



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 					__A       (			lowerCamelCase__   ):





			def 							_snake_case     (self					):
						lowerCamelCase__			:   Tuple										=		self.config_class(**self.inputs_dict					)
						self.parent.assertTrue(hasattr(snake_case__				,     """embed_dim"""					)					)
						self.parent.assertTrue(hasattr(snake_case__				,     """num_heads"""					)					)







class 					__A       :





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





			def 							_snake_case     (self					):
						lowerCamelCase__			:   List[Any]										=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

						lowerCamelCase__			:   List[str]										=		self.get_config()
						return config, pixel_values, labels





			def 							_snake_case     (self					):
						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				,     __magic_name__				,     __magic_name__				,     __magic_name__					):
						lowerCamelCase__			:   Tuple										=		TFCvtModel(config=snake_case__					)
						lowerCamelCase__			:   List[Any]										=		model(snake_case__				,     training=snake_case__					)
						lowerCamelCase__			:   str										=		(self.image_size, self.image_size)
						lowerCamelCase__			:   Any										=		image_size[0], image_size[1]
						for i in range(len(self.depth					)					):
									lowerCamelCase__			:   List[Any]										=		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1					)
									lowerCamelCase__			:   List[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				,     __magic_name__				,     __magic_name__				,     __magic_name__					):
						lowerCamelCase__			:   Tuple										=		self.num_labels
						lowerCamelCase__			:   str										=		TFCvtForImageClassification(snake_case__					)
						lowerCamelCase__			:   Optional[Any]										=		model(snake_case__				,     labels=snake_case__				,     training=snake_case__					)
						self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)					)





			def 							_snake_case     (self					):
						lowerCamelCase__			:   List[Any]										=		self.prepare_config_and_inputs()
						lowerCamelCase__			:   int										=		config_and_inputs
						lowerCamelCase__			:   Any										=		{"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_tf
class 					__A       (			lowerCamelCase__       ,			lowerCamelCase__       ,			unittest.TestCase   ):
			UpperCamelCase       :List[str]	 =      (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
			UpperCamelCase       :Union[str, Any]	 =      (
			    {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
			    if is_tf_available()
			    else {}
			)
			UpperCamelCase       :str	 =      False
			UpperCamelCase       :Dict	 =      False
			UpperCamelCase       :List[Any]	 =      False
			UpperCamelCase       :Union[str, Any]	 =      False
			UpperCamelCase       :Dict	 =      False





			def 							_snake_case     (self					):
						lowerCamelCase__			:   int										=		TFCvtModelTester(self					)
						lowerCamelCase__			:   Union[str, Any]										=		TFCvtConfigTester(self				,     config_class=snake_case__				,     has_text_modality=snake_case__				,     hidden_size=37					)





			def 							_snake_case     (self					):
						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					):
						pass





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





			@unittest.skip(reason="""Cvt does not support input and output embeddings"""					)
			def 							_snake_case     (self					):
						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					):
						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					):
						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					):
						lowerCamelCase__			:   List[str]										=		tf.keras.mixed_precision.Policy("""mixed_float16"""					)
						tf.keras.mixed_precision.set_global_policy(snake_case__					)
						super().test_keras_fit()
						tf.keras.mixed_precision.set_global_policy("""float32"""					)





			def 							_snake_case     (self					):
						lowerCamelCase__			:   Tuple										=		self.model_tester.prepare_config_and_inputs_for_common()

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

									lowerCamelCase__			:   Union[str, Any]										=		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]				,     snake_case__					)





			def 							_snake_case     (self					):
						def check_hidden_states_output(__magic_name__				,     __magic_name__				,     __magic_name__					):
									lowerCamelCase__			:   List[str]										=		model_class(snake_case__					)

									lowerCamelCase__			:   List[str]										=		model(**self._prepare_for_class(snake_case__				,     snake_case__					)					)
									lowerCamelCase__			:   Dict										=		outputs.hidden_states

									lowerCamelCase__			:   int										=		len(self.model_tester.depth					)
									self.assertEqual(len(snake_case__					)				,     snake_case__					)

									# 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,
									    ]				,     )

						lowerCamelCase__			:   Optional[int]										=		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									lowerCamelCase__			:   Optional[int]										=		True
									check_hidden_states_output(snake_case__				,     snake_case__				,     snake_case__					)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									lowerCamelCase__			:   Optional[int]										=		True

									check_hidden_states_output(snake_case__				,     snake_case__				,     snake_case__					)





			def 							_snake_case     (self					):
						lowerCamelCase__			:   Optional[Any]										=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case__					)





			def 							_snake_case     (self					):
						lowerCamelCase__			:   Dict										=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case__					)





			@slow
			def 							_snake_case     (self					):
						for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase__			:   Dict										=		TFCvtModel.from_pretrained(snake_case__					)
									self.assertIsNotNone(snake_case__					)







def   _A					()	->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__			:   Any										=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
			return image
@require_tf
@require_vision
class 					__A       (			unittest.TestCase   ):





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





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

						lowerCamelCase__			:   Union[str, Any]										=		self.default_image_processor
						lowerCamelCase__			:   Any										=		prepare_img()
						lowerCamelCase__			:   Any										=		image_processor(images=snake_case__				,     return_tensors="""tf"""					)

						# forward pass
						lowerCamelCase__			:   List[Any]										=		model(**snake_case__					)

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

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










UpperCAmelCase_							=				{"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
UpperCAmelCase_							=				["""a""", """b""", """c""", """d""", """e"""]





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

							"""simple docstring"""



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


if __name__ == "__main__":
							UpperCAmelCase_							=				topological_sort("""a""", [], [])
							print(sort)