



"""simple docstring"""
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"Pix2StructImageProcessor"
							__lowerCAmelCase    					=					("T5Tokenizer", "T5TokenizerFast")






							def __init__(   self			,     a_			,     a_      ):
									a_     :      Tuple 						=     False
									super().__init__(a_			,     a_      )






							def __call__(   self			,     a_=None			,     a_ = None			,     a_ = True			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = 2_0_4_8			,     a_ = 0			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = True			,     a_ = None			,     **a_			,     ):
									if images is None and text is None:
											raise ValueError("You have to specify either images or text."      )

									# Get only text
									if images is None and not self.image_processor.is_vqa:
											a_     :      int 						=     self.tokenizer
											a_     :      List[Any] 						=     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_			,     )
											return text_encoding

									if not self.image_processor.is_vqa:
											# add pixel_values
											a_     :      Dict 						=     self.image_processor(
											    a_			,     return_tensors=a_			,     max_patches=a_			,     **a_      )
									else:
											# add pixel_values and bbox
											a_     :      Union[str, Any] 						=     self.image_processor(
											    a_			,     return_tensors=a_			,     max_patches=a_			,     header_text=a_			,     **a_      )

									if text is not None and not self.image_processor.is_vqa:
											a_     :      Tuple 						=     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_			,     )

											if "attention_mask" in text_encoding:
													a_     :      List[Any] 						=     text_encoding.pop("attention_mask"      )
											if "input_ids" in text_encoding:
													a_     :      List[Any] 						=     text_encoding.pop("input_ids"      )
									else:
											a_     :      Any 						=     None

									if text_encoding is not None:
											encoding_image_processor.update(a_      )

									return encoding_image_processor






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.batch_decode(*a_			,     **a_      )






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.decode(*a_			,     **a_      )






							@property
							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     self.tokenizer.model_input_names
									a_     :      Optional[int] 						=     self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"realm"






							def __init__(   self			,     a_=3_0_5_2_2			,     a_=7_6_8			,     a_=1_2_8			,     a_=1_2			,     a_=1_2			,     a_=8			,     a_=3_0_7_2			,     a_="gelu_new"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=2			,     a_=0.02			,     a_=1e-12			,     a_=2_5_6			,     a_=1_0			,     a_=1e-3			,     a_=5			,     a_=3_2_0			,     a_=1_3_3_5_3_7_1_8			,     a_=5_0_0_0			,     a_=1			,     a_=0			,     a_=2			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

									# Common config
									a_     :      Optional[int] 						=     vocab_size
									a_     :      List[Any] 						=     max_position_embeddings
									a_     :      Optional[Any] 						=     hidden_size
									a_     :      Optional[Any] 						=     retriever_proj_size
									a_     :      List[str] 						=     num_hidden_layers
									a_     :      List[Any] 						=     num_attention_heads
									a_     :      Tuple 						=     num_candidates
									a_     :      str 						=     intermediate_size
									a_     :      Optional[int] 						=     hidden_act
									a_     :      List[str] 						=     hidden_dropout_prob
									a_     :      List[str] 						=     attention_probs_dropout_prob
									a_     :      Tuple 						=     initializer_range
									a_     :      Tuple 						=     type_vocab_size
									a_     :      str 						=     layer_norm_eps

									# Reader config
									a_     :      str 						=     span_hidden_size
									a_     :      Union[str, Any] 						=     max_span_width
									a_     :      Tuple 						=     reader_layer_norm_eps
									a_     :      List[Any] 						=     reader_beam_size
									a_     :      str 						=     reader_seq_len

									# Retrieval config
									a_     :      str 						=     num_block_records
									a_     :      int 						=     searcher_beam_size