



'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase:					Any						       =						logging.get_logger(__name__)

lowerCAmelCase:					Optional[Any]						       =						{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class 							a__(					__lowerCamelCase	):
    lowercase__ =    "gpt_neo"
    lowercase__ =    ["past_key_values"]
    lowercase__ =    {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







    def __init__(      self :  Union[str, Any]						,			__snake_case :  Union[str, Any]=5_02_57						,			__snake_case :  Any=20_48						,			__snake_case :  Optional[Any]=20_48						,			__snake_case :  Any=24						,			__snake_case :  Union[str, Any]=[[["global", "local"], 12]]						,			__snake_case :  str=16						,			__snake_case :  Optional[int]=None						,			__snake_case :  Union[str, Any]=2_56						,			__snake_case :  Any="gelu_new"						,			__snake_case :  Dict=0.0						,			__snake_case :  Optional[int]=0.0						,			__snake_case :  int=0.0						,			__snake_case :  List[str]=0.1						,			__snake_case :  Any=1e-5						,			__snake_case :  int=0.02						,			__snake_case :  List[str]=True						,			__snake_case :  Tuple=5_02_56						,			__snake_case :  Optional[Any]=5_02_56						,			**__snake_case :  Optional[Any]						,			):
      a						:					List[Any]        =     vocab_size
      a						:					Tuple        =     max_position_embeddings
      a						:					Union[str, Any]        =     hidden_size
      a						:					Dict        =     num_layers
      a						:					int        =     num_heads
      a						:					Tuple        =     intermediate_size
      a						:					int        =     window_size
      a						:					Dict        =     activation_function
      a						:					Union[str, Any]        =     resid_dropout
      a						:					str        =     embed_dropout
      a						:					Optional[int]        =     attention_dropout
      a						:					Union[str, Any]        =     classifier_dropout
      a						:					int        =     layer_norm_epsilon
      a						:					int        =     initializer_range
      a						:					int        =     use_cache

      a						:					Optional[Any]        =     bos_token_id
      a						:					List[Any]        =     eos_token_id

      a						:					Any        =     attention_types
      a						:					Optional[Any]        =     self.expand_attention_types_params(__A	)

      if len(self.attention_layers	) != self.num_layers:
        raise ValueError(
            'Configuration for convolutional module is incorrect. '
            'It is required that `len(config.attention_layers)` == `config.num_layers` '
            F"""but is `len(config.attention_layers) = {len(self.attention_layers	)}`, """
            F"""`config.num_layers = {self.num_layers}`. """
            '`config.attention_layers` is prepared using `config.attention_types`. '
            'Please verify the value of `config.attention_types` argument.'	)

      super().__init__(bos_token_id=__A						,			eos_token_id=__A						,			**__A	)







    @staticmethod
    def 			lowercase_       (      __snake_case :  Tuple	):
      a						:					Union[str, Any]        =     []
      for item in attention_types:
        for _ in range(item[1]	):
          attentions.extend(item[0]	)
      return attentions
def       lowerCamelCase__       (						_A ,			_A ,			_A ,			_A				):
  import torch

  a						:					Any        =     input.size()
  a						:					Union[str, Any]        =     len(__lowercase				)
  a						:					Any        =     shape[dimension]

  a						:					Any        =     torch.arange(0 ,			__lowercase ,			__lowercase				)
  a						:					str        =     torch.div(sizedim - size ,			__lowercase ,			rounding_mode='floor'				) + 1
  a						:					str        =     torch.arange(__lowercase				) + low_indices[:min_length][:, None]

  a						:					Optional[int]        =     [slice(__lowercase				)] * rank
  a						:					Any        =     indices
  a						:					List[Any]        =     input[s]

  a						:					Optional[Any]        =     list(range(0 ,			rank + 1				)				)
  perm.append(perm.pop(dimension + 1				)				)

  return sliced.permute(__lowercase				)
def       lowerCamelCase__       (						_A ,			_A				):
  import torch

  a						:					Union[str, Any]        =     torch.arange(1 ,			__lowercase				)
  a						:					Tuple        =     torch.remainder(__lowercase ,			__lowercase				)
  a						:					List[str]        =     remainders == 0
  a						:					Optional[int]        =     candidates[divisor_indices]
  a						:					Optional[int]        =     torch.max(__lowercase				)
  return largest_divisor, torch.div(__lowercase ,			__lowercase ,			rounding_mode='floor'				)

class 							a__(					__lowerCamelCase	):







    @property
    def 			lowercase_       (      self :  Tuple	):
      a						:					Any        =     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}	)
      if self.use_past:
        self.fill_with_past_key_values_(__A						,			direction='inputs'	)
        a						:					List[str]        =     {0: 'batch', 1: 'past_sequence + sequence'}
      else:
        a						:					Optional[int]        =     {0: 'batch', 1: 'sequence'}

      return common_inputs







    @property
    def 			lowercase_       (      self :  int	):
      return self._config.num_heads







    def 			lowercase_       (      self :  List[str]						,			__snake_case :  PreTrainedTokenizer						,			__snake_case :  int = -1						,			__snake_case :  int = -1						,			__snake_case :  bool = False						,			__snake_case :  Optional[TensorType] = None						,			):
      a						:					Dict        =     super(__A						,			self	).generate_dummy_inputs(
          __A						,			batch_size=__A						,			seq_length=__A						,			is_pair=__A						,			framework=__A	)

      # We need to order the input in the way they appears in the forward()
      a						:					str        =     OrderedDict({'input_ids': common_inputs['input_ids']}	)

      # Need to add the past_keys
      if self.use_past:
        if not is_torch_available():
          raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
        else:
          import torch

          a   ,	a						:					Union[str, Any]        =     common_inputs['input_ids'].shape
          # Not using the same length for past_key_values
          a						:					Any        =     seqlen + 2
          a						:					List[str]        =     (
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
          a						:					Any        =     [
              (torch.zeros(__A	), torch.zeros(__A	)) for _ in range(self.num_layers	)
          ]

      a						:					Optional[int]        =     common_inputs['attention_mask']
      if self.use_past:
        a						:					Any        =     ordered_inputs['attention_mask'].dtype
        a						:					Any        =     torch.cat(
            [ordered_inputs['attention_mask'], torch.ones(__A						,			__A						,			dtype=__A	)]						,			dim=1	)

      return ordered_inputs







    @property
    def 			lowercase_       (      self :  Dict	):
      return 13






'''simple docstring'''
# 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.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
    from PIL import Image

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="naver-clova-ix/donut-base-finetuned-docvqa"
 SCREAMING_SNAKE_CASE_	:					Dict					=(
     "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
     "should be the document containing the information, as well as a `question` that is the question about the "
     "document. It returns a text that contains the answer to the question."
 )
 SCREAMING_SNAKE_CASE_	:					List[str]					="document_qa"
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=AutoProcessor
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=VisionEncoderDecoderModel

 SCREAMING_SNAKE_CASE_	:					List[Any]					=["image", "text"]
 SCREAMING_SNAKE_CASE_	:					Any					=["text"]






 def __init__(       self       :							Optional[int]							,	*__A       :							List[str]							,	**__A       :							List[Any]	):
        if not is_vision_available():
               raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'	)

        super().__init__(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							"Image"							,	__A       :							str	):
        __UpperCamelCase       =  '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
        __UpperCamelCase       =  task_prompt.replace('{user_input}'							,	__A	)
        __UpperCamelCase       =  self.pre_processor.tokenizer(
            __A							,	add_special_tokens=__A							,	return_tensors='pt'	).input_ids
        __UpperCamelCase       =  self.pre_processor(__A							,	return_tensors='pt'	).pixel_values

        return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Optional[Any]	):
        return self.model.generate(
            inputs['pixel_values'].to(self.device	)							,	decoder_input_ids=inputs['decoder_input_ids'].to(self.device	)							,	max_length=self.model.decoder.config.max_position_embeddings							,	early_stopping=__A							,	pad_token_id=self.pre_processor.tokenizer.pad_token_id							,	eos_token_id=self.pre_processor.tokenizer.eos_token_id							,	use_cache=__A							,	num_beams=1							,	bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]							,	return_dict_in_generate=__A							,	).sequences






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							List[Any]	):
        __UpperCamelCase       =  self.pre_processor.batch_decode(__A	)[0]
        __UpperCamelCase       =  sequence.replace(self.pre_processor.tokenizer.eos_token							,	''	)
        __UpperCamelCase       =  sequence.replace(self.pre_processor.tokenizer.pad_token							,	''	)
        __UpperCamelCase       =  re.sub(R'<.*?>'							,	''							,	__A							,	count=1	).strip()  # remove first task start token
        __UpperCamelCase       =  self.pre_processor.tokenajson(__A	)

        return sequence["answer"]
