



'''simple docstring'''





import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BlipImageProcessor






class __A      (  unittest.TestCase						):



 '''simple docstring'''







 def __init__(self			,       A			,       A=7			,       A=3			,       A=18			,       A=30			,       A=400			,       A=True			,       A=None			,       A=True			,       A=False			,       A=[0.48145466, 0.4578275, 0.40821073]			,       A=[0.26862954, 0.26130258, 0.27577711]			,       A=True			,       )   ->					Optional[int]:


      """simple docstring"""



      _a     =  size if size is not None else {'''height''': 20, '''width''': 20}
      _a     =  parent
      _a     =  batch_size
      _a     =  num_channels
      _a     =  image_size
      _a     =  min_resolution
      _a     =  max_resolution
      _a     =  do_resize
      _a     =  size
      _a     =  do_normalize
      _a     =  image_mean
      _a     =  image_std
      _a     =  do_pad
      _a     =  do_convert_rgb



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


      """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_convert_rgb": self.do_convert_rgb,
          "do_pad": self.do_pad,
      }



 def  a__  (self			,       A=False			,       A=False			,       A=False	)   ->					List[str]:


      """simple docstring"""



      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
           _a     =  []
           for i in range(self.batch_size	):
                image_inputs.append(
                    np.random.randint(
                        255			,       size=(self.num_channels, self.max_resolution, self.max_resolution)			,       dtype=np.uinta	)	)
      else:
           _a     =  []
           for i in range(self.batch_size	):
                _a  ,   _a     =  np.random.choice(np.arange(self.min_resolution			,       self.max_resolution	)			,       2	)
                image_inputs.append(np.random.randint(255			,       size=(self.num_channels, width, height)			,       dtype=np.uinta	)	)

      if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           _a     =  [Image.fromarray(np.moveaxis(A			,       0			,       -1	)	) for x in image_inputs]

      if torchify:
           _a     =  [torch.from_numpy(A	) for x in image_inputs]

      return image_inputs






@require_torch
@require_vision
class __A      (  A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Tuple										=	BlipImageProcessor if is_vision_available() else None



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  BlipImageProcessingTester(self	)



 @property
 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



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


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(A			,       '''do_resize'''	)	)
      self.assertTrue(hasattr(A			,       '''size'''	)	)
      self.assertTrue(hasattr(A			,       '''do_normalize'''	)	)
      self.assertTrue(hasattr(A			,       '''image_mean'''	)	)
      self.assertTrue(hasattr(A			,       '''image_std'''	)	)
      self.assertTrue(hasattr(A			,       '''do_convert_rgb'''	)	)



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


      """simple docstring"""



      pass



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


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      _a     =  self.image_processor_tester.prepare_inputs(equal_resolution=A	)
      for image in image_inputs:
           self.assertIsInstance(A			,       Image.Image	)

      # Test not batched input
      _a     =  image_processor(image_inputs[0]			,       return_tensors='''pt'''	).pixel_values
      self.assertEqual(
          encoded_images.shape			,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )

      # Test batched
      _a     =  image_processor(A			,       return_tensors='''pt'''	).pixel_values
      self.assertEqual(
          encoded_images.shape			,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )



 def  a__  (self	)   ->					str:


      """simple docstring"""



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

      # Test not batched input
      _a     =  image_processor(image_inputs[0]			,       return_tensors='''pt'''	).pixel_values
      self.assertEqual(
          encoded_images.shape			,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )

      # Test batched
      _a     =  image_processor(A			,       return_tensors='''pt'''	).pixel_values
      self.assertEqual(
          encoded_images.shape			,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )



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


      """simple docstring"""



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

      # Test not batched input
      _a     =  image_processor(image_inputs[0]			,       return_tensors='''pt'''	).pixel_values
      self.assertEqual(
          encoded_images.shape			,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )

      # Test batched
      _a     =  image_processor(A			,       return_tensors='''pt'''	).pixel_values
      self.assertEqual(
          encoded_images.shape			,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )






@require_torch
@require_vision
class __A      (  A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	int										=	BlipImageProcessor if is_vision_available() else None



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  BlipImageProcessingTester(self			,       num_channels=4	)
      _a     =  3



 @property
 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



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


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(A			,       '''do_resize'''	)	)
      self.assertTrue(hasattr(A			,       '''size'''	)	)
      self.assertTrue(hasattr(A			,       '''do_normalize'''	)	)
      self.assertTrue(hasattr(A			,       '''image_mean'''	)	)
      self.assertTrue(hasattr(A			,       '''image_std'''	)	)
      self.assertTrue(hasattr(A			,       '''do_convert_rgb'''	)	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      pass



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      _a     =  self.image_processor_tester.prepare_inputs(equal_resolution=A	)
      for image in image_inputs:
           self.assertIsInstance(A			,       Image.Image	)

      # Test not batched input
      _a     =  image_processor(image_inputs[0]			,       return_tensors='''pt'''	).pixel_values
      self.assertEqual(
          encoded_images.shape			,       (
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )

      # Test batched
      _a     =  image_processor(A			,       return_tensors='''pt'''	).pixel_values
      self.assertEqual(
          encoded_images.shape			,       (
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )








'''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_mbartaa import MBartaaTokenizer
else:
  lowercase_			=  None


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase_			=  {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_			=  {
    "facebook/mbart-large-50-one-to-many-mmt": 1_024,
}

# 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", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	List[str]										=	VOCAB_FILES_NAMES
 __lowerCamelCase   :	int										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase   :	Optional[int]										=	PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase   :	Any										=	['input_ids', 'attention_mask']
 __lowerCamelCase   :	Optional[Any]										=	MBartaaTokenizer

 __lowerCamelCase   :	List[int]										=	[]
 __lowerCamelCase   :	List[int]										=	[]



 def __init__(self			,       A=None			,       A=None			,       A=None			,       A=None			,       A="</s>"			,       A="</s>"			,       A="<s>"			,       A="<unk>"			,       A="<pad>"			,       A="<mask>"			,       **A			,       )   ->					List[str]:


      """simple docstring"""



      _a     =  AddedToken(A			,       lstrip=A			,       rstrip=A	) if isinstance(A			,       A	) else mask_token

      _a     =  kwargs.get('''additional_special_tokens'''			,       []	)
      kwargs["additional_special_tokens"] += [
          code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          A			,       src_lang=A			,       tgt_lang=A			,       tokenizer_file=A			,       eos_token=A			,       sep_token=A			,       cls_token=A			,       unk_token=A			,       pad_token=A			,       mask_token=A			,       **A			,       )

      _a     =  vocab_file
      _a     =  False if not self.vocab_file else True

      _a     =  {
          lang_code: self.convert_tokens_to_ids(A	) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      _a     =  src_lang if src_lang is not None else '''en_XX'''
      _a     =  tgt_lang
      _a     =  self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang	)



 @property
 def  a__  (self	)   ->					str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang	)



 def  a__  (self			,       A			,       A = 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  a__  (self			,       A			,       A = "en_XX"			,       A = None			,       A = "ro_RO"			,       **A			,       )   ->					BatchEncoding:


      """simple docstring"""



      _a     =  src_lang
      _a     =  tgt_lang
      return super().prepare_seqaseq_batch(A			,       A			,       **A	)



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


      """simple docstring"""



      return self.set_src_lang_special_tokens(self.src_lang	)



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


      """simple docstring"""



      return self.set_tgt_lang_special_tokens(self.tgt_lang	)



 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  [self.cur_lang_code_id]
      _a     =  [self.eos_token_id]

      _a     =  self.convert_ids_to_tokens(self.prefix_tokens	)
      _a     =  self.convert_ids_to_tokens(self.suffix_tokens	)

      _a     =  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  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  [self.cur_lang_code_id]
      _a     =  [self.eos_token_id]

      _a     =  self.convert_ids_to_tokens(self.prefix_tokens	)
      _a     =  self.convert_ids_to_tokens(self.suffix_tokens	)

      _a     =  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  a__  (self			,       A			,       A			,       A			,       A			,       **A	)   ->					Optional[int]:


      """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     =  src_lang
      _a     =  self(A			,       add_special_tokens=A			,       return_tensors=A			,       **A	)
      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  tgt_lang_id
      return inputs



 def  a__  (self			,       A			,       A = 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(A	):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
           return
      _a     =  os.path.join(
          A			,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

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

      return (out_vocab_file,)



