
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


_snake_case      :							Dict     	=    logging.get_logger(__name__)

_snake_case      :							Union[str, Any]     	=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case      :							int     	=    {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case      :							Any     	=    {
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}





class   UpperCamelCase_      (      _lowerCamelCase		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[Any]				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Optional[Any]				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Any				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  List[str]				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Any				   =					RobertaTokenizer





						def __init__(			self							:Dict		,    lowerCAmelCase__							:List[Any]=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:List[str]="replace"		,    lowerCAmelCase__							:str="<s>"		,    lowerCAmelCase__							:Tuple="</s>"		,    lowerCAmelCase__							:int="</s>"		,    lowerCAmelCase__							:Optional[int]="<s>"		,    lowerCAmelCase__							:str="<unk>"		,    lowerCAmelCase__							:Union[str, Any]="<pad>"		,    lowerCAmelCase__							:Any="<mask>"		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:List[str]=True		,    **lowerCAmelCase__							:Tuple		,    )  ->Any:
										super().__init__(
										    lowercase_		,    lowercase_		,    tokenizer_file=lowercase_		,    errors=lowercase_		,    bos_token=lowercase_		,    eos_token=lowercase_		,    sep_token=lowercase_		,    cls_token=lowercase_		,    unk_token=lowercase_		,    pad_token=lowercase_		,    mask_token=lowercase_		,    add_prefix_space=lowercase_		,    trim_offsets=lowercase_		,    **lowercase_		,    )

										lowercase         =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
										if pre_tok_state.get("add_prefix_space"		,    lowercase_     ) != add_prefix_space:
														lowercase         =      getattr(lowercase_		,    pre_tok_state.pop("type"     )     )
														lowercase         =      add_prefix_space
														lowercase         =      pre_tok_class(**lowercase_     )

										lowercase         =      add_prefix_space

										lowercase         =      "post_processor"
										lowercase         =      getattr(self.backend_tokenizer		,    lowercase_		,    lowercase_     )
										if tokenizer_component_instance:
														lowercase         =      json.loads(tokenizer_component_instance.__getstate__()     )

														# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
														if "sep" in state:
																		lowercase         =      tuple(state["sep"]     )
														if "cls" in state:
																		lowercase         =      tuple(state["cls"]     )

														lowercase         =      False

														if state.get("add_prefix_space"		,    lowercase_     ) != add_prefix_space:
																		lowercase         =      add_prefix_space
																		lowercase         =      True

														if state.get("trim_offsets"		,    lowercase_     ) != trim_offsets:
																		lowercase         =      trim_offsets
																		lowercase         =      True

														if changes_to_apply:
																		lowercase         =      getattr(lowercase_		,    state.pop("type"     )     )
																		lowercase         =      component_class(**lowercase_     )
																		setattr(self.backend_tokenizer		,    lowercase_		,    lowercase_     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->List[str]:
										if self._mask_token is None:
														if self.verbose:
																		logger.error("Using mask_token, but it is not set yet."     )
														return None
										return str(self._mask_token     )





						@mask_token.setter
						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Tuple     )  ->Optional[Any]:
										lowercase         =      AddedToken(lowercase_		,    lstrip=lowercase_		,    rstrip=lowercase_     ) if isinstance(lowercase_		,    lowercase_     ) else value
										lowercase         =      value





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    *lowerCAmelCase__							:Any		,    **lowerCAmelCase__							:int     )  ->int:
										lowercase         =      kwargs.get("is_split_into_words"		,    lowercase_     )
										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowercase_		,    **lowercase_     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    *lowerCAmelCase__							:Union[str, Any]		,    **lowerCAmelCase__							:Tuple     )  ->Optional[Any]:
										lowercase         =      kwargs.get("is_split_into_words"		,    lowercase_     )

										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowercase_		,    **lowercase_     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = None     )  ->Dict:
										lowercase         =      self._tokenizer.model.save(lowercase_		,    name=lowercase_     )
										return tuple(lowercase_     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Union[str, Any]=None     )  ->Optional[int]:
										lowercase         =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->Dict:
										lowercase         =      [self.sep_token_id]
										lowercase         =      [self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep     ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]






'''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 		a							(  unittest.TestCase	):
				snake_case_           =						ViTImageProcessor if is_vision_available() else None


				@property
				def     A_					(						self       :   List[str]   ):
									return self.image_processor_tester.prepare_image_processor_dict()


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							(3, 32, 128)
									snake_case_		     =							tempfile.mkdtemp()

									# fmt: off
									snake_case_		     =							['''[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
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )

									snake_case_		     =							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(lowercase_   ) + '''\n'''   )

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


				def     A_					(						self       :   Any			,							**lowercase_       :   str   ):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   List[str]			,							**lowercase_       :   List[Any]   ):
									return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									shutil.rmtree(self.tmpdirname   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta   )

									snake_case_		     =							Image.fromarray(np.moveaxis(lowercase_			,							0			,							-1   )   )

									return image_input


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )
									snake_case_		     =							MgpstrProcessor.from_pretrained(self.tmpdirname			,							use_fast=lowercase_   )

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

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


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )

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

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

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

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


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							image_processor(lowercase_			,							return_tensors='''np'''   )
									snake_case_		     =							processor(images=lowercase_			,							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     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''

									snake_case_		     =							processor(text=lowercase_   )

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


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

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

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

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

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


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

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

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

									self.assertListEqual(lowercase_			,							lowercase_   )


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

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							None
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

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


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							torch.randn(1			,							27			,							38   )
									snake_case_		     =							torch.randn(1			,							27			,							5_0257   )
									snake_case_		     =							torch.randn(1			,							27			,							3_0522   )

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

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

