






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


_a      	=  logging.get_logger(__name__)





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			question_encoder
							_UpperCAmelCase									=			generator
							_UpperCAmelCase									=			self.question_encoder

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if os.path.isfile(UpperCAmelCase    ):
													raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""    )
							os.makedirs(UpperCAmelCase       ,    exist_ok=UpperCAmelCase    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'question_encoder_tokenizer'    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'generator_tokenizer'    )
							self.question_encoder.save_pretrained(UpperCAmelCase    )
							self.generator.save_pretrained(UpperCAmelCase    )

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							from ..auto.tokenization_auto import AutoTokenizer

							_UpperCAmelCase									=			kwargs.pop('config'       ,    UpperCAmelCase    )

							if config is None:
													_UpperCAmelCase									=			RagConfig.from_pretrained(UpperCAmelCase    )

							_UpperCAmelCase									=			AutoTokenizer.from_pretrained(
							    UpperCAmelCase       ,    config=config.question_encoder       ,    subfolder='question_encoder_tokenizer'    )
							_UpperCAmelCase									=			AutoTokenizer.from_pretrained(
							    UpperCAmelCase       ,    config=config.generator       ,    subfolder='generator_tokenizer'    )
							return cls(question_encoder=UpperCAmelCase       ,    generator=UpperCAmelCase    )

	def __call__(							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.current_tokenizer(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.generator.batch_decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.generator.decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.question_encoder

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.generator

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = "longest"       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							warnings.warn(
							    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
							    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
							    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
							    'details'       ,    UpperCAmelCase       ,    )
							if max_length is None:
													_UpperCAmelCase									=			self.current_tokenizer.model_max_length
							_UpperCAmelCase									=			self(
							    UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
							if tgt_texts is None:
													return model_inputs
							# Process tgt_texts
							if max_target_length is None:
													_UpperCAmelCase									=			self.current_tokenizer.model_max_length
							_UpperCAmelCase									=			self(
							    text_target=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
							_UpperCAmelCase									=			labels['input_ids']
							return model_inputs








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 __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["image_processor", "tokenizer"]
	UpperCamelCase__								=      "Pix2StructImageProcessor"
	UpperCamelCase__								=      ("T5Tokenizer", "T5TokenizerFast")

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			False
							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = False       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 2048       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							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:
													_UpperCAmelCase									=			self.tokenizer
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
													return text_encoding

							if not self.image_processor.is_vqa:
													# add pixel_values
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    **UpperCAmelCase    )
							else:
													# add pixel_values and bbox
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    header_text=UpperCAmelCase       ,    **UpperCAmelCase    )

							if text is not None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

													if "attention_mask" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('attention_mask'    )
													if "input_ids" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('input_ids'    )
							else:
													_UpperCAmelCase									=			None

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

							return encoding_image_processor

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.batch_decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.tokenizer.model_input_names
							_UpperCAmelCase									=			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
