



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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	['image_processor', 'tokenizer']
		SCREAMING_SNAKE_CASE__								=	'BlipImageProcessor'
		SCREAMING_SNAKE_CASE__								=	'AutoTokenizer'
		def __init__(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						super().__init__(a_    ,a_						)

						# add QFormer tokenizer
						lowerCAmelCase__		  =  qformer_tokenizer
		def __call__(							self    ,a_ = None    ,a_ = None    ,a_ = True    ,a_ = False    ,a_ = None    ,a_ = None    ,a_ = 0    ,a_ = None    ,a_ = None    ,a_ = False    ,a_ = False    ,a_ = False    ,a_ = False    ,a_ = False    ,a_ = True    ,a_ = None    ,**a_    ,):







						"""simple docstring"""
						if images is None and text is None:
										raise ValueError('You have to specify at least images or text.'						)

						lowerCAmelCase__		  =  BatchFeature()

						if text is not None:
										lowerCAmelCase__		  =  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_						)
										lowerCAmelCase__		  =  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_    ,)
										lowerCAmelCase__		  =  qformer_text_encoding.pop('input_ids'						)
										lowerCAmelCase__		  =  qformer_text_encoding.pop('attention_mask'						)

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

						return encoding
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return self.tokenizer.batch_decode(*a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return self.tokenizer.decode(*a_    ,**a_						)
		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.tokenizer.model_input_names
						lowerCAmelCase__		  =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,**a_						):







						"""simple docstring"""
						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_						)
						lowerCAmelCase__		  =  os.path.join(a_    ,'qformer_tokenizer'						)
						self.qformer_tokenizer.save_pretrained(a_						)
						return super().save_pretrained(a_    ,**a_						)



		@classmethod
		def 						SCREAMING_SNAKE_CASE_					(							cls    ,a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  AutoTokenizer.from_pretrained(a_    ,subfolder='qformer_tokenizer'						)
						lowerCAmelCase__		  =  cls._get_arguments_from_pretrained(a_    ,**a_						)
						args.append(a_						)
						return cls(*a_						)





from typing import Dict

from .base import GenericTensor, Pipeline


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=None    ,a_=None    ,a_=None    ,**a_						):







						"""simple docstring"""
						if tokenize_kwargs is None:
										lowerCAmelCase__		  =  {}

						if truncation is not None:
										if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'						)
										lowerCAmelCase__		  =  truncation

						lowerCAmelCase__		  =  tokenize_kwargs

						lowerCAmelCase__		  =  {}
						if return_tensors is not None:
										lowerCAmelCase__		  =  return_tensors

						return preprocess_params, {}, postprocess_params
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.framework
						lowerCAmelCase__		  =  self.tokenizer(a_    ,return_tensors=a_    ,**a_						)
						return model_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model(**a_						)
						return model_outputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False						):







						"""simple docstring"""
						# [0] is the first available tensor, logits or last_hidden_state.
						if return_tensors:
										return model_outputs[0]
						if self.framework == "pt":
										return model_outputs[0].tolist()
						elif self.framework == "tf":
										return model_outputs[0].numpy().tolist()



		def __call__(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return super().__call__(*a_    ,**a_						)
