




"""simple docstring"""

import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_lowerCamelCase					:     List[str]				       =    logging.getLogger()
_lowerCamelCase					:     int				       =    torch.cuda.is_available()
_lowerCamelCase					:     List[str]				       =    {
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 42,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1E-08,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 12,
    'max_target_length': 12,
    'val_max_target_length': 12,
    'test_max_target_length': 12,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase  ):




	"""simple docstring"""






	A_					:			Tuple						=						'''\n'''.join(_UpperCAmelCase  )
	Path(_UpperCAmelCase  ).open('''w'''  ).writelines(_UpperCAmelCase  )


_lowerCamelCase					:     Optional[int]				       =    [' Sam ate lunch today.', 'Sams lunch ingredients.']
_lowerCamelCase					:     List[Any]				       =    ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_lowerCamelCase					:     List[str]				       =    'patrickvonplaten/t5-tiny-random'
_lowerCamelCase					:     Tuple				       =    'sshleifer/t5-tinier-random'
_lowerCamelCase					:     int				       =    'sshleifer/bart-tiny-random'
_lowerCamelCase					:     Tuple				       =    'sshleifer/tiny-mbart'
_lowerCamelCase					:     Tuple				       =    'sshleifer/tiny-marian-en-de'
_lowerCamelCase					:     Union[str, Any]				       =    'stas/tiny-wmt19-en-de'


_lowerCamelCase					:     Dict				       =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

def 						lowercase_ (						_UpperCAmelCase  ):




	"""simple docstring"""






	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_UpperCAmelCase					, f"""{split}.source"""  )					, _UpperCAmelCase  )
		_dump_articles(os.path.join(_UpperCAmelCase					, f"""{split}.target"""  )					, _UpperCAmelCase  )
	return tmp_dir

class     lowercase (      __UpperCAmelCase):







				@classmethod
				def    a_					(							cls				:   List[Any]    ):



					"""simple docstring"""

					logging.disable(logging.CRITICAL    )  # remove noisy download output from tracebacks
					return cls







				@slow
				@require_torch_gpu
				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						list_models()
					A_					:			Dict						=						'''sshleifer'''
					A_					:			List[str]						=						[x.modelId for x in model_list if x.modelId.startswith(_lowerCamelCase    )]
					A_					:			Union[str, Any]						=						['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
					A_					:			Dict						=						[]
					for m in model_ids:
						if m in allowed_to_be_broken:
							continue
						try:
							AutoConfig.from_pretrained(_lowerCamelCase    )
						except Exception:
							failures.append(_lowerCamelCase    )
					assert not failures, F"""The following models could not be loaded through AutoConfig: {failures}"""







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
					self._test_distiller_cli(_lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						{
					    '''student_encoder_layers''': 2,
					    '''student_decoder_layers''': 1,
					    '''max_epochs''': 4,
					    '''val_check_interval''': 0.25,
					    '''alpha_hid''': 2.0,
					    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
					}
					A_					:			int						=						self._test_distiller_cli(_lowerCamelCase ,       check_contents=_lowerCamelCase    )

					A_					:			str						=						list(Path(model.output_dir    ).glob('''*.ckpt'''    )    )
					self.assertEqual(1 ,       len(_lowerCamelCase    )    )
					A_					:			List[str]						=						list(Path(model.output_dir    ).glob('''**/*.bin'''    )    )
					self.assertEqual(len(_lowerCamelCase    ) ,       2    )
					A_					:			Tuple						=						lmap(str.strip ,       Path(model.hparams.data_dir    ).joinpath('''test.source'''    ).open().readlines()    )
					A_					:			List[Any]						=						tempfile.mktemp()  # XXX: not being cleaned up
					generate_summaries_or_translations(_lowerCamelCase ,       _lowerCamelCase ,       str(model.output_dir / '''best_tfmr'''    )    )
					self.assertTrue(Path(_lowerCamelCase    ).exists()    )

					A_					:			Tuple						=						self.get_auto_remove_tmp_dir()
					convert_pl_to_hf(ckpts[0] ,       transformer_ckpts[0].parent ,       _lowerCamelCase    )
					assert os.path.exists(os.path.join(_lowerCamelCase ,       '''pytorch_model.bin'''    )    )







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase    )
					A_					:			List[str]						=						model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
					A_					:			Optional[int]						=						torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]] ,       dtype=torch.long ,       device=model.device    )
					A_					:			Tuple						=						target_ids[:, :-1].contiguous()  # Why this line?
					A_					:			Optional[int]						=						target_ids[:, 1:].clone()  # why clone?
					A_					:			Optional[Any]						=						model(
					    _lowerCamelCase ,       attention_mask=_lowerCamelCase ,       decoder_input_ids=_lowerCamelCase ,       labels=_lowerCamelCase ,       use_cache=_lowerCamelCase    ).loss

					A_					:			Optional[int]						=						model(_lowerCamelCase ,       attention_mask=_lowerCamelCase ,       decoder_input_ids=_lowerCamelCase ,       use_cache=_lowerCamelCase    ).logits

					A_					:			int						=						nn.functional.log_softmax(_lowerCamelCase ,       dim=-1    )
					A_					:			Optional[int]						=						label_smoothed_nll_loss(
					    _lowerCamelCase ,       _lowerCamelCase ,       0.1 ,       ignore_index=model.config.pad_token_id    )
					with self.assertRaises(_lowerCamelCase    ):
						# TODO: understand why this breaks
						self.assertEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					A_					:			List[Any]						=						{
					    '''student_encoder_layers''': 2,
					    '''student_decoder_layers''': 1,
					    '''num_train_epochs''': 4,
					    '''val_check_interval''': 0.25,
					    '''alpha_hid''': 2.0,
					    '''task''': '''translation''',
					    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
					    '''tokenizer_name''': MBART_TINY,
					    '''teacher''': MBART_TINY,
					    '''src_lang''': '''en_XX''',
					    '''tgt_lang''': '''ro_RO''',
					}
					A_					:			str						=						self._test_distiller_cli(_lowerCamelCase ,       check_contents=_lowerCamelCase    )
					assert model.model.config.model_type == "mbart"

					A_					:			Dict						=						list(Path(model.output_dir    ).glob('''*.ckpt'''    )    )
					self.assertEqual(1 ,       len(_lowerCamelCase    )    )
					A_					:			Union[str, Any]						=						list(Path(model.output_dir    ).glob('''**/*.bin'''    )    )
					A_					:			Union[str, Any]						=						list(Path(model.output_dir    ).glob('''best_tfmr/*'''    )    )
					assert len(_lowerCamelCase    ) > 2
					self.assertEqual(len(_lowerCamelCase    ) ,       2    )







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			str						=						{
					    '''student_encoder_layers''': 1,
					    '''student_decoder_layers''': 1,
					    '''alpha_hid''': 2.0,
					    '''teacher''': T5_TINY,
					    '''model_name_or_path''': T5_TINY,
					    '''tokenizer_name''': T5_TINY,
					}
					self._test_distiller_cli(_lowerCamelCase    )







				def    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

					A_					:			Dict						=						{
					    '''teacher''': T5_TINY,
					    '''student''': T5_TINIER,
					    '''model_name_or_path''': T5_TINIER,
					    '''tokenizer_name''': T5_TINIER,
					}
					self._test_distiller_cli(_lowerCamelCase    )







				def    a_					(							self				:   str ,       _lowerCamelCase				:   List[str] ,       _lowerCamelCase				:   Optional[Any]=True    ):



					"""simple docstring"""

					A_					:			Any						=						{
					    '''label_smoothing''': 0.0,
					    '''early_stopping_patience''': -1,
					    '''train_batch_size''': 1,
					    '''eval_batch_size''': 2,
					    '''max_epochs''': 2,
					    '''alpha_mlm''': 0.2,
					    '''alpha_ce''': 0.8,
					    '''do_predict''': True,
					    '''model_name_or_path''': '''sshleifer/tinier_bart''',
					    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
					    '''val_check_interval''': 0.5,
					}
					default_updates.update(_lowerCamelCase    )
					A_					:			dict						=						CHEAP_ARGS.copy()
					A_					:			int						=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
					A_					:			Dict						=						self.get_auto_remove_tmp_dir()

					args_d.update(data_dir=_lowerCamelCase ,       output_dir=_lowerCamelCase ,       **_lowerCamelCase    )
					A_					:			List[Any]						=						distill_main(argparse.Namespace(**_lowerCamelCase    )    )
					if not check_contents:
						return model
					A_					:			Dict						=						os.listdir(_lowerCamelCase    )
					A_					:			Optional[Any]						=						{os.path.basename(_lowerCamelCase    ) for p in contents}
					A_					:			Union[str, Any]						=						[p for p in contents if p.endswith('''ckpt'''    )]
					assert len(_lowerCamelCase    ) > 0

					self.assertIn('''test_generations.txt''' ,       _lowerCamelCase    )
					self.assertIn('''test_results.txt''' ,       _lowerCamelCase    )

					A_					:			Optional[Any]						=						load_json(model.metrics_save_path    )
					A_					:			Optional[int]						=						metrics['''val'''][-1]
					self.assertGreaterEqual(last_step_stats['''val_avg_gen_time'''] ,       0.01    )
					self.assertGreaterEqual(1.0 ,       last_step_stats['''val_avg_gen_time''']    )
					self.assertIsInstance(last_step_stats[F"""val_avg_{model.val_metric}"""] ,       _lowerCamelCase    )
					A_					:			Dict						=						int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1    )
					self.assertEqual(len(metrics['''val''']    ) ,       _lowerCamelCase    )
					self.assertEqual(len(metrics['''test''']    ) ,       1    )
					return model

class     lowercase (      __UpperCAmelCase):







				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY] ,       )
				def    a_					(							self				:   Dict ,       _lowerCamelCase				:   Any    ):



					"""simple docstring"""

					A_					:			dict						=						CHEAP_ARGS.copy()
					A_					:			str						=						'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
					A_					:			Any						=						0.1 if task == '''translation''' else 0

					A_					:			List[str]						=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
					A_					:			Optional[Any]						=						self.get_auto_remove_tmp_dir()
					args_d.update(
					    data_dir=_lowerCamelCase ,       model_name_or_path=_lowerCamelCase ,       tokenizer_name=_lowerCamelCase ,       train_batch_size=2 ,       eval_batch_size=2 ,       output_dir=_lowerCamelCase ,       do_predict=_lowerCamelCase ,       task=_lowerCamelCase ,       src_lang='''en_XX''' ,       tgt_lang='''ro_RO''' ,       freeze_encoder=_lowerCamelCase ,       freeze_embeds=_lowerCamelCase ,       )
					assert "n_train" in args_d
					A_					:			List[Any]						=						argparse.Namespace(**_lowerCamelCase    )
					A_					:			List[str]						=						main(_lowerCamelCase    )

					A_					:			Any						=						module.model.get_input_embeddings()
					assert not input_embeds.weight.requires_grad
					if model == T5_TINY:
						A_					:			Optional[int]						=						module.model.lm_head
						assert not lm_head.weight.requires_grad
						assert (lm_head.weight == input_embeds.weight).all().item()
					elif model == FSMT_TINY:
						A_					:			str						=						module.model.model
						A_					:			Any						=						fsmt.decoder.embed_positions
						assert not embed_pos.weight.requires_grad
						assert not fsmt.decoder.embed_tokens.weight.requires_grad
						# check that embeds are not the same
						assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
					else:
						A_					:			int						=						module.model.model
						A_					:			str						=						bart.decoder.embed_positions
						assert not embed_pos.weight.requires_grad
						assert not bart.shared.weight.requires_grad
						# check that embeds are the same
						assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
						assert bart.decoder.embed_tokens == bart.shared

					A_					:			Optional[int]						=						load_json(module.output_dir / '''text_batch.json'''    )
					assert isinstance(_lowerCamelCase ,       _lowerCamelCase    )
					assert len(_lowerCamelCase    ) >= 4







				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			dict						=						CHEAP_ARGS.copy()

					A_					:			List[Any]						=						'''summarization'''
					A_					:			Union[str, Any]						=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )

					args_d.update(
					    data_dir=_lowerCamelCase ,       tokenizer_name=_lowerCamelCase ,       train_batch_size=2 ,       eval_batch_size=2 ,       do_predict=_lowerCamelCase ,       task=_lowerCamelCase ,       src_lang='''en_XX''' ,       tgt_lang='''ro_RO''' ,       freeze_encoder=_lowerCamelCase ,       freeze_embeds=_lowerCamelCase ,       )

					# test models whose config includes the extra_model_args
					A_					:			str						=						BART_TINY
					A_					:			List[Any]						=						self.get_auto_remove_tmp_dir()
					A_					:			Union[str, Any]						=						args_d.copy()
					args_da.update(
					    model_name_or_path=_lowerCamelCase ,       output_dir=_lowerCamelCase ,       )
					A_					:			Dict						=						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
					for p in extra_model_params:
						A_					:			int						=						0.5
					A_					:			Any						=						argparse.Namespace(**_lowerCamelCase    )
					A_					:			int						=						main(_lowerCamelCase    )
					for p in extra_model_params:
						assert getattr(model.config ,       _lowerCamelCase    ) == 0.5, F"""failed to override the model config for param {p}"""

					# test models whose config doesn't include the extra_model_args
					A_					:			Any						=						T5_TINY
					A_					:			Any						=						self.get_auto_remove_tmp_dir()
					A_					:			Optional[Any]						=						args_d.copy()
					args_da.update(
					    model_name_or_path=_lowerCamelCase ,       output_dir=_lowerCamelCase ,       )
					A_					:			List[str]						=						'''encoder_layerdrop'''
					A_					:			Any						=						0.5
					A_					:			str						=						argparse.Namespace(**_lowerCamelCase    )
					with pytest.raises(_lowerCamelCase    ) as excinfo:
						A_					:			Optional[int]						=						main(_lowerCamelCase    )
					assert str(excinfo.value    ) == F"""model config doesn't have a `{unsupported_param}` attribute"""







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_					:			dict						=						CHEAP_ARGS.copy()

					A_					:			Union[str, Any]						=						'''summarization'''
					A_					:			Union[str, Any]						=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )

					A_					:			List[str]						=						BART_TINY
					A_					:			int						=						self.get_auto_remove_tmp_dir()

					args_d.update(
					    data_dir=_lowerCamelCase ,       model_name_or_path=_lowerCamelCase ,       output_dir=_lowerCamelCase ,       tokenizer_name=_lowerCamelCase ,       train_batch_size=2 ,       eval_batch_size=2 ,       do_predict=_lowerCamelCase ,       task=_lowerCamelCase ,       src_lang='''en_XX''' ,       tgt_lang='''ro_RO''' ,       freeze_encoder=_lowerCamelCase ,       freeze_embeds=_lowerCamelCase ,       )

					# emulate finetune.py
					A_					:			Dict						=						argparse.ArgumentParser()
					A_					:			Optional[Any]						=						pl.Trainer.add_argparse_args(_lowerCamelCase    )
					A_					:			Tuple						=						SummarizationModule.add_model_specific_args(_lowerCamelCase ,       os.getcwd()    )
					A_					:			int						=						{'''--help''': True}

					# --help test
					with pytest.raises(_lowerCamelCase    ) as excinfo:
						with CaptureStdout() as cs:
							A_					:			Tuple						=						parser.parse_args(_lowerCamelCase    )
						assert False, "--help is expected to sys.exit"
					assert excinfo.type == SystemExit
					A_					:			Optional[Any]						=						lightning_base.arg_to_scheduler_metavar
					assert expected in cs.out, "--help is expected to list the supported schedulers"

					# --lr_scheduler=non_existing_scheduler test
					A_					:			List[str]						=						'''non_existing_scheduler'''
					A_					:			List[Any]						=						{F"""--lr_scheduler={unsupported_param}"""}
					with pytest.raises(_lowerCamelCase    ) as excinfo:
						with CaptureStderr() as cs:
							A_					:			str						=						parser.parse_args(_lowerCamelCase    )
						assert False, "invalid argument is expected to sys.exit"
					assert excinfo.type == SystemExit
					A_					:			List[Any]						=						F"""invalid choice: '{unsupported_param}'"""
					assert expected in cs.err, F"""should have bailed on invalid choice of scheduler {unsupported_param}"""

					# --lr_scheduler=existing_scheduler test
					A_					:			Union[str, Any]						=						'''cosine'''
					A_					:			Optional[int]						=						args_d.copy()
					A_					:			Optional[int]						=						supported_param
					A_					:			List[Any]						=						argparse.Namespace(**_lowerCamelCase    )
					A_					:			int						=						main(_lowerCamelCase    )
					assert (
					    getattr(model.hparams ,       '''lr_scheduler'''    ) == supported_param
					), F"""lr_scheduler={supported_param} shouldn't fail"""











"""simple docstring"""

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						tempfile.mkdtemp()

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).tokenizer







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).image_processor







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

					A_					:			Any						=						[Image.fromarray(np.moveaxis(_lowerCamelCase ,       0 ,       -1    )    ) for x in image_inputs]

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.from_pretrained(
					    self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=_lowerCamelCase ,       padding_value=1.0    )

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

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







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			Optional[Any]						=						processor(images=_lowerCamelCase ,       return_tensors='''np'''    )

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







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.get_image_processor()
					A_					:			Union[str, Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

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







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

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

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







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			List[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Dict						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			int						=						'''lower newer'''
					A_					:			Union[str, Any]						=						self.prepare_image_inputs()

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )





