



'''simple docstring'''


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)


lowercase_      :				List[Any]   					=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase_      :				Dict   					=    {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowercase_      :				Any   					=    {
    '''allenai/led-base-16384''': 1_6384,
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					VOCAB_FILES_NAMES
	__A			  =					PRETRAINED_VOCAB_FILES_MAP
	__A			  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A			  =					LEDTokenizer
	__A			  =					['''input_ids''', '''attention_mask''']







	def __init__(    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase="replace" ,				_lowerCAmelCase="<s>" ,				_lowerCAmelCase="</s>" ,				_lowerCAmelCase="</s>" ,				_lowerCAmelCase="<s>" ,				_lowerCAmelCase="<unk>" ,				_lowerCAmelCase="<pad>" ,				_lowerCAmelCase="<mask>" ,				_lowerCAmelCase=False ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    Union[str, Any]:
							'''simple docstring'''






							super().__init__(
							    _lowerCAmelCase ,				_lowerCAmelCase ,				tokenizer_file=_lowerCAmelCase ,				errors=_lowerCAmelCase ,				bos_token=_lowerCAmelCase ,				eos_token=_lowerCAmelCase ,				sep_token=_lowerCAmelCase ,				cls_token=_lowerCAmelCase ,				unk_token=_lowerCAmelCase ,				pad_token=_lowerCAmelCase ,				mask_token=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase ,				**_lowerCAmelCase ,				)

							lowercase        =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
							if pre_tok_state.get("""add_prefix_space""" ,				_lowerCAmelCase						) != add_prefix_space:
													lowercase        =							getattr(_lowerCAmelCase ,				pre_tok_state.pop("""type"""						)						)
													lowercase        =							add_prefix_space
													lowercase        =							pre_tok_class(**_lowerCAmelCase						)

							lowercase        =							add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							lowercase        =							"""post_processor"""
							lowercase        =							getattr(self.backend_tokenizer ,				_lowerCAmelCase ,				_lowerCAmelCase						)
							if tokenizer_component_instance:
													lowercase        =							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:
																			lowercase        =							tuple(state["""sep"""]						)
													if "cls" in state:
																			lowercase        =							tuple(state["""cls"""]						)

													lowercase        =							False

													if state.get("""add_prefix_space""" ,				_lowerCAmelCase						) != add_prefix_space:
																			lowercase        =							add_prefix_space
																			lowercase        =							True

													if state.get("""trim_offsets""" ,				_lowerCAmelCase						) != trim_offsets:
																			lowercase        =							trim_offsets
																			lowercase        =							True

													if changes_to_apply:
																			lowercase        =							getattr(_lowerCAmelCase ,				state.pop("""type"""						)						)
																			lowercase        =							component_class(**_lowerCAmelCase						)
																			setattr(self.backend_tokenizer ,				_lowerCAmelCase ,				_lowerCAmelCase						)







	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							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   _a   (    self ,				_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






							lowercase        =							AddedToken(_lowerCAmelCase ,				lstrip=_lowerCAmelCase ,				rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase ,				_lowerCAmelCase						) else value
							lowercase        =							value







	def   _a   (    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    BatchEncoding:
							'''simple docstring'''






							lowercase        =							kwargs.get("""is_split_into_words""" ,				_lowerCAmelCase						)

							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(*_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    BatchEncoding:
							'''simple docstring'''






							lowercase        =							kwargs.get("""is_split_into_words""" ,				_lowerCAmelCase						)

							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(*_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    Tuple[str]:
							'''simple docstring'''






							lowercase        =							self._tokenizer.model.save(_lowerCAmelCase ,				name=_lowerCAmelCase						)
							return tuple(_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    str:
							'''simple docstring'''






							lowercase        =							[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   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    List[int]:
							'''simple docstring'''






							lowercase        =							[self.sep_token_id]
							lowercase        =							[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]







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = PaddingStrategy.DO_NOT_PAD ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				)    ->    dict:
							'''simple docstring'''






							lowercase        =							super()._pad(
							    encoded_inputs=_lowerCAmelCase ,				max_length=_lowerCAmelCase ,				padding_strategy=_lowerCAmelCase ,				pad_to_multiple_of=_lowerCAmelCase ,				return_attention_mask=_lowerCAmelCase ,				)

							# Load from model defaults
							if return_attention_mask is None:
													lowercase        =							"""attention_mask""" in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
													lowercase        =							encoded_inputs[self.model_input_names[0]]
													# `global_attention_mask` need to have the same length as other (sequential) inputs.
													lowercase        =							len(encoded_inputs["""global_attention_mask"""]						) != len(_lowerCAmelCase						)

													if needs_to_be_padded:
																			lowercase        =							len(_lowerCAmelCase						) - len(encoded_inputs["""global_attention_mask"""]						)

																			if self.padding_side == "right":
																									# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																									lowercase        =							(
																									    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																									)
																			elif self.padding_side == "left":
																									lowercase        =							[-1] * difference + encoded_inputs[
																									    """global_attention_mask"""
																									]
																			else:
																									raise ValueError("""Invalid padding strategy:""" + str(self.padding_side						)						)

							return encoded_inputs







'''simple docstring'''


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)




@add_end_docstrings(_UpperCAmelCase			)
class      __UpperCamelCase       (_UpperCAmelCase			):







	def __init__(    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Dict:
							'''simple docstring'''






							super().__init__(*_lowerCAmelCase ,				**_lowerCAmelCase						)
							requires_backends(self ,				"""vision"""						)
							self.check_model_type(
							    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING						)







	def   _a   (    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None						)    ->    str:
							'''simple docstring'''






							lowercase        =							{}
							lowercase        =							{}

							if prompt is not None:
													lowercase        =							prompt

							if generate_kwargs is not None:
													lowercase        =							generate_kwargs
							if max_new_tokens is not None:
													if "generate_kwargs" not in forward_kwargs:
																			lowercase        =							{}
													if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																			raise ValueError(
																			    """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
																			    """ please use only one"""						)
													lowercase        =							max_new_tokens
							return preprocess_params, forward_kwargs, {}







	def __call__(    self ,				_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							return super().__call__(_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							load_image(_lowerCAmelCase						)

							if prompt is not None:
													if not isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																			raise ValueError(
																			    F"""Received an invalid text input, got - {type(_lowerCAmelCase						)} - but expected a single string. """
																			    """Note also that one single text can be provided for conditional image to text generation."""						)

													lowercase        =							self.model.config.model_type

													if model_type == "git":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(text=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						).input_ids
																			lowercase        =							[self.tokenizer.cls_token_id] + input_ids
																			lowercase        =							torch.tensor(_lowerCAmelCase						).unsqueeze(0						)
																			model_inputs.update({"""input_ids""": input_ids}						)

													elif model_type == "pix2struct":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				header_text=_lowerCAmelCase ,				return_tensors=self.framework						)

													elif model_type != "vision-encoder-decoder":
																			# vision-encoder-decoder does not support conditional generation
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(_lowerCAmelCase ,				return_tensors=self.framework						)
																			model_inputs.update(_lowerCAmelCase						)

													else:
																			raise ValueError(F"""Model type {model_type} does not support conditional text generation"""						)

							else:
													lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)

							if self.model.config.model_type == "git" and prompt is None:
													lowercase        =							None

							return model_inputs







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    Union[str, Any]:
							'''simple docstring'''






							if (
							    "input_ids" in model_inputs
							    and isinstance(model_inputs["""input_ids"""] ,				_lowerCAmelCase						)
							    and all(x is None for x in model_inputs["""input_ids"""]						)
							):
													lowercase        =							None

							if generate_kwargs is None:
													lowercase        =							{}
							# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
							#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
							#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
							#  in the `_prepare_model_inputs` method.
							lowercase        =							model_inputs.pop(self.model.main_input_name						)
							lowercase        =							self.model.generate(_lowerCAmelCase ,				**_lowerCAmelCase ,				**_lowerCAmelCase						)
							return model_outputs







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							[]
							for output_ids in model_outputs:
													lowercase        =							{
													    """generated_text""": self.tokenizer.decode(
													        _lowerCAmelCase ,				skip_special_tokens=_lowerCAmelCase ,				)
													}
													records.append(_lowerCAmelCase						)
							return records


