


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 :       str						=   logging.get_logger(__name__)

A :       Union[str, Any]						=   "Hello world! cécé herlolip"

def  lowercase_						(						_A     :       str       ,   _A     :       str       ,   _A     :       bool		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[int]       =    FairseqRobertaModel.from_pretrained(_A		)
      roberta.eval()  # disable dropout
      lowerCamelCase__						:							List[str]       =    roberta.model.encoder.sentence_encoder
      lowerCamelCase__						:							int       =    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=514       ,   type_vocab_size=1       ,   layer_norm_eps=1E-5       ,   )
      if classification_head:
            lowerCamelCase__						:							Any       =    roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
      print("Our BERT config:"       ,   _A		)

      lowerCamelCase__						:							int       =    RobertaForSequenceClassification(_A		) if classification_head else RobertaForMaskedLM(_A		)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      lowerCamelCase__						:							List[Any]       =    roberta_sent_encoder.embed_tokens.weight
      lowerCamelCase__						:							List[str]       =    roberta_sent_encoder.embed_positions.weight
      lowerCamelCase__						:							Dict       =    torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.
      lowerCamelCase__						:							List[Any]       =    roberta_sent_encoder.emb_layer_norm.weight
      lowerCamelCase__						:							Optional[int]       =    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__						:							Tuple       =    roberta_layer.self_attn.q_proj.weight
            lowerCamelCase__						:							Union[str, Any]       =    roberta_layer.self_attn.q_proj.bias
            lowerCamelCase__						:							str       =    roberta_layer.self_attn.k_proj.weight
            lowerCamelCase__						:							Tuple       =    roberta_layer.self_attn.k_proj.bias
            lowerCamelCase__						:							Tuple       =    roberta_layer.self_attn.v_proj.weight
            lowerCamelCase__						:							Optional[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__						:							int       =    roberta_layer.self_attn.out_proj.weight
            lowerCamelCase__						:							str       =    roberta_layer.self_attn.out_proj.bias
            lowerCamelCase__						:							Optional[int]       =    roberta_layer.self_attn_layer_norm.weight
            lowerCamelCase__						:							Any       =    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__						:							int       =    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__						:							int       =    roberta_layer.fca.bias
            lowerCamelCase__						:							List[str]       =    roberta_layer.final_layer_norm.weight
            lowerCamelCase__						:							int       =    roberta_layer.final_layer_norm.bias
            # end of layer

      if classification_head:
            lowerCamelCase__						:							Dict       =    roberta.model.classification_heads["mnli"].dense.weight
            lowerCamelCase__						:							List[str]       =    roberta.model.classification_heads["mnli"].dense.bias
            lowerCamelCase__						:							Tuple       =    roberta.model.classification_heads["mnli"].out_proj.weight
            lowerCamelCase__						:							List[str]       =    roberta.model.classification_heads["mnli"].out_proj.bias
      else:
            # LM Head
            lowerCamelCase__						:							Tuple       =    roberta.model.encoder.lm_head.dense.weight
            lowerCamelCase__						:							List[Any]       =    roberta.model.encoder.lm_head.dense.bias
            lowerCamelCase__						:							List[str]       =    roberta.model.encoder.lm_head.layer_norm.weight
            lowerCamelCase__						:							Tuple       =    roberta.model.encoder.lm_head.layer_norm.bias
            lowerCamelCase__						:							Any       =    roberta.model.encoder.lm_head.weight
            lowerCamelCase__						:							str       =    roberta.model.encoder.lm_head.bias

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

      lowerCamelCase__						:							int       =    model(_A		)[0]
      if classification_head:
            lowerCamelCase__						:							int       =    roberta.model.classification_heads["mnli"](roberta.extract_features(_A		)		)
      else:
            lowerCamelCase__						:							Optional[int]       =    roberta.model(_A		)[0]
      print(our_output.shape       ,   their_output.shape		)
      lowerCamelCase__						:							Optional[int]       =    torch.max(torch.abs(our_output - their_output		)		).item()
      print(F"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
      lowerCamelCase__						:							int       =    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 :       Dict						=   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
    )






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def  lowercase_						(						_A     :       str		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    min(re.search("^ +"       ,   _A		).end() if t.startswith(" "		) else 0 for t in string.splitlines()		)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A		)]		)


A :       Optional[int]						=   "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

A :       str						=   "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


A :       Optional[Any]						=   "\\n# Begin of markdown\n\nSome cool dataset card\n"







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             with tempfile.TemporaryDirectory() as tmp_dir:
                   lowerCamelCase__						:							List[str]       =    Path(__lowerCamelCase			) / "README.md"
                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Dict       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Optional[Any]       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							int       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertEqual(__lowerCamelCase   , {}			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "			)
             with self.assertRaises(__lowerCamelCase			):
                   DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Optional[Any]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)


