
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()
_A						      =				logging.get_logger(__name__)

_A						      =				"Hello world! cécé herlolip"



def 		lowercase_    (     A__				,	A__				,	A__	)							->							Optional[int]:
				"""simple docstring"""

				snake_case				  =      FairseqRobertaModel.from_pretrained(A__	)
				roberta.eval()  # disable dropout
				snake_case				  =      roberta.model.encoder.sentence_encoder
				snake_case				  =      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=514				,	type_vocab_size=1				,	layer_norm_eps=1e-5				,	)
				if classification_head:
								snake_case				  =      roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

				print("Our RoBERTa config:"				,	A__	)

				snake_case				  =      XLMRobertaXLForSequenceClassification(A__	) if classification_head else XLMRobertaXLForMaskedLM(A__	)
				model.eval()

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

				snake_case				  =      roberta_sent_encoder.layer_norm.weight
				snake_case				  =      roberta_sent_encoder.layer_norm.bias

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

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

								# self attention
								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)	)
								)

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

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

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

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

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

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

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

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A						      =				{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Wav2Vec2ForAudioFrameClassification",
       "Wav2Vec2ForCTC",
       "Wav2Vec2ForMaskedLM",
       "Wav2Vec2ForPreTraining",
       "Wav2Vec2ForSequenceClassification",
       "Wav2Vec2ForXVector",
       "Wav2Vec2Model",
       "Wav2Vec2PreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFWav2Vec2ForCTC",
       "TFWav2Vec2Model",
       "TFWav2Vec2PreTrainedModel",
       "TFWav2Vec2ForSequenceClassification",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "FlaxWav2Vec2ForCTC",
       "FlaxWav2Vec2ForPreTraining",
       "FlaxWav2Vec2Model",
       "FlaxWav2Vec2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

   _A						      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

