






'''simple docstring'''







import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   '▁'

__lowerCAmelCase									=   {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__lowerCAmelCase									=   {
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

__lowerCAmelCase									=   {
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
__lowerCAmelCase									=   {
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					VOCAB_FILES_NAMES
			snake_case__	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__	=					PRETRAINED_VOCAB_FILES_MAP
			snake_case__	=					["input_ids", "attention_mask"]

			snake_case__	=					[]
			snake_case__	=					[]






			def __init__(			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=None	,    __SCREAMING_SNAKE_CASE   :      str=None	,    __SCREAMING_SNAKE_CASE   :      int="<s>"	,    __SCREAMING_SNAKE_CASE   :      Any="</s>"	,    __SCREAMING_SNAKE_CASE   :      List[Any]="</s>"	,    __SCREAMING_SNAKE_CASE   :      int="<pad>"	,    __SCREAMING_SNAKE_CASE   :      str="<unk>"	,    __SCREAMING_SNAKE_CASE   :      List[Any]="m2m100"	,    __SCREAMING_SNAKE_CASE   :      Optional[Dict[str, Any]] = None	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=8	,    **__SCREAMING_SNAKE_CASE   :      Tuple	,    )      ->      None:
							a_		:	Tuple									=   {} if sp_model_kwargs is None else sp_model_kwargs

							a_		:	Optional[Any]									=   language_codes
							a_		:	List[str]									=   FAIRSEQ_LANGUAGE_CODES[language_codes]
							a_		:	Optional[int]									=   {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}

							a_		:	List[Any]									=   kwargs.get('''additional_special_tokens'''	,    []       )
							kwargs["additional_special_tokens"] += [
							    self.get_lang_token(__SCREAMING_SNAKE_CASE       )
							    for lang_code in fairseq_language_code
							    if self.get_lang_token(__SCREAMING_SNAKE_CASE       ) not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=__SCREAMING_SNAKE_CASE	,    tgt_lang=__SCREAMING_SNAKE_CASE	,    bos_token=__SCREAMING_SNAKE_CASE	,    eos_token=__SCREAMING_SNAKE_CASE	,    sep_token=__SCREAMING_SNAKE_CASE	,    unk_token=__SCREAMING_SNAKE_CASE	,    pad_token=__SCREAMING_SNAKE_CASE	,    language_codes=__SCREAMING_SNAKE_CASE	,    sp_model_kwargs=self.sp_model_kwargs	,    num_madeup_words=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )

							a_		:	List[Any]									=   vocab_file
							a_		:	Dict									=   load_json(__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   {v: k for k, v in self.encoder.items()}
							a_		:	Any									=   spm_file
							a_		:	Optional[Any]									=   load_spm(__SCREAMING_SNAKE_CASE	,    self.sp_model_kwargs       )

							a_		:	List[Any]									=   len(self.encoder       )

							a_		:	int									=   {
							    self.get_lang_token(__SCREAMING_SNAKE_CASE       ): self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE       )
							}
							a_		:	List[str]									=   {lang_code: self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE       )}
							a_		:	Any									=   {v: k for k, v in self.lang_token_to_id.items()}

							a_		:	int									=   src_lang if src_lang is not None else '''en'''
							a_		:	Any									=   tgt_lang
							a_		:	Union[str, Any]									=   self.get_lang_id(self._src_lang       )
							self.set_src_lang_special_tokens(self._src_lang       )

							a_		:	Optional[Any]									=   num_madeup_words






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							return len(self.encoder       ) + len(self.lang_token_to_id       )






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      str:
							return self._src_lang






			@src_lang.setter
			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      None:
							a_		:	str									=   new_src_lang
							self.set_src_lang_special_tokens(self._src_lang       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE	,    out_type=__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      Union[str, Any]:
							if token in self.lang_token_to_id:
											return self.lang_token_to_id[token]
							return self.encoder.get(__SCREAMING_SNAKE_CASE	,    self.encoder[self.unk_token]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      str:
							if index in self.id_to_lang_token:
											return self.id_to_lang_token[index]
							return self.decoder.get(__SCREAMING_SNAKE_CASE	,    self.unk_token       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      List[str]       )      ->      Optional[Any]:
							a_		:	Any									=   []
							a_		:	Optional[Any]									=   ''''''
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE       ) + token
															a_		:	Union[str, Any]									=   []
											else:
															current_sub_tokens.append(__SCREAMING_SNAKE_CASE       )
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE       )
							return out_string.strip()






			def       SCREAMING_SNAKE_CASE   (			self   :      int	,    __SCREAMING_SNAKE_CASE   :      List[int]	,    __SCREAMING_SNAKE_CASE   :      Optional[List[int]] = None	,    __SCREAMING_SNAKE_CASE   :      bool = False       )      ->      List[int]:

							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__SCREAMING_SNAKE_CASE	,    token_ids_a=__SCREAMING_SNAKE_CASE	,    already_has_special_tokens=__SCREAMING_SNAKE_CASE       )

							a_		:	List[str]									=   [1] * len(self.prefix_tokens       )
							a_		:	List[Any]									=   [1] * len(self.suffix_tokens       )
							if token_ids_a is None:
											return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE       )) + suffix_ones
							return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE       )) + ([0] * len(__SCREAMING_SNAKE_CASE       )) + suffix_ones






			def       SCREAMING_SNAKE_CASE   (			self   :      int	,    __SCREAMING_SNAKE_CASE   :      List[int]	,    __SCREAMING_SNAKE_CASE   :      Optional[List[int]] = None       )      ->      List[int]:
							if token_ids_a is None:
											return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Dict:
							a_		:	List[str]									=   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab






			def __getstate__(			self   :      int       )      ->      Dict:
							a_		:	Optional[Any]									=   self.__dict__.copy()
							a_		:	Any									=   None
							return state






			def __setstate__(			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Dict       )      ->      None:
							a_		:	int									=   d

							# for backward compatibility
							if not hasattr(self	,    '''sp_model_kwargs'''       ):
											a_		:	Any									=   {}

							a_		:	Tuple									=   load_spm(self.spm_file	,    self.sp_model_kwargs       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[str] = None       )      ->      Tuple[str]:
							a_		:	Dict									=   Path(__SCREAMING_SNAKE_CASE       )
							if not save_dir.is_dir():
											raise OSError(f'{save_directory} should be a directory'       )
							a_		:	int									=   save_dir / (
							    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
							)
							a_		:	int									=   save_dir / (
							    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
							)

							save_json(self.encoder	,    __SCREAMING_SNAKE_CASE       )

							if os.path.abspath(self.spm_file       ) != os.path.abspath(__SCREAMING_SNAKE_CASE       ) and os.path.isfile(self.spm_file       ):
											copyfile(self.spm_file	,    __SCREAMING_SNAKE_CASE       )
							elif not os.path.isfile(self.spm_file       ):
											with open(__SCREAMING_SNAKE_CASE	,    '''wb'''       ) as fi:
															a_		:	List[str]									=   self.sp_model.serialized_model_proto()
															fi.write(__SCREAMING_SNAKE_CASE       )

							return (str(__SCREAMING_SNAKE_CASE       ), str(__SCREAMING_SNAKE_CASE       ))






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      str = "en"	,    __SCREAMING_SNAKE_CASE   :      Optional[List[str]] = None	,    __SCREAMING_SNAKE_CASE   :      str = "ro"	,    **__SCREAMING_SNAKE_CASE   :      Optional[Any]	,    )      ->      BatchEncoding:
							a_		:	Union[str, Any]									=   src_lang
							a_		:	Dict									=   tgt_lang
							self.set_src_lang_special_tokens(self.src_lang       )
							return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[str]	,    __SCREAMING_SNAKE_CASE   :      Optional[str]	,    **__SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      Optional[Any]:
							if src_lang is None or tgt_lang is None:
											raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
							a_		:	Any									=   src_lang
							a_		:	Union[str, Any]									=   self(__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   self.get_lang_id(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tgt_lang_id
							return inputs






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[Any]:
							self.set_src_lang_special_tokens(self.src_lang       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Optional[Any]:
							self.set_tgt_lang_special_tokens(self.tgt_lang       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      None:
							a_		:	int									=   self.get_lang_token(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   self.lang_token_to_id[lang_token]
							a_		:	Tuple									=   [self.cur_lang_id]
							a_		:	Tuple									=   [self.eos_token_id]






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      None:
							a_		:	List[Any]									=   self.get_lang_token(__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   self.lang_token_to_id[lang_token]
							a_		:	List[Any]									=   [self.cur_lang_id]
							a_		:	List[Any]									=   [self.eos_token_id]






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      str:
							return self.lang_code_to_token[lang]







			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      int:
							a_		:	List[str]									=   self.get_lang_token(__SCREAMING_SNAKE_CASE       )
							return self.lang_token_to_id[lang_token]





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Dict[str, Any]   ):
				a_		:	Optional[int]									=   sentencepiece.SentencePieceProcessor(**__A   )
				spm.Load(str(__A   )   )
				return spm





def 		_UpperCAmelCase (				__A				: str   ):
				with open(__A    ,		'''r'''   ) as f:
								return json.load(__A   )





def 		_UpperCAmelCase (				__A				: Dict    ,		__A				: str   ):
				with open(__A    ,		'''w'''   ) as f:
								json.dump(__A    ,		__A    ,		indent=2   )












'''simple docstring'''







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 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):
			snake_case__	=					ViTImageProcessor if is_vision_available() else None






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   (3, 32, 128)
							a_		:	Optional[Any]									=   tempfile.mkdtemp()

							# fmt: off
							a_		:	Union[str, Any]									=   ['''[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
							a_		:	List[Any]									=   dict(zip(__SCREAMING_SNAKE_CASE	,    range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

							a_		:	Union[str, Any]									=   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(__SCREAMING_SNAKE_CASE       ) + '''\n'''       )

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






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Dict:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






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






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							a_		:	Optional[Any]									=   np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta       )

							a_		:	Optional[int]									=   Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,    0	,    -1       )       )

							return image_input






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	str									=   self.get_tokenizer()
							a_		:	List[Any]									=   self.get_image_processor()

							a_		:	Union[str, Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )
							a_		:	Tuple									=   MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=__SCREAMING_SNAKE_CASE       )

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	Any									=   self.get_tokenizer()
							a_		:	Dict									=   self.get_image_processor()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )

							a_		:	str									=   self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''       )
							a_		:	str									=   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							a_		:	Optional[int]									=   MgpstrProcessor.from_pretrained(
							    self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   self.prepare_image_inputs()

							a_		:	List[Any]									=   image_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )
							a_		:	int									=   processor(images=__SCREAMING_SNAKE_CASE	,    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       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	int									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   '''test'''

							a_		:	Any									=   processor(text=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   tokenizer(__SCREAMING_SNAKE_CASE       )
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	Union[str, Any]									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   '''test'''
							a_		:	str									=   self.prepare_image_inputs()

							a_		:	Tuple									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							a_		:	int									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	Dict									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a_		:	List[Any]									=   processor.char_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   [seq.replace(''' '''	,    ''''''       ) for seq in decoded_tok]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	int									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   None
							a_		:	Tuple									=   self.prepare_image_inputs()

							a_		:	Optional[int]									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

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







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Union[str, Any]:
							a_		:	List[str]									=   self.get_image_processor()
							a_		:	str									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   torch.randn(1	,    27	,    38       )
							a_		:	int									=   torch.randn(1	,    27	,    5_0257       )
							a_		:	Union[str, Any]									=   torch.randn(1	,    27	,    3_0522       )

							a_		:	Tuple									=   processor.batch_decode([char_input, bpe_input, wp_input]       )

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




