'''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 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	int = 13      ,       SCREAMING_SNAKE_CASE__   :	int = 64      ,       SCREAMING_SNAKE_CASE__   :	int = 2      ,       SCREAMING_SNAKE_CASE__   :	int = 3      ,       SCREAMING_SNAKE_CASE__   :	int = 3      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	int = 1_28      ,       SCREAMING_SNAKE_CASE__   :	int=[16, 32, 64, 1_28]      ,       SCREAMING_SNAKE_CASE__   :	int = 7      ,       SCREAMING_SNAKE_CASE__   :	int = 4      ,       SCREAMING_SNAKE_CASE__   :	int = 37      ,       SCREAMING_SNAKE_CASE__   :	str = "gelu"      ,       SCREAMING_SNAKE_CASE__   :	float = 0.1      ,       SCREAMING_SNAKE_CASE__   :	float = 0.1      ,       SCREAMING_SNAKE_CASE__   :	int = 10      ,       SCREAMING_SNAKE_CASE__   :	float = 0.0_2      ,       SCREAMING_SNAKE_CASE__   :	int = 2      ,       SCREAMING_SNAKE_CASE__   :	int = 1      ,       SCREAMING_SNAKE_CASE__   :	int = 1_28      ,       SCREAMING_SNAKE_CASE__   :	List[int] = [2, 2, 2, 2]      ,       SCREAMING_SNAKE_CASE__   :	int = 2      ,       SCREAMING_SNAKE_CASE__   :	int = 2      ,       )  ->    Any:
  __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 a					(      self   :	Union[str, Any]							)  ->    List[Any]:
  __lowerCAmelCase											=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

  __lowerCAmelCase											=						self.get_config()

  return config, pixel_values, labels
 def a					(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  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=SCREAMING_SNAKE_CASE__      ,       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 a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    Any:
  __lowerCAmelCase											=						TFEfficientFormerModel(config=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       training=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    List[Any]:
  __lowerCAmelCase											=						self.type_sequence_label_size
  __lowerCAmelCase											=						TFEfficientFormerForImageClassification(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       labels=SCREAMING_SNAKE_CASE__      ,       training=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)							)

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

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





 def a					(      self   :	Union[str, Any]							)  ->    int:
  __lowerCAmelCase											=						self.prepare_config_and_inputs()
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						config_and_inputs
  __lowerCAmelCase											=						{"""pixel_values""": pixel_values}
  return config, inputs_dict







@require_tf
class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






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

 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					False

 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					False
 def a					(      self   :	Optional[int]							)  ->    Tuple:
  __lowerCAmelCase											=						TFEfficientFormerModelTester(self							)
  __lowerCAmelCase											=						ConfigTester(
      self      ,       config_class=SCREAMING_SNAKE_CASE__      ,       has_text_modality=SCREAMING_SNAKE_CASE__      ,       hidden_size=37							)
 def a					(      self   :	Optional[Any]							)  ->    List[str]:
  self.config_tester.run_common_tests()
 @unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""							)
 def a					(      self   :	str							)  ->    List[str]:
  pass
 @unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""							)
 def a					(      self   :	int							)  ->    Tuple:
  pass
 def a					(      self   :	int							)  ->    str:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   __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]      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[Any]							)  ->    List[Any]:
  def check_hidden_states_output(SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	str							):
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)      ,       training=SCREAMING_SNAKE_CASE__							)
   __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(SCREAMING_SNAKE_CASE__							)      ,       SCREAMING_SNAKE_CASE__							)

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

    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(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

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

   check_hidden_states_output(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=False							)  ->    List[Any]:
  __lowerCAmelCase											=						super()._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       return_labels=SCREAMING_SNAKE_CASE__							)

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

  return inputs_dict
 def a					(      self   :	str							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)
 @unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""							)
 def a					(      self   :	List[str]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[str]							)  ->    List[Any]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__							)
 @slow
 def a					(      self   :	List[Any]							)  ->    List[str]:
  for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __lowerCAmelCase											=						TFEfficientFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Tuple							)  ->    int:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()
  __lowerCAmelCase											=						True

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

  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(SCREAMING_SNAKE_CASE__							)

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

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

   __lowerCAmelCase											=						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       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 a					(      self   :	Dict							)  ->    List[Any]:
  # 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(SCREAMING_SNAKE_CASE__							)
   # 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=SCREAMING_SNAKE_CASE__							)
       for key, val in model.input_signature.items()
       if key in model.dummy_inputs
   }
   __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)
   self.assertTrue(outputs_dict is not None							)







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






 '''simple docstring'''

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







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


 '''simple docstring'''






 @cached_property
 def a					(      self   :	Any							)  ->    Any:
  return (
      EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""							)
      if is_vision_available()
      else None
  )
 @slow
 def a					(      self   :	Optional[Any]							)  ->    Any:
  __lowerCAmelCase											=						TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""							)
  __lowerCAmelCase											=						self.default_image_processor
  __lowerCAmelCase											=						prepare_img()
  __lowerCAmelCase											=						image_processor(images=SCREAMING_SNAKE_CASE__      ,       return_tensors="""tf"""							)
  # forward pass
  __lowerCAmelCase											=						model(**SCREAMING_SNAKE_CASE__      ,       training=SCREAMING_SNAKE_CASE__							)
  # verify the logits
  __lowerCAmelCase											=						tf.TensorShape((1, 10_00)							)
  self.assertEqual(outputs.logits.shape      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]							)
  self.assertTrue(np.allclose(outputs.logits[0, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-4							)							)





 @slow
 def a					(      self   :	Tuple							)  ->    Dict:
  __lowerCAmelCase											=						TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
      """snap-research/efficientformer-l1-300"""							)
  __lowerCAmelCase											=						self.default_image_processor
  __lowerCAmelCase											=						prepare_img()
  __lowerCAmelCase											=						image_processor(images=SCREAMING_SNAKE_CASE__      ,       return_tensors="""tf"""							)
  # forward pass
  __lowerCAmelCase											=						model(**SCREAMING_SNAKE_CASE__      ,       training=SCREAMING_SNAKE_CASE__							)
  # verify the logits
  __lowerCAmelCase											=						tf.TensorShape((1, 10_00)							)
  self.assertEqual(outputs.logits.shape      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]							)
  self.assertTrue(np.allclose(outputs.logits[0, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-4							)							)







'''simple docstring'''

from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






