





"""simple docstring"""


import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
   from comet_ml import OfflineExperiment

if is_tensorboard_available():
   import struct

   import tensorboard.compat.proto.event_pba as event_pba

a__					:     Union[str, Any]							=				logging.getLogger(__name__)



@require_tensorboard
class      lowercase_				(     unittest.TestCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"test_project_with_config"
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
         accelerator.init_trackers(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
         accelerator.end_training()
         for child in Path(F"{dirpath}/{project_name}"     ).glob("*/**"     ):
          snake_case__    							=				list(filter(lambda __SCREAMING_SNAKE_CASE     : x.is_file()   ,					child.iterdir()     )     )[0]
         self.assertNotEqual(str(__SCREAMING_SNAKE_CASE     )   ,					""     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"test_project_with_log"
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE     )
         accelerator.init_trackers(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
         accelerator.log(__SCREAMING_SNAKE_CASE   ,					step=0     )
         accelerator.end_training()
         # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
         # Names are randomly generated each time
         snake_case__    							=				list(filter(lambda __SCREAMING_SNAKE_CASE     : x.is_file()   ,					Path(F"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
         self.assertNotEqual(str(__SCREAMING_SNAKE_CASE     )   ,					""     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"test_project_with_log"
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE     )
         accelerator.init_trackers(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"tensor": torch.tensor(1     )}
         accelerator.log(__SCREAMING_SNAKE_CASE   ,					step=0     )
         accelerator.end_training()
         # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
         # Names are randomly generated each time
         snake_case__    							=				list(filter(lambda __SCREAMING_SNAKE_CASE     : x.is_file()   ,					Path(F"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
         # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
         with open(__SCREAMING_SNAKE_CASE   ,					"rb"     ) as f:
          snake_case__    							=				f.read()
         snake_case__    							=				False
         while data:
          snake_case__    							=				struct.unpack("Q"   ,					data[:8]     )

          snake_case__    							=				data[12 : 12 + int(header[0]     )]  # 8+4
          snake_case__    							=				data[12 + int(header[0]     ) + 4 :]
          snake_case__    							=				event_pba.Event()

          event.ParseFromString(__SCREAMING_SNAKE_CASE     )
          if event.HasField("summary"     ):
           for value in event.summary.value:
            if value.simple_value == 1.0 and value.tag == "tensor":
             snake_case__    							=				True
         self.assertTrue(__SCREAMING_SNAKE_CASE   ,					"Converted tensor was not found in the log file!"     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        with self.assertRaisesRegex(__SCREAMING_SNAKE_CASE   ,					"Logging with `tensorboard` requires a `logging_dir`"     ):
         snake_case__    							=				Accelerator(log_with="tensorboard"     )
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				ProjectConfiguration(total_limit=30     )
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE   ,					project_config=__SCREAMING_SNAKE_CASE     )



@require_wandb
@mock.patch.dict(os.environ				,{"""WANDB_MODE""": """offline"""}							)
class      lowercase_				(     __lowerCAmelCase				,__lowerCAmelCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        super().setUp()
        # wandb let's us override where logs are stored to via the WANDB_DIR env var
        self.add_mocks(mock.patch.dict(os.environ   ,					{"WANDB_DIR": self.tmpdir}     )     )

       @staticmethod
       def 		_SCREAMING_SNAKE_CASE(			__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = True     ):






        '''simple docstring'''

        snake_case__    							=				RF"{section} ([\S\s]*?)\n\n"
        if record:
         snake_case__    							=				RF"Record: {pattern}"
        snake_case__    							=				re.findall(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )[0]
        # A config
        if section == "config" or section == "history":
         snake_case__    							=				re.findall(R"\"([a-zA-Z0-9_.,]+)"   ,					__SCREAMING_SNAKE_CASE     )
         return {key: val for key, val in zip(cleaned_record[0::2]   ,					cleaned_record[1::2]     )}
        # Everything else
        else:
         return dict(re.findall(R"(\w+): \"([^\s]+)\""   ,					__SCREAMING_SNAKE_CASE     )     )

       @skip
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"test_project_with_config"
        snake_case__    							=				Accelerator(log_with="wandb"     )
        snake_case__    							=				{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
        snake_case__    							=				{"wandb": {"tags": ["my_tag"]}}
        accelerator.init_trackers(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
        accelerator.log(__SCREAMING_SNAKE_CASE   ,					step=0     )
        accelerator.end_training()
        # The latest offline log is stored at wandb/latest-run/*.wandb
        for child in Path(F"{self.tmpdir}/wandb/latest-run"     ).glob("*"     ):
         if child.is_file() and child.suffix == ".wandb":
          snake_case__    							=				subprocess.check_output(
              ["wandb", "sync", "--view", "--verbose", str(__SCREAMING_SNAKE_CASE     )]   ,					env=os.environ.copy()     ).decode("utf8"   ,					"ignore"     )
          break

        # Check HPS through careful parsing and cleaning
        snake_case__    							=				self.parse_log(__SCREAMING_SNAKE_CASE   ,					"config"     )
        self.assertEqual(logged_items["num_iterations"]   ,					"12"     )
        self.assertEqual(logged_items["learning_rate"]   ,					"0.01"     )
        self.assertEqual(logged_items["some_boolean"]   ,					"false"     )
        self.assertEqual(logged_items["some_string"]   ,					"some_value"     )
        self.assertEqual(logged_items["some_string"]   ,					"some_value"     )

        # Run tags
        snake_case__    							=				self.parse_log(__SCREAMING_SNAKE_CASE   ,					"run"   ,					__SCREAMING_SNAKE_CASE     )
        self.assertEqual(logged_items["tags"]   ,					"my_tag"     )

        # Actual logging
        snake_case__    							=				self.parse_log(__SCREAMING_SNAKE_CASE   ,					"history"     )
        self.assertEqual(logged_items["total_loss"]   ,					"0.1"     )
        self.assertEqual(logged_items["iteration"]   ,					"1"     )
        self.assertEqual(logged_items["my_text"]   ,					"some_value"     )
        self.assertEqual(logged_items["_step"]   ,					"0"     )





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

 '''simple docstring'''






 snake_case__    							=				run_name
 snake_case__    							=				OfflineExperiment(project_name=_UpperCAmelCase	,     offline_directory=_UpperCAmelCase	)
 logger.info(f"Initialized offline CometML project {self.run_name}"	)
 logger.info("Make sure to log any initial configurations with `self.store_init_configuration` before training!"	)



@require_comet_ml
@mock.patch.object(__lowerCAmelCase				,"""__init__"""				,__lowerCAmelCase							)
class      lowercase_				(     unittest.TestCase							):

       @staticmethod
       def 		_SCREAMING_SNAKE_CASE(			__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = False     ):






        '''simple docstring'''

        for log in log_list:
         snake_case__    							=				json.loads(__SCREAMING_SNAKE_CASE     )["payload"]
         if is_param and "param" in j.keys():
          if j["param"]["paramName"] == key:
           return j["param"]["paramValue"]
         if "log_other" in j.keys():
          if j["log_other"]["key"] == key:
           return j["log_other"]["val"]
         if "metric" in j.keys():
          if j["metric"]["metricName"] == key:
           return j["metric"]["metricValue"]

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        with tempfile.TemporaryDirectory() as d:
         snake_case__    							=				CometMLTracker("test_project_with_config"   ,					__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				Accelerator(log_with=__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
         accelerator.init_trackers(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
         accelerator.end_training()
         snake_case__    							=				os.listdir(__SCREAMING_SNAKE_CASE     )[0]  # Comet is nice, it's just a zip file here
         # We parse the raw logs
         snake_case__    							=				os.path.join(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				zipfile.ZipFile(__SCREAMING_SNAKE_CASE   ,					"r"     )
         snake_case__    							=				archive.open("messages.json"     ).read().decode("utf-8"     )
        snake_case__    							=				log.split("\n"     )[:-1]
        self.assertEqual(self.get_value_from_key(__SCREAMING_SNAKE_CASE   ,					"num_iterations"   ,					__SCREAMING_SNAKE_CASE     )   ,					12     )
        self.assertEqual(self.get_value_from_key(__SCREAMING_SNAKE_CASE   ,					"learning_rate"   ,					__SCREAMING_SNAKE_CASE     )   ,					0.01     )
        self.assertEqual(self.get_value_from_key(__SCREAMING_SNAKE_CASE   ,					"some_boolean"   ,					__SCREAMING_SNAKE_CASE     )   ,					__SCREAMING_SNAKE_CASE     )
        self.assertEqual(self.get_value_from_key(__SCREAMING_SNAKE_CASE   ,					"some_string"   ,					__SCREAMING_SNAKE_CASE     )   ,					"some_value"     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        with tempfile.TemporaryDirectory() as d:
         snake_case__    							=				CometMLTracker("test_project_with_config"   ,					__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				Accelerator(log_with=__SCREAMING_SNAKE_CASE     )
         accelerator.init_trackers(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
         accelerator.log(__SCREAMING_SNAKE_CASE   ,					step=0     )
         accelerator.end_training()
         snake_case__    							=				os.listdir(__SCREAMING_SNAKE_CASE     )[0]  # Comet is nice, it's just a zip file here
         # We parse the raw logs
         snake_case__    							=				os.path.join(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				zipfile.ZipFile(__SCREAMING_SNAKE_CASE   ,					"r"     )
         snake_case__    							=				archive.open("messages.json"     ).read().decode("utf-8"     )
        snake_case__    							=				log.split("\n"     )[:-1]
        self.assertEqual(self.get_value_from_key(__SCREAMING_SNAKE_CASE   ,					"curr_step"   ,					__SCREAMING_SNAKE_CASE     )   ,					0     )
        self.assertEqual(self.get_value_from_key(__SCREAMING_SNAKE_CASE   ,					"total_loss"     )   ,					0.1     )
        self.assertEqual(self.get_value_from_key(__SCREAMING_SNAKE_CASE   ,					"iteration"     )   ,					1     )
        self.assertEqual(self.get_value_from_key(__SCREAMING_SNAKE_CASE   ,					"my_text"     )   ,					"some_value"     )



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Dict											=     [
           """total_loss""",
           """iteration""",
           """my_text""",
           """learning_rate""",
           """num_iterations""",
           """some_boolean""",
           """some_string""",
       ]

       snake_case__				:							Optional[int]											=     """my_custom_tracker"""
       snake_case__				:							List[Any]											=     False

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				open(F"{dir}/log.csv"   ,					"w+"     )
        snake_case__    							=				csv.DictWriter(self.f   ,					fieldnames=self._col_names     )
        self.writer.writeheader()

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        return self.writer

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        logger.info("Call init"     )
        self.writer.writerow(__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        logger.info("Call log"     )
        self.writer.writerow(__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        self.f.close()



class      lowercase_				(     unittest.TestCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        with tempfile.TemporaryDirectory() as d:
         snake_case__    							=				MyCustomTracker(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				Accelerator(log_with=__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
         accelerator.init_trackers("Some name"   ,					__SCREAMING_SNAKE_CASE     )
         accelerator.end_training()
         with open(F"{d}/log.csv"   ,					"r"     ) as f:
          snake_case__    							=				csv.DictReader(__SCREAMING_SNAKE_CASE     )
          snake_case__    							=				next(__SCREAMING_SNAKE_CASE     )
          snake_case__    							=				{
              "total_loss": "",
              "iteration": "",
              "my_text": "",
              "learning_rate": "0.01",
              "num_iterations": "12",
              "some_boolean": "False",
              "some_string": "some_value",
          }
          self.assertDictEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        with tempfile.TemporaryDirectory() as d:
         snake_case__    							=				MyCustomTracker(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				Accelerator(log_with=__SCREAMING_SNAKE_CASE     )
         accelerator.init_trackers("Some name"     )
         snake_case__    							=				{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
         accelerator.log(__SCREAMING_SNAKE_CASE   ,					step=0     )
         accelerator.end_training()
         with open(F"{d}/log.csv"   ,					"r"     ) as f:
          snake_case__    							=				csv.DictReader(__SCREAMING_SNAKE_CASE     )
          snake_case__    							=				next(__SCREAMING_SNAKE_CASE     )
          snake_case__    							=				{
              "total_loss": "0.1",
              "iteration": "1",
              "my_text": "some_value",
              "learning_rate": "",
              "num_iterations": "",
              "some_boolean": "",
              "some_string": "",
          }
          self.assertDictEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )








"""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      lowercase_				(     unittest.TestCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				tempfile.mkdtemp()

        # fmt: off
        snake_case__    							=				["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
        # fmt: on
        snake_case__    							=				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]     )     )

        snake_case__    							=				{
            "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,
        }

        snake_case__    							=				os.path.join(self.tmpdirname   ,					__SCREAMING_SNAKE_CASE     )
        with open(self.image_processor_file   ,					"w"   ,					encoding="utf-8"     ) as fp:
         json.dump(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				[np.random.randint(255   ,					size=(3, 30, 400)   ,					dtype=np.uinta     )]

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

        return image_inputs

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_tokenizer()
        snake_case__    							=				self.get_rust_tokenizer()
        snake_case__    							=				self.get_image_processor()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )
        processor_slow.save_pretrained(self.tmpdirname     )
        snake_case__    							=				FlavaProcessor.from_pretrained(self.tmpdirname   ,					use_fast=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )
        processor_fast.save_pretrained(self.tmpdirname     )
        snake_case__    							=				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   ,					__SCREAMING_SNAKE_CASE     )
        self.assertIsInstance(processor_fast.tokenizer   ,					__SCREAMING_SNAKE_CASE     )

        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   ,					__SCREAMING_SNAKE_CASE     )
        self.assertIsInstance(processor_fast.image_processor   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				FlavaProcessor(tokenizer=self.get_tokenizer()   ,					image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case__    							=				self.get_tokenizer(bos_token="(BOS)"   ,					eos_token="(EOS)"     )
        snake_case__    							=				self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE   ,					padding_value=1.0     )

        snake_case__    							=				FlavaProcessor.from_pretrained(
            self.tmpdirname   ,					bos_token="(BOS)"   ,					eos_token="(EOS)"   ,					do_normalize=__SCREAMING_SNAKE_CASE   ,					padding_value=1.0     )

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

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				self.prepare_image_inputs()

        snake_case__    							=				image_processor(__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )
        snake_case__    							=				processor(images=__SCREAMING_SNAKE_CASE   ,					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     )
        snake_case__    							=				image_processor(
            __SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )
        random.seed(1234     )
        snake_case__    							=				processor(
            images=__SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__SCREAMING_SNAKE_CASE   ,					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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				"lower newer"

        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				tokenizer(__SCREAMING_SNAKE_CASE     )

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				"lower newer"
        snake_case__    							=				self.prepare_image_inputs()

        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE   ,					images=__SCREAMING_SNAKE_CASE     )

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

        # add extra args
        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE   ,					images=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_SNAKE_CASE     )

        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(__SCREAMING_SNAKE_CASE     ):
         processor()

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

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

        snake_case__    							=				processor.batch_decode(__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     )

        self.assertListEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				"lower newer"
        snake_case__    							=				self.prepare_image_inputs()

        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE   ,					images=__SCREAMING_SNAKE_CASE     )

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

