'''simple docstring'''

import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 CvtForImageClassification, CvtModel
       from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor





class 							lowercase						(						__lowerCamelCase ):


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					int:
         lowercase_     =      self.config_class(**self.inputs_dict)
         self.parent.assertTrue(hasattr(__lowerCAmelCase   ,		"embed_dim"))
         self.parent.assertTrue(hasattr(__lowerCAmelCase   ,		"num_heads"))





class 							lowercase						:


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	Optional[Any]=13   ,		__lowerCAmelCase				:	Any=64   ,		__lowerCAmelCase				:	Tuple=3   ,		__lowerCAmelCase				:	Optional[Any]=[16, 48, 96]   ,		__lowerCAmelCase				:	Any=[1, 3, 6]   ,		__lowerCAmelCase				:	Union[str, Any]=[1, 2, 10]   ,		__lowerCAmelCase				:	Optional[int]=[7, 3, 3]   ,		__lowerCAmelCase				:	List[str]=[4, 2, 2]   ,		__lowerCAmelCase				:	Optional[int]=[2, 1, 1]   ,		__lowerCAmelCase				:	Optional[Any]=[2, 2, 2]   ,		__lowerCAmelCase				:	Tuple=[False, False, True]   ,		__lowerCAmelCase				:	Optional[int]=[0.0, 0.0, 0.0]   ,		__lowerCAmelCase				:	int=0.02   ,		__lowerCAmelCase				:	List[Any]=1e-12   ,		__lowerCAmelCase				:	Union[str, Any]=True   ,		__lowerCAmelCase				:	List[Any]=True   ,		__lowerCAmelCase				:	Optional[Any]=2   ,		)      ->					Optional[Any]:
         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 __UpperCAmelCase       (   self				:	Optional[Any])      ->					Any:
         lowercase_     =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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


   def __UpperCAmelCase       (   self				:	Dict)      ->					Dict:
         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 __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Any)      ->					Tuple:
         lowercase_     =      CvtModel(config=__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()
         lowercase_     =      model(__lowerCAmelCase)
         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 __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	List[str])      ->					List[str]:
         lowercase_     =      self.num_labels
         lowercase_     =      CvtForImageClassification(__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()
         lowercase_     =      model(__lowerCAmelCase   ,		labels=__lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels))


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[Any]:
         lowercase_     =      self.prepare_config_and_inputs()
         lowercase_ ,							lowercase_ ,							lowercase_     =      config_and_inputs
         lowercase_     =      {"pixel_values": pixel_values}
         return config, inputs_dict





@require_torch
class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=(CvtModel, CvtForImageClassification) if is_torch_available() else ()
   lowerCamelCase_    					=(
       {'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
       if is_torch_available()
       else {}
   )

   lowerCamelCase_    					=False
   lowerCamelCase_    					=False
   lowerCamelCase_    					=False
   lowerCamelCase_    					=False
   lowerCamelCase_    					=False


   def __UpperCAmelCase       (   self				:	Any)      ->					Optional[int]:
         lowercase_     =      CvtModelTester(self)
         lowercase_     =      ConfigTester(self   ,		config_class=__lowerCAmelCase   ,		has_text_modality=__lowerCAmelCase   ,		hidden_size=37)


   def __UpperCAmelCase       (   self				:	int)      ->					Any:
         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 __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Any:
         return


   @unittest.skip(reason="Cvt does not output attentions")
   def __UpperCAmelCase       (   self				:	int)      ->					Tuple:
         pass


   @unittest.skip(reason="Cvt does not use inputs_embeds")
   def __UpperCAmelCase       (   self				:	str)      ->					Any:
         pass


   @unittest.skip(reason="Cvt does not support input and output embeddings")
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[str]:
         pass


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Union[str, Any]:
         lowercase_ ,							lowercase_     =      self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase_     =      ["pixel_values"]
               self.assertListEqual(arg_names[:1]   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Any)      ->					int:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Union[str, Any]:
         def check_hidden_states_output(__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	List[Any]):
               lowercase_     =      model_class(__lowerCAmelCase)
               model.to(__lowerCAmelCase)
               model.eval()

               with torch.no_grad():
                     lowercase_     =      model(**self._prepare_for_class(__lowerCAmelCase   ,		__lowerCAmelCase))

               lowercase_     =      outputs.hidden_states

               lowercase_     =      len(self.model_tester.depth)
               self.assertEqual(len(__lowerCAmelCase)   ,		__lowerCAmelCase)

               # 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(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)

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

               check_hidden_states_output(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Any:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase)


   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
   def __UpperCAmelCase       (   self				:	Any)      ->					Dict:
         pass


   @slow
   def __UpperCAmelCase       (   self				:	Any)      ->					Any:
         for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase_     =      CvtModel.from_pretrained(__lowerCAmelCase)
               self.assertIsNotNone(__lowerCAmelCase)




def __a (	)	->							List[Any]:
      '''simple docstring'''


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






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


   @cached_property
   def __UpperCAmelCase       (   self				:	List[str])      ->					Optional[int]:
         return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])


   @slow
   def __UpperCAmelCase       (   self				:	List[Any])      ->					int:
         lowercase_     =      CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__lowerCAmelCase)

         lowercase_     =      self.default_image_processor
         lowercase_     =      prepare_img()
         lowercase_     =      image_processor(images=__lowerCAmelCase   ,		return_tensors="pt").to(__lowerCAmelCase)

         # forward pass
         with torch.no_grad():
               lowercase_     =      model(**__lowerCAmelCase)

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

         lowercase_     =      torch.tensor([0.9285, 0.9015, -0.3150]).to(__lowerCAmelCase)

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







'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCAmelCase_ : Dict						  =      TypeVar("T")





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	bool = True)      ->					None:
         lowercase_     =      {}  # dictionary of lists
         lowercase_     =      directed


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	T)      ->					GraphAdjacencyList[T]:

         if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      [source_vertex]
         else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      []
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      []

         return self


   def __repr__(   self				:	str)      ->					str:
         return pformat(self.adj_list)






