



import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


A_		:  str										=  10_00



class 		_lowercase		:

   def __init__(   self      :							Tuple				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Optional[int]=1.2				, __lowerCAmelCase      :							Dict="g"				, __lowerCAmelCase      :							List[Any]=0.5				, __lowerCAmelCase      :							int="-"				, __lowerCAmelCase      :							Optional[Any]="test_out.jpg"				, __lowerCAmelCase      :							Optional[int]=True				, __lowerCAmelCase      :							List[Any]=False				, __lowerCAmelCase      :							Optional[int]=None				, __lowerCAmelCase      :							Optional[Any]=None				, __lowerCAmelCase      :							str=0.7				, )							->					Dict:


    """simple docstring"""






    if isinstance(__lowerCAmelCase				, torch.Tensor				):
     a							=		img.numpy().astype("np.uint8"				)
    if isinstance(__lowerCAmelCase				, __lowerCAmelCase				):
     a							=		img_tensorize(__lowerCAmelCase				)
    assert isinstance(__lowerCAmelCase				, np.ndarray				)

    a		,	a							=		img.shape[1], img.shape[0]
    a							=		mplfigure.Figure(frameon=__lowerCAmelCase				)
    a							=		fig.get_dpi()
    a							=		(width * scale + 1E-2) / dpi
    a							=		(height * scale + 1E-2) / dpi
    fig.set_size_inches(__lowerCAmelCase				, __lowerCAmelCase				)
    a							=		fig.add_axes([0.0, 0.0, 1.0, 1.0]				)
    ax.axis("off"				)
    ax.set_xlim(0.0				, __lowerCAmelCase				)
    ax.set_ylim(__lowerCAmelCase				)

    a							=		saveas
    a							=		rgb
    a							=		pynb
    a							=		img
    a							=		edgecolor
    a							=		0.5
    a							=		linestyle
    a							=		int(np.sqrt(min(__lowerCAmelCase				, __lowerCAmelCase				)				) * scale // 3				)
    a							=		width
    a							=		height
    a							=		scale
    a							=		fig
    a							=		ax
    a							=		pad
    a							=		idaobj
    a							=		idaattr
    a							=		FigureCanvasAgg(__lowerCAmelCase				)

   def 		A				(   self      :							str				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							List[str]=None				)							->					Tuple:


    """simple docstring"""






    if color is None:
     a							=		self.edgecolor
    ((a)		,	(a)		,	(a)		,	(a))							=		box
    a							=		xa - xa
    a							=		ya - ya
    self.ax.add_patch(
        mpl.patches.Rectangle(
            (xa, ya)				, __lowerCAmelCase				, __lowerCAmelCase				, fill=__lowerCAmelCase				, edgecolor=__lowerCAmelCase				, linewidth=self.font_size // 3				, alpha=self.alpha				, linestyle=self.linestyle				, )				)

   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							str=None				, __lowerCAmelCase      :							List[Any]=None				, __lowerCAmelCase      :							Any=None				, __lowerCAmelCase      :							List[Any]=None				)							->					Dict:


    """simple docstring"""






    if len(boxes.shape				) > 2:
     a							=		boxes[0]
    if len(obj_ids.shape				) > 1:
     a							=		obj_ids[0]
    if len(obj_scores.shape				) > 1:
     a							=		obj_scores[0]
    if len(attr_ids.shape				) > 1:
     a							=		attr_ids[0]
    if len(attr_scores.shape				) > 1:
     a							=		attr_scores[0]
    if isinstance(__lowerCAmelCase				, torch.Tensor				):
     a							=		boxes.numpy()
    if isinstance(__lowerCAmelCase				, __lowerCAmelCase				):
     a							=		np.array(__lowerCAmelCase				)
    assert isinstance(__lowerCAmelCase				, np.ndarray				)
    a							=		np.prod(boxes[:, 2:] - boxes[:, :2]				, axis=1				)
    a							=		np.argsort(-areas				).tolist()
    a							=		boxes[sorted_idxs] if boxes is not None else None
    a							=		obj_ids[sorted_idxs] if obj_ids is not None else None
    a							=		obj_scores[sorted_idxs] if obj_scores is not None else None
    a							=		attr_ids[sorted_idxs] if attr_ids is not None else None
    a							=		attr_scores[sorted_idxs] if attr_scores is not None else None

    a							=		[self._random_color(maximum=1				) for _ in range(len(__lowerCAmelCase				)				)]
    a							=		[assigned_colors[idx] for idx in sorted_idxs]
    if obj_ids is not None:
     a							=		self._create_text_labels_attr(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)
     for i in range(len(__lowerCAmelCase				)				):
      a							=		assigned_colors[i]
      self.add_box(boxes[i]				, __lowerCAmelCase				)
      self.draw_labels(labels[i]				, boxes[i]				, __lowerCAmelCase				)

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				)							->					Optional[int]:


    """simple docstring"""






    a		,	a		,	a		,	a							=		box
    a							=		(xa, ya)
    a							=		(ya - ya) * (xa - xa)
    a							=		_SMALL_OBJ * self.scale
    if instance_area < small or ya - ya < 40 * self.scale:
     if ya >= self.height - 5:
      a							=		(xa, ya)
     else:
      a							=		(xa, ya)

    a							=		(ya - ya) / np.sqrt(self.height * self.width				)
    a							=		self._change_color_brightness(__lowerCAmelCase				, brightness_factor=0.7				)
    a							=		np.clip((height_ratio - 0.0_2) / 0.0_8 + 1				, 1.2				, 2				)
    font_size *= 0.7_5 * self.font_size

    self.draw_text(
        text=__lowerCAmelCase				, position=__lowerCAmelCase				, color=__lowerCAmelCase				, )

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Union[str, Any]="g"				, __lowerCAmelCase      :							List[Any]="left"				, )							->					Any:


    """simple docstring"""






    a							=		0
    a							=		self.font_size
    a							=		np.maximum(list(mplc.to_rgb(__lowerCAmelCase				)				)				, 0.2				)
    a							=		max(0.8				, np.max(__lowerCAmelCase				)				)
    a							=		{
        "facecolor": "black",
        "alpha": self.alpha,
        "pad": self.pad,
        "edgecolor": "none",
    }
    a		,	a							=		position
    self.ax.text(
        __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, size=font_size * self.scale				, family="sans-serif"				, bbox=__lowerCAmelCase				, verticalalignment="top"				, horizontalalignment=__lowerCAmelCase				, color=__lowerCAmelCase				, zorder=10				, rotation=__lowerCAmelCase				, )

   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							int=None				)							->					Any:


    """simple docstring"""






    if saveas is None:
     a							=		self.saveas
    if saveas.lower().endswith(".jpg"				) or saveas.lower().endswith(".png"				):
     cva.imwrite(
         __lowerCAmelCase				, self._get_buffer()[:, :, ::-1]				, )
    else:
     self.fig.savefig(__lowerCAmelCase				)

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Dict				)							->					str:


    """simple docstring"""






    a							=		[self.idaobj[i] for i in classes]
    a							=		[self.idaattr[i] for i in attr_classes]
    a							=		[
        f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
        for label, score, attr, attr_score in zip(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)
    ]
    return labels

   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		[self.idaobj[i] for i in classes]
    if scores is not None:
     if labels is None:
      a							=		["{:.0f}%".format(s * 100				) for s in scores]
     else:
      a							=		["{} {:.0f}%".format(__lowerCAmelCase				, s * 100				) for li, s in zip(__lowerCAmelCase				, __lowerCAmelCase				)]
    return labels

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							str=255				)							->					List[Any]:


    """simple docstring"""






    a							=		np.random.randint(0				, len(_COLORS				)				)
    a							=		_COLORS[idx] * maximum
    if not self.rgb:
     a							=		ret[::-1]
    return ret

   def 		A				(   self      :							Any				)							->					Any:


    """simple docstring"""






    if not self.pynb:
     a		,	a							=		self.canvas.print_to_buffer()
     if (width, height) != (self.width, self.height):
      a							=		cva.resize(self.img				, (width, height)				)
     else:
      a							=		self.img
    else:
     a							=		io.BytesIO()  # works for cairo backend
     self.canvas.print_rgba(__lowerCAmelCase				)
     a		,	a							=		self.width, self.height
     a							=		buf.getvalue()
     a							=		self.img

    a							=		np.frombuffer(__lowerCAmelCase				, dtype="uint8"				)
    a							=		buffer.reshape(__lowerCAmelCase				, __lowerCAmelCase				, 4				)
    a		,	a							=		np.split(__lowerCAmelCase				, [3]				, axis=2				)

    try:
     import numexpr as ne  # fuse them with numexpr

     a							=		ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"				)
    except ImportError:
     a							=		alpha.astype("float32"				) / 2_5_5.0
     a							=		img * (1 - alpha) + rgb * alpha

    return visualized_image.astype("uint8"				)




   def 		A				(   self      :							str				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    assert brightness_factor >= -1.0 and brightness_factor <= 1.0
    a							=		mplc.to_rgb(__lowerCAmelCase				)
    a							=		colorsys.rgb_to_hls(*mplc.to_rgb(__lowerCAmelCase				)				)
    a							=		polygon_color[1] + (brightness_factor * polygon_color[1])
    a							=		0.0 if modified_lightness < 0.0 else modified_lightness
    a							=		1.0 if modified_lightness > 1.0 else modified_lightness
    a							=		colorsys.hls_to_rgb(polygon_color[0]				, __lowerCAmelCase				, polygon_color[2]				)
    return modified_color

A_		:  int										=  (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)



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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase__						:	Any	     = logging.get_logger(__name__)

UpperCAmelCase__						:	Optional[Any]	     = {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Optional[Any]												=       '''blenderbot'''
   __UpperCamelCase						:       Optional[int]												=       ['''past_key_values''']
   __UpperCamelCase						:       Union[str, Any]												=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


   def __init__(   self	:				Any     ,     lowerCAmelCase_	:				List[str]=8_0_0_8     ,     lowerCAmelCase_	:				Any=1_2_8     ,     lowerCAmelCase_	:				List[str]=2     ,     lowerCAmelCase_	:				Dict=1_0_2_4_0     ,     lowerCAmelCase_	:				Dict=3_2     ,     lowerCAmelCase_	:				Union[str, Any]=2_4     ,     lowerCAmelCase_	:				Union[str, Any]=1_0_2_4_0     ,     lowerCAmelCase_	:				Union[str, Any]=3_2     ,     lowerCAmelCase_	:				Optional[Any]=0.0     ,     lowerCAmelCase_	:				Optional[int]=0.0     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				Optional[Any]=True     ,     lowerCAmelCase_	:				Union[str, Any]="gelu"     ,     lowerCAmelCase_	:				Optional[int]=2_5_6_0     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				Any=0.0     ,     lowerCAmelCase_	:				Union[str, Any]=0.0     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				List[Any]=1     ,     lowerCAmelCase_	:				List[str]=False     ,     lowerCAmelCase_	:				str=0     ,     lowerCAmelCase_	:				str=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				List[str]=3     ,     lowerCAmelCase_	:				int=2     ,     **lowerCAmelCase_	:				Tuple     ,     ):

      """simple docstring"""
      _A:    Any						     =			vocab_size
      _A:    Optional[int]						     =			max_position_embeddings
      _A:    int						     =			d_model
      _A:    Optional[int]						     =			encoder_ffn_dim
      _A:    Optional[int]						     =			encoder_layers
      _A:    int						     =			encoder_attention_heads
      _A:    Optional[Any]						     =			decoder_ffn_dim
      _A:    Dict						     =			decoder_layers
      _A:    int						     =			decoder_attention_heads
      _A:    List[str]						     =			dropout
      _A:    Union[str, Any]						     =			attention_dropout
      _A:    Tuple						     =			activation_dropout
      _A:    Dict						     =			activation_function
      _A:    Optional[Any]						     =			init_std
      _A:    Optional[int]						     =			encoder_layerdrop
      _A:    Any						     =			decoder_layerdrop
      _A:    List[str]						     =			use_cache
      _A:    Dict						     =			encoder_layers
      _A:    int						     =			scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     eos_token_id=lowerCAmelCase_     ,     is_encoder_decoder=lowerCAmelCase_     ,     decoder_start_token_id=lowerCAmelCase_     ,     encoder_no_repeat_ngram_size=lowerCAmelCase_     ,     forced_eos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]	)
         if self.use_past:
            _A:    int						     =			{0: '''batch'''}
            _A:    Optional[int]						     =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
            _A:    List[Any]						     =			{0: '''batch''', 1: '''decoder_sequence'''}
            _A:    Tuple						     =			{0: '''batch''', 1: '''decoder_sequence'''}
         if self.use_past:
            self.fill_with_past_key_values_(lowerCAmelCase_     ,     direction='''inputs'''	)
      elif self.task == "causal-lm":
         _A:    str						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]	)
         if self.use_past:
            _A     ,    _A:    Tuple						     =			self.num_layers
            for i in range(lowerCAmelCase_	):
               _A:    str						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
               _A:    List[str]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
         _A:    List[Any]						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                 ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
             ]	)

      return common_inputs


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    List[str]						     =			super().outputs
      else:
         _A:    Tuple						     =			super(lowerCAmelCase_     ,     self	).outputs
         if self.use_past:
            _A     ,    _A:    Optional[int]						     =			self.num_layers
            for i in range(lowerCAmelCase_	):
               _A:    Union[str, Any]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
               _A:    Optional[int]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      # Generate decoder inputs
      _A:    int						     =			seq_length if not self.use_past else 1
      _A:    Any						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Optional[int]						     =			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
      _A:    Union[str, Any]						     =			dict(**lowerCAmelCase_     ,     **lowerCAmelCase_	)

      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:    str						     =			common_inputs['''input_ids'''].shape
         _A:    Optional[Any]						     =			common_inputs['''decoder_input_ids'''].shape[1]
         _A     ,    _A:    Optional[Any]						     =			self.num_attention_heads
         _A:    List[str]						     =			(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _A:    Union[str, Any]						     =			decoder_seq_length
         _A:    Tuple						     =			(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         _A:    Union[str, Any]						     =			torch.cat(
             [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase_     ,     lowerCAmelCase_	)]     ,     dim=1	)
         _A:    List[Any]						     =			[]
         _A     ,    _A:    Union[str, Any]						     =			self.num_layers

         for _ in range(lowerCAmelCase_	):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                )	)
      return common_inputs


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Tuple						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)

      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:    Optional[int]						     =			common_inputs['''input_ids'''].shape
         _A:    Union[str, Any]						     =			seqlen
         _A     ,    _A:    Tuple						     =			self.num_layers
         _A     ,    _A:    Dict						     =			self.num_attention_heads
         _A:    Tuple						     =			(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _A:    List[Any]						     =			common_inputs['''attention_mask'''].dtype
         _A:    Optional[int]						     =			torch.cat(
             [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase_     ,     lowerCAmelCase_     ,     dtype=lowerCAmelCase_	)]     ,     dim=1	)
         _A:    Tuple						     =			[
             (torch.zeros(lowerCAmelCase_	), torch.zeros(lowerCAmelCase_	)) for _ in range(lowerCAmelCase_	)
         ]
      return common_inputs


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      _A:    Optional[int]						     =			compute_effective_axis_dimension(
          lowerCAmelCase_     ,     fixed_dimension=OnnxConfig.default_fixed_batch     ,     num_token_to_add=0	)

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      _A:    int						     =			tokenizer.num_special_tokens_to_add(lowerCAmelCase_	)
      _A:    Any						     =			compute_effective_axis_dimension(
          lowerCAmelCase_     ,     fixed_dimension=OnnxConfig.default_fixed_sequence     ,     num_token_to_add=lowerCAmelCase_	)

      # Generate dummy inputs according to compute batch and sequence
      _A:    Tuple						     =			[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
      _A:    Any						     =			dict(tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_	)	)
      return common_inputs


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)

      elif self.task == "causal-lm":
         _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_causal_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)
      else:
         _A:    Tuple						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)

      return common_inputs


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				List[str]	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    int						     =			super()._flatten_past_key_values_(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      else:
         _A:    Optional[int]						     =			super(lowerCAmelCase_     ,     self	)._flatten_past_key_values_(
             lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				Mapping[str, Mapping[int, str]]     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      if direction not in ["inputs", "outputs"]:
         raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""	)

      _A:    Tuple						     =			'''past_key_values''' if direction == '''inputs''' else '''present'''
      _A     ,    _A:    int						     =			self.num_layers

      _A:    int						     =			'''past_encoder_sequence'''
      _A:    int						     =			'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

      for i in range(lowerCAmelCase_	):
         _A:    Optional[int]						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    Dict						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    int						     =			{0: '''batch''', 2: encoder_sequence}
         _A:    Optional[int]						     =			{0: '''batch''', 2: encoder_sequence}



