
"""simple docstring"""

import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
			import onnxruntime as ort


_A						=			logging.get_logger(__name__)

_A						=			{
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}






class 	_lowercase       :




	def __init__(			self		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Union[str, Any]:
			logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
			lowerCamelCase					:      List[str]          =   model
			lowerCamelCase					:      Union[str, Any]          =   kwargs.get('model_save_dir'		,			UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   kwargs.get('latest_model_name'		,			UpperCAmelCase_ )




	def __call__(			self		,			**UpperCAmelCase_ )		->							int:
			lowerCamelCase					:      List[Any]          =   {k: np.array(UpperCAmelCase_ ) for k, v in kwargs.items()}
			return self.model.run(UpperCAmelCase_		,			UpperCAmelCase_ )




	@staticmethod
	def       _UpperCamelCase   (			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None )		->							Dict:
			if provider is None:
					logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
					lowerCamelCase					:      List[str]          =   'CPUExecutionProvider'

			return ort.InferenceSession(UpperCAmelCase_		,			providers=[provider]		,			sess_options=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_ )		->							str:
			lowerCamelCase					:      Optional[Any]          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME

			lowerCamelCase					:      Optional[Any]          =   self.model_save_dir.joinpath(self.latest_model_name )
			lowerCamelCase					:      Union[str, Any]          =   Path(UpperCAmelCase_ ).joinpath(UpperCAmelCase_ )
			try:
					shutil.copyfile(UpperCAmelCase_		,			UpperCAmelCase_ )
			except shutil.SameFileError:
					pass

			# copy external weights (for models >2GB)
			lowerCamelCase					:      List[str]          =   self.model_save_dir.joinpath(UpperCAmelCase_ )
			if src_path.exists():
					lowerCamelCase					:      str          =   Path(UpperCAmelCase_ ).joinpath(UpperCAmelCase_ )
					try:
							shutil.copyfile(UpperCAmelCase_		,			UpperCAmelCase_ )
					except shutil.SameFileError:
							pass




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			**UpperCAmelCase_		,			)		->							Any:
			if os.path.isfile(UpperCAmelCase_ ):
					logger.error(F"""Provided path ({save_directory}) should be a directory, not a file""" )
					return

			os.makedirs(UpperCAmelCase_		,			exist_ok=UpperCAmelCase_ )

			# saving model weights/files
			self._save_pretrained(UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Any:
			lowerCamelCase					:      List[str]          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME
			# load model from local directory
			if os.path.isdir(UpperCAmelCase_ ):
					lowerCamelCase					:      Optional[int]          =   OnnxRuntimeModel.load_model(
					    os.path.join(UpperCAmelCase_		,			UpperCAmelCase_ )		,			provider=UpperCAmelCase_		,			sess_options=UpperCAmelCase_ )
					lowerCamelCase					:      Optional[int]          =   Path(UpperCAmelCase_ )
			# load model from hub
			else:
					# download model
					lowerCamelCase					:      Optional[Any]          =   hf_hub_download(
					    repo_id=UpperCAmelCase_		,			filename=UpperCAmelCase_		,			use_auth_token=UpperCAmelCase_		,			revision=UpperCAmelCase_		,			cache_dir=UpperCAmelCase_		,			force_download=UpperCAmelCase_		,			)
					lowerCamelCase					:      Dict          =   Path(UpperCAmelCase_ ).parent
					lowerCamelCase					:      Any          =   Path(UpperCAmelCase_ ).name
					lowerCamelCase					:      str          =   OnnxRuntimeModel.load_model(UpperCAmelCase_		,			provider=UpperCAmelCase_		,			sess_options=UpperCAmelCase_ )
			return cls(model=UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Optional[Any]:
			lowerCamelCase					:      Any          =   None
			if len(str(UpperCAmelCase_ ).split('@' ) ) == 2:
					lowerCamelCase					:      Optional[int]          =   model_id.split('@' )

			return cls._from_pretrained(
			    model_id=UpperCAmelCase_		,			revision=UpperCAmelCase_		,			cache_dir=UpperCAmelCase_		,			force_download=UpperCAmelCase_		,			use_auth_token=UpperCAmelCase_		,			**UpperCAmelCase_		,			)








"""simple docstring"""

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_bart import BartTokenizer


_A						=			logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
_A						=			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_A						=			{
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     VOCAB_FILES_NAMES
	lowercase_				=     PRETRAINED_VOCAB_FILES_MAP
	lowercase_				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_				=     ['input_ids', 'attention_mask']
	lowercase_				=     BartTokenizer




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

			lowerCamelCase					:      List[Any]          =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
			if pre_tok_state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
					lowerCamelCase					:      Tuple          =   getattr(UpperCAmelCase_		,			pre_tok_state.pop('type' ) )
					lowerCamelCase					:      Optional[Any]          =   add_prefix_space
					lowerCamelCase					:      str          =   pre_tok_class(**UpperCAmelCase_ )

			lowerCamelCase					:      Optional[Any]          =   add_prefix_space

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

					lowerCamelCase					:      Optional[Any]          =   False

					if state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
							lowerCamelCase					:      Dict          =   add_prefix_space
							lowerCamelCase					:      Tuple          =   True

					if state.get('trim_offsets'		,			UpperCAmelCase_ ) != trim_offsets:
							lowerCamelCase					:      Tuple          =   trim_offsets
							lowerCamelCase					:      Dict          =   True

					if changes_to_apply:
							lowerCamelCase					:      Optional[int]          =   getattr(UpperCAmelCase_		,			state.pop('type' ) )
							lowerCamelCase					:      Any          =   component_class(**UpperCAmelCase_ )
							setattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   AddedToken(UpperCAmelCase_		,			lstrip=UpperCAmelCase_		,			rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) else value
			lowerCamelCase					:      int          =   value




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      str          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			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(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      Optional[Any]          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			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(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
			lowerCamelCase					:      Any          =   self._tokenizer.model.save(UpperCAmelCase_		,			name=UpperCAmelCase_ )
			return tuple(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							List[Any]:
			lowerCamelCase					:      Union[str, 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		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:
			lowerCamelCase					:      List[Any]          =   [self.sep_token_id]
			lowerCamelCase					:      str          =   [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]






