code: 
  
  
  '''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
  
  
  A__	:      List[Any]				=						1000
  
  
  
  
  
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=1.2    ,  SCREAMING_SNAKE_CASE_="g"    ,  SCREAMING_SNAKE_CASE_=0.5    ,  SCREAMING_SNAKE_CASE_="-"    ,  SCREAMING_SNAKE_CASE_="test_out.jpg"    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=False    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=0.7    ,  )       ->  int:
           if isinstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			):
                __lowerCamelCase     :			int              =	img.numpy().astype('np.uint8'			)
           if isinstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			):
                __lowerCamelCase     :			List[Any]              =	img_tensorize(SCREAMING_SNAKE_CASE_			)
           assert isinstance(SCREAMING_SNAKE_CASE_    ,  np.ndarray			)
  
           __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	img.shape[1], img.shape[0]
           __lowerCamelCase     :			str              =	mplfigure.Figure(frameon=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[str]              =	fig.get_dpi()
           __lowerCamelCase     :			List[Any]              =	(width * scale + 1E-2) / dpi
           __lowerCamelCase     :			str              =	(height * scale + 1E-2) / dpi
           fig.set_size_inches(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Any              =	fig.add_axes([0.0, 0.0, 1.0, 1.0]			)
           ax.axis('off'			)
           ax.set_xlim(0.0    ,  SCREAMING_SNAKE_CASE_			)
           ax.set_ylim(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			str              =	saveas
           __lowerCamelCase     :			str              =	rgb
           __lowerCamelCase     :			Tuple              =	pynb
           __lowerCamelCase     :			Tuple              =	img
           __lowerCamelCase     :			Optional[int]              =	edgecolor
           __lowerCamelCase     :			str              =	0.5
           __lowerCamelCase     :			int              =	linestyle
           __lowerCamelCase     :			Tuple              =	int(np.sqrt(min(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			) * scale // 3			)
           __lowerCamelCase     :			Union[str, Any]              =	width
           __lowerCamelCase     :			List[Any]              =	height
           __lowerCamelCase     :			Optional[Any]              =	scale
           __lowerCamelCase     :			Optional[int]              =	fig
           __lowerCamelCase     :			Optional[int]              =	ax
           __lowerCamelCase     :			Any              =	pad
           __lowerCamelCase     :			Union[str, Any]              =	idaobj
           __lowerCamelCase     :			Optional[Any]              =	idaattr
           __lowerCamelCase     :			Optional[Any]              =	FigureCanvasAgg(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=None			)       ->  List[str]:
           if color is None:
                __lowerCamelCase     :			Union[str, Any]              =	self.edgecolor
           ((__lowerCamelCase)       , (__lowerCamelCase)       , (__lowerCamelCase)       , (__lowerCamelCase))     :			Dict              =	box
           __lowerCamelCase     :			Union[str, Any]              =	xa - xa
           __lowerCamelCase     :			int              =	ya - ya
           self.ax.add_patch(
               mpl.patches.Rectangle(
                   (xa, ya)    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  fill=SCREAMING_SNAKE_CASE_    ,  edgecolor=SCREAMING_SNAKE_CASE_    ,  linewidth=self.font_size // 3    ,  alpha=self.alpha    ,  linestyle=self.linestyle    ,  )			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=None			)       ->  Union[str, Any]:
           if len(boxes.shape			) > 2:
                __lowerCamelCase     :			Tuple              =	boxes[0]
           if len(obj_ids.shape			) > 1:
                __lowerCamelCase     :			int              =	obj_ids[0]
           if len(obj_scores.shape			) > 1:
                __lowerCamelCase     :			List[str]              =	obj_scores[0]
           if len(attr_ids.shape			) > 1:
                __lowerCamelCase     :			Union[str, Any]              =	attr_ids[0]
           if len(attr_scores.shape			) > 1:
                __lowerCamelCase     :			Tuple              =	attr_scores[0]
           if isinstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			):
                __lowerCamelCase     :			Tuple              =	boxes.numpy()
           if isinstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			):
                __lowerCamelCase     :			Union[str, Any]              =	np.array(SCREAMING_SNAKE_CASE_			)
           assert isinstance(SCREAMING_SNAKE_CASE_    ,  np.ndarray			)
           __lowerCamelCase     :			int              =	np.prod(boxes[:, 2:] - boxes[:, :2]    ,  axis=1			)
           __lowerCamelCase     :			List[str]              =	np.argsort(-areas			).tolist()
           __lowerCamelCase     :			Dict              =	boxes[sorted_idxs] if boxes is not None else None
           __lowerCamelCase     :			str              =	obj_ids[sorted_idxs] if obj_ids is not None else None
           __lowerCamelCase     :			Tuple              =	obj_scores[sorted_idxs] if obj_scores is not None else None
           __lowerCamelCase     :			Union[str, Any]              =	attr_ids[sorted_idxs] if attr_ids is not None else None
           __lowerCamelCase     :			Optional[int]              =	attr_scores[sorted_idxs] if attr_scores is not None else None
  
           __lowerCamelCase     :			List[str]              =	[self._random_color(maximum=1			) for _ in range(len(SCREAMING_SNAKE_CASE_			)			)]
           __lowerCamelCase     :			List[str]              =	[assigned_colors[idx] for idx in sorted_idxs]
           if obj_ids is not None:
                __lowerCamelCase     :			Optional[int]              =	self._create_text_labels_attr(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
                for i in range(len(SCREAMING_SNAKE_CASE_			)			):
                     __lowerCamelCase     :			List[Any]              =	assigned_colors[i]
                     self.add_box(boxes[i]    ,  SCREAMING_SNAKE_CASE_			)
                     self.draw_labels(labels[i]    ,  boxes[i]    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
           __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			List[str]              =	box
           __lowerCamelCase     :			str              =	(xa, ya)
           __lowerCamelCase     :			Optional[int]              =	(ya - ya) * (xa - xa)
           __lowerCamelCase     :			int              =	_SMALL_OBJ * self.scale
           if instance_area < small or ya - ya < 40 * self.scale:
                if ya >= self.height - 5:
                     __lowerCamelCase     :			Optional[int]              =	(xa, ya)
                else:
                     __lowerCamelCase     :			Tuple              =	(xa, ya)
  
           __lowerCamelCase     :			Optional[Any]              =	(ya - ya) / np.sqrt(self.height * self.width			)
           __lowerCamelCase     :			List[str]              =	self._change_color_brightness(SCREAMING_SNAKE_CASE_    ,  brightness_factor=0.7			)
           __lowerCamelCase     :			str              =	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=SCREAMING_SNAKE_CASE_    ,  position=SCREAMING_SNAKE_CASE_    ,  color=SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_="g"    ,  SCREAMING_SNAKE_CASE_="left"    ,  )       ->  Dict:
           __lowerCamelCase     :			str              =	0
           __lowerCamelCase     :			List[Any]              =	self.font_size
           __lowerCamelCase     :			Union[str, Any]              =	np.maximum(list(mplc.to_rgb(SCREAMING_SNAKE_CASE_			)			)    ,  0.2			)
           __lowerCamelCase     :			Tuple              =	max(0.8    ,  np.max(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			List[Any]              =	{
               'facecolor': 'black',
               'alpha': self.alpha,
               'pad': self.pad,
               'edgecolor': 'none',
           }
           __lowerCamelCase       , __lowerCamelCase     :			Any              =	position
           self.ax.text(
               SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  size=font_size * self.scale    ,  family='sans-serif'    ,  bbox=SCREAMING_SNAKE_CASE_    ,  verticalalignment='top'    ,  horizontalalignment=SCREAMING_SNAKE_CASE_    ,  color=SCREAMING_SNAKE_CASE_    ,  zorder=10    ,  rotation=SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_=None			)       ->  Dict:
           if saveas is None:
                __lowerCamelCase     :			Dict              =	self.saveas
           if saveas.lower().endswith('.jpg'			) or saveas.lower().endswith('.png'			):
                cva.imwrite(
                    SCREAMING_SNAKE_CASE_    ,  self._get_buffer()[:, :, ::-1]    ,  )
           else:
                self.fig.savefig(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  str:
           __lowerCamelCase     :			List[Any]              =	[self.idaobj[i] for i in classes]
           __lowerCamelCase     :			Any              =	[self.idaattr[i] for i in attr_classes]
           __lowerCamelCase     :			List[Any]              =	[
               f'{label} {score:.2f} {attr} {attr_score:.2f}'
               for label, score, attr, attr_score in zip(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
           ]
           return labels
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           __lowerCamelCase     :			str              =	[self.idaobj[i] for i in classes]
           if scores is not None:
                if labels is None:
                     __lowerCamelCase     :			List[Any]              =	['{:.0f}%'.format(s * 1_00			) for s in scores]
                else:
                     __lowerCamelCase     :			List[str]              =	['{} {:.0f}%'.format(SCREAMING_SNAKE_CASE_    ,  s * 1_00			) for li, s in zip(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)]
           return labels
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_=2_55			)       ->  Optional[int]:
           __lowerCamelCase     :			List[str]              =	np.random.randint(0    ,  len(_COLORS			)			)
           __lowerCamelCase     :			Union[str, Any]              =	_COLORS[idx] * maximum
           if not self.rgb:
                __lowerCamelCase     :			Optional[int]              =	ret[::-1]
           return ret
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           if not self.pynb:
                __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	self.canvas.print_to_buffer()
                if (width, height) != (self.width, self.height):
                     __lowerCamelCase     :			Dict              =	cva.resize(self.img    ,  (width, height)			)
                else:
                     __lowerCamelCase     :			int              =	self.img
           else:
                __lowerCamelCase     :			str              =	io.BytesIO()  # works for cairo backend
                self.canvas.print_rgba(SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase       , __lowerCamelCase     :			str              =	self.width, self.height
                __lowerCamelCase     :			int              =	buf.getvalue()
                __lowerCamelCase     :			Any              =	self.img
  
           __lowerCamelCase     :			Any              =	np.frombuffer(SCREAMING_SNAKE_CASE_    ,  dtype='uint8'			)
           __lowerCamelCase     :			int              =	buffer.reshape(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  4			)
           __lowerCamelCase       , __lowerCamelCase     :			Union[str, Any]              =	np.split(SCREAMING_SNAKE_CASE_    ,  [3]    ,  axis=2			)
  
           try:
                import numexpr as ne  # fuse them with numexpr
  
                __lowerCamelCase     :			Optional[Any]              =	ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'			)
           except ImportError:
                __lowerCamelCase     :			Optional[int]              =	alpha.astype('float32'			) / 2_5_5.0
                __lowerCamelCase     :			List[Any]              =	img * (1 - alpha) + rgb * alpha
  
           return visualized_image.astype('uint8'			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           assert brightness_factor >= -1.0 and brightness_factor <= 1.0
           __lowerCamelCase     :			str              =	mplc.to_rgb(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	colorsys.rgb_to_hls(*mplc.to_rgb(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			str              =	polygon_color[1] + (brightness_factor * polygon_color[1])
           __lowerCamelCase     :			int              =	0.0 if modified_lightness < 0.0 else modified_lightness
           __lowerCamelCase     :			int              =	1.0 if modified_lightness > 1.0 else modified_lightness
           __lowerCamelCase     :			str              =	colorsys.hls_to_rgb(polygon_color[0]    ,  SCREAMING_SNAKE_CASE_    ,  polygon_color[2]			)
           return modified_color
  
  
  
  
  
  
  
  A__	:      List[Any]				=						(
      np.array(
          [
              0.0_0_0,
              0.4_4_7,
              0.7_4_1,
              0.8_5_0,
              0.3_2_5,
              0.0_9_8,
              0.9_2_9,
              0.6_9_4,
              0.1_2_5,
              0.4_9_4,
              0.1_8_4,
              0.5_5_6,
              0.4_6_6,
              0.6_7_4,
              0.1_8_8,
              0.3_0_1,
              0.7_4_5,
              0.9_3_3,
              0.6_3_5,
              0.0_7_8,
              0.1_8_4,
              0.3_0_0,
              0.3_0_0,
              0.3_0_0,
              0.6_0_0,
              0.6_0_0,
              0.6_0_0,
              1.0_0_0,
              0.0_0_0,
              0.0_0_0,
              1.0_0_0,
              0.5_0_0,
              0.0_0_0,
              0.7_4_9,
              0.7_4_9,
              0.0_0_0,
              0.0_0_0,
              1.0_0_0,
              0.0_0_0,
              0.0_0_0,
              0.0_0_0,
              1.0_0_0,
              0.6_6_7,
              0.0_0_0,
              1.0_0_0,
              0.3_3_3,
              0.3_3_3,
              0.0_0_0,
              0.3_3_3,
              0.6_6_7,
              0.0_0_0,
              0.3_3_3,
              1.0_0_0,
              0.0_0_0,
              0.6_6_7,
              0.3_3_3,
              0.0_0_0,
              0.6_6_7,
              0.6_6_7,
              0.0_0_0,
              0.6_6_7,
              1.0_0_0,
              0.0_0_0,
              1.0_0_0,
              0.3_3_3,
              0.0_0_0,
              1.0_0_0,
              0.6_6_7,
              0.0_0_0,
              1.0_0_0,
              1.0_0_0,
              0.0_0_0,
              0.0_0_0,
              0.3_3_3,
              0.5_0_0,
              0.0_0_0,
              0.6_6_7,
              0.5_0_0,
              0.0_0_0,
              1.0_0_0,
              0.5_0_0,
              0.3_3_3,
              0.0_0_0,
              0.5_0_0,
              0.3_3_3,
              0.3_3_3,
              0.5_0_0,
              0.3_3_3,
              0.6_6_7,
              0.5_0_0,
              0.3_3_3,
              1.0_0_0,
              0.5_0_0,
              0.6_6_7,
              0.0_0_0,
              0.5_0_0,
              0.6_6_7,
              0.3_3_3,
              0.5_0_0,
              0.6_6_7,
              0.6_6_7,
              0.5_0_0,
              0.6_6_7,
              1.0_0_0,
              0.5_0_0,
              1.0_0_0,
              0.0_0_0,
              0.5_0_0,
              1.0_0_0,
              0.3_3_3,
              0.5_0_0,
              1.0_0_0,
              0.6_6_7,
              0.5_0_0,
              1.0_0_0,
              1.0_0_0,
              0.5_0_0,
              0.0_0_0,
              0.3_3_3,
              1.0_0_0,
              0.0_0_0,
              0.6_6_7,
              1.0_0_0,
              0.0_0_0,
              1.0_0_0,
              1.0_0_0,
              0.3_3_3,
              0.0_0_0,
              1.0_0_0,
              0.3_3_3,
              0.3_3_3,
              1.0_0_0,
              0.3_3_3,
              0.6_6_7,
              1.0_0_0,
              0.3_3_3,
              1.0_0_0,
              1.0_0_0,
              0.6_6_7,
              0.0_0_0,
              1.0_0_0,
              0.6_6_7,
              0.3_3_3,
              1.0_0_0,
              0.6_6_7,
              0.6_6_7,
              1.0_0_0,
              0.6_6_7,
              1.0_0_0,
              1.0_0_0,
              1.0_0_0,
              0.0_0_0,
              1.0_0_0,
              1.0_0_0,
              0.3_3_3,
              1.0_0_0,
              1.0_0_0,
              0.6_6_7,
              1.0_0_0,
              0.3_3_3,
              0.0_0_0,
              0.0_0_0,
              0.5_0_0,
              0.0_0_0,
              0.0_0_0,
              0.6_6_7,
              0.0_0_0,
              0.0_0_0,
              0.8_3_3,
              0.0_0_0,
              0.0_0_0,
              1.0_0_0,
              0.0_0_0,
              0.0_0_0,
              0.0_0_0,
              0.1_6_7,
              0.0_0_0,
              0.0_0_0,
              0.3_3_3,
              0.0_0_0,
              0.0_0_0,
              0.5_0_0,
              0.0_0_0,
              0.0_0_0,
              0.6_6_7,
              0.0_0_0,
              0.0_0_0,
              0.8_3_3,
              0.0_0_0,
              0.0_0_0,
              1.0_0_0,
              0.0_0_0,
              0.0_0_0,
              0.0_0_0,
              0.1_6_7,
              0.0_0_0,
              0.0_0_0,
              0.3_3_3,
              0.0_0_0,
              0.0_0_0,
              0.5_0_0,
              0.0_0_0,
              0.0_0_0,
              0.6_6_7,
              0.0_0_0,
              0.0_0_0,
              0.8_3_3,
              0.0_0_0,
              0.0_0_0,
              1.0_0_0,
              0.0_0_0,
              0.0_0_0,
              0.0_0_0,
              0.1_4_3,
              0.1_4_3,
              0.1_4_3,
              0.8_5_7,
              0.8_5_7,
              0.8_5_7,
              1.0_0_0,
              1.0_0_0,
              1.0_0_0,
          ]
      )
      .astype(np.floataa)
      .reshape(-1, 3)
  )
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  import pathlib
  
  import fairseq
  import torch
  from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  from transformers.utils import logging
  
  
  if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
        raise Exception("""requires fairseq >= 0.9.0""")
  
  
  logging.set_verbosity_info()
  A__	:      List[str]				=						logging.get_logger(__name__)
  
  A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
       __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
       roberta.eval()  # disable dropout
       __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
       __lowerCamelCase     :			Optional[Any]              =	RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,					hidden_size=roberta.args.encoder_embed_dim   ,					num_hidden_layers=roberta.args.encoder_layers   ,					num_attention_heads=roberta.args.encoder_attention_heads   ,					intermediate_size=roberta.args.encoder_ffn_embed_dim   ,					max_position_embeddings=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
       if classification_head:
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'   ,					UpperCAmelCase_							)
  
       __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
       __lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
       __lowerCamelCase     :			Tuple              =	torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
       __lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
       __lowerCamelCase     :			Tuple              =	roberta_sent_encoder.emb_layer_norm.bias
  
       for i in range(config.num_hidden_layers							):
            # Encoder: start of layer
            __lowerCamelCase     :			BertLayer              =	model.roberta.encoder.layer[i]
            __lowerCamelCase     :			TransformerSentenceEncoderLayer              =	roberta_sent_encoder.layers[i]
  
            # self attention
            __lowerCamelCase     :			BertSelfAttention              =	layer.attention.self
            assert (
                roberta_layer.self_attn.k_proj.weight.data.shape
                == roberta_layer.self_attn.q_proj.weight.data.shape
                == roberta_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)							)
            )
  
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias
  
            # self-attention output
            __lowerCamelCase     :			BertSelfOutput              =	layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
            __lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias
  
            # intermediate
            __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias
  
            # output
            __lowerCamelCase     :			BertOutput              =	layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			str              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Tuple              =	roberta_layer.fca.bias
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
            # end of layer
  
       if classification_head:
            __lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
            __lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
            __lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
            __lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
       else:
            # LM Head
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
            __lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
            __lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
            __lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias
  
       # Let's check that we get the same results.
       __lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1
  
       __lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
       if classification_head:
            __lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
       else:
            __lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
       print(our_output.shape   ,					their_output.shape							)
       __lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
       print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
       __lowerCamelCase     :			List[str]              =	torch.allclose(UpperCAmelCase_   ,					UpperCAmelCase_   ,					atol=1e-3							)
       print('Do both models output the same tensors?'   ,					'🔥' if success else '💩'							)
       if not success:
            raise Exception('Something went wRoNg'							)
  
       pathlib.Path(UpperCAmelCase_							).mkdir(parents=UpperCAmelCase_   ,					exist_ok=UpperCAmelCase_							)
       print(F'Saving model to {pytorch_dump_folder_path}'							)
       model.save_pretrained(UpperCAmelCase_							)
  
  
  if __name__ == "__main__":
        A__	:      Optional[Any]				=						argparse.ArgumentParser()
        # Required parameters
        parser.add_argument(
            """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
        )
        parser.add_argument(
            """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
        )
        parser.add_argument(
            """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
        )
        A__	:      Optional[Any]				=						parser.parse_args()
        convert_roberta_checkpoint_to_pytorch(
            args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
        )
  
  
  
  
  
  
  