

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_mvp import MvpTokenizer


a_      =     logging.get_logger(__name__)


a_      =     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
a_      =     {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

a_      =     {
    'RUCAIBox/mvp': 1024,
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =VOCAB_FILES_NAMES
			lowerCamelCase__							     =PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__							     =['input_ids', 'attention_mask']
			lowerCamelCase__							     =MvpTokenizer
			def __init__(      self				:			int   ,   a				:			List[str]=None   ,   a				:			List[str]=None   ,   a				:			List[str]=None   ,   a				:			int="replace"   ,   a				:			Tuple="<s>"   ,   a				:			List[Any]="</s>"   ,   a				:			int="</s>"   ,   a				:			Optional[Any]="<s>"   ,   a				:			int="<unk>"   ,   a				:			int="<pad>"   ,   a				:			List[Any]="<mask>"   ,   a				:			Union[str, Any]=False   ,   a				:			str=True   ,   **a				:			Optional[int]   ,   )				->							int:

				"""simple docstring"""

				super().__init__(
				    a   ,   a   ,   tokenizer_file=a   ,   errors=a   ,   bos_token=a   ,   eos_token=a   ,   sep_token=a   ,   cls_token=a   ,   unk_token=a   ,   pad_token=a   ,   mask_token=a   ,   add_prefix_space=a   ,   trim_offsets=a   ,   **a   ,   )

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
				if pre_tok_state.get("add_prefix_space"   ,   a						) != add_prefix_space:
					SCREAMING_SNAKE_CASE :   Dict					  =							getattr(a   ,   pre_tok_state.pop("type"						)						)
					SCREAMING_SNAKE_CASE :   str					  =							add_prefix_space
					SCREAMING_SNAKE_CASE :   List[str]					  =							pre_tok_class(**a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				SCREAMING_SNAKE_CASE :   List[str]					  =							"post_processor"
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							getattr(self.backend_tokenizer   ,   a   ,   a						)
				if tokenizer_component_instance:
					SCREAMING_SNAKE_CASE :   Any					  =							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:
						SCREAMING_SNAKE_CASE :   str					  =							tuple(state["sep"]						)
					if "cls" in state:
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							tuple(state["cls"]						)

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							False

					if state.get("add_prefix_space"   ,   a						) != add_prefix_space:
						SCREAMING_SNAKE_CASE :   List[str]					  =							add_prefix_space
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							True

					if state.get("trim_offsets"   ,   a						) != trim_offsets:
						SCREAMING_SNAKE_CASE :   str					  =							trim_offsets
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True

					if changes_to_apply:
						SCREAMING_SNAKE_CASE :   Tuple					  =							getattr(a   ,   state.pop("type"						)						)
						SCREAMING_SNAKE_CASE :   Tuple					  =							component_class(**a						)
						setattr(self.backend_tokenizer   ,   a   ,   a						)
			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				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 				__UpperCamelCase			(      self				:			str   ,   a				:			Tuple						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							AddedToken(a   ,   lstrip=a   ,   rstrip=a						) if isinstance(a   ,   a						) else value
				SCREAMING_SNAKE_CASE :   str					  =							value
			def 				__UpperCamelCase			(      self				:			Tuple   ,   *a				:			List[Any]   ,   **a				:			List[str]						)				->							BatchEncoding:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							kwargs.get("is_split_into_words"   ,   a						)

				if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
					    "to use it with pretokenized inputs."						)

				return super()._batch_encode_plus(*a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   *a				:			Union[str, Any]   ,   **a				:			Dict						)				->							BatchEncoding:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							kwargs.get("is_split_into_words"   ,   a						)

				if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
					    "to use it with pretokenized inputs."						)

				return super()._encode_plus(*a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			str   ,   a				:			Optional[str] = None						)				->							Tuple[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self._tokenizer.model.save(a   ,   name=a						)
				return tuple(a						)
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			str   ,   a				:			Optional[Any]=None						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							[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 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			List[int]   ,   a				:			Optional[List[int]] = None						)				->							List[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							[self.sep_token_id]
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[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 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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

				A__											=					BlipImageProcessor()
				A__											=					GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"		)

				A__											=					BlipaProcessor(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				processor.save_pretrained(self.tmpdirname		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).tokenizer


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).image_processor


			def   snake_case__	(      self		)      ->	str:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]

				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]

				return image_inputs


			def   snake_case__	(      self		)      ->	str:
				A__											=					BlipaProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				A__											=					self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"		)
				A__											=					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					BlipaProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()		)
				self.assertIsInstance(processor.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   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	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   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	return_token_type_ids=SCREAMING_SNAKE_CASE__		)

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


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

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

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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

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




