



"""simple docstring"""

lowercase__					:						List[str]												=			{'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowercase__					:						str												=			['''a''', '''b''', '''c''', '''d''', '''e''']




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  List[str]       =			start
    # add current to visited
    visited.append(lowerCamelCase__					)
    snake_case_				:  Optional[int]       =			edges[current]
    for neighbor in neighbors:
        # if neighbor not in visited, visit
        if neighbor not in visited:
            snake_case_				:  int       =			topological_sort(lowerCamelCase__	,    lowerCamelCase__	,    lowerCamelCase__					)
    # if all neighbors visited add current to sort
    sort.append(lowerCamelCase__					)
    # if all vertices haven't been visited select a new one to visit
    if len(lowerCamelCase__					) != len(lowerCamelCase__					):
        for vertice in vertices:
            if vertice not in visited:
                snake_case_				:  Tuple       =			topological_sort(lowerCamelCase__	,    lowerCamelCase__	,    lowerCamelCase__					)
    # return sort
    return sort


if __name__ == "__main__":
     lowercase__					:						Any												=			topological_sort('''a''', [], [])
     print(sort)





"""simple docstring"""

from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
     from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor




class  _UpperCAmelCase (    lowerCAmelCase__):

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Any       =			self.config_class(**self.inputs_dict       )
     self.parent.assertTrue(hasattr(lowercase_						,  '''embed_dim'''       )       )
     self.parent.assertTrue(hasattr(lowercase_						,  '''num_heads'''       )       )





class  _UpperCAmelCase :

 def __init__(  self	: List[str]						,  lowercase_	: Optional[Any]						,  lowercase_	: int=13						,  lowercase_	: Optional[int]=64						,  lowercase_	: Any=3						,  lowercase_	: Any=[16, 48, 96]						,  lowercase_	: List[Any]=[1, 3, 6]						,  lowercase_	: Union[str, Any]=[1, 2, 10]						,  lowercase_	: Optional[Any]=[7, 3, 3]						,  lowercase_	: Union[str, Any]=[4, 2, 2]						,  lowercase_	: Tuple=[2, 1, 1]						,  lowercase_	: List[str]=[2, 2, 2]						,  lowercase_	: Union[str, Any]=[False, False, True]						,  lowercase_	: Optional[int]=[0.0, 0.0, 0.0]						,  lowercase_	: str=0.02						,  lowercase_	: Optional[Any]=1E-12						,  lowercase_	: Optional[int]=True						,  lowercase_	: Optional[int]=True						,  lowercase_	: Optional[Any]=2						,  ):
     snake_case_				:  List[Any]       =			parent
     snake_case_				:  int       =			batch_size
     snake_case_				:  Union[str, Any]       =			image_size
     snake_case_				:  Tuple       =			patch_sizes
     snake_case_				:  List[Any]       =			patch_stride
     snake_case_				:  Dict       =			patch_padding
     snake_case_				:  Any       =			is_training
     snake_case_				:  Any       =			use_labels
     snake_case_				:  str       =			num_labels
     snake_case_				:  Optional[Any]       =			num_channels
     snake_case_				:  Optional[Any]       =			embed_dim
     snake_case_				:  int       =			num_heads
     snake_case_				:  List[str]       =			stride_kv
     snake_case_				:  Any       =			depth
     snake_case_				:  Dict       =			cls_token
     snake_case_				:  Dict       =			attention_drop_rate
     snake_case_				:  int       =			initializer_range
     snake_case_				:  Tuple       =			layer_norm_eps

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  str       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

     snake_case_				:  str       =			None
     if self.use_labels:
         # create a random int32 tensor of given shape
         snake_case_				:  Union[str, Any]       =			ids_tensor([self.batch_size]						,  self.num_labels       )

     snake_case_				:  Dict       =			self.get_config()
     return config, pixel_values, labels

 def 		_snake_case      (  self	: int       ):
     return CvtConfig(
         image_size=self.image_size						,  num_labels=self.num_labels						,  num_channels=self.num_channels						,  embed_dim=self.embed_dim						,  num_heads=self.num_heads						,  patch_sizes=self.patch_sizes						,  patch_padding=self.patch_padding						,  patch_stride=self.patch_stride						,  stride_kv=self.stride_kv						,  depth=self.depth						,  cls_token=self.cls_token						,  attention_drop_rate=self.attention_drop_rate						,  initializer_range=self.initializer_range						,  )

 def 		_snake_case      (  self	: int						,  lowercase_	: List[str]						,  lowercase_	: Optional[Any]						,  lowercase_	: Union[str, Any]       ):
     snake_case_				:  Tuple       =			TFCvtModel(config=lowercase_       )
     snake_case_				:  Tuple       =			model(lowercase_						,  training=lowercase_       )
     snake_case_				:  int       =			(self.image_size, self.image_size)
     snake_case_,			snake_case_				:  List[str]       =			image_size[0], image_size[1]
     for i in range(len(self.depth       )       ):
         snake_case_				:  str       =			floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
         snake_case_				:  str       =			floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
     self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.embed_dim[-1], height, width)       )

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Tuple						,  lowercase_	: Union[str, Any]						,  lowercase_	: List[str]       ):
     snake_case_				:  int       =			self.num_labels
     snake_case_				:  Any       =			TFCvtForImageClassification(lowercase_       )
     snake_case_				:  List[Any]       =			model(lowercase_						,  labels=lowercase_						,  training=lowercase_       )
     self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)       )






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





@require_tf
class  _UpperCAmelCase (    lowerCAmelCase__					,  lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Optional[int]	      =	(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
 _lowerCAmelCase     :    str	      =	(
     {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
     if is_tf_available()
     else {}
 )
 _lowerCAmelCase     :    str	      =	False
 _lowerCAmelCase     :    int	      =	False
 _lowerCAmelCase     :    Tuple	      =	False
 _lowerCAmelCase     :    int	      =	False
 _lowerCAmelCase     :    int	      =	False

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Optional[int]       =			TFCvtModelTester(self       )
     snake_case_				:  str       =			TFCvtConfigTester(self						,  config_class=lowercase_						,  has_text_modality=lowercase_						,  hidden_size=37       )

 def 		_snake_case      (  self	: int       ):
     self.config_tester.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()

 @unittest.skip(reason='''Cvt does not output attentions'''       )
 def 		_snake_case      (  self	: Any       ):
     pass

 @unittest.skip(reason='''Cvt does not use inputs_embeds'''       )
 def 		_snake_case      (  self	: str       ):
     pass

 @unittest.skip(reason='''Cvt does not support input and output embeddings'''       )
 def 		_snake_case      (  self	: Union[str, Any]       ):
     pass

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''       )       ) == 0						,  reason='''TF does not support backprop for grouped convolutions on CPU.'''						,  )
 def 		_snake_case      (  self	: Tuple       ):
     super().test_dataset_conversion()

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''       )       ) == 0						,  reason='''TF does not support backprop for grouped convolutions on CPU.'''						,  )
 @slow
 def 		_snake_case      (  self	: Tuple       ):
     super().test_keras_fit()

 @unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'''       )
 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Optional[Any]       =			tf.keras.mixed_precision.Policy('''mixed_float16'''       )
     tf.keras.mixed_precision.set_global_policy(lowercase_       )
     super().test_keras_fit()
     tf.keras.mixed_precision.set_global_policy('''float32'''       )

 def 		_snake_case      (  self	: int       ):
     snake_case_,			snake_case_				:  List[str]       =			self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case_				:  int       =			['''pixel_values''']
         self.assertListEqual(arg_names[:1]						,  lowercase_       )

 def 		_snake_case      (  self	: List[str]       ):
     def check_hidden_states_output(lowercase_	: Optional[int]						,  lowercase_	: Union[str, Any]						,  lowercase_	: str       ):
         snake_case_				:  Any       =			model_class(lowercase_       )

         snake_case_				:  Optional[Any]       =			model(**self._prepare_for_class(lowercase_						,  lowercase_       )       )
         snake_case_				:  Tuple       =			outputs.hidden_states

         snake_case_				:  str       =			len(self.model_tester.depth       )
         self.assertEqual(len(lowercase_       )						,  lowercase_       )

         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-3:]       )						,  [
                 self.model_tester.embed_dim[0],
                 self.model_tester.image_size // 4,
                 self.model_tester.image_size // 4,
             ]						,  )

     snake_case_,			snake_case_				:  List[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_				:  Dict       =			True
         check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_       )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case_				:  Tuple       =			True

         check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Dict       =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_       )

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  str       =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_       )






 @slow
 def 		_snake_case      (  self	: Optional[Any]       ):
     for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_				:  int       =			TFCvtModel.from_pretrained(lowercase_       )
         self.assertIsNotNone(lowercase_       )





def __lowercase    (							):
    snake_case_				:  Tuple       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
    return image






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

 @cached_property
 def 		_snake_case      (  self	: List[Any]       ):
     return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )






 @slow
 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  int       =			TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

     snake_case_				:  Any       =			self.default_image_processor
     snake_case_				:  Union[str, Any]       =			prepare_img()
     snake_case_				:  int       =			image_processor(images=lowercase_						,  return_tensors='''tf'''       )

     # forward pass
     snake_case_				:  Tuple       =			model(**lowercase_       )

     # verify the logits
     snake_case_				:  Optional[Any]       =			tf.TensorShape((1, 1000)       )
     self.assertEqual(outputs.logits.shape						,  lowercase_       )

     snake_case_				:  Tuple       =			tf.constant([0.92_85, 0.90_15, -0.31_50]       )
     self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()						,  lowercase_						,  atol=1E-4       )       )





