

import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
    from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Tuple=13     , __lowerCamelCase					:			Optional[Any]=32     , __lowerCamelCase					:			List[Any]=3     , __lowerCamelCase					:			List[str]=4     , __lowerCamelCase					:			str=[10, 20, 30, 40]     , __lowerCamelCase					:			Any=[2, 2, 3, 2]     , __lowerCamelCase					:			Union[str, Any]=True     , __lowerCamelCase					:			List[str]=True     , __lowerCamelCase					:			Any=37     , __lowerCamelCase					:			str="gelu"     , __lowerCamelCase					:			List[str]=10     , __lowerCamelCase					:			Optional[Any]=0.02     , __lowerCamelCase					:			Tuple=["stage2", "stage3", "stage4"]     , __lowerCamelCase					:			Union[str, Any]=[2, 3, 4]     , __lowerCamelCase					:			int=None     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   num_stages
       SCREAMING_SNAKE_CASE										=   hidden_sizes
       SCREAMING_SNAKE_CASE										=   depths
       SCREAMING_SNAKE_CASE										=   is_training
       SCREAMING_SNAKE_CASE										=   use_labels
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   num_labels
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   out_features
       SCREAMING_SNAKE_CASE										=   out_indices
       SCREAMING_SNAKE_CASE										=   scope





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       SCREAMING_SNAKE_CASE										=   None
       if self.use_labels:
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size]     , self.num_labels			)

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





   def      _snake_case		(       self					:			List[Any]			):
       return ConvNextConfig(
           num_channels=self.num_channels     , hidden_sizes=self.hidden_sizes     , depths=self.depths     , num_stages=self.num_stages     , hidden_act=self.hidden_act     , is_decoder=__lowerCamelCase     , initializer_range=self.initializer_range     , out_features=self.out_features     , out_indices=self.out_indices     , num_labels=self.num_labels     , )





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   ConvNextModel(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape     , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     , )





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   ConvNextForImageClassification(__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , labels=__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)			)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Any			):
       SCREAMING_SNAKE_CASE										=   ConvNextBackbone(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps			)     , len(config.out_features			)			)
       self.parent.assertListEqual(list(result.feature_maps[0].shape			)     , [self.batch_size, self.hidden_sizes[1], 4, 4]			)

       # verify channels
       self.parent.assertEqual(len(model.channels			)     , len(config.out_features			)			)
       self.parent.assertListEqual(model.channels     , config.hidden_sizes[1:]			)

       # verify backbone works with out_features=None
       SCREAMING_SNAKE_CASE										=   None
       SCREAMING_SNAKE_CASE										=   ConvNextBackbone(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps			)     , 1			)
       self.parent.assertListEqual(list(result.feature_maps[0].shape			)     , [self.batch_size, self.hidden_sizes[-1], 1, 1]			)

       # verify channels
       self.parent.assertEqual(len(model.channels			)     , 1			)
       self.parent.assertListEqual(model.channels     , [config.hidden_sizes[-1]]			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   config_and_inputs
       SCREAMING_SNAKE_CASE										=   {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (
       (
           ConvNextModel,
           ConvNextForImageClassification,
           ConvNextBackbone,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase__				=      (
       {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
       if is_torch_available()
       else {}
   )

   lowerCamelCase__				=      True
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   ConvNextModelTester(self			)
       SCREAMING_SNAKE_CASE										=   ConfigTester(self     , config_class=__lowerCamelCase     , has_text_modality=__lowerCamelCase     , hidden_size=37			)





   def      _snake_case		(       self					:			Optional[int]			):
       self.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()





   def      _snake_case		(       self					:			Dict			):
       return





   @unittest.skip(reason="ConvNext does not use inputs_embeds"			)
   def      _snake_case		(       self					:			Optional[int]			):
       pass





   @unittest.skip(reason="ConvNext does not support input and output embeddings"			)
   def      _snake_case		(       self					:			int			):
       pass





   @unittest.skip(reason="ConvNext does not use feedforward chunking"			)
   def      _snake_case		(       self					:			List[Any]			):
       pass





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

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

           SCREAMING_SNAKE_CASE										=   ["pixel_values"]
           self.assertListEqual(arg_names[:1]     , __lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Tuple			):
       def check_hidden_states_output(__lowerCamelCase					:			Tuple     , __lowerCamelCase					:			int     , __lowerCamelCase					:			List[str]			):
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()

           with torch.no_grad():
               SCREAMING_SNAKE_CASE										=   model(**self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase			)			)

           SCREAMING_SNAKE_CASE										=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           SCREAMING_SNAKE_CASE										=   self.model_tester.num_stages
           self.assertEqual(len(__lowerCamelCase			)     , expected_num_stages + 1			)

           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]			)     , [self.model_tester.image_size // 4, self.model_tester.image_size // 4]     , )

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   True
           check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

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

           check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





   @slow
   def      _snake_case		(       self					:			Dict			):
       for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE										=   ConvNextModel.from_pretrained(__lowerCamelCase			)
           self.assertIsNotNone(__lowerCamelCase			)





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





@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   @cached_property
   def      _snake_case		(       self					:			Any			):
       return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224"			) if is_vision_available() else None





   @slow
   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"			).to(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.default_image_processor
       SCREAMING_SNAKE_CASE										=   prepare_img()
       SCREAMING_SNAKE_CASE										=   image_processor(images=__lowerCamelCase     , return_tensors="pt"			).to(__lowerCamelCase			)

       # forward pass
       with torch.no_grad():
           SCREAMING_SNAKE_CASE										=   model(**__lowerCamelCase			)

       # verify the logits
       SCREAMING_SNAKE_CASE										=   torch.Size((1, 1000)			)
       self.assertEqual(outputs.logits.shape     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   torch.tensor([-0.0_260, -0.4_739, 0.1_911]			).to(__lowerCamelCase			)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]     , __lowerCamelCase     , atol=1e-4			)			)





@require_torch
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase					,       __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      (ConvNextBackbone,) if is_torch_available() else ()
   lowerCamelCase__				=      ConvNextConfig

   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   ConvNextModelTester(self			)


from __future__ import annotations

__A			:					str						=			list[tuple[int, int]]

__A			:					Optional[int]						=			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__A			:					List[str]						=			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			float     , __lowerCamelCase					:			Node | None     , ):
       SCREAMING_SNAKE_CASE										=   pos_x
       SCREAMING_SNAKE_CASE										=   pos_y
       SCREAMING_SNAKE_CASE										=   (pos_y, pos_x)
       SCREAMING_SNAKE_CASE										=   goal_x
       SCREAMING_SNAKE_CASE										=   goal_y
       SCREAMING_SNAKE_CASE										=   g_cost
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   self.calculate_heuristic()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   abs(self.pos_x - self.goal_x			)
       SCREAMING_SNAKE_CASE										=   abs(self.pos_y - self.goal_y			)
       return dx + dy





   def __lt__(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       return self.f_cost < other.f_cost





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			tuple[int, int]     , __lowerCamelCase					:			tuple[int, int]			):
       SCREAMING_SNAKE_CASE										=   Node(start[1]     , start[0]     , goal[1]     , goal[0]     , 0     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   Node(goal[1]     , goal[0]     , goal[1]     , goal[0]     , 99999     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   [self.start]
       SCREAMING_SNAKE_CASE										=   []

       SCREAMING_SNAKE_CASE										=   False





   def      _snake_case		(       self					:			Optional[Any]			):
       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(0			)

           if current_node.pos == self.target.pos:
               SCREAMING_SNAKE_CASE										=   True
               return self.retrace_path(__lowerCamelCase			)

           self.closed_nodes.append(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self.get_successors(__lowerCamelCase			)

           for child_node in successors:
               if child_node in self.closed_nodes:
                   continue

               if child_node not in self.open_nodes:
                   self.open_nodes.append(__lowerCamelCase			)
               else:
                   # retrieve the best current path
                   SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase			)			)

                   if child_node.g_cost < better_node.g_cost:
                       self.open_nodes.append(__lowerCamelCase			)
                   else:
                       self.open_nodes.append(__lowerCamelCase			)

       if not self.reached:
           return [self.start.pos]
       return None





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Node			):
       SCREAMING_SNAKE_CASE										=   []
       for action in delta:
           SCREAMING_SNAKE_CASE										=   parent.pos_x + action[1]
           SCREAMING_SNAKE_CASE										=   parent.pos_y + action[0]

           if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(__lowerCamelCase			) - 1):
               continue

           if grid[pos_y][pos_x] != 0:
               continue

           successors.append(
               Node(
                   __lowerCamelCase     , __lowerCamelCase     , self.target.pos_y     , self.target.pos_x     , parent.g_cost + 1     , __lowerCamelCase     , )			)
       return successors





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Node | None			):
       SCREAMING_SNAKE_CASE										=   node
       SCREAMING_SNAKE_CASE										=   []
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)			)
           SCREAMING_SNAKE_CASE										=   current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
    __A			:					Optional[Any]						=			(0, 0)
    __A			:					Optional[int]						=			(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print('------')

    __A			:					List[str]						=			GreedyBestFirst(init, goal)
    __A			:					Tuple						=			greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            __A			:					Optional[Any]						=			2

        for elem in grid:
            print(elem)