



'''simple docstring'''




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    UpperCamelCase_       :  List[str]					=  False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor



class    lowerCamelCase__				(			unittest.TestCase						):






      """simple docstring"""




      def __init__(       self							:     str			,a__							:     int			,a__							:     Dict=7			,a__							:     List[Any]=3			,a__							:     int=18			,a__							:     List[str]=30			,a__							:     str=4_00			,a__							:     int=None			,a__							:     List[Any]=True			,a__							:     Optional[int]=True			,a__							:     Union[str, Any]=None			,):
             a__								= size if size is not None else {"height": 20, "width": 20}
             a__								= parent
             a__								= batch_size
             a__								= num_channels
             a__								= image_size
             a__								= min_resolution
             a__								= max_resolution
             a__								= size
             a__								= do_normalize
             a__								= do_convert_rgb
             a__								= [5_12, 10_24, 20_48, 40_96]
             a__								= patch_size if patch_size is not None else {"height": 16, "width": 16}




      def lowerCAmelCase_					(       self							:     Any   ):
             return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




      def lowerCAmelCase_					(       self							:     int   ):
             a__								= "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
             a__								= Image.open(requests.get(a__			,stream=a__   ).raw   ).convert("RGB"   )
             return raw_image



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	,    reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''	,    )
@require_torch
@require_vision
class    lowerCamelCase__				(			_a	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     PixaStructImageProcessor if is_vision_available() else None




      def lowerCAmelCase_					(       self							:     str   ):
             a__								= PixaStructImageProcessingTester(self   )




      @property
      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def lowerCAmelCase_					(       self							:     str   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             self.assertTrue(hasattr(a__			,"do_normalize"   )   )
             self.assertTrue(hasattr(a__			,"do_convert_rgb"   )   )




      def lowerCAmelCase_					(       self							:     Any   ):
             a__								= self.image_processor_tester.prepare_dummy_image()

             a__								= self.image_processing_class(**self.image_processor_dict   )
             a__								= 20_48

             a__								= image_processor(a__			,return_tensors="pt"			,max_patches=a__   )
             self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,torch.tensor(0.0606   )			,atol=1e-3			,rtol=1e-3   )   )




      def lowerCAmelCase_					(       self							:     int   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             a__								= prepare_image_inputs(self.image_processor_tester			,equal_resolution=a__   )
             for image in image_inputs:
                    self.assertIsInstance(a__			,Image.Image   )

             # Test not batched input
             a__								= (
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    a__								= image_processor(
                        image_inputs[0]			,return_tensors="pt"			,max_patches=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(1, max_patch, expected_hidden_dim)			,)

                    # Test batched
                    a__								= image_processor(
                        a__			,return_tensors="pt"			,max_patches=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,)




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             a__								= prepare_image_inputs(self.image_processor_tester			,equal_resolution=a__   )
             for image in image_inputs:
                    self.assertIsInstance(a__			,Image.Image   )

             # Test not batched input
             a__								= (
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             a__								= True

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    with self.assertRaises(a__   ):
                           a__								= image_processor(
                               image_inputs[0]			,return_tensors="pt"			,max_patches=a__   ).flattened_patches

                    a__								= "Hello"

                    a__								= image_processor(
                        image_inputs[0]			,return_tensors="pt"			,max_patches=a__			,header_text=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(1, max_patch, expected_hidden_dim)			,)

                    # Test batched
                    a__								= image_processor(
                        a__			,return_tensors="pt"			,max_patches=a__			,header_text=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,)




      def lowerCAmelCase_					(       self							:     int   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             # create random numpy tensors
             a__								= prepare_image_inputs(self.image_processor_tester			,equal_resolution=a__			,numpify=a__   )
             for image in image_inputs:
                    self.assertIsInstance(a__			,np.ndarray   )

             a__								= (
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    a__								= image_processor(
                        image_inputs[0]			,return_tensors="pt"			,max_patches=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(1, max_patch, expected_hidden_dim)			,)

                    # Test batched
                    a__								= image_processor(
                        a__			,return_tensors="pt"			,max_patches=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,)




      def lowerCAmelCase_					(       self							:     Any   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             # create random PyTorch tensors
             a__								= prepare_image_inputs(self.image_processor_tester			,equal_resolution=a__			,torchify=a__   )
             for image in image_inputs:
                    self.assertIsInstance(a__			,torch.Tensor   )

             # Test not batched input
             a__								= (
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    a__								= image_processor(
                        image_inputs[0]			,return_tensors="pt"			,max_patches=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(1, max_patch, expected_hidden_dim)			,)

                    # Test batched
                    a__								= image_processor(
                        a__			,return_tensors="pt"			,max_patches=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	,    reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''	,    )
@require_torch
@require_vision
class    lowerCamelCase__				(			_a	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     PixaStructImageProcessor if is_vision_available() else None




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= PixaStructImageProcessingTester(self			,num_channels=4   )
             a__								= 3




      @property
      def lowerCAmelCase_					(       self							:     str   ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             self.assertTrue(hasattr(a__			,"do_normalize"   )   )
             self.assertTrue(hasattr(a__			,"do_convert_rgb"   )   )




      def lowerCAmelCase_					(       self							:     Optional[Any]   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             a__								= prepare_image_inputs(self.image_processor_tester			,equal_resolution=a__   )
             for image in image_inputs:
                    self.assertIsInstance(a__			,Image.Image   )

             # Test not batched input
             a__								= (
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * (self.image_processor_tester.num_channels - 1)
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    a__								= image_processor(
                        image_inputs[0]			,return_tensors="pt"			,max_patches=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(1, max_patch, expected_hidden_dim)			,)

                    # Test batched
                    a__								= image_processor(
                        a__			,return_tensors="pt"			,max_patches=a__   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape			,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,)








# 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

_SCREAMING_SNAKE_CASE   =  TypeVar("T")






class 					SCREAMING_SNAKE_CASE_  (       Generic[T] ):
       """simple docstring"""


       def __init__(				self	:List[Any],      snake_case	:bool = True):



          """simple docstring"""

          _lowercase              ={}  # dictionary of lists
          _lowercase              =directed





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:T,      snake_case	:T):



          """simple docstring"""

          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(snake_case)
                self.adj_list[destination_vertex].append(snake_case)
             # 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(snake_case)
                _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(snake_case)
                _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(snake_case)
             # 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(snake_case)
                _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	:Optional[int]):



          """simple docstring"""

          return pformat(self.adj_list)


