
   return (
       EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"     )
       if is_vision_available()
       else None
   )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"     )
   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  image_processor(images=UpperCamelCase			,  return_tensors="tf"     )
   # forward pass
   lowerCamelCase_      =  model(**UpperCamelCase			,  training=UpperCamelCase     )
   # verify the logits
   lowerCamelCase_      =  tf.TensorShape((1, 1000)     )
   self.assertEqual(outputs.logits.shape			,  UpperCamelCase     )
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
       "snap-research/efficientformer-l1-300"     )
   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  image_processor(images=UpperCamelCase			,  return_tensors="tf"     )
   # forward pass
   lowerCamelCase_      =  model(**UpperCamelCase			,  training=UpperCamelCase     )
   # verify the logits
   lowerCamelCase_      =  tf.TensorShape((1, 1000)     )
   self.assertEqual(outputs.logits.shape			,  UpperCamelCase     )
   lowerCamelCase_      =  tf.constant([-0.1_312, 0.4_353, -1.0_499]     )
   self.assertTrue(np.allclose(outputs.logits[0, :3]			,  UpperCamelCase			,  atol=1e-4     )     )



