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

_A										= 'Hello world! cécé herlolip'

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							int  					=       FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_				)
     roberta.eval()  # disable dropout
     lowercase_       :							Dict  					=       roberta.model.encoder.sentence_encoder
     lowercase_       :							List[Any]  					=       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:
          lowercase_       :							Dict  					=       roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
     print('Our BERT config:'	,							SCREAMING_SNAKE_CASE_				)

     lowercase_       :							Union[str, Any]  					=       RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_				) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_				)
     model.eval()

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

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

          # self attention
          lowercase_       :							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)				)
          )

          lowercase_       :							Optional[Any]  					=       roberta_layer.self_attn.q_proj.weight
          lowercase_       :							Optional[int]  					=       roberta_layer.self_attn.q_proj.bias
          lowercase_       :							List[Any]  					=       roberta_layer.self_attn.k_proj.weight
          lowercase_       :							Tuple  					=       roberta_layer.self_attn.k_proj.bias
          lowercase_       :							Any  					=       roberta_layer.self_attn.v_proj.weight
          lowercase_       :							List[Any]  					=       roberta_layer.self_attn.v_proj.bias

          # self-attention output
          lowercase_       :							BertSelfOutput  					=       layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          lowercase_       :							str  					=       roberta_layer.self_attn.out_proj.weight
          lowercase_       :							Union[str, Any]  					=       roberta_layer.self_attn.out_proj.bias
          lowercase_       :							Dict  					=       roberta_layer.self_attn_layer_norm.weight
          lowercase_       :							List[Any]  					=       roberta_layer.self_attn_layer_norm.bias

          # intermediate
          lowercase_       :							BertIntermediate  					=       layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          lowercase_       :							Tuple  					=       roberta_layer.fca.weight
          lowercase_       :							Any  					=       roberta_layer.fca.bias

          # output
          lowercase_       :							BertOutput  					=       layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          lowercase_       :							List[str]  					=       roberta_layer.fca.weight
          lowercase_       :							Union[str, Any]  					=       roberta_layer.fca.bias
          lowercase_       :							Optional[Any]  					=       roberta_layer.final_layer_norm.weight
          lowercase_       :							str  					=       roberta_layer.final_layer_norm.bias
          # end of layer

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

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

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

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


if __name__ == "__main__":
 _A										= 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										= parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )





'''simple docstring'''




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_A										= get_logger(__name__)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     if version.parse(SCREAMING_SNAKE_CASE_				) < version.parse(SCREAMING_SNAKE_CASE_				):
          if "dev" in min_version:
               lowercase_       :							Tuple  					=       (
                   'This example requires a source install from HuggingFace diffusers (see '
                   '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
               )
          else:
               lowercase_       :							List[Any]  					=       f'''This example requires a minimum version of {min_version},'''
          error_message += f''' but the version found is {__version__}.\n'''
          raise ImportError(SCREAMING_SNAKE_CASE_				)




