



"""simple docstring"""




import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


_lowerCAmelCase    : List[str]							=							os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"{bindir}/../../examples/pytorch/translation"):
 from run_translation import main  # noqa


set_seed(42)
_lowerCAmelCase    : str							=							"sshleifer/student_marian_en_ro_6_1"
_lowerCAmelCase    : Union[str, Any]							=							"sshleifer/tiny-mbart"






@require_torch
class      UpperCAmelCase_  (    _UpperCamelCase						):



      def 						snake_case_ (					self					:     Any					,     A					:     List[str]=False					,     A					:     Optional[Any]=None					,     A					:     Union[str, Any]=True					,     A					:     Optional[int]=True					,     A					:     int=True					,     A					:     Any=True					,     ):
           _UpperCAmelCase      :     int        =   self.run_trainer(
               eval_steps=1					,     max_len=1_2					,     model_name=A					,     num_train_epochs=1					,     distributed=A					,     extra_args_str=A					,     predict_with_generate=A					,     do_train=A					,     do_eval=A					,     do_predict=A					,     )
           _UpperCAmelCase      :     Any        =   TrainerState.load_from_json(os.path.join(A					,     "trainer_state.json"						)						).log_history

           if not do_eval:
                return

           _UpperCAmelCase      :     Optional[Any]        =   [log for log in logs if "eval_loss" in log.keys()]

           _UpperCAmelCase      :     List[str]        =   eval_metrics[0]
           if predict_with_generate:
                assert "eval_bleu" in first_step_stats

                _UpperCAmelCase      :     List[Any]        =   eval_metrics[-1]
                assert isinstance(last_step_stats["eval_bleu"]					,     A						)
                assert not math.isnan(float(last_step_stats["eval_loss"]						)						), "eval_loss must not be `nan`"



      @require_torch_non_multi_gpu
      def 						snake_case_ (					self					:     Union[str, Any]						):
           self.run_seqaseq_quick()



      @require_torch_multi_gpu
      def 						snake_case_ (					self					:     List[Any]						):
           self.run_seqaseq_quick(distributed=A						)



      @require_torch_multi_gpu
      def 						snake_case_ (					self					:     Any						):
           self.run_seqaseq_quick(distributed=A						)



      @unittest.skip("Requires an update of the env running those tests"						)
      @require_torch_multi_gpu
      @require_fairscale
      def 						snake_case_ (					self					:     Union[str, Any]						):
           self.run_seqaseq_quick(distributed=A					,     extra_args_str="--sharded_ddp simple"						)



      @unittest.skip("Requires an update of the env running those tests"						)
      @require_torch_multi_gpu
      @require_fairscale
      def 						snake_case_ (					self					:     int						):
           self.run_seqaseq_quick(distributed=A					,     extra_args_str="--sharded_ddp simple --fp16"						)



      @unittest.skip("Requires an update of the env running those tests"						)
      @require_torch_multi_gpu
      @require_fairscale
      def 						snake_case_ (					self					:     Tuple						):
           self.run_seqaseq_quick(distributed=A					,     extra_args_str="--sharded_ddp zero_dp_2"					,     predict_with_generate=A						)



      @unittest.skip("Requires an update of the env running those tests"						)
      @require_torch_multi_gpu
      @require_fairscale
      def 						snake_case_ (					self					:     str						):
           self.run_seqaseq_quick(
               distributed=A					,     extra_args_str="--sharded_ddp zero_dp_2 --fp16"					,     predict_with_generate=A						)



      @require_apex
      @require_torch_gpu
      def 						snake_case_ (					self					:     Tuple						):
           # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
           # program and it breaks other tests that run from the same pytest worker, therefore until this is
           # sorted out it must be run only in an external program, that is distributed=True in this
           # test and only under one or more gpus - if we want cpu will need to make a special test
           #
           # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
           # 2nd main() call it botches the future eval.
           #
           self.run_seqaseq_quick(distributed=A					,     extra_args_str="--fp16 --fp16_backend=apex"						)
           # test 2nd time - was getting eval_loss': nan'
           # to reproduce the problem set distributed=False
           self.run_seqaseq_quick(distributed=A					,     extra_args_str="--fp16 --fp16_backend=apex"						)



      @parameterized.expand(["base", "low", "high", "mixed"]						)
      @require_torch_multi_gpu
      def 						snake_case_ (					self					:     List[Any]					,     A					:     List[str]						):
           # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
           _UpperCAmelCase      :     Optional[Any]        =   {
               # test with the default log_level - should be info and thus log info once
               "base": {"extra_args_str": "", "n_matches": 1},
               # test with low log_level and log_level_replica - should be noisy on all processes
               # now the info string should appear twice on 2 processes
               "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
               # test with high log_level and low log_level_replica
               # now the info string should appear once only on the replica
               "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
               # test with high log_level and log_level_replica - should be quiet on all processes
               "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
           }

           _UpperCAmelCase      :     int        =   experiments[experiment_id]
           _UpperCAmelCase      :     Dict        =   {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
           _UpperCAmelCase      :     Optional[Any]        =   "Running training"
           with CaptureStderr() as cl:
                self.run_seqaseq_quick(**A					,     extra_args_str=data["extra_args_str"]						)
           _UpperCAmelCase      :     List[str]        =   len(re.findall(A					,     cl.err						)						)
           self.assertEqual(A					,     data["n_matches"]						)



      @slow
      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Any        =   self.run_trainer(
               eval_steps=2					,     max_len=1_2_8					,     model_name=A					,     learning_rate=3e-4					,     num_train_epochs=1_0					,     distributed=A					,     )

           # Check metrics
           _UpperCAmelCase      :     List[str]        =   TrainerState.load_from_json(os.path.join(A					,     "trainer_state.json"						)						).log_history
           _UpperCAmelCase      :     List[str]        =   [log for log in logs if "eval_loss" in log.keys()]
           _UpperCAmelCase      :     Optional[int]        =   eval_metrics[0]
           _UpperCAmelCase      :     Optional[Any]        =   eval_metrics[-1]

           assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
           assert isinstance(last_step_stats["eval_bleu"]					,     A						)

           # test if do_predict saves generations and metrics
           _UpperCAmelCase      :     int        =   os.listdir(A						)
           _UpperCAmelCase      :     Optional[int]        =   {os.path.basename(A						) for p in contents}
           assert "generated_predictions.txt" in contents
           assert "predict_results.json" in contents



      @slow
      @require_bitsandbytes
      def 						snake_case_ (					self					:     Tuple						):
           from transformers.training_args import OptimizerNames

           def train_and_return_metrics(A					:     str						) -> Tuple[int, float]:
                _UpperCAmelCase      :     List[str]        =   "--skip_memory_metrics 0"

                _UpperCAmelCase      :     str        =   self.run_trainer(
                    max_len=1_2_8					,     model_name=A					,     learning_rate=3e-4					,     num_train_epochs=1					,     optim=A					,     distributed=A					,     extra_args_str=A					,     do_eval=A					,     do_predict=A					,     n_gpus_to_use=1					,     )

                # Check metrics
                _UpperCAmelCase      :     Any        =   TrainerState.load_from_json(Path(A					,     "trainer_state.json"						)						).log_history
                _UpperCAmelCase      :     Tuple        =   int(logs[0]["train_mem_gpu_peaked_delta"] / 2**2_0						)
                _UpperCAmelCase      :     Optional[int]        =   int(logs[0]["train_mem_gpu_alloc_delta"] / 2**2_0						)

                _UpperCAmelCase      :     Union[str, Any]        =   logs[0]["train_loss"]
                return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     str        =   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value						)
           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Union[str, Any]        =   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value						)

           _UpperCAmelCase      :     List[str]        =   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

           _UpperCAmelCase      :     Optional[Any]        =   gpu_peak_mem_orig + gpu_alloc_mem_orig
           _UpperCAmelCase      :     Dict        =   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
           _UpperCAmelCase      :     Any        =   gpu_total_mem_orig - gpu_total_mem_bnb

           # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
           # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
           # in 2 bytes and the diff in optim memory usage is derived as so:
           #
           # - normal 25*8=~200MB (8 bytes per param)
           # - bnb    25*2= ~50MB (2 bytes per param)
           #
           # Thus we should expect ~150MB total memory saved.
           #
           # Peak memory should be the same - the total should be different by about that same margin
           #
           # After leaving a small margin to accommodate for differences between gpus let's check
           # that we have at least 120MB in savings
           _UpperCAmelCase      :     Union[str, Any]        =   1_2_0

           # uncomment the following if this test starts failing - requires py38 for a new print feature
           # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
           # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
           # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
           # print(f"{gpu_alloc_mem_diff=}MB")
           # print(f"{gpu_peak_mem_diff=}MB")
           # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
           # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

           self.assertGreater(
               A					,     A					,     "should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
               f' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'
               f' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'					,     )

           self.assertGreater(
               A					,     A					,     "should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
               f' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'
               f' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'					,     )

           self.assertEqual(
               A					,     A					,     f'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'						)



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     int					,     A					:     str					,     A					:     int					,     A					:     float = 3e-3					,     A					:     str = "adafactor"					,     A					:     bool = False					,     A					:     str = None					,     A					:     int = 0					,     A					:     bool = True					,     A					:     bool = True					,     A					:     bool = True					,     A					:     bool = True					,     A					:     int = None					,     ):
           _UpperCAmelCase      :     Union[str, Any]        =   self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_auto_remove_tmp_dir()
           _UpperCAmelCase      :     Optional[Any]        =   f'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(A						)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(A						)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

           _UpperCAmelCase      :     Tuple        =   f'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(A						)}\n        '.split()

           _UpperCAmelCase      :     List[Any]        =   "\n            --do_predict\n        ".split()

           _UpperCAmelCase      :     List[str]        =   []
           if do_train:
                args += args_train

           if do_eval:
                args += args_eval

           if do_predict:
                args += args_predict

           if predict_with_generate:
                args += "--predict_with_generate".split()

           if do_train:
                if optim == "adafactor":
                     args += "--adafactor".split()
                else:
                     args += f'--optim {optim}'.split()

           if extra_args_str is not None:
                args += extra_args_str.split()

           if distributed:
                if n_gpus_to_use is None:
                     _UpperCAmelCase      :     Union[str, Any]        =   get_gpu_count()
                _UpperCAmelCase      :     Optional[Any]        =   get_torch_dist_unique_port()
                _UpperCAmelCase      :     List[str]        =   f'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
                _UpperCAmelCase      :     Optional[int]        =   [sys.executable] + distributed_args + args
                # keep for quick debug
                # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                execute_subprocess_async(A					,     env=self.get_env()						)
           else:
                _UpperCAmelCase      :     List[str]        =   ["run_translation.py"] + args
                with patch.object(A					,     "argv"					,     A						):
                     main()

           return output_dir





"""simple docstring"""




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      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     List[str]        =   tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase      :     Union[str, Any]        =   ["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
           _UpperCAmelCase      :     List[Any]        =   dict(zip(A					,     range(len(A						)						)						)						)
           _UpperCAmelCase      :     Union[str, Any]        =   ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase      :     Optional[int]        =   {"unk_token": "<unk>"}

           _UpperCAmelCase      :     Optional[Any]        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]						)
           _UpperCAmelCase      :     Optional[Any]        =   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(A						) + "\n"						)
           with open(self.merges_file					,     "w"					,     encoding="utf-8"						) as fp:
                fp.write("\n".join(A						)						)

           _UpperCAmelCase      :     List[str]        =   {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "do_normalize": True,
               "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           _UpperCAmelCase      :     Any        =   os.path.join(self.tmpdirname					,     A						)
           with open(self.image_processor_file					,     "w"					,     encoding="utf-8"						) as fp:
                json.dump(A					,     A						)



      def 						snake_case_ (					self					:     List[Any]					,     **A					:     Union[str, Any]						):
           return CLIPTokenizer.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     int					,     **A					:     Any						):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     **A					:     Optional[Any]						):
           return CLIPImageProcessor.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           shutil.rmtree(self.tmpdirname						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     int        =   [np.random.randint(2_5_5					,     size=(3, 3_0, 4_0_0)					,     dtype=np.uinta						)]

           _UpperCAmelCase      :     Dict        =   [Image.fromarray(np.moveaxis(A					,     0					,     -1						)						) for x in image_inputs]

           return image_inputs



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     int        =   self.get_tokenizer()
           _UpperCAmelCase      :     Dict        =   self.get_rust_tokenizer()
           _UpperCAmelCase      :     int        =   self.get_image_processor()

           _UpperCAmelCase      :     List[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)
           processor_slow.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     Optional[Any]        =   CLIPProcessor.from_pretrained(self.tmpdirname					,     use_fast=A						)

           _UpperCAmelCase      :     Optional[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)
           processor_fast.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     Optional[int]        =   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					,     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 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[str]        =   CLIPProcessor(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      :     Any        =   self.get_image_processor(do_normalize=A					,     padding_value=1.0						)

           _UpperCAmelCase      :     Any        =   CLIPProcessor.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 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     str        =   self.get_image_processor()
           _UpperCAmelCase      :     List[str]        =   self.get_tokenizer()

           _UpperCAmelCase      :     Any        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Dict        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[int]        =   image_processor(A					,     return_tensors="np"						)
           _UpperCAmelCase      :     Any        =   processor(images=A					,     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 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   self.get_image_processor()
           _UpperCAmelCase      :     int        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[str]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Optional[int]        =   "lower newer"

           _UpperCAmelCase      :     Union[str, Any]        =   processor(text=A						)

           _UpperCAmelCase      :     Optional[int]        =   tokenizer(A						)

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



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     Tuple        =   self.get_tokenizer()

           _UpperCAmelCase      :     str        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

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

           _UpperCAmelCase      :     str        =   processor(text=A					,     images=A						)

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

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



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     List[str]        =   self.get_image_processor()
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     str        =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase      :     List[str]        =   processor.batch_decode(A						)
           _UpperCAmelCase      :     int        =   tokenizer.batch_decode(A						)

           self.assertListEqual(A					,     A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     int        =   self.get_tokenizer()

           _UpperCAmelCase      :     int        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     str        =   "lower newer"
           _UpperCAmelCase      :     int        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[Any]        =   processor(text=A					,     images=A						)

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