# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							"""dandelin/vilt-b32-finetuned-vqa"""
							lowercase__						:       List[Any]								=							(
							    """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
							    """image containing the information, as well as a `question` which should be the question in English. It """
							    """returns a text that is the answer to the question."""
							)
							lowercase__						:       Optional[int]								=							"""image_qa"""
							lowercase__						:       Dict								=							AutoProcessor
							lowercase__						:       str								=							AutoModelForVisualQuestionAnswering

							lowercase__						:       List[str]								=							["""image""", """text"""]
							lowercase__						:       List[Any]								=							["""text"""]




							def __init__(						self   :					Tuple	,							*_UpperCamelCase   :					Optional[int]	,							**_UpperCamelCase   :					str      )					->				List[Any]:



												'''simple docstring'''

												requires_backends(self	,							["vision"]      )
												super().__init__(*_UpperCamelCase	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					"Image"	,							_UpperCamelCase   :					str      )					->				Optional[Any]:



												'''simple docstring'''

												return self.pre_processor(_UpperCamelCase	,							_UpperCamelCase	,							return_tensors="pt"      )




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					int      )					->				List[Any]:



												'''simple docstring'''

												with torch.no_grad():
																	return self.model(**_UpperCamelCase      ).logits





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       outputs.argmax(-1      ).item()
												return self.model.config.idalabel[idx]





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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase					:   Dict							=		logging.get_logger(__name__)

_lowerCamelCase					:   List[Any]							=		'''▁'''

_lowerCamelCase					:   Optional[int]							=		{'''vocab_file''': '''prophetnet.tokenizer'''}

_lowerCamelCase					:   str							=		{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]  ):
					SCREAMING_SNAKE_CASE    			=       collections.OrderedDict()
					with open(UpperCAmelCase__      , "r"      , encoding="utf-8"  ) as reader:
										SCREAMING_SNAKE_CASE    			=       reader.readlines()
					for index, token in enumerate(UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       token.rstrip("\n"  )
										SCREAMING_SNAKE_CASE    			=       index
					return vocab


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							VOCAB_FILES_NAMES
							lowercase__						:       Optional[Any]								=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__						:       Union[str, Any]								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__						:       Any								=							["""input_ids""", """attention_mask"""]




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					str="[SEP]"	,							_UpperCamelCase   :					str="[SEP]"	,							_UpperCamelCase   :					Dict="[SEP]"	,							_UpperCamelCase   :					Tuple="[UNK]"	,							_UpperCamelCase   :					Dict="[PAD]"	,							_UpperCamelCase   :					Any="[CLS]"	,							_UpperCamelCase   :					Optional[Any]="[MASK]"	,							_UpperCamelCase   :					Optional[Dict[str, Any]] = None	,							**_UpperCamelCase   :					Dict	,							)					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {} if sp_model_kwargs is None else sp_model_kwargs

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

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"      )
																	raise

												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(str(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												SCREAMING_SNAKE_CASE    			=       {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

												for i in range(10      ):
																	SCREAMING_SNAKE_CASE    			=       F"[unused{i}]"
																	SCREAMING_SNAKE_CASE    			=       5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												SCREAMING_SNAKE_CASE    			=       12
												SCREAMING_SNAKE_CASE    			=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(_UpperCamelCase      )




							def __getstate__(						self   :					Dict      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.__dict__.copy()
												SCREAMING_SNAKE_CASE    			=       None
												return state




							def __setstate__(						self   :					List[Any]	,							_UpperCamelCase   :					Union[str, Any]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"      )
																	raise

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

												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(self.vocab_file      )




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None	,							_UpperCamelCase   :					bool = False      )					->				List[int]:



												'''simple docstring'''

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

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




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None      )					->				List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]

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




							@property
							def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												return len(self.sp_model      ) + self.fairseq_offset




							def 	__snake_case(						self   :					Optional[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					str      )					->				str:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												SCREAMING_SNAKE_CASE    			=       self.sp_model.PieceToId(_UpperCamelCase      )

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					str      )					->				int:



												'''simple docstring'''

												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset      )




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "".join(_UpperCamelCase      ).replace(_UpperCamelCase	,							" "      ).strip()
												return out_string




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[str] = None      )					->				Tuple[str]:



												'''simple docstring'''

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

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

												return (out_vocab_file,)





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None      )					->				List[int]:



												'''simple docstring'''

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




