


"""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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
   raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
__snake_case		=       logging.get_logger(__name__)

__snake_case		=       '''Hello world! cécé herlolip'''
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					bool       ):






     """simple docstring"""

     _a    	=				FairseqRobertaModel.from_pretrained(_lowerCAmelCase       )
     roberta.eval()  # disable dropout
     _a    	=				roberta.model.encoder.sentence_encoder
     _a    	=				XLMRobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,     hidden_size=roberta.cfg.model.encoder_embed_dim,     num_hidden_layers=roberta.cfg.model.encoder_layers,     num_attention_heads=roberta.cfg.model.encoder_attention_heads,     intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim,     max_position_embeddings=5_14,     type_vocab_size=1,     layer_norm_eps=1e-5,     )
     if classification_head:
          _a    	=				roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

     print('''Our RoBERTa config:''',     _lowerCAmelCase       )

     _a    	=				XLMRobertaXLForSequenceClassification(_lowerCAmelCase       ) if classification_head else XLMRobertaXLForMaskedLM(_lowerCAmelCase       )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     _a    	=				roberta_sent_encoder.embed_tokens.weight
     _a    	=				roberta_sent_encoder.embed_positions.weight
     _a    	=				torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight       )  # just zero them out b/c RoBERTa doesn't use them.

     _a    	=				roberta_sent_encoder.layer_norm.weight
     _a    	=				roberta_sent_encoder.layer_norm.bias

     for i in range(config.num_hidden_layers       ):
          # Encoder: start of layer
          _a    	=				model.roberta.encoder.layer[i]
          _a    	=				roberta_sent_encoder.layers[i]

          _a    	=				layer.attention
          _a    	=				roberta_layer.self_attn_layer_norm.weight
          _a    	=				roberta_layer.self_attn_layer_norm.bias

          # self attention
          _a    	=				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)       )
          )

          _a    	=				roberta_layer.self_attn.q_proj.weight
          _a    	=				roberta_layer.self_attn.q_proj.bias
          _a    	=				roberta_layer.self_attn.k_proj.weight
          _a    	=				roberta_layer.self_attn.k_proj.bias
          _a    	=				roberta_layer.self_attn.v_proj.weight
          _a    	=				roberta_layer.self_attn.v_proj.bias

          # self-attention output
          _a    	=				layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          _a    	=				roberta_layer.self_attn.out_proj.weight
          _a    	=				roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          _a    	=				roberta_layer.final_layer_norm.weight
          _a    	=				roberta_layer.final_layer_norm.bias

          # intermediate
          _a    	=				layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          _a    	=				roberta_layer.fca.weight
          _a    	=				roberta_layer.fca.bias

          # output
          _a    	=				layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          _a    	=				roberta_layer.fca.weight
          _a    	=				roberta_layer.fca.bias
          # end of layer

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

     # Let's check that we get the same results.
     _a    	=				roberta.encode(_lowerCAmelCase       ).unsqueeze(0       )  # batch of size 1

     _a    	=				model(_lowerCAmelCase       )[0]
     if classification_head:
          _a    	=				roberta.model.classification_heads['''mnli'''](roberta.extract_features(_lowerCAmelCase       )       )
     else:
          _a    	=				roberta.model(_lowerCAmelCase       )[0]
     print(our_output.shape,     their_output.shape       )
     _a    	=				torch.max(torch.abs(our_output - their_output       )       ).item()
     print(f'max_absolute_diff = {max_absolute_diff}'       )  # ~ 1e-7
     _a    	=				torch.allclose(_lowerCAmelCase,     _lowerCAmelCase,     atol=1e-3       )
     print('''Do both models output the same tensors?''',     '''🔥''' if success else '''💩'''       )
     if not success:
          raise Exception('''Something went wRoNg'''       )

     pathlib.Path(_lowerCAmelCase       ).mkdir(parents=_lowerCAmelCase,     exist_ok=_lowerCAmelCase       )
     print(f'Saving model to {pytorch_dump_folder_path}'       )
     model.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   __snake_case		=       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.'''
   )
   __snake_case		=       parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case		=       {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''Pix2StructImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Pix2StructPreTrainedModel''',
       '''Pix2StructForConditionalGeneration''',
       '''Pix2StructVisionModel''',
       '''Pix2StructTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_pixastruct import (
       PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       PixaStructConfig,
       PixaStructTextConfig,
       PixaStructVisionConfig,
   )
   from .processing_pixastruct import PixaStructProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_pixastruct import PixaStructImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pixastruct import (
          PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
          PixaStructForConditionalGeneration,
          PixaStructPreTrainedModel,
          PixaStructTextModel,
          PixaStructVisionModel,
      )

else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)