






"""simple docstring"""


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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

if is_vision_available():
   from PIL import Image

   from transformers import DetrImageProcessor






class 						snake_case  (  unittest.TestCase	):





    def __init__(							self     : Optional[Any]    ,		A     : Optional[int]    ,		A     : List[str]=7    ,		A     : int=3    ,		A     : Optional[Any]=3_0    ,		A     : Tuple=4_0_0    ,		A     : int=True    ,		A     : Union[str, Any]=None    ,		A     : Any=True    ,		A     : Union[str, Any]=1 / 2_5_5    ,		A     : Optional[Any]=True    ,		A     : Union[str, Any]=[0.5, 0.5, 0.5]    ,		A     : Optional[Any]=[0.5, 0.5, 0.5]    ,		A     : Any=True    ,		):


      '''simple docstring'''




      a					:      Dict    =       size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
      a					:      List[str]    =       parent
      a					:      Union[str, Any]    =       batch_size
      a					:      str    =       num_channels
      a					:      List[Any]    =       min_resolution
      a					:      List[str]    =       max_resolution
      a					:      Union[str, Any]    =       do_resize
      a					:      Tuple    =       size
      a					:      Union[str, Any]    =       do_rescale
      a					:      int    =       rescale_factor
      a					:      int    =       do_normalize
      a					:      Any    =       image_mean
      a					:      Dict    =       image_std
      a					:      Tuple    =       do_pad





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_pad": self.do_pad,
      }





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : Optional[Any]    ,		A     : str=False      ):


      '''simple docstring'''




      if not batched:
        a					:      Tuple    =       image_inputs[0]
        if isinstance(A    ,		Image.Image      ):
          a,					a					:      Union[str, Any]    =       image.size
        else:
          a,					a					:      Optional[Any]    =       image.shape[1], image.shape[2]
        if w < h:
          a					:      Optional[Any]    =       int(self.size['shortest_edge'] * h / w      )
          a					:      Any    =       self.size['shortest_edge']
        elif w > h:
          a					:      str    =       self.size['shortest_edge']
          a					:      int    =       int(self.size['shortest_edge'] * w / h      )
        else:
          a					:      Dict    =       self.size['shortest_edge']
          a					:      Tuple    =       self.size['shortest_edge']

      else:
        a					:      Union[str, Any]    =       []
        for image in image_inputs:
          a,					a					:      int    =       self.get_expected_values([image]      )
          expected_values.append((expected_height, expected_width)      )
        a					:      str    =       max(A    ,		key=lambda A      : item[0]      )[0]
        a					:      Tuple    =       max(A    ,		key=lambda A      : item[1]      )[1]

      return expected_height, expected_width






@require_torch
@require_vision
class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					DetrImageProcessor if is_vision_available() else None





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      List[str]    =       DetrImageProcessingTester(self      )





    @property
    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a					:      Optional[int]    =       self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(A    ,		'image_mean'      )      )
      self.assertTrue(hasattr(A    ,		'image_std'      )      )
      self.assertTrue(hasattr(A    ,		'do_normalize'      )      )
      self.assertTrue(hasattr(A    ,		'do_rescale'      )      )
      self.assertTrue(hasattr(A    ,		'rescale_factor'      )      )
      self.assertTrue(hasattr(A    ,		'do_resize'      )      )
      self.assertTrue(hasattr(A    ,		'size'      )      )
      self.assertTrue(hasattr(A    ,		'do_pad'      )      )





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Any    =       self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size    ,		{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}      )
      self.assertEqual(image_processor.do_pad    ,		A      )

      a					:      Any    =       self.image_processing_class.from_dict(
          self.image_processor_dict    ,		size=4_2    ,		max_size=8_4    ,		pad_and_return_pixel_mask=A      )
      self.assertEqual(image_processor.size    ,		{'shortest_edge': 4_2, 'longest_edge': 8_4}      )
      self.assertEqual(image_processor.do_pad    ,		A      )





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      pass





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      int    =       self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      a					:      Optional[int]    =       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					:      Optional[int]    =       image_processing(image_inputs[0]    ,		return_tensors='pt'      ).pixel_values

      a,					a					:      List[Any]    =       self.image_processor_tester.get_expected_values(A      )

      self.assertEqual(
          encoded_images.shape    ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,		)

      # Test batched
      a,					a					:      List[str]    =       self.image_processor_tester.get_expected_values(A    ,		batched=A      )

      a					:      Any    =       image_processing(A    ,		return_tensors='pt'      ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,		)





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      a					:      Tuple    =       prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=A    ,		numpify=A      )
      for image in image_inputs:
        self.assertIsInstance(A    ,		np.ndarray      )

      # Test not batched input
      a					:      Optional[int]    =       image_processing(image_inputs[0]    ,		return_tensors='pt'      ).pixel_values

      a,					a					:      int    =       self.image_processor_tester.get_expected_values(A      )

      self.assertEqual(
          encoded_images.shape    ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,		)

      # Test batched
      a					:      Tuple    =       image_processing(A    ,		return_tensors='pt'      ).pixel_values

      a,					a					:      Optional[Any]    =       self.image_processor_tester.get_expected_values(A    ,		batched=A      )

      self.assertEqual(
          encoded_images.shape    ,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,		)





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Dict    =       self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      a					:      Optional[int]    =       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					:      Optional[int]    =       image_processing(image_inputs[0]    ,		return_tensors='pt'      ).pixel_values

      a,					a					:      List[Any]    =       self.image_processor_tester.get_expected_values(A      )

      self.assertEqual(
          encoded_images.shape    ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,		)

      # Test batched
      a					:      List[str]    =       image_processing(A    ,		return_tensors='pt'      ).pixel_values

      a,					a					:      Dict    =       self.image_processor_tester.get_expected_values(A    ,		batched=A      )

      self.assertEqual(
          encoded_images.shape    ,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,		)





    @slow
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      str    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,		'r'      ) as f:
        a					:      List[Any]    =       json.loads(f.read()      )

      a					:      Tuple    =       {'image_id': 3_9_7_6_9, 'annotations': target}

      # encode them
      a					:      List[str]    =       DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'      )
      a					:      Tuple    =       image_processing(images=A    ,		annotations=A    ,		return_tensors='pt'      )

      # verify pixel values
      a					:      List[str]    =       torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
      self.assertEqual(encoding['pixel_values'].shape    ,		A      )

      a					:      Tuple    =       torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,		A    ,		atol=1E-4      )      )

      # verify area
      a					:      Optional[Any]    =       torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,		A      )      )
      # verify boxes
      a					:      Dict    =       torch.Size([6, 4]      )
      self.assertEqual(encoding['labels'][0]['boxes'].shape    ,		A      )
      a					:      Union[str, Any]    =       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,		A    ,		atol=1E-3      )      )
      # verify image_id
      a					:      Any    =       torch.tensor([3_9_7_6_9]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,		A      )      )
      # verify is_crowd
      a					:      Dict    =       torch.tensor([0, 0, 0, 0, 0, 0]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,		A      )      )
      # verify class_labels
      a					:      Dict    =       torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,		A      )      )
      # verify orig_size
      a					:      str    =       torch.tensor([4_8_0, 6_4_0]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,		A      )      )
      # verify size
      a					:      int    =       torch.tensor([8_0_0, 1_0_6_6]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,		A      )      )





    @slow
    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      int    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
      with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,		'r'      ) as f:
        a					:      Dict    =       json.loads(f.read()      )

      a					:      Dict    =       {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

      a					:      Dict    =       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

      # encode them
      a					:      Dict    =       DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'      )
      a					:      Union[str, Any]    =       image_processing(images=A    ,		annotations=A    ,		masks_path=A    ,		return_tensors='pt'      )

      # verify pixel values
      a					:      Tuple    =       torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
      self.assertEqual(encoding['pixel_values'].shape    ,		A      )

      a					:      Any    =       torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,		A    ,		atol=1E-4      )      )

      # verify area
      a					:      Optional[int]    =       torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,		A      )      )
      # verify boxes
      a					:      str    =       torch.Size([6, 4]      )
      self.assertEqual(encoding['labels'][0]['boxes'].shape    ,		A      )
      a					:      Dict    =       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,		A    ,		atol=1E-3      )      )
      # verify image_id
      a					:      Any    =       torch.tensor([3_9_7_6_9]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,		A      )      )
      # verify is_crowd
      a					:      str    =       torch.tensor([0, 0, 0, 0, 0, 0]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,		A      )      )
      # verify class_labels
      a					:      Dict    =       torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,		A      )      )
      # verify masks
      a					:      Optional[Any]    =       8_2_2_8_7_3
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,		A      )
      # verify orig_size
      a					:      Optional[int]    =       torch.tensor([4_8_0, 6_4_0]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,		A      )      )
      # verify size
      a					:      Dict    =       torch.tensor([8_0_0, 1_0_6_6]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,		A      )      )












"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase    :   int   			=						get_tests_dir('fixtures/test_sentencepiece_bpe.model')






class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					BartphoTokenizer
    __magic_name__   					=					False
    __magic_name__   					=					True





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      super().setUp()

      a					:      Any    =       ['▁This', '▁is', '▁a', '▁t', 'est']
      a					:      List[Any]    =       dict(zip(A    ,		range(len(A      )      )      )      )
      a					:      int    =       {'unk_token': '<unk>'}

      a					:      Tuple    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['monolingual_vocab_file']      )
      with open(self.monolingual_vocab_file    ,		'w'    ,		encoding='utf-8'      ) as fp:
        for token in vocab_tokens:
          fp.write(F'''{token} {vocab_tokens[token]}\n'''      )

      a					:      Optional[int]    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      tokenizer.save_pretrained(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Dict    ,		**A     : str      ):


      '''simple docstring'''




      kwargs.update(self.special_tokens_map      )
      return BartphoTokenizer.from_pretrained(self.tmpdirname    ,		**A      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       'This is a là test'
      a					:      List[Any]    =       'This is a<unk><unk> test'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      a					:      int    =       'This is a là test'
      a					:      int    =       '▁This ▁is ▁a ▁l à ▁t est'.split()
      a					:      str    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      Union[str, Any]    =       tokens + [tokenizer.unk_token]
      a					:      Dict    =       [4, 5, 6, 3, 3, 7, 8, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		A      )




