





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   LongformerTokenizer
       _UpperCAmelCase				:  Dict			   =   True
       _UpperCAmelCase				:  List[str]			   =   LongformerTokenizerFast
       _UpperCAmelCase				:  str			   =   True







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[int]:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowerCamelCase__       :						List[str]        =							[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          lowerCamelCase__       :						Optional[Any]        =							dict(zip(A		, range(len(A      )      )      )      )
          lowerCamelCase__       :						Union[str, Any]        =							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          lowerCamelCase__       :						Tuple        =							{'''unk_token''': '''<unk>'''}

          lowerCamelCase__       :						Any        =							os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''vocab_file''']      )
          lowerCamelCase__       :						Tuple        =							os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''merges_file''']      )
          with open(self.vocab_file		, '''w'''		, encoding='''utf-8'''      ) as fp:
             fp.write(json.dumps(A      ) + '''\n'''      )
          with open(self.merges_file		, '''w'''		, encoding='''utf-8'''      ) as fp:
             fp.write('''\n'''.join(A      )      )







       def 						__lowerCamelCase   (				self					:     Optional[int]		, **A					:     Dict      )       ->Tuple:
          kwargs.update(self.special_tokens_map      )
          return self.tokenizer_class.from_pretrained(self.tmpdirname		, **A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, **A					:     Any      )       ->Dict:
          kwargs.update(self.special_tokens_map      )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		, **A      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     Optional[Any]      )       ->Tuple:
          lowerCamelCase__       :						int        =							'''lower newer'''
          lowerCamelCase__       :						Tuple        =							'''lower newer'''
          return input_text, output_text







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Dict:
          lowerCamelCase__       :						List[str]        =							self.tokenizer_class(self.vocab_file		, self.merges_file		, **self.special_tokens_map      )
          lowerCamelCase__       :						Union[str, Any]        =							'''lower newer'''
          lowerCamelCase__       :						Optional[int]        =							['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
          lowerCamelCase__       :						Union[str, Any]        =							tokenizer.tokenize(A      )  # , add_prefix_space=True)
          self.assertListEqual(A		, A      )

          lowerCamelCase__       :						Optional[Any]        =							tokens + [tokenizer.unk_token]
          lowerCamelCase__       :						Optional[int]        =							[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )		, A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[Any]:
          lowerCamelCase__       :						Union[str, Any]        =							self.get_tokenizer()

          self.assertListEqual(tokenizer.encode('''Hello world!'''		, add_special_tokens=A      )		, [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]      )
          self.assertListEqual(
              tokenizer.encode('''Hello world! cécé herlolip 418'''		, add_special_tokens=A      )		, [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]		, )







       @slow
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Dict:
          lowerCamelCase__       :						List[str]        =							self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''      )

          lowerCamelCase__       :						Union[str, Any]        =							tokenizer.encode('''sequence builders'''		, add_special_tokens=A      )
          lowerCamelCase__       :						Union[str, Any]        =							tokenizer.encode('''multi-sequence build'''		, add_special_tokens=A      )

          lowerCamelCase__       :						Optional[Any]        =							tokenizer.encode(
              '''sequence builders'''		, add_special_tokens=A		, add_prefix_space=A      )
          lowerCamelCase__       :						Optional[int]        =							tokenizer.encode(
              '''sequence builders'''		, '''multi-sequence build'''		, add_special_tokens=A		, add_prefix_space=A      )

          lowerCamelCase__       :						List[str]        =							tokenizer.build_inputs_with_special_tokens(A      )
          lowerCamelCase__       :						List[Any]        =							tokenizer.build_inputs_with_special_tokens(A		, A      )

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode







       def 						__lowerCamelCase   (				self					:     Any      )       ->List[Any]:
          lowerCamelCase__       :						Optional[int]        =							self.get_tokenizer()

          lowerCamelCase__       :						Optional[int]        =							'''Encode this sequence.'''
          lowerCamelCase__       :						Optional[int]        =							tokenizer.byte_encoder[''' '''.encode('''utf-8'''      )[0]]

          # Testing encoder arguments
          lowerCamelCase__       :						Union[str, Any]        =							tokenizer.encode(A		, add_special_tokens=A		, add_prefix_space=A      )
          lowerCamelCase__       :						List[Any]        =							tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
          self.assertNotEqual(A		, A      )

          lowerCamelCase__       :						str        =							tokenizer.encode(A		, add_special_tokens=A		, add_prefix_space=A      )
          lowerCamelCase__       :						List[Any]        =							tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
          self.assertEqual(A		, A      )

          tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}      )
          lowerCamelCase__       :						Optional[int]        =							tokenizer.encode(A		, add_special_tokens=A      )
          lowerCamelCase__       :						Optional[Any]        =							tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
          self.assertNotEqual(A		, A      )

          # Testing spaces after special tokens
          lowerCamelCase__       :						Optional[int]        =							'''<mask>'''
          tokenizer.add_special_tokens(
              {'''mask_token''': AddedToken(A		, lstrip=A		, rstrip=A      )}      )  # mask token has a left space
          lowerCamelCase__       :						Dict        =							tokenizer.convert_tokens_to_ids(A      )

          lowerCamelCase__       :						Optional[int]        =							'''Encode <mask> sequence'''
          lowerCamelCase__       :						str        =							'''Encode <mask>sequence'''

          lowerCamelCase__       :						Optional[Any]        =							tokenizer.encode(A      )
          lowerCamelCase__       :						List[str]        =							encoded.index(A      )
          lowerCamelCase__       :						str        =							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
          self.assertEqual(A		, A      )

          lowerCamelCase__       :						Optional[Any]        =							tokenizer.encode(A      )
          lowerCamelCase__       :						Optional[int]        =							encoded.index(A      )
          lowerCamelCase__       :						Tuple        =							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
          self.assertNotEqual(A		, A      )







       def 						__lowerCamelCase   (				self					:     str      )       ->str:
          pass







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                lowerCamelCase__       :						Optional[Any]        =							self.rust_tokenizer_class.from_pretrained(A		, **A      )
                lowerCamelCase__       :						str        =							self.tokenizer_class.from_pretrained(A		, **A      )
                lowerCamelCase__       :						List[str]        =							'''A, <mask> AllenNLP sentence.'''
                lowerCamelCase__       :						Dict        =							tokenizer_r.encode_plus(A		, add_special_tokens=A		, return_token_type_ids=A      )
                lowerCamelCase__       :						Tuple        =							tokenizer_p.encode_plus(A		, add_special_tokens=A		, return_token_type_ids=A      )

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r['''token_type_ids''']      )		, sum(tokens_p['''token_type_ids''']      )      )

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r['''attention_mask''']      ) / len(tokens_r['''attention_mask''']      )		, sum(tokens_p['''attention_mask''']      ) / len(tokens_p['''attention_mask''']      )		, )

                lowerCamelCase__       :						List[Any]        =							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
                lowerCamelCase__       :						List[str]        =							tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']      )

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p['''input_ids''']		, [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
                self.assertSequenceEqual(tokens_r['''input_ids''']		, [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

                self.assertSequenceEqual(
                    A		, ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )
                self.assertSequenceEqual(
                    A		, ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[Any]:
          for trim_offsets, add_prefix_space in itertools.product([True, False]		, repeat=2      ):
             lowerCamelCase__       :						Tuple        =							self.rust_tokenizer_class.from_pretrained(
                 self.tmpdirname		, use_fast=A		, add_prefix_space=A		, trim_offsets=A      )

             lowerCamelCase__       :						Optional[int]        =							json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
             lowerCamelCase__       :						Any        =							json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

             self.assertEqual(pre_tokenizer_state['''add_prefix_space''']		, A      )

             self.assertEqual(post_processor_state['''add_prefix_space''']		, A      )
             self.assertEqual(post_processor_state['''trim_offsets''']		, A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
          # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
          # `trim_offsets`
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                lowerCamelCase__       :						int        =							'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                lowerCamelCase__       :						Any        =							F"{text_of_1_token} {text_of_1_token}"

                lowerCamelCase__       :						List[Any]        =							self.rust_tokenizer_class.from_pretrained(
                    A		, use_fast=A		, add_prefix_space=A		, trim_offsets=A      )
                lowerCamelCase__       :						Optional[int]        =							tokenizer_r(A		, return_offsets_mapping=A		, add_special_tokens=A      )
                self.assertEqual(encoding.offset_mapping[0]		, (0, len(A      ))      )
                self.assertEqual(
                    encoding.offset_mapping[1]		, (len(A      ) + 1, len(A      ) + 1 + len(A      ))		, )

                lowerCamelCase__       :						int        =							self.rust_tokenizer_class.from_pretrained(
                    A		, use_fast=A		, add_prefix_space=A		, trim_offsets=A      )
                lowerCamelCase__       :						Optional[Any]        =							tokenizer_r(A		, return_offsets_mapping=A		, add_special_tokens=A      )
                self.assertEqual(encoding.offset_mapping[0]		, (0, len(A      ))      )
                self.assertEqual(
                    encoding.offset_mapping[1]		, (len(A      ) + 1, len(A      ) + 1 + len(A      ))		, )

                lowerCamelCase__       :						Union[str, Any]        =							self.rust_tokenizer_class.from_pretrained(
                    A		, use_fast=A		, add_prefix_space=A		, trim_offsets=A      )
                lowerCamelCase__       :						Dict        =							tokenizer_r(A		, return_offsets_mapping=A		, add_special_tokens=A      )
                self.assertEqual(encoding.offset_mapping[0]		, (0, len(A      ))      )
                self.assertEqual(
                    encoding.offset_mapping[1]		, (len(A      ), len(A      ) + 1 + len(A      ))		, )

                lowerCamelCase__       :						Optional[Any]        =							self.rust_tokenizer_class.from_pretrained(
                    A		, use_fast=A		, add_prefix_space=A		, trim_offsets=A      )
                lowerCamelCase__       :						Tuple        =							tokenizer_r(A		, return_offsets_mapping=A		, add_special_tokens=A      )
                self.assertEqual(encoding.offset_mapping[0]		, (0, len(A      ))      )
                self.assertEqual(
                    encoding.offset_mapping[1]		, (len(A      ), len(A      ) + 1 + len(A      ))		, )

                lowerCamelCase__       :						List[str]        =							F" {text}"

                # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                # )
                # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                # self.assertEqual(
                #     encoding.offset_mapping[1],
                #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                # )

                lowerCamelCase__       :						str        =							self.rust_tokenizer_class.from_pretrained(
                    A		, use_fast=A		, add_prefix_space=A		, trim_offsets=A      )
                lowerCamelCase__       :						Union[str, Any]        =							tokenizer_r(A		, return_offsets_mapping=A		, add_special_tokens=A      )
                self.assertEqual(encoding.offset_mapping[0]		, (1, 1 + len(A      ))      )
                self.assertEqual(
                    encoding.offset_mapping[1]		, (1 + len(A      ) + 1, 1 + len(A      ) + 1 + len(A      ))		, )

                lowerCamelCase__       :						Optional[int]        =							self.rust_tokenizer_class.from_pretrained(
                    A		, use_fast=A		, add_prefix_space=A		, trim_offsets=A      )
                lowerCamelCase__       :						int        =							tokenizer_r(A		, return_offsets_mapping=A		, add_special_tokens=A      )
                self.assertEqual(encoding.offset_mapping[0]		, (0, 1 + len(A      ))      )
                self.assertEqual(
                    encoding.offset_mapping[1]		, (1 + len(A      ), 1 + len(A      ) + 1 + len(A      ))		, )

                lowerCamelCase__       :						Tuple        =							self.rust_tokenizer_class.from_pretrained(
                    A		, use_fast=A		, add_prefix_space=A		, trim_offsets=A      )
                lowerCamelCase__       :						Union[str, Any]        =							tokenizer_r(A		, return_offsets_mapping=A		, add_special_tokens=A      )
                self.assertEqual(encoding.offset_mapping[0]		, (0, 1 + len(A      ))      )
                self.assertEqual(
                    encoding.offset_mapping[1]		, (1 + len(A      ), 1 + len(A      ) + 1 + len(A      ))		, )










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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def __init__(				self					:     Optional[int]		, A					:     Union[str, Any]		, A					:     Any=7		, A					:     Optional[int]=3		, A					:     Tuple=3_0		, A					:     List[Any]=4_0_0		, A					:     str=True		, A					:     Optional[int]=None		, A					:     Tuple=True		, A					:     Union[str, Any]=1 / 2_5_5		, A					:     Any=True		, A					:     Optional[int]=[0.5, 0.5, 0.5]		, A					:     Optional[int]=[0.5, 0.5, 0.5]		, A					:     str=True		, )       ->List[Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          lowerCamelCase__       :						Union[str, Any]        =							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
          lowerCamelCase__       :						List[str]        =							parent
          lowerCamelCase__       :						Union[str, Any]        =							batch_size
          lowerCamelCase__       :						int        =							num_channels
          lowerCamelCase__       :						Optional[Any]        =							min_resolution
          lowerCamelCase__       :						List[Any]        =							max_resolution
          lowerCamelCase__       :						int        =							do_resize
          lowerCamelCase__       :						List[str]        =							size
          lowerCamelCase__       :						Union[str, Any]        =							do_rescale
          lowerCamelCase__       :						Optional[int]        =							rescale_factor
          lowerCamelCase__       :						List[str]        =							do_normalize
          lowerCamelCase__       :						Tuple        =							image_mean
          lowerCamelCase__       :						str        =							image_std
          lowerCamelCase__       :						List[Any]        =							do_pad







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

          else:
             lowerCamelCase__       :						Optional[Any]        =							[]
             for image in image_inputs:
                lowerCamelCase__       ,							lowerCamelCase__       :						Any        =							self.get_expected_values([image]      )
                expected_values.append((expected_height, expected_width)      )
             lowerCamelCase__       :						Dict        =							max(A		, key=lambda A      : item[0]      )[0]
             lowerCamelCase__       :						Dict        =							max(A		, key=lambda A      : item[1]      )[1]

          return expected_height, expected_width


@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   DetrImageProcessor if is_vision_available() else None







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessingTester(self      )







       @property
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          return self.image_processor_tester.prepare_image_processor_dict()







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[Any]        =							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					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						List[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      )

          lowerCamelCase__       :						str        =							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					:     Union[str, Any]      )       ->Optional[Any]:
          pass







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[Any]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          lowerCamelCase__       :						str        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, Image.Image      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							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
          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						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[str]      )       ->List[str]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          lowerCamelCase__       :						int        =							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
          lowerCamelCase__       :						List[str]        =							image_processing(image_inputs[0]		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							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
          lowerCamelCase__       :						List[Any]        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						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					:     Any      )       ->Any:
          # Initialize image_processing
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowerCamelCase__       :						List[str]        =							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
          lowerCamelCase__       :						str        =							image_processing(image_inputs[0]		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							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
          lowerCamelCase__       :						str        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							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					:     Tuple      )       ->List[Any]:
          # prepare image and target
          lowerCamelCase__       :						Union[str, Any]        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		, '''r'''      ) as f:
             lowerCamelCase__       :						Union[str, Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						List[str]        =							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

          # encode them
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''      )
          lowerCamelCase__       :						List[Any]        =							image_processing(images=A		, annotations=A		, return_tensors='''pt'''      )

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

          lowerCamelCase__       :						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
          lowerCamelCase__       :						Dict        =							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
          lowerCamelCase__       :						Any        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						str        =							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
          lowerCamelCase__       :						str        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						List[str]        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						List[Any]        =							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
          lowerCamelCase__       :						str        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						str        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[str]:
          # prepare image, target and masks_path
          lowerCamelCase__       :						Any        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		, '''r'''      ) as f:
             lowerCamelCase__       :						Optional[Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						Union[str, Any]        =							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

          lowerCamelCase__       :						List[Any]        =							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

          # encode them
          lowerCamelCase__       :						Any        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''      )
          lowerCamelCase__       :						Tuple        =							image_processing(images=A		, annotations=A		, masks_path=A		, return_tensors='''pt'''      )

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

          lowerCamelCase__       :						str        =							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
          lowerCamelCase__       :						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
          lowerCamelCase__       :						Optional[Any]        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						Optional[Any]        =							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
          lowerCamelCase__       :						int        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						Dict        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						int        =							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
          lowerCamelCase__       :						Union[str, Any]        =							8_2_2_8_7_3
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		, A      )
          # verify orig_size
          lowerCamelCase__       :						List[Any]        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						Tuple        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )



