"""simple docstring"""
import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor




class        __snake_case	(					unittest.TestCase    ):



      def __init__(						self   ,      lowercase   ,      lowercase=7   ,      lowercase=3   ,      lowercase=30   ,      lowercase=4_00   ,      lowercase=True   ,      lowercase=None   ,      lowercase=True   ,      lowercase=1 / 2_55   ,      lowercase=True   ,      lowercase=[0.5, 0.5, 0.5]   ,      lowercase=[0.5, 0.5, 0.5]   ,      lowercase=True   ,      )     ->      Any:





        '''simple docstring'''





        a__:					str      							=     size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
        a__:					List[str]      							=     parent
        a__:					Optional[int]      							=     batch_size
        a__:					int      							=     num_channels
        a__:					Any      							=     min_resolution
        a__:					Tuple      							=     max_resolution
        a__:					Optional[Any]      							=     do_resize
        a__:					Any      							=     size
        a__:					int      							=     do_rescale
        a__:					List[Any]      							=     rescale_factor
        a__:					Union[str, Any]      							=     do_normalize
        a__:					List[Any]      							=     image_mean
        a__:					Tuple      							=     image_std
        a__:					Any      							=     do_pad



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=False)     ->      Tuple:





        '''simple docstring'''





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

        else:
          a__:					int      							=     []
          for image in image_inputs:
            a__							,   a__:					Any      							=     self.get_expected_values([image])
            expected_values.append((expected_height, expected_width))
          a__:					List[Any]      							=     max(lowercase   ,      key=lambda lowercase: item[0])[0]
          a__:					Any      							=     max(lowercase   ,      key=lambda lowercase: item[1])[1]

        return expected_height, expected_width







@require_torch
@require_vision
class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		DetrImageProcessor if is_vision_available() else None



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





        '''simple docstring'''





        a__:					int      							=     DetrImageProcessingTester(self)



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase   ,      'image_mean'))
        self.assertTrue(hasattr(lowercase   ,      'image_std'))
        self.assertTrue(hasattr(lowercase   ,      'do_normalize'))
        self.assertTrue(hasattr(lowercase   ,      'do_rescale'))
        self.assertTrue(hasattr(lowercase   ,      'rescale_factor'))
        self.assertTrue(hasattr(lowercase   ,      'do_resize'))
        self.assertTrue(hasattr(lowercase   ,      'size'))
        self.assertTrue(hasattr(lowercase   ,      'do_pad'))



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





        '''simple docstring'''





        a__:					Any      							=     self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size   ,      {'shortest_edge': 18, 'longest_edge': 13_33})
        self.assertEqual(image_processor.do_pad   ,      lowercase)

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



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





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

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

        a__							,   a__:					Dict      							=     self.image_processor_tester.get_expected_values(lowercase)

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

        # Test batched
        a__							,   a__:					Tuple      							=     self.image_processor_tester.get_expected_values(lowercase   ,      batched=lowercase)

        a__:					Any      							=     image_processing(lowercase   ,      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)     ->      Tuple:





        '''simple docstring'''





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

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

        a__							,   a__:					Any      							=     self.image_processor_tester.get_expected_values(lowercase)

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

        # Test batched
        a__:					Any      							=     image_processing(lowercase   ,      return_tensors='pt').pixel_values

        a__							,   a__:					Optional[Any]      							=     self.image_processor_tester.get_expected_values(lowercase   ,      batched=lowercase)

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



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





        '''simple docstring'''





        a__:					int      							=     self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        a__:					Optional[int]      							=     prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=lowercase   ,      torchify=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase   ,      torch.Tensor)

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

        a__							,   a__:					Dict      							=     self.image_processor_tester.get_expected_values(lowercase)

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

        # Test batched
        a__:					Optional[int]      							=     image_processing(lowercase   ,      return_tensors='pt').pixel_values

        a__							,   a__:					Union[str, Any]      							=     self.image_processor_tester.get_expected_values(lowercase   ,      batched=lowercase)

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



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





        '''simple docstring'''





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

        a__:					Tuple      							=     {'image_id': 3_97_69, 'annotations': target}

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

        # verify pixel values
        a__:					Optional[Any]      							=     torch.Size([1, 3, 8_00, 10_66])
        self.assertEqual(encoding['pixel_values'].shape   ,      lowercase)

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

        # verify area
        a__:					Optional[int]      							=     torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,      lowercase))
        # verify boxes
        a__:					Optional[int]      							=     torch.Size([6, 4])
        self.assertEqual(encoding['labels'][0]['boxes'].shape   ,      lowercase)
        a__:					List[str]      							=     torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,      lowercase   ,      atol=1e-3))
        # verify image_id
        a__:					Dict      							=     torch.tensor([3_97_69])
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,      lowercase))
        # verify is_crowd
        a__:					str      							=     torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,      lowercase))
        # verify class_labels
        a__:					Union[str, Any]      							=     torch.tensor([75, 75, 63, 65, 17, 17])
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,      lowercase))
        # verify orig_size
        a__:					List[Any]      							=     torch.tensor([4_80, 6_40])
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,      lowercase))
        # verify size
        a__:					str      							=     torch.tensor([8_00, 10_66])
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,      lowercase))



      @slow
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





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

        a__:					int      							=     {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

        a__:					str      							=     pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

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

        # verify pixel values
        a__:					Any      							=     torch.Size([1, 3, 8_00, 10_66])
        self.assertEqual(encoding['pixel_values'].shape   ,      lowercase)

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

        # verify area
        a__:					List[str]      							=     torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,      lowercase))
        # verify boxes
        a__:					Union[str, Any]      							=     torch.Size([6, 4])
        self.assertEqual(encoding['labels'][0]['boxes'].shape   ,      lowercase)
        a__:					Union[str, Any]      							=     torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,      lowercase   ,      atol=1e-3))
        # verify image_id
        a__:					List[Any]      							=     torch.tensor([3_97_69])
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,      lowercase))
        # verify is_crowd
        a__:					Optional[Any]      							=     torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,      lowercase))
        # verify class_labels
        a__:					Dict      							=     torch.tensor([17, 17, 63, 75, 75, 93])
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,      lowercase))
        # verify masks
        a__:					Any      							=     82_28_73
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,      lowercase)
        # verify orig_size
        a__:					Union[str, Any]      							=     torch.tensor([4_80, 6_40])
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,      lowercase))
        # verify size
        a__:					Optional[int]      							=     torch.tensor([8_00, 10_66])
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,      lowercase))



"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbart import MBartTokenizer
else:
 lowercase__ 	=     None


lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__ 	=     {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowercase__ 	=     {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
lowercase__ 	=     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		["""input_ids""", """attention_mask"""]
      a__				=		MBartTokenizer

      a__				=		[]
      a__				=		[]



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase="<s>"   ,      lowercase="</s>"   ,      lowercase="</s>"   ,      lowercase="<s>"   ,      lowercase="<unk>"   ,      lowercase="<pad>"   ,      lowercase="<mask>"   ,      lowercase=None   ,      lowercase=None   ,      lowercase=None   ,      **lowercase   ,      )     ->      Tuple:





        '''simple docstring'''





        a__:					List[str]      							=     AddedToken(lowercase   ,      lstrip=lowercase   ,      rstrip=lowercase) if isinstance(lowercase   ,      lowercase) else mask_token

        super().__init__(
            vocab_file=lowercase   ,      tokenizer_file=lowercase   ,      bos_token=lowercase   ,      eos_token=lowercase   ,      sep_token=lowercase   ,      cls_token=lowercase   ,      unk_token=lowercase   ,      pad_token=lowercase   ,      mask_token=lowercase   ,      src_lang=lowercase   ,      tgt_lang=lowercase   ,      additional_special_tokens=lowercase   ,      **lowercase   ,      )

        a__:					Tuple      							=     vocab_file
        a__:					Union[str, Any]      							=     False if not self.vocab_file else True

        a__:					Union[str, Any]      							=     FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          _additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in _additional_special_tokens])

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens})
        a__:					int      							=     {
            lang_code: self.convert_tokens_to_ids(lowercase) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        a__:					List[Any]      							=     src_lang if src_lang is not None else 'en_XX'
        a__:					Tuple      							=     self.convert_tokens_to_ids(self._src_lang)
        a__:					str      							=     tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return self._src_lang



      @src_lang.setter
      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					Tuple      							=     new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        a__:					Any      							=     [self.sep_token_id]
        a__:					List[str]      							=     [self.cls_token_id]

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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      **lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





        if src_lang is None or tgt_lang is None:
          raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
        a__:					Union[str, Any]      							=     src_lang
        a__:					Any      							=     self(lowercase   ,      add_special_tokens=lowercase   ,      return_tensors=lowercase   ,      **lowercase)
        a__:					str      							=     self.convert_tokens_to_ids(lowercase)
        a__:					Any      							=     tgt_lang_id
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = "en_XX"   ,      lowercase = None   ,      lowercase = "ro_RO"   ,      **lowercase   ,      )     ->      BatchEncoding:





        '''simple docstring'''





        a__:					Any      							=     src_lang
        a__:					List[Any]      							=     tgt_lang
        return super().prepare_seqaseq_batch(lowercase   ,      lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        return self.set_src_lang_special_tokens(self.src_lang)



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





        '''simple docstring'''





        return self.set_tgt_lang_special_tokens(self.tgt_lang)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					int      							=     self.convert_tokens_to_ids(lowercase)
        a__:					List[Any]      							=     []
        a__:					List[str]      							=     [self.eos_token_id, self.cur_lang_code]

        a__:					Dict      							=     self.convert_ids_to_tokens(self.prefix_tokens)
        a__:					Any      							=     self.convert_ids_to_tokens(self.suffix_tokens)

        a__:					int      							=     processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str   ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens))   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					str      							=     self.convert_tokens_to_ids(lowercase)
        a__:					List[Any]      							=     []
        a__:					Dict      							=     [self.eos_token_id, self.cur_lang_code]

        a__:					Any      							=     self.convert_ids_to_tokens(self.prefix_tokens)
        a__:					Optional[Any]      							=     self.convert_ids_to_tokens(self.suffix_tokens)

        a__:					str      							=     processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str   ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens))   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Tuple[str]:





        '''simple docstring'''





        if not self.can_save_slow_tokenizer:
          raise ValueError(
              'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
              'tokenizer.')

        if not os.path.isdir(lowercase):
          logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
          return
        a__:					Any      							=     os.path.join(
            lowercase   ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

        if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase):
          copyfile(self.vocab_file   ,      lowercase)

        return (out_vocab_file,)


