






'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__     =       get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
     from transformers.models.mbart.modeling_mbart import shift_tokens_right

SCREAMING_SNAKE_CASE__     =       2_5_0_0_0_4
SCREAMING_SNAKE_CASE__     =       2_5_0_0_2_0






@require_sentencepiece
@require_tokenizers
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				MBartaaTokenizer
  lowercase							=				MBartaaTokenizerFast
  lowercase							=				True
  lowercase							=				True

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        super().setUp()

        # We have a SentencePiece fixture for testing
        UpperCamelCase      = MBartaaTokenizer(_SCREAMING_SNAKE_CASE      ,		src_lang="""en_XX"""      ,		tgt_lang="""ro_RO"""      ,		keep_accents=_SCREAMING_SNAKE_CASE				)
        tokenizer.save_pretrained(self.tmpdirname				)

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



        """simple docstring"""






        UpperCamelCase      = """<s>"""
        UpperCamelCase      = 0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = list(self.get_tokenizer().get_vocab().keys()				)

        self.assertEqual(vocab_keys[0]      ,		"""<s>"""				)
        self.assertEqual(vocab_keys[1]      ,		"""<pad>"""				)
        self.assertEqual(vocab_keys[-1]      ,		"""<mask>"""				)
        self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		1054				)

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



        """simple docstring"""






        self.assertEqual(self.get_tokenizer().vocab_size      ,		1054				)

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = MBartaaTokenizer(_SCREAMING_SNAKE_CASE      ,		src_lang="""en_XX"""      ,		tgt_lang="""ro_RO"""      ,		keep_accents=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = tokenizer.tokenize("""This is a test"""				)
        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)      ,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,		)

        UpperCamelCase      = tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
        self.assertListEqual(
            _SCREAMING_SNAKE_CASE      ,		[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """."""]      ,		)
        UpperCamelCase      = tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(
            _SCREAMING_SNAKE_CASE      ,		[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]      ,		)

        UpperCamelCase      = tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(
            _SCREAMING_SNAKE_CASE      ,		[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """."""]      ,		)

  @slow
  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = {"""input_ids""": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=_SCREAMING_SNAKE_CASE      ,		model_name="""facebook/mbart-large-50"""      ,		revision="""d3913889c59cd5c9e456b269c376325eabad57e2"""      ,		)


  def   A__						(				self				)  ->				int:



        """simple docstring"""






        if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

        UpperCamelCase      = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                    UpperCamelCase      = self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

                    UpperCamelCase      = tempfile.mkdtemp()

                    UpperCamelCase      = tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

                    # Checks it save with the same files + the tokenizer.json file for the fast one
                    self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)
                    UpperCamelCase      = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f				)
                    self.assertSequenceEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

                    # Checks everything loads correctly in the same way
                    UpperCamelCase      = tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)
                          # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                          # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                    shutil.rmtree(_SCREAMING_SNAKE_CASE				)

                    # Save tokenizer rust, legacy_format=True
                    UpperCamelCase      = tempfile.mkdtemp()

                    UpperCamelCase      = tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE      ,		legacy_format=_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

                    # Checks it save with the same files
                    self.assertSequenceEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

                    # Checks everything loads correctly in the same way
                    UpperCamelCase      = tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)

                    shutil.rmtree(_SCREAMING_SNAKE_CASE				)

                    # Save tokenizer rust, legacy_format=False
                    UpperCamelCase      = tempfile.mkdtemp()

                    UpperCamelCase      = tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE      ,		legacy_format=_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

                    # Checks it saved the tokenizer.json file
                    self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)

                    # Checks everything loads correctly in the same way
                    UpperCamelCase      = tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)

                    shutil.rmtree(_SCREAMING_SNAKE_CASE				)






@require_torch
@require_sentencepiece
@require_tokenizers
class     a_						(      unittest.TestCase    ):
  lowercase							=				"""facebook/mbart-large-50-one-to-many-mmt"""
  lowercase							=				[
      """ UN Chief Says There Is No Military Solution in Syria""",
      """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
  ]
  lowercase							=				[
      """Şeful ONU declară că nu există o soluţie militară în Siria""",
      """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
      """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
      """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
  ]
  lowercase							=				[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]

  @classmethod
  def   A__						(				cls				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = MBartaaTokenizer.from_pretrained(
            cls.checkpoint_name      ,		src_lang="""en_XX"""      ,		tgt_lang="""ro_RO"""				)
        UpperCamelCase      = 1
        return cls

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]      ,		250001				)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]      ,		250004				)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]      ,		250020				)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]      ,		250038				)

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



        """simple docstring"""






        UpperCamelCase      = self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
        self.assertListEqual(self.expected_src_tokens      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        self.assertIn(_SCREAMING_SNAKE_CASE      ,		self.tokenizer.all_special_ids				)
        UpperCamelCase      = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
        UpperCamelCase      = self.tokenizer.decode(_SCREAMING_SNAKE_CASE      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = self.tokenizer.decode(generated_ids[1:]      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertNotIn(self.tokenizer.eos_token      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = ["""this is gunna be a long sentence """ * 20]
        assert isinstance(src_text[0]      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = 10
        UpperCamelCase      = self.tokenizer(_SCREAMING_SNAKE_CASE      ,		max_length=_SCREAMING_SNAKE_CASE      ,		truncation=_SCREAMING_SNAKE_CASE				).input_ids[0]
        self.assertEqual(ids[0]      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(ids[-1]      ,		2				)
        self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]				)      ,		[250053, 250001]				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = tempfile.mkdtemp()
        UpperCamelCase      = self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = MBartaaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,		_SCREAMING_SNAKE_CASE				)

  @require_torch
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.tokenizer(self.src_text      ,		text_target=self.tgt_text      ,		padding=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)
        UpperCamelCase      = shift_tokens_right(batch["""labels"""]      ,		self.tokenizer.pad_token_id				)

        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        assert batch.input_ids[1][0] == EN_CODE
        assert batch.input_ids[1][-1] == 2
        assert batch.labels[1][0] == RO_CODE
        assert batch.labels[1][-1] == 2
        assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

  @require_torch
  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.tokenizer(
            self.src_text      ,		text_target=self.tgt_text      ,		padding=_SCREAMING_SNAKE_CASE      ,		truncation=_SCREAMING_SNAKE_CASE      ,		max_length=len(self.expected_src_tokens				)      ,		return_tensors="""pt"""      ,		)
        UpperCamelCase      = shift_tokens_right(batch["""labels"""]      ,		self.tokenizer.pad_token_id				)

        self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

        self.assertEqual((2, 14)      ,		batch.input_ids.shape				)
        self.assertEqual((2, 14)      ,		batch.attention_mask.shape				)
        UpperCamelCase      = batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(2      ,		batch.decoder_input_ids[0, 0]				)  # decoder_start_token_id
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens      ,		[EN_CODE]				)
        self.assertEqual(self.tokenizer.suffix_tokens      ,		[self.tokenizer.eos_token_id]				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = self.tokenizer(self.src_text      ,		padding=_SCREAMING_SNAKE_CASE      ,		truncation=_SCREAMING_SNAKE_CASE      ,		max_length=3      ,		return_tensors="""pt"""				)
        UpperCamelCase      = self.tokenizer(
            text_target=self.tgt_text      ,		padding=_SCREAMING_SNAKE_CASE      ,		truncation=_SCREAMING_SNAKE_CASE      ,		max_length=10      ,		return_tensors="""pt"""				)
        UpperCamelCase      = targets["""input_ids"""]
        UpperCamelCase      = shift_tokens_right(_SCREAMING_SNAKE_CASE      ,		self.tokenizer.pad_token_id				)

        self.assertEqual(batch.input_ids.shape[1]      ,		3				)
        self.assertEqual(batch.decoder_input_ids.shape[1]      ,		10				)


  @require_torch
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = self.tokenizer._build_translation_inputs(
            """A test"""      ,		return_tensors="""pt"""      ,		src_lang="""en_XX"""      ,		tgt_lang="""ar_AR"""				)

        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE				)      ,		{
                # en_XX, A, test, EOS
                """input_ids""": [[250004, 62, 3034, 2]],
                """attention_mask""": [[1, 1, 1, 1]],
                # ar_AR
                """forced_bos_token_id""": 250001,
            }      ,		)








'''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 ConditionalDetrImageProcessor






class     a_						(      unittest.TestCase    ):

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



        """simple docstring"""






        UpperCamelCase      = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = min_resolution
        UpperCamelCase      = max_resolution
        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = do_normalize
        UpperCamelCase      = image_mean
        UpperCamelCase      = image_std
        UpperCamelCase      = do_rescale
        UpperCamelCase      = rescale_factor
        UpperCamelCase      = do_pad

  def   A__						(				self				)  ->				str:



        """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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Optional[int]:



        """simple docstring"""






        if not batched:
              UpperCamelCase      = image_inputs[0]
              if isinstance(_SCREAMING_SNAKE_CASE      ,		Image.Image				):
                    UpperCamelCase  ,UpperCamelCase      = image.size
              else:
                    UpperCamelCase  ,UpperCamelCase      = image.shape[1], image.shape[2]
              if w < h:
                    UpperCamelCase      = int(self.size["""shortest_edge"""] * h / w				)
                    UpperCamelCase      = self.size["""shortest_edge"""]
              elif w > h:
                    UpperCamelCase      = self.size["""shortest_edge"""]
                    UpperCamelCase      = int(self.size["""shortest_edge"""] * w / h				)
              else:
                    UpperCamelCase      = self.size["""shortest_edge"""]
                    UpperCamelCase      = self.size["""shortest_edge"""]

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

        return expected_height, expected_width






@require_torch
@require_vision
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				ConditionalDetrImageProcessor if is_vision_available() else None

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



        """simple docstring"""






        UpperCamelCase      = ConditionalDetrImageProcessingTester(self				)

  @property
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_mean"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_std"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_normalize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_resize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""size"""				)				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = 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      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = self.image_processing_class.from_dict(
            self.image_processor_dict      ,		size=42      ,		max_size=84      ,		pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE				)
        self.assertEqual(image_processor.size      ,		{"""shortest_edge""": 42, """longest_edge""": 84}				)
        self.assertEqual(image_processor.do_pad      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        pass

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



        """simple docstring"""






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

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		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   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






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

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

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

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



        """simple docstring"""






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

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

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

  @slow
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,		"""r"""				) as f:
              UpperCamelCase      = json.loads(f.read()				)

        UpperCamelCase      = {"""image_id""": 39769, """annotations""": target}

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([75, 75, 63, 65, 17, 17]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)


  @slow
  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






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

        UpperCamelCase      = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

        UpperCamelCase      = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor(format="""coco_panoptic"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		masks_path=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([17, 17, 63, 75, 75, 93]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify masks
        UpperCamelCase      = 822873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,		_SCREAMING_SNAKE_CASE				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
