
"""simple docstring"""







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       : Union[str, Any] )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  tempfile.mkdtemp()

      # fmt: off
      UpperCAmelCase_						:    Union[str, Any]    					=  ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
      # fmt: on
      UpperCAmelCase_						:    List[Any]    					=  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_						:    Union[str, Any]    					=  {
          """image_mean""": FLAVA_IMAGE_MEAN,
          """image_std""": FLAVA_IMAGE_STD,
          """do_normalize""": True,
          """do_resize""": True,
          """size""": 2_24,
          """do_center_crop""": True,
          """crop_size""": 2_24,
          """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""": 1_12,
          """codebook_do_center_crop""": True,
          """codebook_crop_size""": 1_12,
          """codebook_do_map_pixels""": True,
          """codebook_do_normalize""": True,
          """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
          """codebook_image_std""": FLAVA_CODEBOOK_STD,
      }

      UpperCAmelCase_						:    Tuple    					=  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       : Optional[Any]     ,   **a_       : Optional[Any] )->					Union[str, Any]:



      """simple docstring"""

      return BertTokenizer.from_pretrained(self.tmpdirname     ,   **a_ )




 def   a      ( self       : str     ,   **a_       : Union[str, Any] )->					Dict:



      """simple docstring"""

      return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **a_ )




 def   a      ( self       : Tuple     ,   **a_       : Optional[int] )->					Optional[Any]:



      """simple docstring"""

      return FlavaImageProcessor.from_pretrained(self.tmpdirname     ,   **a_ )




 def   a      ( self       : Optional[Any] )->					Tuple:



      """simple docstring"""

      shutil.rmtree(self.tmpdirname )




 def   a      ( self       : Optional[Any] )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  [np.random.randint(2_55     ,   size=(3, 30, 4_00)     ,   dtype=np.uinta )]

      UpperCAmelCase_						:    Tuple    					=  [Image.fromarray(np.moveaxis(a_     ,   0     ,   -1 ) ) for x in image_inputs]

      return image_inputs




 def   a      ( self       : int )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.get_tokenizer()
      UpperCAmelCase_						:    Union[str, Any]    					=  self.get_rust_tokenizer()
      UpperCAmelCase_						:    Tuple    					=  self.get_image_processor()

      UpperCAmelCase_						:    Any    					=  FlavaProcessor(tokenizer=a_     ,   image_processor=a_ )
      processor_slow.save_pretrained(self.tmpdirname )
      UpperCAmelCase_						:    List[Any]    					=  FlavaProcessor.from_pretrained(self.tmpdirname     ,   use_fast=a_ )

      UpperCAmelCase_						:    List[Any]    					=  FlavaProcessor(tokenizer=a_     ,   image_processor=a_ )
      processor_fast.save_pretrained(self.tmpdirname )
      UpperCAmelCase_						:    Tuple    					=  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       : int )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  FlavaProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor() )
      processor.save_pretrained(self.tmpdirname )

      UpperCAmelCase_						:    Any    					=  self.get_tokenizer(bos_token="""(BOS)"""     ,   eos_token="""(EOS)""" )
      UpperCAmelCase_						:    List[Any]    					=  self.get_image_processor(do_normalize=a_     ,   padding_value=1.0 )

      UpperCAmelCase_						:    int    					=  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       : Any )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.get_image_processor()
      UpperCAmelCase_						:    Tuple    					=  self.get_tokenizer()

      UpperCAmelCase_						:    Optional[Any]    					=  FlavaProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    Tuple    					=  self.prepare_image_inputs()

      UpperCAmelCase_						:    Any    					=  image_processor(a_     ,   return_tensors="""np""" )
      UpperCAmelCase_						:    Union[str, Any]    					=  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(12_34 )
      UpperCAmelCase_						:    Tuple    					=  image_processor(
          a_     ,   return_image_mask=a_     ,   return_codebook_pixels=a_     ,   return_tensors="""np""" )
      random.seed(12_34 )
      UpperCAmelCase_						:    List[str]    					=  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       : List[str] )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.get_image_processor()
      UpperCAmelCase_						:    List[str]    					=  self.get_tokenizer()

      UpperCAmelCase_						:    str    					=  FlavaProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    Optional[int]    					=  """lower newer"""

      UpperCAmelCase_						:    int    					=  processor(text=a_ )

      UpperCAmelCase_						:    Optional[Any]    					=  tokenizer(a_ )

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




 def   a      ( self       : str )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  self.get_image_processor()
      UpperCAmelCase_						:    Optional[Any]    					=  self.get_tokenizer()

      UpperCAmelCase_						:    Union[str, Any]    					=  FlavaProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  """lower newer"""
      UpperCAmelCase_						:    List[str]    					=  self.prepare_image_inputs()

      UpperCAmelCase_						:    Dict    					=  processor(text=a_     ,   images=a_ )

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

      # add extra args
      UpperCAmelCase_						:    Union[str, Any]    					=  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] )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.get_image_processor()
      UpperCAmelCase_						:    str    					=  self.get_tokenizer()

      UpperCAmelCase_						:    List[str]    					=  FlavaProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    int    					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      UpperCAmelCase_						:    List[Any]    					=  processor.batch_decode(a_ )
      UpperCAmelCase_						:    Union[str, Any]    					=  tokenizer.batch_decode(a_ )

      self.assertListEqual(a_     ,   a_ )






 def   a      ( self       : str )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  self.get_image_processor()
      UpperCAmelCase_						:    Dict    					=  self.get_tokenizer()

      UpperCAmelCase_						:    int    					=  FlavaProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    List[Any]    					=  """lower newer"""
      UpperCAmelCase_						:    List[str]    					=  self.prepare_image_inputs()

      UpperCAmelCase_						:    str    					=  processor(text=a_     ,   images=a_ )

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



"""simple docstring"""







import warnings

from .generation import GenerationMixin



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 warnings.warn(
     """Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will """
     """be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."""  , lowerCamelCase_  , )

