






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__UpperCamelCase   :   Optional[Any]      =    logging.get_logger(__name__)

__UpperCamelCase   :   List[str]      =    """▁"""

__UpperCamelCase   :   str      =    {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

__UpperCamelCase   :   Any      =    {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

__UpperCamelCase   :   List[Any]      =    {"""vinai/bartpho-syllable""": 1024}


class 							__UpperCamelCase     (  _A ):
	__snake_case						:int =							VOCAB_FILES_NAMES
	__snake_case						:Tuple =							PRETRAINED_VOCAB_FILES_MAP
	__snake_case						:Optional[int] =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case						:Tuple =							["input_ids", "attention_mask"]







	def __init__(	self   :				Union[str, Any]							,					_lowerCAmelCase   :				Dict							,					_lowerCAmelCase   :				List[str]							,					_lowerCAmelCase   :				Union[str, Any]="<s>"							,					_lowerCAmelCase   :				Dict="</s>"							,					_lowerCAmelCase   :				List[Any]="</s>"							,					_lowerCAmelCase   :				Optional[Any]="<s>"							,					_lowerCAmelCase   :				Tuple="<unk>"							,					_lowerCAmelCase   :				int="<pad>"							,					_lowerCAmelCase   :				Optional[Any]="<mask>"							,					_lowerCAmelCase   :				Optional[Dict[str, Any]] = None							,					**_lowerCAmelCase   :				Tuple							,					)		->			None:
		"""simple docstring"""







		__lowercase   							=	AddedToken(__lowerCAmelCase							,					lstrip=__lowerCAmelCase							,					rstrip=__lowerCAmelCase      ) if isinstance(__lowerCAmelCase							,					__lowerCAmelCase      ) else mask_token

		__lowercase   							=	{} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    bos_token=__lowerCAmelCase							,					eos_token=__lowerCAmelCase							,					unk_token=__lowerCAmelCase							,					sep_token=__lowerCAmelCase							,					cls_token=__lowerCAmelCase							,					pad_token=__lowerCAmelCase							,					mask_token=__lowerCAmelCase							,					sp_model_kwargs=self.sp_model_kwargs							,					**__lowerCAmelCase							,					)

		__lowercase   							=	vocab_file
		__lowercase   							=	monolingual_vocab_file
		__lowercase   							=	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
		self.sp_model.Load(str(__lowerCAmelCase      )      )

		# Load the reduced vocab

		# Keep order of special tokens for backward compatibility
		__lowercase   							=	{}
		__lowercase   							=	0
		for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
			if str(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
				__lowercase   							=	cnt
				cnt += 1
		with open(__lowerCAmelCase							,					"""r"""							,					encoding="""utf-8"""      ) as f:
			for line in f.readlines():
				__lowercase   							=	line.strip().split()[0]
				__lowercase   							=	len(self.fairseq_tokens_to_ids      )
		if str(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
			__lowercase   							=	len(self.fairseq_tokens_to_ids      )

		__lowercase   							=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def __getstate__(	self   :				Any      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	self.__dict__.copy()
		__lowercase   							=	None
		__lowercase   							=	self.sp_model.serialized_model_proto()
		return state







	def __setstate__(	self   :				Union[str, Any]							,					_lowerCAmelCase   :				Dict      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	d

		# for backward compatibility
		if not hasattr(self							,					"""sp_model_kwargs"""      ):
			__lowercase   							=	{}

		__lowercase   							=	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
		self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )







	def 			_a (	self   :				Optional[Any]							,					_lowerCAmelCase   :				List[int]							,					_lowerCAmelCase   :				Optional[List[int]] = None      )		->			List[int]:
		"""simple docstring"""







		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		__lowercase   							=	[self.cls_token_id]
		__lowercase   							=	[self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep







	def 			_a (	self   :				List[Any]							,					_lowerCAmelCase   :				List[int]							,					_lowerCAmelCase   :				Optional[List[int]] = None							,					_lowerCAmelCase   :				bool = False      )		->			List[int]:
		"""simple docstring"""







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

		if token_ids_a is None:
			return [1] + ([0] * len(__lowerCAmelCase      )) + [1]
		return [1] + ([0] * len(__lowerCAmelCase      )) + [1, 1] + ([0] * len(__lowerCAmelCase      )) + [1]







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				List[int]							,					_lowerCAmelCase   :				Optional[List[int]] = None      )		->			List[int]:
		"""simple docstring"""







		__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]







	@property
	def 			_a (	self   :				Optional[int]      )		->			Union[str, Any]:
		"""simple docstring"""







		return len(self.fairseq_ids_to_tokens      )







	def 			_a (	self   :				Dict      )		->			Optional[Any]:
		"""simple docstring"""







		__lowercase   							=	{self.convert_ids_to_tokens(__lowerCAmelCase      ): i for i in range(self.vocab_size      )}
		vocab.update(self.added_tokens_encoder      )
		return vocab







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				str      )		->			List[str]:
		"""simple docstring"""







		return self.sp_model.encode(__lowerCAmelCase							,					out_type=__lowerCAmelCase      )







	def 			_a (	self   :				str							,					_lowerCAmelCase   :				Optional[Any]      )		->			Dict:
		"""simple docstring"""







		if token in self.fairseq_tokens_to_ids:
			return self.fairseq_tokens_to_ids[token]
		else:
			return self.unk_token_id







	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Optional[int]      )		->			List[str]:
		"""simple docstring"""







		return self.fairseq_ids_to_tokens[index]







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				Union[str, Any]      )		->			Tuple:
		"""simple docstring"""







		__lowercase   							=	"""""".join(__lowerCAmelCase      ).replace(__lowerCAmelCase							,					""" """      ).strip()
		return out_string







	def 			_a (	self   :				str							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				Optional[str] = None      )		->			Tuple[str]:
		"""simple docstring"""







		if not os.path.isdir(__lowerCAmelCase      ):
			logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
			return
		__lowercase   							=	os.path.join(
		    __lowerCAmelCase							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
		__lowercase   							=	os.path.join(
		    __lowerCAmelCase							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]							,					)

		if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCAmelCase      ) and os.path.isfile(self.vocab_file      ):
			copyfile(self.vocab_file							,					__lowerCAmelCase      )
		elif not os.path.isfile(self.vocab_file      ):
			with open(__lowerCAmelCase							,					"""wb"""      ) as fi:
				__lowercase   							=	self.sp_model.serialized_model_proto()
				fi.write(__lowerCAmelCase      )

		if os.path.abspath(self.monolingual_vocab_file      ) != os.path.abspath(
		    __lowerCAmelCase      ) and os.path.isfile(self.monolingual_vocab_file      ):
			copyfile(self.monolingual_vocab_file							,					__lowerCAmelCase      )
		elif not os.path.isfile(self.monolingual_vocab_file      ):
			with open(__lowerCAmelCase							,					"""w"""							,					encoding="""utf-8"""      ) as fp:
				for token in self.fairseq_tokens_to_ids:
					if token not in self.all_special_tokens:
						fp.write(F'{str(__lowerCAmelCase      )} \n'      )

		return out_vocab_file, out_monolingual_vocab_file






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

UpperCAmelCase_		 =					logging.get_logger(__name__)


@add_end_docstrings(_A)
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[str]      )   ->	List[str]:
    super().__init__(*__lowerCAmelCase			,						**__lowerCAmelCase      )
    requires_backends(self			,						'''vision'''      )
    self.check_model_type(
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING      )

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Optional[Any]=None      )   ->	int:
    _A      =       {}
    _A      =       {}

    if prompt is not None:
       _A      =       prompt

    if generate_kwargs is not None:
       _A      =       generate_kwargs
    if max_new_tokens is not None:
       if "generate_kwargs" not in forward_kwargs:
          _A      =       {}
       if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
          raise ValueError(
              '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
              ''' please use only one'''      )
       _A      =       max_new_tokens
    return preprocess_params, forward_kwargs, {}

 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, List[str], "Image.Image", List["Image.Image"]]			,						**__lowerCAmelCase					:					Union[str, Any]      )   ->	Union[str, Any]:
    return super().__call__(__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, Any]=None      )   ->	int:
    _A      =       load_image(__lowerCAmelCase      )

    if prompt is not None:
       if not isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          raise ValueError(
              f'''Received an invalid text input, got - {type(__lowerCAmelCase      )} - but expected a single string. '''
              '''Note also that one single text can be provided for conditional image to text generation.'''      )

       _A      =       self.model.config.model_type

       if model_type == "git":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(text=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids
          _A      =       [self.tokenizer.cls_token_id] + input_ids
          _A      =       torch.tensor(__lowerCAmelCase      ).unsqueeze(0      )
          model_inputs.update({'''input_ids''': input_ids}      )

       elif model_type == "pix2struct":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						header_text=__lowerCAmelCase			,						return_tensors=self.framework      )

       elif model_type != "vision-encoder-decoder":
          # vision-encoder-decoder does not support conditional generation
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(__lowerCAmelCase			,						return_tensors=self.framework      )
          model_inputs.update(__lowerCAmelCase      )

       else:
          raise ValueError(f'''Model type {model_type} does not support conditional text generation'''      )

    else:
       _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )

    if self.model.config.model_type == "git" and prompt is None:
       _A      =       None

    return model_inputs

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Dict=None      )   ->	str:
    # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
    # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
    if (
        "input_ids" in model_inputs
        and isinstance(model_inputs['''input_ids''']			,						__lowerCAmelCase      )
        and all(x is None for x in model_inputs['''input_ids''']      )
    ):
       _A      =       None

    if generate_kwargs is None:
       _A      =       {}
    # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
    #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
    #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
    #  in the `_prepare_model_inputs` method.
    _A      =       model_inputs.pop(self.model.main_input_name      )
    _A      =       self.model.generate(__lowerCAmelCase			,						**__lowerCAmelCase			,						**__lowerCAmelCase      )
    return model_outputs

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       []
    for output_ids in model_outputs:
       _A      =       {
           '''generated_text''': self.tokenizer.decode(
               __lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase			,						)
       }
       records.append(__lowerCAmelCase      )
    return records



