# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


__a  :					List[Any]          =  '''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

Can you answer this question about the text: \'{question}\''''




class        _UpperCamelCase				(						UpperCamelCase__			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''google/flan-t5-base'''
			__a  :			Any      			=						(
			    '''This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '''
			    '''text where to find the answer, and `question`, which is the question, and returns the answer to the question.'''
			)
			__a  :			str      			=						'''text_qa'''
			__a  :			Union[str, Any]      			=						AutoTokenizer
			__a  :			int      			=						AutoModelForSeqaSeqLM

			__a  :			Dict      			=						['''text''', '''text''']
			__a  :			Optional[Any]      			=						['''text''']





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							QA_PROMPT.format(text=__lowerCamelCase				,       question=__lowerCamelCase						)
							return self.pre_processor(__lowerCamelCase				,       return_tensors='''pt'''						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							self.model.generate(**__lowerCamelCase						)

							__lowercase			      =							inputs["input_ids"].shape
							__lowercase			      =							output_ids.shape[0]

							return output_ids.reshape(__lowerCamelCase				,       out_b // in_b				,       *output_ids.shape[1:]						)[0][0]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							return self.pre_processor.decode(__lowerCamelCase				,       skip_special_tokens=__lowerCamelCase				,       clean_up_tokenization_spaces=__lowerCamelCase						)
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__a  :					int          =  logging.get_logger(__name__)

__a  :					Optional[Any]          =  {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							vocab_size
							__lowercase			      =							hidden_size
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							hidden_act
							__lowercase			      =							intermediate_size
							__lowercase			      =							hidden_dropout_prob
							__lowercase			      =							attention_probs_dropout_prob
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							type_vocab_size
							__lowercase			      =							initializer_range
							__lowercase			      =							layer_norm_eps
							__lowercase			      =							position_embedding_type
							__lowercase			      =							use_cache
							__lowercase			      =							max_ad_position_embeddings




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
							        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
							    ]						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							super().generate_dummy_inputs(
							    lowerCAmelCase__				,       batch_size=lowerCAmelCase__				,       seq_length=lowerCAmelCase__				,       is_pair=lowerCAmelCase__				,       framework=lowerCAmelCase__						)

							# Generate a dummy bbox
							__lowercase			      =							[48, 84, 73, 1_28]

							if not framework == TensorType.PYTORCH:
											raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''						)

							if not is_torch_available():
											raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''						)
							import torch

							__lowercase      ,					__lowercase			      =							input_dict['''input_ids'''].shape
							__lowercase			      =							torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__				,       1				,       1						)
							return input_dict