






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


A__			:    List[Any]         =				logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A__			:    Dict         =				{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
A__			:    int         =				{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
A__			:    int         =				sorted(arg_to_scheduler.keys())
A__			:    Optional[Any]         =				'{' + ', '.join(arg_to_scheduler_choices) + '}'
class    _UpperCAmelCase      ( pl.LightningModule			):

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					argparse.Namespace,						lowerCamelCase		:					Optional[Any]=None,						lowerCamelCase		:					Union[str, Any]="base",						lowerCamelCase		:					List[str]=None,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					str=None,						**lowerCamelCase		:					Dict,						):

								'''simple docstring'''
								super().__init__()
								# TODO: move to self.save_hyperparameters()
								# self.save_hyperparameters()
								# can also expand arguments into trainer signature for easier reading

								self.save_hyperparameters(lowerCamelCase						)
								lowercase__     				=    0
								lowercase__     				=    Path(self.hparams.output_dir						)
								lowercase__     				=    self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
												lowercase__     				=    AutoConfig.from_pretrained(
												    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,						**({'''num_labels''': num_labels} if num_labels is not None else {}),						cache_dir=lowerCamelCase,						**lowerCamelCase,						)
								else:
												lowercase__     				=    config

								lowercase__     				=    ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
								for p in extra_model_params:
												if getattr(self.hparams,						lowerCamelCase,						lowerCamelCase						):
																assert hasattr(self.config,						lowerCamelCase						), F"""model config doesn't have a `{p}` attribute"""
																setattr(self.config,						lowerCamelCase,						getattr(self.hparams,						lowerCamelCase						)						)

								if tokenizer is None:
												lowercase__     				=    AutoTokenizer.from_pretrained(
												    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,						cache_dir=lowerCamelCase,						)
								else:
												lowercase__     				=    tokenizer
								lowercase__     				=    MODEL_MODES[mode]
								if model is None:
												lowercase__     				=    self.model_type.from_pretrained(
												    self.hparams.model_name_or_path,						from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path						),						config=self.config,						cache_dir=lowerCamelCase,						)
								else:
												lowercase__     				=    model




				def        lowercase__     (  self		:					Dict,						*lowerCamelCase		:					List[str],						**lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.model_type.from_pretrained(*lowerCamelCase,						**lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    arg_to_scheduler[self.hparams.lr_scheduler]
								lowercase__     				=    get_schedule_func(
								    self.opt,						num_warmup_steps=self.hparams.warmup_steps,						num_training_steps=self.total_steps()						)
								lowercase__     				=    {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
								return scheduler




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.model
								lowercase__     				=    ['''bias''', '''LayerNorm.weight''']
								lowercase__     				=    [
								    {
								        '''params''': [
								            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)
								        ],  # check this named paramters
								        '''weight_decay''': self.hparams.weight_decay,
								    },
								    {
								        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
								        '''weight_decay''': 0.0,
								    },
								]
								if self.hparams.adafactor:
												lowercase__     				=    Adafactor(
												    lowerCamelCase,						lr=self.hparams.learning_rate,						scale_parameter=lowerCamelCase,						relative_step=lowerCamelCase						)

								else:
												lowercase__     				=    AdamW(
												    lowerCamelCase,						lr=self.hparams.learning_rate,						eps=self.hparams.adam_epsilon						)
								lowercase__     				=    optimizer

								lowercase__     				=    self.get_lr_scheduler()

								return [optimizer], [scheduler]




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								return self.validation_step(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return self.validation_end(lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    max(1,						self.hparams.gpus						)  # TODO: consider num_tpu_cores
								lowercase__     				=    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
								return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								if stage == "test":
												lowercase__     				=    len(self.test_dataloader().dataset						)
								else:
												lowercase__     				=    self.get_dataloader('''train''',						self.hparams.train_batch_size,						shuffle=lowerCamelCase						)
												lowercase__     				=    len(self.train_dataloader().dataset						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					bool = False						):

								'''simple docstring'''
								raise NotImplementedError('''You must implement this for your task'''						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								return self.train_loader




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								return self.get_dataloader('''dev''',						self.hparams.eval_batch_size,						shuffle=lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								return self.get_dataloader('''test''',						self.hparams.eval_batch_size,						shuffle=lowerCamelCase						)




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								return os.path.join(
								    self.hparams.data_dir,						'''cached_{}_{}_{}'''.format(
								        lowerCamelCase,						list(filter(lowerCamelCase,						self.hparams.model_name_or_path.split('''/'''						)						)						).pop(),						str(self.hparams.max_seq_length						),						),						)




				@pl.utilities.rank_zero_only
				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					Dict[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.output_dir.joinpath('''best_tfmr'''						)
								lowercase__     				=    self.step_count
								self.model.save_pretrained(lowerCamelCase						)
								self.tokenizer.save_pretrained(lowerCamelCase						)




				@staticmethod
				def        lowercase__     (  lowerCamelCase		:					Tuple,						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								parser.add_argument(
								    '''--model_name_or_path''',						default=lowerCamelCase,						type=lowerCamelCase,						required=lowerCamelCase,						help='''Path to pretrained model or model identifier from huggingface.co/models''',						)
								parser.add_argument(
								    '''--config_name''',						default='''''',						type=lowerCamelCase,						help='''Pretrained config name or path if not the same as model_name'''						)
								parser.add_argument(
								    '''--tokenizer_name''',						default=lowerCamelCase,						type=lowerCamelCase,						help='''Pretrained tokenizer name or path if not the same as model_name''',						)
								parser.add_argument(
								    '''--cache_dir''',						default=str(Path(lowerCamelCase						).parent / '''test_run''' / '''cache'''						),						type=lowerCamelCase,						help='''Where do you want to store the pre-trained models downloaded from huggingface.co''',						)
								parser.add_argument(
								    '''--encoder_layerdrop''',						type=lowerCamelCase,						help='''Encoder layer dropout probability (Optional). Goes into model.config''',						)
								parser.add_argument(
								    '''--decoder_layerdrop''',						type=lowerCamelCase,						help='''Decoder layer dropout probability (Optional). Goes into model.config''',						)
								parser.add_argument(
								    '''--dropout''',						type=lowerCamelCase,						help='''Dropout probability (Optional). Goes into model.config''',						)
								parser.add_argument(
								    '''--attention_dropout''',						type=lowerCamelCase,						help='''Attention dropout probability (Optional). Goes into model.config''',						)
								parser.add_argument('''--learning_rate''',						default=5E-5,						type=lowerCamelCase,						help='''The initial learning rate for Adam.'''						)
								parser.add_argument(
								    '''--lr_scheduler''',						default='''linear''',						choices=lowerCamelCase,						metavar=lowerCamelCase,						type=lowerCamelCase,						help='''Learning rate scheduler''',						)
								parser.add_argument('''--weight_decay''',						default=0.0,						type=lowerCamelCase,						help='''Weight decay if we apply some.'''						)
								parser.add_argument('''--adam_epsilon''',						default=1E-8,						type=lowerCamelCase,						help='''Epsilon for Adam optimizer.'''						)
								parser.add_argument('''--warmup_steps''',						default=0,						type=lowerCamelCase,						help='''Linear warmup over warmup_steps.'''						)
								parser.add_argument('''--num_workers''',						default=4,						type=lowerCamelCase,						help='''kwarg passed to DataLoader'''						)
								parser.add_argument('''--num_train_epochs''',						dest='''max_epochs''',						default=3,						type=lowerCamelCase						)
								parser.add_argument('''--train_batch_size''',						default=32,						type=lowerCamelCase						)
								parser.add_argument('''--eval_batch_size''',						default=32,						type=lowerCamelCase						)
								parser.add_argument('''--adafactor''',						action='''store_true'''						)



class    _UpperCAmelCase      ( pl.Callback			):

				"""simple docstring"""




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					str,						lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								if (
								    trainer.is_global_zero and trainer.global_rank == 0
								):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
												pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class    _UpperCAmelCase      ( pl.Callback			):

				"""simple docstring"""




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								# print(pl_module.model.rag)
								for name, param in pl_module.model.rag.named_parameters():
												if param.grad is None:
																print(lowerCamelCase						)



class    _UpperCAmelCase      ( pl.Callback			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int,						lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    trainer.lr_schedulers[0]['''scheduler''']
								lowercase__     				=    {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
								pl_module.logger.log_metrics(lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					pl.Trainer,						lowerCamelCase		:					pl.LightningModule						):

								'''simple docstring'''
								rank_zero_info('''***** Validation results *****'''						)
								lowercase__     				=    trainer.callback_metrics
								# Log results
								for key in sorted(lowerCamelCase						):
												if key not in ["log", "progress_bar"]:
																rank_zero_info('''{} = {}\n'''.format(lowerCamelCase,						str(metrics[key]						)						)						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					pl.Trainer,						lowerCamelCase		:					pl.LightningModule						):

								'''simple docstring'''
								rank_zero_info('''***** Test results *****'''						)
								lowercase__     				=    trainer.callback_metrics
								# Log and save results to file
								lowercase__     				=    os.path.join(pl_module.hparams.output_dir,						'''test_results.txt'''						)
								with open(lowerCamelCase,						'''w'''						) as writer:
												for key in sorted(lowerCamelCase						):
																if key not in ["log", "progress_bar"]:
																				rank_zero_info('''{} = {}\n'''.format(lowerCamelCase,						str(metrics[key]						)						)						)
																				writer.write('''{} = {}\n'''.format(lowerCamelCase,						str(metrics[key]						)						)						)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '''--output_dir'''	,     default=str(Path(lowerCamelCase_       ).parent / '''test_run''' / '''model_checkpoints'''       )	,     type=lowerCamelCase_	,     help='''The output directory where the model predictions and checkpoints will be written.'''	,     )
				parser.add_argument(
				    '''--fp16'''	,     action='''store_true'''	,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''	,     )

				parser.add_argument(
				    '''--fp16_opt_level'''	,     type=lowerCamelCase_	,     default='''O2'''	,     help=(
				        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
				        '''See details at https://nvidia.github.io/apex/amp.html'''
				    )	,     )
				parser.add_argument('''--n_tpu_cores'''	,     dest='''tpu_cores'''	,     type=lowerCamelCase_       )
				parser.add_argument('''--max_grad_norm'''	,     dest='''gradient_clip_val'''	,     default=1.0	,     type=lowerCamelCase_	,     help='''Max gradient norm'''       )
				parser.add_argument('''--do_train'''	,     action='''store_true'''	,     help='''Whether to run training.'''       )
				parser.add_argument('''--do_predict'''	,     action='''store_true'''	,     help='''Whether to run predictions on the test set.'''       )
				parser.add_argument(
				    '''--gradient_accumulation_steps'''	,     dest='''accumulate_grad_batches'''	,     type=lowerCamelCase_	,     default=1	,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''	,     )
				parser.add_argument('''--seed'''	,     type=lowerCamelCase_	,     default=42	,     help='''random seed for initialization'''       )
				parser.add_argument(
				    '''--data_dir'''	,     default=str(Path(lowerCamelCase_       ).parent / '''test_run''' / '''dummy-train-data'''       )	,     type=lowerCamelCase_	,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''	,     )






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_=None	,     lowerCamelCase_=True	,     lowerCamelCase_=[]	,     lowerCamelCase_=None	,     lowerCamelCase_=None	,     **lowerCamelCase_	,     ):






				'''simple docstring'''
				pl.seed_everything(args.seed       )

				# init model
				lowercase__     				=    Path(model.hparams.output_dir       )
				odir.mkdir(exist_ok=lowerCamelCase_       )

				# add custom checkpoints
				if checkpoint_callback is None:
								lowercase__     				=    pl.callbacks.ModelCheckpoint(
								    filepath=args.output_dir	,     prefix='''checkpoint'''	,     monitor='''val_loss'''	,     mode='''min'''	,     save_top_k=1       )
				if early_stopping_callback:
								extra_callbacks.append(lowerCamelCase_       )
				if logging_callback is None:
								lowercase__     				=    LoggingCallback()

				lowercase__     				=    {}

				if args.fpaa:
								lowercase__     				=    16

				if args.gpus > 1:
								lowercase__     				=    '''auto'''
								lowercase__     				=    '''ddp'''

				lowercase__     				=    args.accumulate_grad_batches
				lowercase__     				=    None
				lowercase__     				=    '''auto'''

				lowercase__     				=    pl.Trainer.from_argparse_args(
				    lowerCamelCase_	,     weights_summary=lowerCamelCase_	,     callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]	,     logger=lowerCamelCase_	,     val_check_interval=1	,     num_sanity_val_steps=2	,     **lowerCamelCase_	,     )

				if args.do_train:
								trainer.fit(lowerCamelCase_       )

				else:
								print('''RAG modeling tests with new set functions successfuly executed!'''       )
				return trainer












import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= ViTImageProcessor if is_vision_available() else None




				@property
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    (3, 32, 128)
								lowercase__     				=    tempfile.mkdtemp()

								# fmt: off
								lowercase__     				=    ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
								# fmt: on
								lowercase__     				=    dict(zip(lowerCamelCase,						range(len(lowerCamelCase						)						)						)						)

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

								lowercase__     				=    {
								    '''do_normalize''': False,
								    '''do_resize''': True,
								    '''image_processor_type''': '''ViTImageProcessor''',
								    '''resample''': 3,
								    '''size''': {'''height''': 32, '''width''': 128},
								}
								lowercase__     				=    os.path.join(self.tmpdirname,						lowerCamelCase						)
								with open(self.image_processor_file,						'''w''',						encoding='''utf-8'''						) as fp:
												json.dump(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					int,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								return MgpstrTokenizer.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					str,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								return ViTImageProcessor.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								shutil.rmtree(self.tmpdirname						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    np.random.randint(255,						size=(3, 30, 400),						dtype=np.uinta						)

								lowercase__     				=    Image.fromarray(np.moveaxis(lowerCamelCase,						0,						-1						)						)

								return image_input




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)
								lowercase__     				=    MgpstrProcessor.from_pretrained(self.tmpdirname,						use_fast=lowerCamelCase						)

								self.assertEqual(processor.char_tokenizer.get_vocab(),						tokenizer.get_vocab()						)
								self.assertIsInstance(processor.char_tokenizer,						lowerCamelCase						)

								self.assertEqual(processor.image_processor.to_json_string(),						image_processor.to_json_string()						)
								self.assertIsInstance(processor.image_processor,						lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)

								lowercase__     				=    self.get_tokenizer(bos_token='''(BOS)''',						eos_token='''(EOS)'''						)
								lowercase__     				=    self.get_image_processor(do_normalize=lowerCamelCase,						padding_value=1.0						)

								lowercase__     				=    MgpstrProcessor.from_pretrained(
								    self.tmpdirname,						bos_token='''(BOS)''',						eos_token='''(EOS)''',						do_normalize=lowerCamelCase,						padding_value=1.0						)

								self.assertEqual(processor.char_tokenizer.get_vocab(),						tokenizer_add_kwargs.get_vocab()						)
								self.assertIsInstance(processor.char_tokenizer,						lowerCamelCase						)

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''np'''						)
								lowercase__     				=    processor(images=lowerCamelCase,						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        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''

								lowercase__     				=    processor(text=lowerCamelCase						)

								lowercase__     				=    tokenizer(lowerCamelCase						)
								for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key],						encoded_processor[key]						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

								self.assertListEqual(list(inputs.keys()						),						['''pixel_values''', '''labels''']						)

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




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

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

								lowercase__     				=    processor.char_decode(lowerCamelCase						)
								lowercase__     				=    tokenizer.batch_decode(lowerCamelCase						)
								lowercase__     				=    [seq.replace(''' ''',						''''''						) for seq in decoded_tok]

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    None
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

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




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    torch.randn(1,						27,						38						)
								lowercase__     				=    torch.randn(1,						27,						50_257						)
								lowercase__     				=    torch.randn(1,						27,						30_522						)

								lowercase__     				=    processor.batch_decode([char_input, bpe_input, wp_input]						)

								self.assertListEqual(list(results.keys()						),						['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']						)




