import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPImageProcessor, CLIPProcessor


@require_vision
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Optional[int]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  tempfile.mkdtemp()

        # fmt: off
        SCREAMING_SNAKE_CASE_          =  ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
        # fmt: on
        SCREAMING_SNAKE_CASE_          =  dict(zip(__magic_name__  ,     range(len(__magic_name__   )   )   )   )
        SCREAMING_SNAKE_CASE_          =  ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        SCREAMING_SNAKE_CASE_          =  {"unk_token": "<unk>"}

        SCREAMING_SNAKE_CASE_          =  os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["vocab_file"]   )
        SCREAMING_SNAKE_CASE_          =  os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["merges_file"]   )
        with open(self.vocab_file  ,     "w"  ,     encoding="utf-8"   ) as fp:
              fp.write(json.dumps(__magic_name__   ) + "\n"   )
        with open(self.merges_file  ,     "w"  ,     encoding="utf-8"   ) as fp:
              fp.write("\n".join(__magic_name__   )   )

        SCREAMING_SNAKE_CASE_          =  {
            "do_resize": True,
            "size": 20,
            "do_center_crop": True,
            "crop_size": 18,
            "do_normalize": True,
            "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
            "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        SCREAMING_SNAKE_CASE_          =  os.path.join(self.tmpdirname  ,     __magic_name__   )
        with open(self.image_processor_file  ,     "w"  ,     encoding="utf-8"   ) as fp:
              json.dump(__magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Dict  ,     **__magic_name__ :       Optional[Any]   )  ->	str:
        return CLIPTokenizer.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







  def    __A     (							self :       Optional[Any]  ,     **__magic_name__ :       str   )  ->	str:
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







  def    __A     (							self :       int  ,     **__magic_name__ :       Any   )  ->	Union[str, Any]:
        return CLIPImageProcessor.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	Any:
        shutil.rmtree(self.tmpdirname   )







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

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

        return image_inputs







  def    __A     (							self :       Union[str, Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()
        SCREAMING_SNAKE_CASE_          =  self.get_rust_tokenizer()
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )
        processor_slow.save_pretrained(self.tmpdirname   )
        SCREAMING_SNAKE_CASE_          =  CLIPProcessor.from_pretrained(self.tmpdirname  ,     use_fast=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )
        processor_fast.save_pretrained(self.tmpdirname   )
        SCREAMING_SNAKE_CASE_          =  CLIPProcessor.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  ,     __magic_name__   )
        self.assertIsInstance(processor_fast.tokenizer  ,     __magic_name__   )

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







  def    __A     (							self :       Union[str, Any]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=self.get_tokenizer()  ,     image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

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

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor.from_pretrained(
            self.tmpdirname  ,     bos_token="(BOS)"  ,     eos_token="(EOS)"  ,     do_normalize=__magic_name__  ,     padding_value=1.0   )

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

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







  def    __A     (							self :       Dict   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE_          =  image_processor(__magic_name__  ,     return_tensors="np"   )
        SCREAMING_SNAKE_CASE_          =  processor(images=__magic_name__  ,     return_tensors="np"   )

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







  def    __A     (							self :       List[str]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "lower newer"

        SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__   )

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







  def    __A     (							self :       str   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

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

        SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__  ,     images=__magic_name__   )

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

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







  def    __A     (							self :       Any   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

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

        SCREAMING_SNAKE_CASE_          =  processor.batch_decode(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  tokenizer.batch_decode(__magic_name__   )

        self.assertListEqual(__magic_name__  ,     __magic_name__   )







  def    __A     (							self :       str   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

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

        SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__  ,     images=__magic_name__   )

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



import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  logging.get_logger()

        # the current default level is logging.WARNING
        SCREAMING_SNAKE_CASE_          =  logging.get_verbosity()

        logging.set_verbosity_error()
        self.assertEqual(logger.getEffectiveLevel()  ,     logging.get_verbosity()   )

        logging.set_verbosity_warning()
        self.assertEqual(logger.getEffectiveLevel()  ,     logging.get_verbosity()   )

        logging.set_verbosity_info()
        self.assertEqual(logger.getEffectiveLevel()  ,     logging.get_verbosity()   )

        logging.set_verbosity_debug()
        self.assertEqual(logger.getEffectiveLevel()  ,     logging.get_verbosity()   )

        # restore to the original level
        logging.set_verbosity(__magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  logging.get_verbosity()

        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )
        SCREAMING_SNAKE_CASE_          =  "Testing 1, 2, 3"

        # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
        if level_origin <= logging.WARNING:
              with CaptureLogger(__magic_name__   ) as cl:
                    logger.warning(__magic_name__   )
              self.assertEqual(cl.out  ,     msg + "\n"   )

        # this is setting the level for all of `transformers.*` loggers
        logging.set_verbosity_error()

        # should not be able to log warnings
        with CaptureLogger(__magic_name__   ) as cl:
              logger.warning(__magic_name__   )
        self.assertEqual(cl.out  ,     ""   )

        # should be able to log warnings again
        logging.set_verbosity_warning()
        with CaptureLogger(__magic_name__   ) as cl:
              logger.warning(__magic_name__   )
        self.assertEqual(cl.out  ,     msg + "\n"   )

        # restore to the original level
        logging.set_verbosity(__magic_name__   )







  @mockenv(TRANSFORMERS_VERBOSITY="error"   )
  def    __A     (							self :       int   )  ->	Optional[Any]:
        # reset for the env var to take effect, next time some logger call is made
        transformers.utils.logging._reset_library_root_logger()
        # this action activates the env var
        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )

        SCREAMING_SNAKE_CASE_          =  os.getenv("TRANSFORMERS_VERBOSITY"  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  logging.log_levels[env_level_str]

        SCREAMING_SNAKE_CASE_          =  logging.get_verbosity()
        self.assertEqual(
            __magic_name__  ,     __magic_name__  ,     F'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''  ,     )

        # restore to the original level
        SCREAMING_SNAKE_CASE_          =  ""
        transformers.utils.logging._reset_library_root_logger()







  @mockenv(TRANSFORMERS_VERBOSITY="super-error"   )
  def    __A     (							self :       Dict   )  ->	List[Any]:
        # reset for the env var to take effect, next time some logger call is made
        transformers.utils.logging._reset_library_root_logger()
        SCREAMING_SNAKE_CASE_          =  logging.logging.getLogger()
        with CaptureLogger(__magic_name__   ) as cl:
              # this action activates the env var
              logging.get_logger("transformers.models.bart.tokenization_bart"   )
        self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error"  ,     cl.out   )

        # no need to restore as nothing was changed







  def    __A     (							self :       Dict   )  ->	Optional[int]:
        # testing `logger.warning_advice()`
        transformers.utils.logging._reset_library_root_logger()

        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )
        SCREAMING_SNAKE_CASE_          =  "Testing 1, 2, 3"

        with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1"   ):
              # nothing should be logged as env var disables this method
              with CaptureLogger(__magic_name__   ) as cl:
                    logger.warning_advice(__magic_name__   )
              self.assertEqual(cl.out  ,     ""   )

        with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""   ):
              # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
              with CaptureLogger(__magic_name__   ) as cl:
                    logger.warning_advice(__magic_name__   )
              self.assertEqual(cl.out  ,     msg + "\n"   )



def      a__ (		):
      disable_progress_bar()
      assert are_progress_bars_disabled()

      enable_progress_bar()
      assert not are_progress_bars_disabled()


