





"""simple docstring"""




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


lowerCamelCase_					:						str      = 10_00

class 			_UpperCAmelCase  :



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_=1.2 ,				snake_case_="g" ,				snake_case_=0.5 ,				snake_case_="-" ,				snake_case_="test_out.jpg" ,				snake_case_=True ,				snake_case_=False ,				snake_case_=None ,				snake_case_=None ,				snake_case_=0.7 ,				):

        """simple docstring"""

        if isinstance(snake_case_ ,				torch.Tensor  ):
          A_  :	Union[str, Any]    						=  img.numpy().astype('np.uint8'  )
        if isinstance(snake_case_ ,				snake_case_  ):
          A_  :	str    						=  img_tensorize(snake_case_  )
        assert isinstance(snake_case_ ,				np.ndarray  )

        A_  :	Optional[Any]    						=  img.shape[1], img.shape[0]
        A_  :	str    						=  mplfigure.Figure(frameon=snake_case_  )
        A_  :	int    						=  fig.get_dpi()
        A_  :	int    						=  (width * scale + 1E-2) / dpi
        A_  :	List[str]    						=  (height * scale + 1E-2) / dpi
        fig.set_size_inches(snake_case_ ,				snake_case_  )
        A_  :	Any    						=  fig.add_axes([0.0, 0.0, 1.0, 1.0]  )
        ax.axis('off'  )
        ax.set_xlim(0.0 ,				snake_case_  )
        ax.set_ylim(snake_case_  )

        A_  :	Union[str, Any]    						=  saveas
        A_  :	List[Any]    						=  rgb
        A_  :	Union[str, Any]    						=  pynb
        A_  :	Optional[int]    						=  img
        A_  :	Any    						=  edgecolor
        A_  :	Any    						=  0.5
        A_  :	Optional[int]    						=  linestyle
        A_  :	Union[str, Any]    						=  int(np.sqrt(min(snake_case_ ,				snake_case_  )  ) * scale // 3  )
        A_  :	Union[str, Any]    						=  width
        A_  :	List[str]    						=  height
        A_  :	Dict    						=  scale
        A_  :	Dict    						=  fig
        A_  :	Tuple    						=  ax
        A_  :	Optional[Any]    						=  pad
        A_  :	Any    						=  idaobj
        A_  :	int    						=  idaattr
        A_  :	Any    						=  FigureCanvasAgg(snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_=None  ):

        """simple docstring"""

        if color is None:
          A_  :	Union[str, Any]    						=  self.edgecolor
        (A_)  :	int    						=  box
        A_  :	Any    						=  xa - xa
        A_  :	Any    						=  ya - ya
        self.ax.add_patch(
            mpl.patches.Rectangle(
                (xa, ya) ,				snake_case_ ,				snake_case_ ,				fill=snake_case_ ,				edgecolor=snake_case_ ,				linewidth=self.font_size // 3 ,				alpha=self.alpha ,				linestyle=self.linestyle ,				)  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None  ):

        """simple docstring"""

        if len(boxes.shape  ) > 2:
          A_  :	Dict    						=  boxes[0]
        if len(obj_ids.shape  ) > 1:
          A_  :	Optional[Any]    						=  obj_ids[0]
        if len(obj_scores.shape  ) > 1:
          A_  :	int    						=  obj_scores[0]
        if len(attr_ids.shape  ) > 1:
          A_  :	Any    						=  attr_ids[0]
        if len(attr_scores.shape  ) > 1:
          A_  :	Union[str, Any]    						=  attr_scores[0]
        if isinstance(snake_case_ ,				torch.Tensor  ):
          A_  :	Optional[int]    						=  boxes.numpy()
        if isinstance(snake_case_ ,				snake_case_  ):
          A_  :	List[str]    						=  np.array(snake_case_  )
        assert isinstance(snake_case_ ,				np.ndarray  )
        A_  :	int    						=  np.prod(boxes[:, 2:] - boxes[:, :2] ,				axis=1  )
        A_  :	Union[str, Any]    						=  np.argsort(-areas  ).tolist()
        A_  :	str    						=  boxes[sorted_idxs] if boxes is not None else None
        A_  :	Optional[int]    						=  obj_ids[sorted_idxs] if obj_ids is not None else None
        A_  :	Optional[int]    						=  obj_scores[sorted_idxs] if obj_scores is not None else None
        A_  :	Optional[int]    						=  attr_ids[sorted_idxs] if attr_ids is not None else None
        A_  :	List[Any]    						=  attr_scores[sorted_idxs] if attr_scores is not None else None

        A_  :	Optional[int]    						=  [self._random_color(maximum=1  ) for _ in range(len(snake_case_  )  )]
        A_  :	Optional[int]    						=  [assigned_colors[idx] for idx in sorted_idxs]
        if obj_ids is not None:
          A_  :	Union[str, Any]    						=  self._create_text_labels_attr(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  )
          for i in range(len(snake_case_  )  ):
            A_  :	int    						=  assigned_colors[i]
            self.add_box(boxes[i] ,				snake_case_  )
            self.draw_labels(labels[i] ,				boxes[i] ,				snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Dict    						=  box
        A_  :	Dict    						=  (xa, ya)
        A_  :	List[str]    						=  (ya - ya) * (xa - xa)
        A_  :	List[Any]    						=  _SMALL_OBJ * self.scale
        if instance_area < small or ya - ya < 4_0 * self.scale:
          if ya >= self.height - 5:
            A_  :	List[str]    						=  (xa, ya)
          else:
            A_  :	Tuple    						=  (xa, ya)

        A_  :	List[Any]    						=  (ya - ya) / np.sqrt(self.height * self.width  )
        A_  :	Optional[int]    						=  self._change_color_brightness(snake_case_ ,				brightness_factor=0.7  )
        A_  :	Optional[Any]    						=  np.clip((height_ratio - 0.02) / 0.08 + 1 ,				1.2 ,				2  )
        font_size *= 0.75 * self.font_size

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

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_="g" ,				snake_case_="left" ,				):

        """simple docstring"""

        A_  :	Any    						=  0
        A_  :	Optional[Any]    						=  self.font_size
        A_  :	Any    						=  np.maximum(list(mplc.to_rgb(snake_case_  )  ) ,				0.2  )
        A_  :	Tuple    						=  max(0.8 ,				np.max(snake_case_  )  )
        A_  :	Union[str, Any]    						=  {
            'facecolor': 'black',
            'alpha': self.alpha,
            'pad': self.pad,
            'edgecolor': 'none',
        }
        A_  :	Tuple    						=  position
        self.ax.text(
            snake_case_ ,				snake_case_ ,				snake_case_ ,				size=font_size * self.scale ,				family='sans-serif' ,				bbox=snake_case_ ,				verticalalignment='top' ,				horizontalalignment=snake_case_ ,				color=snake_case_ ,				zorder=1_0 ,				rotation=snake_case_ ,				)

      def  lowerCamelCase_      (					self ,				snake_case_=None  ):

        """simple docstring"""

        if saveas is None:
          A_  :	Tuple    						=  self.saveas
        if saveas.lower().endswith('.jpg'  ) or saveas.lower().endswith('.png'  ):
          cva.imwrite(
              snake_case_ ,				self._get_buffer()[:, :, ::-1] ,				)
        else:
          self.fig.savefig(snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Tuple    						=  [self.idaobj[i] for i in classes]
        A_  :	Union[str, Any]    						=  [self.idaattr[i] for i in attr_classes]
        A_  :	Optional[Any]    						=  [
            F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
            for label, score, attr, attr_score in zip(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  )
        ]
        return labels

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  [self.idaobj[i] for i in classes]
        if scores is not None:
          if labels is None:
            A_  :	Dict    						=  ['{:.0f}%'.format(s * 1_0_0  ) for s in scores]
          else:
            A_  :	int    						=  ['{} {:.0f}%'.format(snake_case_ ,				s * 1_0_0  ) for li, s in zip(snake_case_ ,				snake_case_  )]
        return labels

      def  lowerCamelCase_      (					self ,				snake_case_=2_5_5  ):

        """simple docstring"""

        A_  :	Tuple    						=  np.random.randint(0 ,				len(_COLORS  )  )
        A_  :	Optional[int]    						=  _COLORS[idx] * maximum
        if not self.rgb:
          A_  :	List[str]    						=  ret[::-1]
        return ret

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        if not self.pynb:
          A_  :	Optional[int]    						=  self.canvas.print_to_buffer()
          if (width, height) != (self.width, self.height):
            A_  :	int    						=  cva.resize(self.img ,				(width, height)  )
          else:
            A_  :	str    						=  self.img
        else:
          A_  :	Tuple    						=  io.BytesIO()  # works for cairo backend
          self.canvas.print_rgba(snake_case_  )
          A_  :	List[Any]    						=  self.width, self.height
          A_  :	int    						=  buf.getvalue()
          A_  :	List[Any]    						=  self.img

        A_  :	Tuple    						=  np.frombuffer(snake_case_ ,				dtype='uint8'  )
        A_  :	Tuple    						=  buffer.reshape(snake_case_ ,				snake_case_ ,				4  )
        A_  :	Union[str, Any]    						=  np.split(snake_case_ ,				[3] ,				axis=2  )

        try:
          import numexpr as ne  # fuse them with numexpr

          A_  :	Optional[int]    						=  ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'  )
        except ImportError:
          A_  :	str    						=  alpha.astype('float32'  ) / 2_5_5.0
          A_  :	Dict    						=  img * (1 - alpha) + rgb * alpha

        return visualized_image.astype('uint8'  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
        A_  :	Union[str, Any]    						=  mplc.to_rgb(snake_case_  )
        A_  :	Tuple    						=  colorsys.rgb_to_hls(*mplc.to_rgb(snake_case_  )  )
        A_  :	int    						=  polygon_color[1] + (brightness_factor * polygon_color[1])
        A_  :	Optional[int]    						=  0.0 if modified_lightness < 0.0 else modified_lightness
        A_  :	Dict    						=  1.0 if modified_lightness > 1.0 else modified_lightness
        A_  :	List[str]    						=  colorsys.hls_to_rgb(polygon_color[0] ,				snake_case_ ,				polygon_color[2]  )
        return modified_color


lowerCamelCase_					:						Any      = (
    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)
)







"""simple docstring"""




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

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


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

lowerCamelCase_					:						Optional[Any]      = {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}

class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Optional[Any]        =       """gpt2"""
      lowercase_				:			Optional[int]        =       ["""past_key_values"""]
      lowercase_				:			Dict        =       {
          """hidden_size""": """n_embd""",
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }

      def __init__(					self ,				snake_case_=5_0_2_5_7 ,				snake_case_=1_0_2_4 ,				snake_case_=7_6_8 ,				snake_case_=1_2 ,				snake_case_=1_2 ,				snake_case_=None ,				snake_case_="gelu_new" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=1E-5 ,				snake_case_=0.02 ,				snake_case_="cls_index" ,				snake_case_=True ,				snake_case_=None ,				snake_case_=True ,				snake_case_=0.1 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=5_0_2_5_6 ,				snake_case_=5_0_2_5_6 ,				snake_case_=False ,				snake_case_=False ,				**snake_case_ ,				):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  vocab_size
        A_  :	List[Any]    						=  n_positions
        A_  :	List[Any]    						=  n_embd
        A_  :	Tuple    						=  n_layer
        A_  :	List[Any]    						=  n_head
        A_  :	List[str]    						=  n_inner
        A_  :	str    						=  activation_function
        A_  :	Optional[Any]    						=  resid_pdrop
        A_  :	int    						=  embd_pdrop
        A_  :	Tuple    						=  attn_pdrop
        A_  :	str    						=  layer_norm_epsilon
        A_  :	Any    						=  initializer_range
        A_  :	Union[str, Any]    						=  summary_type
        A_  :	Optional[Any]    						=  summary_use_proj
        A_  :	Dict    						=  summary_activation
        A_  :	Optional[int]    						=  summary_first_dropout
        A_  :	Optional[int]    						=  summary_proj_to_labels
        A_  :	Any    						=  scale_attn_weights
        A_  :	Optional[Any]    						=  use_cache
        A_  :	Dict    						=  scale_attn_by_inverse_layer_idx
        A_  :	Any    						=  reorder_and_upcast_attn

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

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


class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_ = "default" ,				snake_case_ = None ,				snake_case_ = False ,				):

        """simple docstring"""

        super().__init__(snake_case_ ,				task=snake_case_ ,				patching_specs=snake_case_ ,				use_past=snake_case_  )
        if not getattr(self._config ,				'pad_token_id' ,				snake_case_  ):
          # TODO: how to do that better?
          A_  :	Tuple    						=  0

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
        if self.use_past:
          self.fill_with_past_key_values_(snake_case_ ,				direction='inputs'  )
          A_  :	List[str]    						=  {0: 'batch', 1: 'past_sequence + sequence'}
        else:
          A_  :	Tuple    						=  {0: 'batch', 1: 'sequence'}

        return common_inputs

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self._config.n_layer

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self._config.n_head

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				):

        """simple docstring"""

        A_  :	int    						=  super(snake_case_ ,				self  ).generate_dummy_inputs(
            snake_case_ ,				batch_size=snake_case_ ,				seq_length=snake_case_ ,				is_pair=snake_case_ ,				framework=snake_case_  )

        # We need to order the input in the way they appears in the forward()
        A_  :	Optional[Any]    						=  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_  :	List[str]    						=  common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            A_  :	Optional[Any]    						=  seqlen + 2
            A_  :	Any    						=  (
                batch,
                self.num_attention_heads,
                past_key_values_length,
                self._config.hidden_size // self.num_attention_heads,
            )
            A_  :	Any    						=  [
                (torch.zeros(snake_case_  ), torch.zeros(snake_case_  )) for _ in range(self.num_layers  )
            ]

        A_  :	List[Any]    						=  common_inputs['attention_mask']
        if self.use_past:
          A_  :	List[Any]    						=  ordered_inputs['attention_mask'].dtype
          A_  :	Any    						=  torch.cat(
              [ordered_inputs['attention_mask'], torch.ones(snake_case_ ,				snake_case_ ,				dtype=snake_case_  )] ,				dim=1  )

        return ordered_inputs

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return 1_3