
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]    		=     1_000






class     a_       :



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase=1.2				,       _lowerCamelCase="g"				,       _lowerCamelCase=0.5				,       _lowerCamelCase="-"				,       _lowerCamelCase="test_out.jpg"				,       _lowerCamelCase=True				,       _lowerCamelCase=False				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       _lowerCamelCase=0.7				,       )					->Tuple:
           if isinstance(_lowerCamelCase				,       torch.Tensor   ):
               SCREAMING_SNAKE_CASE				:				Tuple				      = img.numpy().astype('''np.uint8'''   )
           if isinstance(_lowerCamelCase				,       _lowerCamelCase   ):
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = img_tensorize(_lowerCamelCase   )
           assert isinstance(_lowerCamelCase				,       np.ndarray   )

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

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






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=None   )					->List[str]:
           if color is None:
               SCREAMING_SNAKE_CASE				:				Dict				      = self.edgecolor
           ((SCREAMING_SNAKE_CASE)      ,							(SCREAMING_SNAKE_CASE)      ,							(SCREAMING_SNAKE_CASE)      ,							(SCREAMING_SNAKE_CASE))				:				Tuple				      = box
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = xa - xa
           SCREAMING_SNAKE_CASE				:				List[str]				      = 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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       _lowerCamelCase=None   )					->int:
           if len(boxes.shape   ) > 2:
               SCREAMING_SNAKE_CASE				:				Dict				      = boxes[0]
           if len(obj_ids.shape   ) > 1:
               SCREAMING_SNAKE_CASE				:				str				      = obj_ids[0]
           if len(obj_scores.shape   ) > 1:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = obj_scores[0]
           if len(attr_ids.shape   ) > 1:
               SCREAMING_SNAKE_CASE				:				List[Any]				      = attr_ids[0]
           if len(attr_scores.shape   ) > 1:
               SCREAMING_SNAKE_CASE				:				str				      = attr_scores[0]
           if isinstance(_lowerCamelCase				,       torch.Tensor   ):
               SCREAMING_SNAKE_CASE				:				List[str]				      = boxes.numpy()
           if isinstance(_lowerCamelCase				,       _lowerCamelCase   ):
               SCREAMING_SNAKE_CASE				:				int				      = np.array(_lowerCamelCase   )
           assert isinstance(_lowerCamelCase				,       np.ndarray   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = np.prod(boxes[:, 2:] - boxes[:, :2]				,       axis=1   )
           SCREAMING_SNAKE_CASE				:				Dict				      = np.argsort(-areas   ).tolist()
           SCREAMING_SNAKE_CASE				:				List[str]				      = boxes[sorted_idxs] if boxes is not None else None
           SCREAMING_SNAKE_CASE				:				Tuple				      = obj_ids[sorted_idxs] if obj_ids is not None else None
           SCREAMING_SNAKE_CASE				:				str				      = obj_scores[sorted_idxs] if obj_scores is not None else None
           SCREAMING_SNAKE_CASE				:				str				      = attr_ids[sorted_idxs] if attr_ids is not None else None
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = attr_scores[sorted_idxs] if attr_scores is not None else None

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






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->int:
           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Optional[Any]				      = box
           SCREAMING_SNAKE_CASE				:				Dict				      = (xa, ya)
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = (ya - ya) * (xa - xa)
           SCREAMING_SNAKE_CASE				:				str				      = _SMALL_OBJ * self.scale
           if instance_area < small or ya - ya < 40 * self.scale:
               if ya >= self.height - 5:
                   SCREAMING_SNAKE_CASE				:				List[str]				      = (xa, ya)
               else:
                   SCREAMING_SNAKE_CASE				:				int				      = (xa, ya)

           SCREAMING_SNAKE_CASE				:				Any				      = (ya - ya) / np.sqrt(self.height * self.width   )
           SCREAMING_SNAKE_CASE				:				Any				      = self._change_color_brightness(_lowerCamelCase				,       brightness_factor=0.7   )
           SCREAMING_SNAKE_CASE				:				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=_lowerCamelCase				,       position=_lowerCamelCase				,       color=_lowerCamelCase				,       )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase="g"				,       _lowerCamelCase="left"				,       )					->Tuple:
           SCREAMING_SNAKE_CASE				:				List[str]				      = 0
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.font_size
           SCREAMING_SNAKE_CASE				:				int				      = np.maximum(list(mplc.to_rgb(_lowerCamelCase   )   )				,       0.2   )
           SCREAMING_SNAKE_CASE				:				Any				      = max(0.8				,       np.max(_lowerCamelCase   )   )
           SCREAMING_SNAKE_CASE				:				Any				      = {
               '''facecolor''': '''black''',
               '''alpha''': self.alpha,
               '''pad''': self.pad,
               '''edgecolor''': '''none''',
           }
           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Tuple				      = 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 	__lowerCAmelCase     (		self				,       _lowerCamelCase=None   )					->Dict:
           if saveas is None:
               SCREAMING_SNAKE_CASE				:				Any				      = 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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = [self.idaobj[i] for i in classes]
           SCREAMING_SNAKE_CASE				:				List[Any]				      = [self.idaattr[i] for i in attr_classes]
           SCREAMING_SNAKE_CASE				:				List[str]				      = [
               F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
               for label, score, attr, attr_score in zip(_lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )
           ]
           return labels






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = [self.idaobj[i] for i in classes]
           if scores is not None:
               if labels is None:
                   SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ['''{:.0f}%'''.format(s * 100   ) for s in scores]
               else:
                   SCREAMING_SNAKE_CASE				:				int				      = ['''{} {:.0f}%'''.format(_lowerCamelCase				,       s * 100   ) for li, s in zip(_lowerCamelCase				,       _lowerCamelCase   )]
           return labels






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase=255   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Dict				      = np.random.randint(0				,       len(_COLORS   )   )
           SCREAMING_SNAKE_CASE				:				Any				      = _COLORS[idx] * maximum
           if not self.rgb:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = ret[::-1]
           return ret






       def 	__lowerCAmelCase     (		self   )					->Dict:
           if not self.pynb:
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.canvas.print_to_buffer()
               if (width, height) != (self.width, self.height):
                   SCREAMING_SNAKE_CASE				:				List[Any]				      = cva.resize(self.img				,       (width, height)   )
               else:
                   SCREAMING_SNAKE_CASE				:				int				      = self.img
           else:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = io.BytesIO()  # works for cairo backend
               self.canvas.print_rgba(_lowerCamelCase   )
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				str				      = self.width, self.height
               SCREAMING_SNAKE_CASE				:				List[str]				      = buf.getvalue()
               SCREAMING_SNAKE_CASE				:				List[str]				      = self.img

           SCREAMING_SNAKE_CASE				:				str				      = np.frombuffer(_lowerCamelCase				,       dtype='''uint8'''   )
           SCREAMING_SNAKE_CASE				:				str				      = buffer.reshape(_lowerCamelCase				,       _lowerCamelCase				,       4   )
           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = np.split(_lowerCamelCase				,       [3]				,       axis=2   )

           try:
               import numexpr as ne  # fuse them with numexpr

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

           return visualized_image.astype('''uint8'''   )






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





a__				:      int    		=     (
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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__				:      Dict    		=     logging.get_logger(__name__)

a__				:      str    		=     '''Hello world! cécé herlolip'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = os.path.split(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = DataaVecTextModel.from_pretrained(
        a__ ,						checkpoint_file=a__   )
    dataavec.eval()  # disable dropout
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.models[0]
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.sentence_encoder
    SCREAMING_SNAKE_CASE				:				List[str]				      = DataaVecTextConfig(
        vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings ,						hidden_size=dataavec_model.args.encoder_embed_dim ,						num_hidden_layers=dataavec_model.args.encoder_layers ,						num_attention_heads=dataavec_model.args.encoder_attention_heads ,						intermediate_size=dataavec_model.args.encoder_ffn_embed_dim ,						max_position_embeddings=514 ,						type_vocab_size=1 ,						layer_norm_eps=1e-5 ,						)
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our BERT config:''' ,						a__   )

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = DataaVecTextForSequenceClassification(a__   ) if classification_head else DataaVecTextForMaskedLM(a__   )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec_sent_encoder.embed_tokens.weight
    SCREAMING_SNAKE_CASE				:				int				      = dataavec_sent_encoder.embed_positions.weight
    SCREAMING_SNAKE_CASE				:				int				      = torch.zeros_like(
        model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
    SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_sent_encoder.layernorm_embedding.weight
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers   ):
        # Encoder: start of layer
        SCREAMING_SNAKE_CASE				:				BertLayer				      = model.dataavec_text.encoder.layer[i]
        SCREAMING_SNAKE_CASE				:				TransformerSentenceEncoderLayer				      = dataavec_sent_encoder.layers[i]

        # self attention
        SCREAMING_SNAKE_CASE				:				BertSelfAttention				      = layer.attention.self
        assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )

        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.q_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.q_proj.bias
        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.k_proj.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.self_attn.k_proj.bias
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.v_proj.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        SCREAMING_SNAKE_CASE				:				BertSelfOutput				      = layer.attention.output
        assert (
            self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
        ), F"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.out_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.out_proj.bias
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        SCREAMING_SNAKE_CASE				:				BertIntermediate				      = layer.intermediate
        assert (
            intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_layer.fca.bias

        # output
        SCREAMING_SNAKE_CASE				:				BertOutput				      = layer.output
        assert (
            bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				int				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.fca.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.final_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.bias
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].out_proj.bias
    else:
        # LM Head
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.bias
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_model.encoder.lm_head.layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.lm_head.layer_norm.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_model.encoder.lm_head.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model.encoder.lm_head.bias

    # Let's check that we get the same results.
    SCREAMING_SNAKE_CASE				:				torch.Tensor				      = dataavec.encode(a__   ).unsqueeze(0   )  # batch of size 1

    SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__   )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model(a__   )[0]
    print(our_output.shape ,						their_output.shape   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.max(torch.abs(our_output - their_output   )   ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.allclose(a__ ,						a__ ,						atol=1e-3   )
    print('''Do both models output the same tensors?''' ,						'''🔥''' if success else '''💩'''   )
    if not success:
        raise Exception('''Something went wRoNg'''   )

    pathlib.Path(a__   ).mkdir(parents=a__ ,						exist_ok=a__   )
    print(F"""Saving model to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(a__   )


if __name__ == "__main__":
      a__				:      str    		=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--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__				:      int    		=     parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
