





"""simple docstring"""
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()
__UpperCamelCase			:		Dict		    =   logging.get_logger(__name__)

__UpperCamelCase			:		Union[str, Any]		    =   '''Hello world! cécé herlolip'''

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  str   ,			_UpperCAmelCase :  str   ,			_UpperCAmelCase :  bool       ):
 lowerCAmelCase							,lowerCAmelCase  							=      os.path.split(_UpperCAmelCase       )
 lowerCAmelCase  							=      DataaVecTextModel.from_pretrained(
     _UpperCAmelCase   ,			checkpoint_file=_UpperCAmelCase       )
 dataavec.eval()  # disable dropout
 lowerCAmelCase  							=      dataavec.models[0]
 lowerCAmelCase  							=      dataavec_model.encoder.sentence_encoder
 lowerCAmelCase  							=      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:
  lowerCAmelCase  							=      dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our BERT config:'   ,			_UpperCAmelCase       )

 lowerCAmelCase  							=      DataaVecTextForSequenceClassification(_UpperCAmelCase       ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase       )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 lowerCAmelCase  							=      dataavec_sent_encoder.embed_tokens.weight
 lowerCAmelCase  							=      dataavec_sent_encoder.embed_positions.weight
 lowerCAmelCase  							=      torch.zeros_like(
     model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
 lowerCAmelCase  							=      dataavec_sent_encoder.layernorm_embedding.weight
 lowerCAmelCase  							=      dataavec_sent_encoder.layernorm_embedding.bias

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

  # self attention
  lowerCAmelCase  							=      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)       )}'
  )

  lowerCAmelCase  							=      dataavec_layer.self_attn.q_proj.weight
  lowerCAmelCase  							=      dataavec_layer.self_attn.q_proj.bias
  lowerCAmelCase  							=      dataavec_layer.self_attn.k_proj.weight
  lowerCAmelCase  							=      dataavec_layer.self_attn.k_proj.bias
  lowerCAmelCase  							=      dataavec_layer.self_attn.v_proj.weight
  lowerCAmelCase  							=      dataavec_layer.self_attn.v_proj.bias

  # self-attention output
  lowerCAmelCase  							=      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}'
  lowerCAmelCase  							=      dataavec_layer.self_attn.out_proj.weight
  lowerCAmelCase  							=      dataavec_layer.self_attn.out_proj.bias
  lowerCAmelCase  							=      dataavec_layer.self_attn_layer_norm.weight
  lowerCAmelCase  							=      dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  lowerCAmelCase  							=      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}'
  lowerCAmelCase  							=      dataavec_layer.fca.weight
  lowerCAmelCase  							=      dataavec_layer.fca.bias

  # output
  lowerCAmelCase  							=      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}'
  lowerCAmelCase  							=      dataavec_layer.fca.weight
  lowerCAmelCase  							=      dataavec_layer.fca.bias
  lowerCAmelCase  							=      dataavec_layer.final_layer_norm.weight
  lowerCAmelCase  							=      dataavec_layer.final_layer_norm.bias
  # end of layer

 if classification_head:
  lowerCAmelCase  							=      dataavec.model.classification_heads['mnli'].dense.weight
  lowerCAmelCase  							=      dataavec.model.classification_heads['mnli'].dense.bias
  lowerCAmelCase  							=      dataavec.model.classification_heads['mnli'].out_proj.weight
  lowerCAmelCase  							=      dataavec.model.classification_heads['mnli'].out_proj.bias
 else:
  # LM Head
  lowerCAmelCase  							=      dataavec_model.encoder.lm_head.dense.weight
  lowerCAmelCase  							=      dataavec_model.encoder.lm_head.dense.bias
  lowerCAmelCase  							=      dataavec_model.encoder.lm_head.layer_norm.weight
  lowerCAmelCase  							=      dataavec_model.encoder.lm_head.layer_norm.bias
  lowerCAmelCase  							=      dataavec_model.encoder.lm_head.weight
  lowerCAmelCase  							=      dataavec_model.encoder.lm_head.bias

 # Let's check that we get the same results.
 lowerCAmelCase  							=      dataavec.encode(_UpperCAmelCase       ).unsqueeze(0       )  # batch of size 1

 lowerCAmelCase  							=      model(_UpperCAmelCase       )[0]
 if classification_head:
  lowerCAmelCase  							=      dataavec.model.classification_heads['mnli'](dataavec.extract_features(_UpperCAmelCase       )       )
 else:
  lowerCAmelCase  							=      dataavec_model(_UpperCAmelCase       )[0]
 print(our_output.shape   ,			their_output.shape       )
 lowerCAmelCase  							=      torch.max(torch.abs(our_output - their_output       )       ).item()
 print(F'max_absolute_diff = {max_absolute_diff}'       )  # ~ 1e-7
 lowerCAmelCase  							=      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__":
       __UpperCamelCase			:		int		    =   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.'''
       )
       __UpperCamelCase			:		List[Any]		    =   parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__UpperCamelCase			:		List[Any]		    =   logging.get_logger(__name__)

__UpperCamelCase			:		Union[str, Any]		    =   {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
       import detectrona







class 	a				(					a__   ):
 snake_case__    	=    '''layoutlmv2'''



 def __init__(			self							,    _snake_case=3_05_22							,    _snake_case=7_68							,    _snake_case=12							,    _snake_case=12							,    _snake_case=30_72							,    _snake_case="gelu"							,    _snake_case=0.1							,    _snake_case=0.1							,    _snake_case=5_12							,    _snake_case=2							,    _snake_case=0.02							,    _snake_case=1E-12							,    _snake_case=0							,    _snake_case=10_24							,    _snake_case=1_28							,    _snake_case=32							,    _snake_case=True							,    _snake_case=2_56							,    _snake_case=64							,    _snake_case=True							,    _snake_case=[7, 7, 2_56]							,    _snake_case=1_28							,    _snake_case=1_28							,    _snake_case=True							,    _snake_case=True							,    _snake_case=False							,    _snake_case=None							,    **_snake_case							,    ):







  """simple docstring"""






  super().__init__(
      vocab_size=_snake_case							,    hidden_size=_snake_case							,    num_hidden_layers=_snake_case							,    num_attention_heads=_snake_case							,    intermediate_size=_snake_case							,    hidden_act=_snake_case							,    hidden_dropout_prob=_snake_case							,    attention_probs_dropout_prob=_snake_case							,    max_position_embeddings=_snake_case							,    type_vocab_size=_snake_case							,    initializer_range=_snake_case							,    layer_norm_eps=_snake_case							,    pad_token_id=_snake_case							,    **_snake_case							,    )
  lowerCAmelCase  							=      max_ad_position_embeddings
  lowerCAmelCase  							=      max_rel_pos
  lowerCAmelCase  							=      rel_pos_bins
  lowerCAmelCase  							=      fast_qkv
  lowerCAmelCase  							=      max_rel_ad_pos
  lowerCAmelCase  							=      rel_ad_pos_bins
  lowerCAmelCase  							=      convert_sync_batchnorm
  lowerCAmelCase  							=      image_feature_pool_shape
  lowerCAmelCase  							=      coordinate_size
  lowerCAmelCase  							=      shape_size
  lowerCAmelCase  							=      has_relative_attention_bias
  lowerCAmelCase  							=      has_spatial_attention_bias
  lowerCAmelCase  							=      has_visual_segment_embedding
  lowerCAmelCase  							=      (
      detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  )



 @classmethod
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return {
      "MODEL.MASK_ON": True,
      "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
      "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
      "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
      "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
      "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
      "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
      "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
      "MODEL.POST_NMS_TOPK_TEST": 10_00,
      "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
      "MODEL.ROI_HEADS.NUM_CLASSES": 5,
      "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
      "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
      "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
      "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
      "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
      "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
      "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
      "MODEL.RESNETS.DEPTH": 1_01,
      "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
      "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
      "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.RESNETS.NUM_GROUPS": 32,
      "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
      "MODEL.RESNETS.STRIDE_IN_1X1": False,
  }



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      detectrona.config.get_cfg()
  for k, v in self.detectrona_config_args.items():
   lowerCAmelCase  							=      k.split('.'			)
   lowerCAmelCase  							=      detectrona_config
   for attribute in attributes[:-1]:
    lowerCAmelCase  							=      getattr(_snake_case							,    _snake_case			)
   setattr(_snake_case							,    attributes[-1]							,    _snake_case			)

  return detectrona_config





