
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__    :		int          =   logging.get_logger(__name__)

lowerCamelCase__    :		Optional[int]          =   {
    """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       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			'gpt_neo'
 __lowerCAmelCase  :     Optional[int]        =			['past_key_values']
 __lowerCAmelCase  :     str        =			{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=5_02_57					,  SCREAMING_SNAKE_CASE_=20_48					,  SCREAMING_SNAKE_CASE_=20_48					,  SCREAMING_SNAKE_CASE_=24					,  SCREAMING_SNAKE_CASE_=[[["global", "local"], 12]]					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_="gelu_new"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=1E-5					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=5_02_56					,  SCREAMING_SNAKE_CASE_=5_02_56					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      vocab_size
   lowercase__  :   Optional[Any]    		=      max_position_embeddings
   lowercase__  :   Union[str, Any]    		=      hidden_size
   lowercase__  :   Union[str, Any]    		=      num_layers
   lowercase__  :   Optional[Any]    		=      num_heads
   lowercase__  :   Optional[int]    		=      intermediate_size
   lowercase__  :   List[Any]    		=      window_size
   lowercase__  :   Union[str, Any]    		=      activation_function
   lowercase__  :   int    		=      resid_dropout
   lowercase__  :   Union[str, Any]    		=      embed_dropout
   lowercase__  :   str    		=      attention_dropout
   lowercase__  :   str    		=      classifier_dropout
   lowercase__  :   Tuple    		=      layer_norm_epsilon
   lowercase__  :   Optional[int]    		=      initializer_range
   lowercase__  :   Optional[Any]    		=      use_cache

   lowercase__  :   Tuple    		=      bos_token_id
   lowercase__  :   Dict    		=      eos_token_id

   lowercase__  :   Dict    		=      attention_types
   lowercase__  :   Union[str, Any]    		=      self.expand_attention_types_params(SCREAMING_SNAKE_CASE_)

   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=SCREAMING_SNAKE_CASE_					,  eos_token_id=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)



 @staticmethod
 def      lowercase__     (					SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   str    		=      []
   for item in attention_types:
     for _ in range(item[1]):
       attentions.extend(item[0])
   return attentions

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	Any:


  '''simple docstring'''






  import torch

  lowercase__  :   str    		=      input.size()
  lowercase__  :   int    		=      len(lowercase_   )
  lowercase__  :   Dict    		=      shape[dimension]

  lowercase__  :   Union[str, Any]    		=      torch.arange(0 , lowercase_ , lowercase_   )
  lowercase__  :   Optional[Any]    		=      torch.div(sizedim - size , lowercase_ , rounding_mode="""floor"""   ) + 1
  lowercase__  :   Optional[Any]    		=      torch.arange(lowercase_   ) + low_indices[:min_length][:, None]

  lowercase__  :   Optional[Any]    		=      [slice(lowercase_   )] * rank
  lowercase__  :   Optional[Any]    		=      indices
  lowercase__  :   List[Any]    		=      input[s]

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

  return sliced.permute(lowercase_   )

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Any:


  '''simple docstring'''






  import torch

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



class       _snake_case   (	UpperCAmelCase_							):


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   str    		=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
   if self.use_past:
     self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_					,  direction="""inputs""")
     lowercase__  :   int    		=      {0: """batch""", 1: """past_sequence + sequence"""}
   else:
     lowercase__  :   Union[str, Any]    		=      {0: """batch""", 1: """sequence"""}

   return common_inputs


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return self._config.num_heads


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = -1					,  SCREAMING_SNAKE_CASE_ = -1					,  SCREAMING_SNAKE_CASE_ = False					,  SCREAMING_SNAKE_CASE_ = None					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      super(SCREAMING_SNAKE_CASE_					,  self).generate_dummy_inputs(
       SCREAMING_SNAKE_CASE_					,  batch_size=SCREAMING_SNAKE_CASE_					,  seq_length=SCREAMING_SNAKE_CASE_					,  is_pair=SCREAMING_SNAKE_CASE_					,  framework=SCREAMING_SNAKE_CASE_)

   # We need to order the input in the way they appears in the forward()
   lowercase__  :   Optional[int]    		=      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

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

   lowercase__  :   Tuple    		=      common_inputs["""attention_mask"""]
   if self.use_past:
     lowercase__  :   Tuple    		=      ordered_inputs["""attention_mask"""].dtype
     lowercase__  :   Optional[Any]    		=      torch.cat(
         [ordered_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  dtype=SCREAMING_SNAKE_CASE_)]					,  dim=1)

   return ordered_inputs



 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return 13



# 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 typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Any        =			'dandelin/vilt-b32-finetuned-vqa'
 __lowerCAmelCase  :     Union[str, Any]        =			(
     'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
     'image containing the information, as well as a `question` which should be the question in English. It '
     'returns a text that is the answer to the question.'
 )
 __lowerCAmelCase  :     str        =			'image_qa'
 __lowerCAmelCase  :     int        =			AutoProcessor
 __lowerCAmelCase  :     Optional[Any]        =			AutoModelForVisualQuestionAnswering

 __lowerCAmelCase  :     str        =			['image', 'text']
 __lowerCAmelCase  :     Optional[int]        =			['text']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""vision"""])
   super().__init__(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.pre_processor(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  return_tensors="""pt""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   with torch.no_grad():
     return self.model(**SCREAMING_SNAKE_CASE_).logits



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      outputs.argmax(-1).item()
   return self.model.config.idalabel[idx]






