



"""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      :     str        =   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value						)
											_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 copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
 from ...processing_utils import ProcessorMixin
 from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase    : Dict							=							logging.get_logger(__name__)

_lowerCAmelCase    : List[str]							=							{
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       int								=			'owlvit_text_model'



      def __init__(					self					:     int					,     A					:     int=4_9_4_0_8					,     A					:     Optional[Any]=5_1_2					,     A					:     Optional[Any]=2_0_4_8					,     A					:     str=1_2					,     A					:     int=8					,     A					:     Tuple=1_6					,     A					:     List[Any]="quick_gelu"					,     A					:     Tuple=1e-5					,     A					:     Union[str, Any]=0.0					,     A					:     List[Any]=0.02					,     A					:     str=1.0					,     A					:     str=0					,     A					:     List[str]=4_9_4_0_6					,     A					:     str=4_9_4_0_7					,     **A					:     Optional[Any]					,     ):
           super().__init__(pad_token_id=A					,     bos_token_id=A					,     eos_token_id=A					,     **A						)

           _UpperCAmelCase      :     Union[str, Any]        =   vocab_size
           _UpperCAmelCase      :     str        =   hidden_size
           _UpperCAmelCase      :     List[Any]        =   intermediate_size
           _UpperCAmelCase      :     Any        =   num_hidden_layers
           _UpperCAmelCase      :     str        =   num_attention_heads
           _UpperCAmelCase      :     List[str]        =   max_position_embeddings
           _UpperCAmelCase      :     List[Any]        =   hidden_act
           _UpperCAmelCase      :     Tuple        =   layer_norm_eps
           _UpperCAmelCase      :     List[str]        =   attention_dropout
           _UpperCAmelCase      :     Optional[Any]        =   initializer_range
           _UpperCAmelCase      :     List[Any]        =   initializer_factor



      @classmethod
      def 						snake_case_ (					cls					:     Any					,     A					:     Union[str, os.PathLike]					,     **A					:     Dict						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   cls.get_config_dict(A					,     **A						)

           # get the text config dict if we are loading from OwlViTConfig
           if config_dict.get("model_type"						) == "owlvit":
                _UpperCAmelCase      :     int        =   config_dict["text_config"]

           if "model_type" in config_dict and hasattr(cls					,     "model_type"						) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

           return cls.from_dict(A					,     **A						)







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Tuple								=			'owlvit_vision_model'



      def __init__(					self					:     Union[str, Any]					,     A					:     Optional[int]=7_6_8					,     A					:     int=3_0_7_2					,     A					:     List[str]=1_2					,     A					:     List[str]=1_2					,     A					:     Optional[int]=3					,     A					:     Optional[int]=7_6_8					,     A					:     str=3_2					,     A					:     Tuple="quick_gelu"					,     A					:     Dict=1e-5					,     A					:     Optional[int]=0.0					,     A					:     List[Any]=0.02					,     A					:     str=1.0					,     **A					:     Tuple					,     ):
           super().__init__(**A						)

           _UpperCAmelCase      :     List[str]        =   hidden_size
           _UpperCAmelCase      :     Tuple        =   intermediate_size
           _UpperCAmelCase      :     Optional[Any]        =   num_hidden_layers
           _UpperCAmelCase      :     Dict        =   num_attention_heads
           _UpperCAmelCase      :     Optional[Any]        =   num_channels
           _UpperCAmelCase      :     Union[str, Any]        =   image_size
           _UpperCAmelCase      :     Dict        =   patch_size
           _UpperCAmelCase      :     List[str]        =   hidden_act
           _UpperCAmelCase      :     Union[str, Any]        =   layer_norm_eps
           _UpperCAmelCase      :     Any        =   attention_dropout
           _UpperCAmelCase      :     Tuple        =   initializer_range
           _UpperCAmelCase      :     Tuple        =   initializer_factor



      @classmethod
      def 						snake_case_ (					cls					:     Optional[int]					,     A					:     Union[str, os.PathLike]					,     **A					:     int						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   cls.get_config_dict(A					,     **A						)

           # get the vision config dict if we are loading from OwlViTConfig
           if config_dict.get("model_type"						) == "owlvit":
                _UpperCAmelCase      :     Tuple        =   config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls					,     "model_type"						) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

           return cls.from_dict(A					,     **A						)







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       List[str]								=			'owlvit'
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			True



      def __init__(					self					:     Optional[Any]					,     A					:     Dict=None					,     A					:     Tuple=None					,     A					:     Optional[Any]=5_1_2					,     A					:     Optional[Any]=2.6_592					,     A					:     int=True					,     **A					:     Tuple					,     ):
           super().__init__(**A						)

           if text_config is None:
                _UpperCAmelCase      :     List[Any]        =   {}
                logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."						)

           if vision_config is None:
                _UpperCAmelCase      :     Tuple        =   {}
                logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."						)

           _UpperCAmelCase      :     str        =   OwlViTTextConfig(**A						)
           _UpperCAmelCase      :     int        =   OwlViTVisionConfig(**A						)

           _UpperCAmelCase      :     Optional[Any]        =   projection_dim
           _UpperCAmelCase      :     str        =   logit_scale_init_value
           _UpperCAmelCase      :     Optional[Any]        =   return_dict
           _UpperCAmelCase      :     str        =   1.0



      @classmethod
      def 						snake_case_ (					cls					:     Dict					,     A					:     Union[str, os.PathLike]					,     **A					:     Any						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase    ,	_UpperCAmelCase      :     str        =   cls.get_config_dict(A					,     **A						)

           if "model_type" in config_dict and hasattr(cls					,     "model_type"						) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

           return cls.from_dict(A					,     **A						)



      @classmethod
      def 						snake_case_ (					cls					:     Optional[int]					,     A					:     Dict					,     A					:     Dict					,     **A					:     Optional[Any]						):
           _UpperCAmelCase      :     Optional[Any]        =   {}
           _UpperCAmelCase      :     int        =   text_config
           _UpperCAmelCase      :     Dict        =   vision_config

           return cls.from_dict(A					,     **A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     str        =   copy.deepcopy(self.__dict__						)
           _UpperCAmelCase      :     Optional[int]        =   self.text_config.to_dict()
           _UpperCAmelCase      :     Optional[int]        =   self.vision_config.to_dict()
           _UpperCAmelCase      :     List[Any]        =   self.__class__.model_type
           return output







class      UpperCAmelCase_  (    _UpperCamelCase						):



      @property
      def 						snake_case_ (					self					:     List[str]						):
           return OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "sequence"}),
                   ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                   ("attention_mask", {0: "batch", 1: "sequence"}),
               ]						)



      @property
      def 						snake_case_ (					self					:     Optional[int]						):
           return OrderedDict(
               [
                   ("logits_per_image", {0: "batch"}),
                   ("logits_per_text", {0: "batch"}),
                   ("text_embeds", {0: "batch"}),
                   ("image_embeds", {0: "batch"}),
               ]						)



      @property
      def 						snake_case_ (					self					:     str						):
           return 1e-4



      def 						snake_case_ (					self					:     str					,     A					:     "ProcessorMixin"					,     A					:     int = -1					,     A					:     int = -1					,     A					:     Optional["TensorType"] = None					,     ):
           _UpperCAmelCase      :     Optional[Any]        =   super().generate_dummy_inputs(
               processor.tokenizer					,     batch_size=A					,     seq_length=A					,     framework=A						)
           _UpperCAmelCase      :     Union[str, Any]        =   super().generate_dummy_inputs(
               processor.image_processor					,     batch_size=A					,     framework=A						)
           return {**text_input_dict, **image_input_dict}



      @property
      def 						snake_case_ (					self					:     List[Any]						):
           return 1_4
