

"""simple docstring"""





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer




class 					_lowerCAmelCase  (	__lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					int   		=	["image_processor", "tokenizer"]
 __UpperCAmelCase	:					int   		=	"BlipImageProcessor"
 __UpperCAmelCase	:					Dict   		=	"AutoTokenizer"


 def __init__(			self							:			List[str],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			List[str]	):
    super().__init__(_a,					_a	)

    # add QFormer tokenizer
    __lowercase								=						qformer_tokenizer


 def __call__(			self							:			List[str],					UpperCAmelCase__							:			Dict = None,					UpperCAmelCase__							:			str = None,					UpperCAmelCase__							:			Dict = True,					UpperCAmelCase__							:			Optional[int] = False,					UpperCAmelCase__							:			Dict = None,					UpperCAmelCase__							:			Union[str, Any] = None,					UpperCAmelCase__							:			Any = 0,					UpperCAmelCase__							:			List[Any] = None,					UpperCAmelCase__							:			Union[str, Any] = None,					UpperCAmelCase__							:			int = False,					UpperCAmelCase__							:			Tuple = False,					UpperCAmelCase__							:			Optional[int] = False,					UpperCAmelCase__							:			Union[str, Any] = False,					UpperCAmelCase__							:			List[str] = False,					UpperCAmelCase__							:			Any = True,					UpperCAmelCase__							:			Optional[int] = None,					**UpperCAmelCase__							:			str,					):
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."	)

    __lowercase								=						BatchFeature()

    if text is not None:
       __lowercase								=						self.tokenizer(
           text=_a,					add_special_tokens=_a,					padding=_a,					truncation=_a,					max_length=_a,					stride=_a,					pad_to_multiple_of=_a,					return_attention_mask=_a,					return_overflowing_tokens=_a,					return_special_tokens_mask=_a,					return_offsets_mapping=_a,					return_token_type_ids=_a,					return_length=_a,					verbose=_a,					return_tensors=_a,					**_a,					)
       encoding.update(_a	)
       __lowercase								=						self.qformer_tokenizer(
           text=_a,					add_special_tokens=_a,					padding=_a,					truncation=_a,					max_length=_a,					stride=_a,					pad_to_multiple_of=_a,					return_attention_mask=_a,					return_overflowing_tokens=_a,					return_special_tokens_mask=_a,					return_offsets_mapping=_a,					return_token_type_ids=_a,					return_length=_a,					verbose=_a,					return_tensors=_a,					**_a,					)
       __lowercase								=						qformer_text_encoding.pop("input_ids"	)
       __lowercase								=						qformer_text_encoding.pop("attention_mask"	)

    if images is not None:
       __lowercase								=						self.image_processor(_a,					return_tensors=_a	)
       encoding.update(_a	)

    return encoding


 def 					_lowercase (			self							:			Union[str, Any],					*UpperCAmelCase__							:			List[str],					**UpperCAmelCase__							:			List[str]	):
    return self.tokenizer.batch_decode(*_a,					**_a	)


 def 					_lowercase (			self							:			Any,					*UpperCAmelCase__							:			str,					**UpperCAmelCase__							:			Optional[int]	):
    return self.tokenizer.decode(*_a,					**_a	)


 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						self.tokenizer.model_input_names
    __lowercase								=						self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Optional[int],					**UpperCAmelCase__							:			Optional[Any]	):
    if os.path.isfile(_a	):
       raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""	)
    os.makedirs(_a,					exist_ok=_a	)
    __lowercase								=						os.path.join(_a,					"qformer_tokenizer"	)
    self.qformer_tokenizer.save_pretrained(_a	)
    return super().save_pretrained(_a,					**_a	)



 @classmethod
 def 					_lowercase (			cls							:			Optional[int],					UpperCAmelCase__							:			Dict,					**UpperCAmelCase__							:			str	):
    __lowercase								=						AutoTokenizer.from_pretrained(_a,					subfolder="qformer_tokenizer"	)
    __lowercase								=						cls._get_arguments_from_pretrained(_a,					**_a	)
    args.append(_a	)
    return cls(*_a	)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__				=		logging.get_logger(__name__)

a__				=		{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

												_a	:    Tuple         =		vocab_size
												_a	:    List[Any]         =		hidden_size if embedding_size is None else embedding_size
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    List[Any]         =		num_attention_heads
												_a	:    str         =		hidden_act
												_a	:    Any         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Dict         =		type_vocab_size
												_a	:    List[Any]         =		initializer_range
												_a	:    Dict         =		layer_norm_eps
												_a	:    Dict         =		rotary_value
												_a	:    Dict         =		use_cache



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_a	:    Dict         =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_a	:    List[Any]         =		{0: '''batch''', 1: '''sequence'''}
												_a	:    Union[str, Any]         =		{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]       )



