





'''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()
lowercase_            =				logging.get_logger(__name__)

lowercase_            =				"""Hello world! cécé herlolip"""





def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool					)		->str:
    _SCREAMING_SNAKE_CASE             =    FairseqRobertaModel.from_pretrained(UpperCamelCase__					)
    roberta.eval()  # disable dropout
    _SCREAMING_SNAKE_CASE             =    roberta.model.encoder.sentence_encoder
    _SCREAMING_SNAKE_CASE             =    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:
        _SCREAMING_SNAKE_CASE             =    roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
    print("""Our BERT config:"""      , UpperCamelCase__					)

    _SCREAMING_SNAKE_CASE             =    RobertaForSequenceClassification(UpperCamelCase__					) if classification_head else RobertaForMaskedLM(UpperCamelCase__					)
    model.eval()

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

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

        # self attention
        _SCREAMING_SNAKE_CASE             =    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)					)
        )

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

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

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

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

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

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

    _SCREAMING_SNAKE_CASE             =    model(UpperCamelCase__					)[0]
    if classification_head:
        _SCREAMING_SNAKE_CASE             =    roberta.model.classification_heads["mnli"](roberta.extract_features(UpperCamelCase__					)					)
    else:
        _SCREAMING_SNAKE_CASE             =    roberta.model(UpperCamelCase__					)[0]
    print(our_output.shape      , their_output.shape					)
    _SCREAMING_SNAKE_CASE             =    torch.max(torch.abs(our_output - their_output					)					).item()
    print(F'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
    _SCREAMING_SNAKE_CASE             =    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__":
      lowercase_            =				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."""
      )
      lowercase_            =				parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )










'''simple docstring'''

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List





def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
    if name != "training_function" and name != "main":
        raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
    _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    [], False
    for line in lines:
        if not found_start and F'def {name}' in line:
            _SCREAMING_SNAKE_CASE             =    True
            good_lines.append(__lowerCamelCase					)
            continue
        if found_start:
            if name == "training_function" and "def main" in line:
                return good_lines
            if name == "main" and "if __name__" in line:
                return good_lines
            good_lines.append(__lowerCamelCase					)





def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
    return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]






def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
    with open(__lowerCamelCase      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    with open(__lowerCamelCase      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    if secondary_filename is not None:
        with open(__lowerCamelCase      , """r"""					) as f:
            _SCREAMING_SNAKE_CASE             =    f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
    if parser_only:
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        if secondary_filename is not None:
            _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
    else:
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        if secondary_filename is not None:
            _SCREAMING_SNAKE_CASE             =    clean_lines(
                get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)

    _SCREAMING_SNAKE_CASE             =    """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

    # Specific code in our script that differs from the full version, aka what is new
    _SCREAMING_SNAKE_CASE             =    []
    _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
    _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
    for i in range(len(__lowerCamelCase					) - 1					):
        if i not in passed_idxs:
            _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_feature_code.append(__lowerCamelCase					)
                    passed_idxs.append(__lowerCamelCase					)
                else:
                    # Skip over the `config['num_epochs'] = 2` statement
                    _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)

    # Extract out just the new parts from the full_file_training_func
    _SCREAMING_SNAKE_CASE             =    []
    _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
    for i, line in enumerate(__lowerCamelCase					):
        if i not in passed_idxs:
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_full_example_parts.append(__lowerCamelCase					)
                    passed_idxs.append(__lowerCamelCase					)

    # Finally, get the overall diff
    _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
    if secondary_filename is not None:
        _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
        _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]

    return diff_from_example



