


import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import FlavaImageProcessor, FlavaProcessor
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )






@require_vision
class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def _a   (     self       )    ->			int:
         __UpperCamelCase       				=tempfile.mkdtemp()

         # fmt: off
         __UpperCamelCase       				=['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
         # fmt: on
         __UpperCamelCase       				=os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES['vocab_file']       )

         with open(self.vocab_file   ,  'w'   ,  encoding='utf-8'       ) as fp:
              fp.write(''.join([x + '\n' for x in vocab_tokens]       )       )

         __UpperCamelCase       				={
             'image_mean': FLAVA_IMAGE_MEAN,
             'image_std': FLAVA_IMAGE_STD,
             'do_normalize': True,
             'do_resize': True,
             'size': 224,
             'do_center_crop': True,
             'crop_size': 224,
             'input_size_patches': 14,
             'total_mask_patches': 75,
             'mask_group_max_patches': None,
             'mask_group_min_patches': 16,
             'mask_group_min_aspect_ratio': 0.3,
             'mask_group_max_aspect_ratio': None,
             'codebook_do_resize': True,
             'codebook_size': 112,
             'codebook_do_center_crop': True,
             'codebook_crop_size': 112,
             'codebook_do_map_pixels': True,
             'codebook_do_normalize': True,
             'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
             'codebook_image_std': FLAVA_CODEBOOK_STD,
         }

         __UpperCamelCase       				=os.path.join(self.tmpdirname   ,  A_       )
         with open(self.image_processor_file   ,  'w'   ,  encoding='utf-8'       ) as fp:
              json.dump(A_   ,  A_       )






    def _a   (     self   ,  **A_       )    ->			str:
         return BertTokenizer.from_pretrained(self.tmpdirname   ,  **A_       )






    def _a   (     self   ,  **A_       )    ->			Optional[int]:
         return BertTokenizerFast.from_pretrained(self.tmpdirname   ,  **A_       )






    def _a   (     self   ,  **A_       )    ->			Tuple:
         return FlavaImageProcessor.from_pretrained(self.tmpdirname   ,  **A_       )






    def _a   (     self       )    ->			int:
         shutil.rmtree(self.tmpdirname       )






    def _a   (     self       )    ->			List[Any]:
         __UpperCamelCase       				=[np.random.randint(255   ,  size=(3, 30, 400)   ,  dtype=np.uinta       )]

         __UpperCamelCase       				=[Image.fromarray(np.moveaxis(A_   ,  0   ,  -1       )       ) for x in image_inputs]

         return image_inputs






    def _a   (     self       )    ->			Any:
         __UpperCamelCase       				=self.get_tokenizer()
         __UpperCamelCase       				=self.get_rust_tokenizer()
         __UpperCamelCase       				=self.get_image_processor()

         __UpperCamelCase       				=FlavaProcessor(tokenizer=A_   ,  image_processor=A_       )
         processor_slow.save_pretrained(self.tmpdirname       )
         __UpperCamelCase       				=FlavaProcessor.from_pretrained(self.tmpdirname   ,  use_fast=A_       )

         __UpperCamelCase       				=FlavaProcessor(tokenizer=A_   ,  image_processor=A_       )
         processor_fast.save_pretrained(self.tmpdirname       )
         __UpperCamelCase       				=FlavaProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor_slow.tokenizer.get_vocab()   ,  tokenizer_slow.get_vocab()       )
         self.assertEqual(processor_fast.tokenizer.get_vocab()   ,  tokenizer_fast.get_vocab()       )
         self.assertEqual(tokenizer_slow.get_vocab()   ,  tokenizer_fast.get_vocab()       )
         self.assertIsInstance(processor_slow.tokenizer   ,  A_       )
         self.assertIsInstance(processor_fast.tokenizer   ,  A_       )

         self.assertEqual(processor_slow.image_processor.to_json_string()   ,  image_processor.to_json_string()       )
         self.assertEqual(processor_fast.image_processor.to_json_string()   ,  image_processor.to_json_string()       )
         self.assertIsInstance(processor_slow.image_processor   ,  A_       )
         self.assertIsInstance(processor_fast.image_processor   ,  A_       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=FlavaProcessor(tokenizer=self.get_tokenizer()   ,  image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         __UpperCamelCase       				=self.get_tokenizer(bos_token='(BOS)'   ,  eos_token='(EOS)'       )
         __UpperCamelCase       				=self.get_image_processor(do_normalize=A_   ,  padding_value=1.0       )

         __UpperCamelCase       				=FlavaProcessor.from_pretrained(
             self.tmpdirname   ,  bos_token='(BOS)'   ,  eos_token='(EOS)'   ,  do_normalize=A_   ,  padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()   ,  tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer   ,  A_       )

         self.assertEqual(processor.image_processor.to_json_string()   ,  image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor   ,  A_       )






    def _a   (     self       )    ->			Tuple:
         __UpperCamelCase       				=self.get_image_processor()
         __UpperCamelCase       				=self.get_tokenizer()

         __UpperCamelCase       				=FlavaProcessor(tokenizer=A_   ,  image_processor=A_       )

         __UpperCamelCase       				=self.prepare_image_inputs()

         __UpperCamelCase       				=image_processor(A_   ,  return_tensors='np'       )
         __UpperCamelCase       				=processor(images=A_   ,  return_tensors='np'       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()   ,  input_processor[key].sum()   ,  delta=1E-2       )

         # With rest of the args
         random.seed(1234       )
         __UpperCamelCase       				=image_processor(
             A_   ,  return_image_mask=A_   ,  return_codebook_pixels=A_   ,  return_tensors='np'       )
         random.seed(1234       )
         __UpperCamelCase       				=processor(
             images=A_   ,  return_image_mask=A_   ,  return_codebook_pixels=A_   ,  return_tensors='np'       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()   ,  input_processor[key].sum()   ,  delta=1E-2       )






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.get_image_processor()
         __UpperCamelCase       				=self.get_tokenizer()

         __UpperCamelCase       				=FlavaProcessor(tokenizer=A_   ,  image_processor=A_       )

         __UpperCamelCase       				='lower newer'

         __UpperCamelCase       				=processor(text=A_       )

         __UpperCamelCase       				=tokenizer(A_       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]   ,  encoded_processor[key]       )






    def _a   (     self       )    ->			int:
         __UpperCamelCase       				=self.get_image_processor()
         __UpperCamelCase       				=self.get_tokenizer()

         __UpperCamelCase       				=FlavaProcessor(tokenizer=A_   ,  image_processor=A_       )

         __UpperCamelCase       				='lower newer'
         __UpperCamelCase       				=self.prepare_image_inputs()

         __UpperCamelCase       				=processor(text=A_   ,  images=A_       )

         self.assertListEqual(list(inputs.keys()       )   ,  ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']       )

         # add extra args
         __UpperCamelCase       				=processor(text=A_   ,  images=A_   ,  return_codebook_pixels=A_   ,  return_image_mask=A_       )

         self.assertListEqual(
             list(inputs.keys()       )   ,  [
                 'input_ids',
                 'token_type_ids',
                 'attention_mask',
                 'pixel_values',
                 'codebook_pixel_values',
                 'bool_masked_pos',
             ]   ,  )

         # test if it raises when no input is passed
         with pytest.raises(A_       ):
              processor()






    def _a   (     self       )    ->			Optional[int]:
         __UpperCamelCase       				=self.get_image_processor()
         __UpperCamelCase       				=self.get_tokenizer()

         __UpperCamelCase       				=FlavaProcessor(tokenizer=A_   ,  image_processor=A_       )

         __UpperCamelCase       				=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __UpperCamelCase       				=processor.batch_decode(A_       )
         __UpperCamelCase       				=tokenizer.batch_decode(A_       )

         self.assertListEqual(A_   ,  A_       )






    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				=self.get_image_processor()
         __UpperCamelCase       				=self.get_tokenizer()

         __UpperCamelCase       				=FlavaProcessor(tokenizer=A_   ,  image_processor=A_       )

         __UpperCamelCase       				='lower newer'
         __UpperCamelCase       				=self.prepare_image_inputs()

         __UpperCamelCase       				=processor(text=A_   ,  images=A_       )

         self.assertListEqual(list(inputs.keys()       )   ,  processor.model_input_names       )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flaubert import (
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertPreTrainedModel,
          FlaubertWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_flaubert import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertPreTrainedModel,
          TFFlaubertWithLMHeadModel,
      )

else:
   import sys

   _A									=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





