



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 					_snake_case      (       lowercase__):




				def 				A__   (    self       :    List[Any]    ):
							lowercase__       	=					self.config_class(**self.inputs_dict    )
							self.parent.assertTrue(hasattr(a_,       "embed_dim"    )    )
							self.parent.assertTrue(hasattr(a_,       "num_heads"    )    )
class 					_snake_case      :




				def __init__(    self       :    int,       __lowercase       :    str,       __lowercase       :    Any=13,       __lowercase       :    List[Any]=64,       __lowercase       :    Union[str, Any]=3,       __lowercase       :    Dict=[16, 48, 96],       __lowercase       :    Union[str, Any]=[1, 3, 6],       __lowercase       :    Optional[int]=[1, 2, 10],       __lowercase       :    Dict=[7, 3, 3],       __lowercase       :    Any=[4, 2, 2],       __lowercase       :    List[str]=[2, 1, 1],       __lowercase       :    Dict=[2, 2, 2],       __lowercase       :    Dict=[False, False, True],       __lowercase       :    str=[0.0, 0.0, 0.0],       __lowercase       :    List[Any]=0.02,       __lowercase       :    Union[str, Any]=1e-1_2,       __lowercase       :    Optional[Any]=True,       __lowercase       :    Tuple=True,       __lowercase       :    List[Any]=2,       ):
							lowercase__       	=					parent
							lowercase__       	=					batch_size
							lowercase__       	=					image_size
							lowercase__       	=					patch_sizes
							lowercase__       	=					patch_stride
							lowercase__       	=					patch_padding
							lowercase__       	=					is_training
							lowercase__       	=					use_labels
							lowercase__       	=					num_labels
							lowercase__       	=					num_channels
							lowercase__       	=					embed_dim
							lowercase__       	=					num_heads
							lowercase__       	=					stride_kv
							lowercase__       	=					depth
							lowercase__       	=					cls_token
							lowercase__       	=					attention_drop_rate
							lowercase__       	=					initializer_range
							lowercase__       	=					layer_norm_eps




				def 				A__   (    self       :    Tuple    ):
							lowercase__       	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

							lowercase__       	=					self.get_config()
							return config, pixel_values, labels




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




				def 				A__   (    self       :    str    ):
							lowercase__       	=					self.prepare_config_and_inputs()
							lowercase__	,							lowercase__	,							lowercase__       	=					config_and_inputs
							lowercase__       	=					{"pixel_values": pixel_values}
							return config, inputs_dict
@require_tf
class 					_snake_case      (       lowercase__     ,						lowercase__     ,						unittest.TestCase):
				UpperCamelCase__					:       Union[str, Any]  		=(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
				UpperCamelCase__					:       Optional[int]  		=(
				    {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
				    if is_tf_available()
				    else {}
				)
				UpperCamelCase__					:       List[str]  		=False
				UpperCamelCase__					:       int  		=False
				UpperCamelCase__					:       str  		=False
				UpperCamelCase__					:       int  		=False
				UpperCamelCase__					:       Tuple  		=False




				def 				A__   (    self       :    Any    ):
							lowercase__       	=					TFCvtModelTester(self    )
							lowercase__       	=					TFCvtConfigTester(self,       config_class=a_,       has_text_modality=a_,       hidden_size=37    )




				def 				A__   (    self       :    Optional[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 				A__   (    self       :    Dict    ):
							pass




				@unittest.skip(reason="Cvt does not use inputs_embeds"    )
				def 				A__   (    self       :    List[Any]    ):
							pass




				@unittest.skip(reason="Cvt does not support input and output embeddings"    )
				def 				A__   (    self       :    Dict    ):
							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 				A__   (    self       :    str    ):
							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 				A__   (    self       :    str    ):
							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 				A__   (    self       :    Dict    ):
							lowercase__       	=					tf.keras.mixed_precision.Policy("mixed_float16"    )
							tf.keras.mixed_precision.set_global_policy(a_    )
							super().test_keras_fit()
							tf.keras.mixed_precision.set_global_policy("float32"    )




				def 				A__   (    self       :    Optional[Any]    ):
							lowercase__	,							lowercase__       	=					self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase__       	=					["pixel_values"]
										self.assertListEqual(arg_names[:1],       a_    )




				def 				A__   (    self       :    Union[str, Any]    ):
							def check_hidden_states_output(__lowercase       :    List[str],       __lowercase       :    List[Any],       __lowercase       :    Any    ):
										lowercase__       	=					model_class(a_    )

										lowercase__       	=					model(**self._prepare_for_class(a_,       a_    )    )
										lowercase__       	=					outputs.hidden_states

										lowercase__       	=					len(self.model_tester.depth    )
										self.assertEqual(len(a_    ),       a_    )

										# 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,
										    ],       )

							lowercase__	,							lowercase__       	=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowercase__       	=					True
										check_hidden_states_output(a_,       a_,       a_    )

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

										check_hidden_states_output(a_,       a_,       a_    )




				def 				A__   (    self       :    Any    ):
							lowercase__       	=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*a_    )




				def 				A__   (    self       :    List[Any]    ):
							lowercase__       	=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*a_    )




				@slow
				def 				A__   (    self       :    Any    ):
							for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__       	=					TFCvtModel.from_pretrained(a_    )
										self.assertIsNotNone(a_    )



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




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




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




				@slow
				def 				A__   (    self       :    Optional[Any]    ):
							lowercase__       	=					TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )

							lowercase__       	=					self.default_image_processor
							lowercase__       	=					prepare_img()
							lowercase__       	=					image_processor(images=a_,       return_tensors="tf"    )

							# forward pass
							lowercase__       	=					model(**a_    )

							# verify the logits
							lowercase__       	=					tf.TensorShape((1, 1000)    )
							self.assertEqual(outputs.logits.shape,       a_    )

							lowercase__       	=					tf.constant([0.9285, 0.9015, -0.3150]    )
							self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(),       a_,       atol=1e-4    )    )









import numpy as np


class 	__snake_case   :
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  (0, 0)
						lowerCAmelCase__		  =  None
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
		def __eq__(							self    ,a_						):







						"""simple docstring"""
						return self.position == cell.position



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.position						)



class 	__snake_case   :
		def __init__(							self    ,a_=(5, 5)						):







						"""simple docstring"""
						lowerCAmelCase__		  =  np.zeros(a_						)
						lowerCAmelCase__		  =  world_size[0]
						lowerCAmelCase__		  =  world_size[1]
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.w						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [
						    (-1, -1),
						    (-1, 0),
						    (-1, 1),
						    (0, -1),
						    (0, 1),
						    (1, -1),
						    (1, 0),
						    (1, 1),
						]
						lowerCAmelCase__		  =  cell.position[0]
						lowerCAmelCase__		  =  cell.position[1]
						lowerCAmelCase__		  =  []
						for n in neughbour_cord:
										lowerCAmelCase__		  =  current_x + n[0]
										lowerCAmelCase__		  =  current_y + n[1]
										if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
														lowerCAmelCase__		  =  Cell()
														lowerCAmelCase__		  =  (x, y)
														lowerCAmelCase__		  =  cell
														neighbours.append(a_						)
						return neighbours







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     int:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  []
				_open.append(snake_case__					)

				while _open:
								lowerCAmelCase__		  =  np.argmin([n.f for n in _open]					)
								lowerCAmelCase__		  =  _open[min_f]
								_closed.append(_open.pop(snake_case__					)					)
								if current == goal:
												break
								for n in world.get_neigbours(snake_case__					):
												for c in _closed:
																if c == n:
																				continue
												lowerCAmelCase__		  =  current.g + 1
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  n.position
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  goal.position
												lowerCAmelCase__		  =  (ya - ya) ** 2 + (xa - xa) ** 2
												lowerCAmelCase__		  =  n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(snake_case__					)
				lowerCAmelCase__		  =  []
				while current.parent is not None:
								path.append(current.position					)
								lowerCAmelCase__		  =  current.parent
				path.append(current.position					)
				return path[::-1]


if __name__ == "__main__":
	_lowerCAmelCase	:   str    =   Gridworld()
	# Start position and goal
	_lowerCAmelCase	:   List[Any]    =   Cell()
	_lowerCAmelCase	:   List[str]    =   (0, 0)
	_lowerCAmelCase	:   Optional[int]    =   Cell()
	_lowerCAmelCase	:   int    =   (4, 4)
	print(f"""path from {start.position} to {goal.position}""")
	_lowerCAmelCase	:   Optional[int]    =   astar(world, start, goal)
	#   Just for visual reasons.
	for i in s:
		_lowerCAmelCase	:   int    =   1
	print(world.w)
