code: 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    ):
  
  
  
  
      def 						__lowerCamelCase (					self  :   int   )	-> str:
        a__     		= tempfile.mkdtemp()
  
        # fmt: off
        a__     		= ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
        # fmt: on
        a__     		= 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]   )   )
  
        a__     		= {
            "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,
        }
  
        a__     		= os.path.join(self.tmpdirname      ,      lowerCAmelCase   )
        with open(self.image_processor_file      ,      "w"      ,      encoding="utf-8"   ) as fp:
          json.dump(lowerCAmelCase      ,      lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   str      ,      **lowerCAmelCase  :   List[Any]   )	-> Dict:
        return BertTokenizer.from_pretrained(self.tmpdirname      ,      **lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict      ,      **lowerCAmelCase  :   Dict   )	-> Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname      ,      **lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Union[str, Any]      ,      **lowerCAmelCase  :   Any   )	-> Dict:
        return FlavaImageProcessor.from_pretrained(self.tmpdirname      ,      **lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Optional[Any]:
        shutil.rmtree(self.tmpdirname   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   str   )	-> Dict:
        a__     		= [np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta   )]
  
        a__     		= [Image.fromarray(np.moveaxis(lowerCAmelCase      ,      0      ,      -1   )   ) for x in image_inputs]
  
        return image_inputs
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Tuple:
        a__     		= self.get_tokenizer()
        a__     		= self.get_rust_tokenizer()
        a__     		= self.get_image_processor()
  
        a__     		= FlavaProcessor(tokenizer=lowerCAmelCase      ,      image_processor=lowerCAmelCase   )
        processor_slow.save_pretrained(self.tmpdirname   )
        a__     		= FlavaProcessor.from_pretrained(self.tmpdirname      ,      use_fast=lowerCAmelCase   )
  
        a__     		= FlavaProcessor(tokenizer=lowerCAmelCase      ,      image_processor=lowerCAmelCase   )
        processor_fast.save_pretrained(self.tmpdirname   )
        a__     		= 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      ,      lowerCAmelCase   )
        self.assertIsInstance(processor_fast.tokenizer      ,      lowerCAmelCase   )
  
        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      ,      lowerCAmelCase   )
        self.assertIsInstance(processor_fast.image_processor      ,      lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   str   )	-> List[Any]:
        a__     		= FlavaProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )
  
        a__     		= self.get_tokenizer(bos_token="(BOS)"      ,      eos_token="(EOS)"   )
        a__     		= self.get_image_processor(do_normalize=lowerCAmelCase      ,      padding_value=1.0   )
  
        a__     		= FlavaProcessor.from_pretrained(
            self.tmpdirname      ,      bos_token="(BOS)"      ,      eos_token="(EOS)"      ,      do_normalize=lowerCAmelCase      ,      padding_value=1.0   )
  
        self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()   )
        self.assertIsInstance(processor.tokenizer      ,      lowerCAmelCase   )
  
        self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()   )
        self.assertIsInstance(processor.image_processor      ,      lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> str:
        a__     		= self.get_image_processor()
        a__     		= self.get_tokenizer()
  
        a__     		= FlavaProcessor(tokenizer=lowerCAmelCase      ,      image_processor=lowerCAmelCase   )
  
        a__     		= self.prepare_image_inputs()
  
        a__     		= image_processor(lowerCAmelCase      ,      return_tensors="np"   )
        a__     		= processor(images=lowerCAmelCase      ,      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   )
        a__     		= image_processor(
            lowerCAmelCase      ,      return_image_mask=lowerCAmelCase      ,      return_codebook_pixels=lowerCAmelCase      ,      return_tensors="np"   )
        random.seed(1234   )
        a__     		= processor(
            images=lowerCAmelCase      ,      return_image_mask=lowerCAmelCase      ,      return_codebook_pixels=lowerCAmelCase      ,      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 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> Any:
        a__     		= self.get_image_processor()
        a__     		= self.get_tokenizer()
  
        a__     		= FlavaProcessor(tokenizer=lowerCAmelCase      ,      image_processor=lowerCAmelCase   )
  
        a__     		= "lower newer"
  
        a__     		= processor(text=lowerCAmelCase   )
  
        a__     		= tokenizer(lowerCAmelCase   )
  
        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Union[str, Any]:
        a__     		= self.get_image_processor()
        a__     		= self.get_tokenizer()
  
        a__     		= FlavaProcessor(tokenizer=lowerCAmelCase      ,      image_processor=lowerCAmelCase   )
  
        a__     		= "lower newer"
        a__     		= self.prepare_image_inputs()
  
        a__     		= processor(text=lowerCAmelCase      ,      images=lowerCAmelCase   )
  
        self.assertListEqual(list(inputs.keys()   )      ,      ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]   )
  
        # add extra args
        a__     		= processor(text=lowerCAmelCase      ,      images=lowerCAmelCase      ,      return_codebook_pixels=lowerCAmelCase      ,      return_image_mask=lowerCAmelCase   )
  
        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(lowerCAmelCase   ):
          processor()
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[int]   )	-> str:
        a__     		= self.get_image_processor()
        a__     		= self.get_tokenizer()
  
        a__     		= FlavaProcessor(tokenizer=lowerCAmelCase      ,      image_processor=lowerCAmelCase   )
  
        a__     		= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
        a__     		= processor.batch_decode(lowerCAmelCase   )
        a__     		= tokenizer.batch_decode(lowerCAmelCase   )
  
        self.assertListEqual(lowerCAmelCase      ,      lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> Any:
        a__     		= self.get_image_processor()
        a__     		= self.get_tokenizer()
  
        a__     		= FlavaProcessor(tokenizer=lowerCAmelCase      ,      image_processor=lowerCAmelCase   )
  
        a__     		= "lower newer"
        a__     		= self.prepare_image_inputs()
  
        a__     		= processor(text=lowerCAmelCase      ,      images=lowerCAmelCase   )
  
        self.assertListEqual(list(inputs.keys()   )      ,      processor.model_input_names   )
  
  
style_context: def 				__lowercase	(						lowerCamelCase__ = "mytext.txt" ,				lowerCamelCase__ = False					)				->None:
  
    '''simple docstring'''
  
  
  
  
    import socket
  
    a__     		=     1_23_12  # Reserve a port for your service.
    a__     		=     socket.socket()  # Create a socket object
    a__     		=     socket.gethostname()  # Get local machine name
    sock.bind((host, port)					)  # Bind to the port
    sock.listen(5					)  # Now wait for client connection.
  
    print("Server listening...."					)
  
    while True:
      a__		,a__     		=     sock.accept()  # Establish connection with client.
      print(f"Got connection from {addr}"					)
      a__     		=     conn.recv(10_24					)
      print(f"Server received: {data = }"					)
  
      with open(lowerCamelCase__ ,				"rb"					) as in_file:
        a__     		=     in_file.read(10_24					)
        while data:
          conn.send(lowerCamelCase__					)
          print(f"Sent {data!r}"					)
          a__     		=     in_file.read(10_24					)
  
      print("Done sending"					)
      conn.close()
      if testing:  # Allow the test to complete
        break
  
    sock.shutdown(1					)
    sock.close()
  
  
  if __name__ == "__main__":
       send_file()
  
  