






'''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 DeformableDetrImageProcessor
class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      def __init__(	self			, lowerCamelCase			, lowerCamelCase=7			, lowerCamelCase=3			, lowerCamelCase=30			, lowerCamelCase=400			, lowerCamelCase=True			, lowerCamelCase=None			, lowerCamelCase=True			, lowerCamelCase=[0.5, 0.5, 0.5]			, lowerCamelCase=[0.5, 0.5, 0.5]			, lowerCamelCase=True			, lowerCamelCase=1 / 255			, lowerCamelCase=True			, )				->List[Any]:



         '''simple docstring'''
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         __a					  =     size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
         __a					  =     parent
         __a					  =     batch_size
         __a					  =     num_channels
         __a					  =     min_resolution
         __a					  =     max_resolution
         __a					  =     do_resize
         __a					  =     size
         __a					  =     do_normalize
         __a					  =     image_mean
         __a					  =     image_std
         __a					  =     do_rescale
         __a					  =     rescale_factor
         __a					  =     do_pad




      def 			__UpperCamelCase				(	self   )				->Dict:



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






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=False   )				->Dict:



         '''simple docstring'''
         if not batched:
            __a					  =     image_inputs[0]
            if isinstance(lowerCamelCase			, Image.Image   ):
               __a  ,      __a					  =     image.size
            else:
               __a  ,      __a					  =     image.shape[1], image.shape[2]
            if w < h:
               __a					  =     int(self.size['shortest_edge'] * h / w   )
               __a					  =     self.size['shortest_edge']
            elif w > h:
               __a					  =     self.size['shortest_edge']
               __a					  =     int(self.size['shortest_edge'] * w / h   )
            else:
               __a					  =     self.size['shortest_edge']
               __a					  =     self.size['shortest_edge']

         else:
            __a					  =     []
            for image in image_inputs:
               __a  ,      __a					  =     self.get_expected_values([image]   )
               expected_values.append((expected_height, expected_width)   )
            __a					  =     max(lowerCamelCase			, key=lambda lowerCamelCase   : item[0]   )[0]
            __a					  =     max(lowerCamelCase			, key=lambda lowerCamelCase   : item[1]   )[1]

         return expected_height, expected_width







@require_torch
@require_vision
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =DeformableDetrImageProcessor if is_vision_available() else None




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     DeformableDetrImageProcessingTester(self   )




      @property
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         return self.image_processor_tester.prepare_image_processor_dict()




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(lowerCamelCase			, 'image_mean'   )   )
         self.assertTrue(hasattr(lowerCamelCase			, 'image_std'   )   )
         self.assertTrue(hasattr(lowerCamelCase			, 'do_normalize'   )   )
         self.assertTrue(hasattr(lowerCamelCase			, 'do_resize'   )   )
         self.assertTrue(hasattr(lowerCamelCase			, 'do_rescale'   )   )
         self.assertTrue(hasattr(lowerCamelCase			, 'do_pad'   )   )
         self.assertTrue(hasattr(lowerCamelCase			, 'size'   )   )




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __a					  =     self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size			, {'shortest_edge': 18, 'longest_edge': 1333}   )
         self.assertEqual(image_processor.do_pad			, lowerCamelCase   )

         __a					  =     self.image_processing_class.from_dict(
             self.image_processor_dict			, size=42			, max_size=84			, pad_and_return_pixel_mask=lowerCamelCase   )
         self.assertEqual(image_processor.size			, {'shortest_edge': 42, 'longest_edge': 84}   )
         self.assertEqual(image_processor.do_pad			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         pass




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         # Initialize image_processing
         __a					  =     self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         __a					  =     prepare_image_inputs(self.image_processor_tester			, equal_resolution=lowerCamelCase   )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase			, Image.Image   )

         # Test not batched input
         __a					  =     image_processing(image_inputs[0]			, return_tensors='pt'   ).pixel_values

         __a  ,      __a					  =     self.image_processor_tester.get_expected_values(lowerCamelCase   )

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

         # Test batched
         __a  ,      __a					  =     self.image_processor_tester.get_expected_values(lowerCamelCase			, batched=lowerCamelCase   )

         __a					  =     image_processing(lowerCamelCase			, 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 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         # Initialize image_processing
         __a					  =     self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         __a					  =     prepare_image_inputs(self.image_processor_tester			, equal_resolution=lowerCamelCase			, numpify=lowerCamelCase   )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase			, np.ndarray   )

         # Test not batched input
         __a					  =     image_processing(image_inputs[0]			, return_tensors='pt'   ).pixel_values

         __a  ,      __a					  =     self.image_processor_tester.get_expected_values(lowerCamelCase   )

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

         # Test batched
         __a					  =     image_processing(lowerCamelCase			, return_tensors='pt'   ).pixel_values

         __a  ,      __a					  =     self.image_processor_tester.get_expected_values(lowerCamelCase			, batched=lowerCamelCase   )

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




      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         # Initialize image_processing
         __a					  =     self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         __a					  =     prepare_image_inputs(self.image_processor_tester			, equal_resolution=lowerCamelCase			, torchify=lowerCamelCase   )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase			, torch.Tensor   )

         # Test not batched input
         __a					  =     image_processing(image_inputs[0]			, return_tensors='pt'   ).pixel_values

         __a  ,      __a					  =     self.image_processor_tester.get_expected_values(lowerCamelCase   )

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

         # Test batched
         __a					  =     image_processing(lowerCamelCase			, return_tensors='pt'   ).pixel_values

         __a  ,      __a					  =     self.image_processor_tester.get_expected_values(lowerCamelCase			, batched=lowerCamelCase   )

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




      @slow
      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         # prepare image and target
         __a					  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			, 'r'   ) as f:
            __a					  =     json.loads(f.read()   )

         __a					  =     {'image_id': 3_9769, 'annotations': target}

         # encode them
         __a					  =     DeformableDetrImageProcessor()
         __a					  =     image_processing(images=lowerCamelCase			, annotations=lowerCamelCase			, return_tensors='pt'   )

         # verify pixel values
         __a					  =     torch.Size([1, 3, 800, 1066]   )
         self.assertEqual(encoding['pixel_values'].shape			, lowerCamelCase   )

         __a					  =     torch.tensor([0.2796, 0.3138, 0.3481]   )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			, lowerCamelCase			, atol=1e-4   )   )

         # verify area
         __a					  =     torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']			, lowerCamelCase   )   )
         # verify boxes
         __a					  =     torch.Size([6, 4]   )
         self.assertEqual(encoding['labels'][0]['boxes'].shape			, lowerCamelCase   )
         __a					  =     torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			, lowerCamelCase			, atol=1e-3   )   )
         # verify image_id
         __a					  =     torch.tensor([3_9769]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			, lowerCamelCase   )   )
         # verify is_crowd
         __a					  =     torch.tensor([0, 0, 0, 0, 0, 0]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			, lowerCamelCase   )   )
         # verify class_labels
         __a					  =     torch.tensor([75, 75, 63, 65, 17, 17]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			, lowerCamelCase   )   )
         # verify orig_size
         __a					  =     torch.tensor([480, 640]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			, lowerCamelCase   )   )
         # verify size
         __a					  =     torch.tensor([800, 1066]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']			, lowerCamelCase   )   )






      @slow
      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         # prepare image, target and masks_path
         __a					  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'			, 'r'   ) as f:
            __a					  =     json.loads(f.read()   )

         __a					  =     {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

         __a					  =     pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

         # encode them
         __a					  =     DeformableDetrImageProcessor(format='coco_panoptic'   )
         __a					  =     image_processing(images=lowerCamelCase			, annotations=lowerCamelCase			, masks_path=lowerCamelCase			, return_tensors='pt'   )

         # verify pixel values
         __a					  =     torch.Size([1, 3, 800, 1066]   )
         self.assertEqual(encoding['pixel_values'].shape			, lowerCamelCase   )

         __a					  =     torch.tensor([0.2796, 0.3138, 0.3481]   )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			, lowerCamelCase			, atol=1e-4   )   )

         # verify area
         __a					  =     torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']			, lowerCamelCase   )   )
         # verify boxes
         __a					  =     torch.Size([6, 4]   )
         self.assertEqual(encoding['labels'][0]['boxes'].shape			, lowerCamelCase   )
         __a					  =     torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			, lowerCamelCase			, atol=1e-3   )   )
         # verify image_id
         __a					  =     torch.tensor([3_9769]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			, lowerCamelCase   )   )
         # verify is_crowd
         __a					  =     torch.tensor([0, 0, 0, 0, 0, 0]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			, lowerCamelCase   )   )
         # verify class_labels
         __a					  =     torch.tensor([17, 17, 63, 75, 75, 93]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			, lowerCamelCase   )   )
         # verify masks
         __a					  =     82_2873
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()			, lowerCamelCase   )
         # verify orig_size
         __a					  =     torch.tensor([480, 640]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			, lowerCamelCase   )   )
         # verify size
         __a					  =     torch.tensor([800, 1066]   )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']			, lowerCamelCase   )   )







'''simple docstring'''



import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase   :							int         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["input_ids", "attention_mask"]




      def __init__(	self			, lowerCamelCase="</s>"			, lowerCamelCase="<unk>"			, lowerCamelCase="<pad>"			, lowerCamelCase=125			, lowerCamelCase=None			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
            __a					  =     [F"""<extra_id_{i}>""" for i in range(lowerCamelCase   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra_id special tokens
            __a					  =     len(set(filter(lambda lowerCamelCase   : bool('extra_id' in str(lowerCamelCase   )   )			, lowerCamelCase   )   )   )
            if extra_tokens != extra_ids:
               raise ValueError(
                   F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                   ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
                   ' extra_ids tokens'   )

         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else pad_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else eos_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else unk_token

         super().__init__(
             eos_token=lowerCamelCase			, unk_token=lowerCamelCase			, pad_token=lowerCamelCase			, extra_ids=lowerCamelCase			, additional_special_tokens=lowerCamelCase			, **lowerCamelCase			, )

         __a					  =     extra_ids

         __a					  =     2**8  # utf is 8 bits

         # define special tokens dict
         __a					  =     {
             self.pad_token: 0,
             self.eos_token: 1,
             self.unk_token: 2,
         }
         __a					  =     len(self.special_tokens_encoder   )
         __a					  =     len(lowerCamelCase   )
         for i, token in enumerate(lowerCamelCase   ):
            __a					  =     self.vocab_size + i - n
         __a					  =     {v: k for k, v in self.special_tokens_encoder.items()}




      @property
      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = False   )				->List[int]:



         '''simple docstring'''
         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCamelCase			, token_ids_a=lowerCamelCase			, already_has_special_tokens=lowerCamelCase   )

         # normal case: some special tokens
         if token_ids_a is None:
            return ([0] * len(lowerCamelCase   )) + [1]
         return ([0] * len(lowerCamelCase   )) + [1] + ([0] * len(lowerCamelCase   )) + [1]




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[int]:



         '''simple docstring'''
         if len(lowerCamelCase   ) > 0 and token_ids[-1] == self.eos_token_id:
            warnings.warn(
                F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                ' eos tokens being added.'   )
            return token_ids
         else:
            return token_ids + [self.eos_token_id]




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     [self.eos_token_id]

         if token_ids_a is None:
            return len(token_ids_a + eos   ) * [0]
         return len(token_ids_a + eos + token_ids_a + eos   ) * [0]




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     self._add_eos_if_not_present(lowerCamelCase   )
         if token_ids_a is None:
            return token_ids_a
         else:
            __a					  =     self._add_eos_if_not_present(lowerCamelCase   )
            return token_ids_a + token_ids_a




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     [chr(lowerCamelCase   ) for i in text.encode('utf-8'   )]
         return tokens




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Optional[Any]:



         '''simple docstring'''
         if token in self.special_tokens_encoder:
            __a					  =     self.special_tokens_encoder[token]
         elif token in self.added_tokens_encoder:
            __a					  =     self.added_tokens_encoder[token]
         elif len(lowerCamelCase   ) != 1:
            __a					  =     self.unk_token_id
         else:
            __a					  =     ord(lowerCamelCase   ) + self._num_special_tokens
         return token_id




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         if index in self.special_tokens_decoder:
            __a					  =     self.special_tokens_decoder[index]
         else:
            __a					  =     chr(index - self._num_special_tokens   )
         return token




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     B''
         for token in tokens:
            if token in self.special_tokens_decoder:
               __a					  =     self.special_tokens_decoder[token].encode('utf-8'   )
            elif token in self.added_tokens_decoder:
               __a					  =     self.special_tokens_decoder[token].encode('utf-8'   )
            elif token in self.special_tokens_encoder:
               __a					  =     token.encode('utf-8'   )
            elif token in self.added_tokens_encoder:
               __a					  =     token.encode('utf-8'   )
            else:
               __a					  =     bytes([ord(lowerCamelCase   )]   )
            bstring += tok_string
         __a					  =     bstring.decode('utf-8'			, errors='ignore'   )
         return string






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->Tuple[str]:



         '''simple docstring'''
         return ()