




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

snake_case__ 							=    """Hello world! cécé herlolip"""




def     snake_case__     (			lowerCamelCase__					: str      ,		lowerCamelCase__					: str      ,		lowerCamelCase__					: bool      ) -> List[str]:
					A_			:  Optional[int]			=				FairseqRobertaModel.from_pretrained(lowerCamelCase__      )
					roberta.eval()  # disable dropout
					A_			:  Optional[Any]			=				roberta.model.encoder.sentence_encoder
					A_			:  Optional[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=5_1_4      ,		type_vocab_size=1      ,		layer_norm_eps=1e-5      ,		)
					if classification_head:
										A_			:  int			=				roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
					print('''Our BERT config:'''      ,		lowerCamelCase__      )

					A_			:  Optional[int]			=				RobertaForSequenceClassification(lowerCamelCase__      ) if classification_head else RobertaForMaskedLM(lowerCamelCase__      )
					model.eval()

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

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

										# self attention
										A_			:  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)      )
										)

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

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

										# intermediate
										A_			:  BertIntermediate			=				layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										A_			:  Union[str, Any]			=				roberta_layer.fca.weight
										A_			:  str			=				roberta_layer.fca.bias

										# output
										A_			:  BertOutput			=				layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										A_			:  Optional[int]			=				roberta_layer.fca.weight
										A_			:  Optional[Any]			=				roberta_layer.fca.bias
										A_			:  Optional[int]			=				roberta_layer.final_layer_norm.weight
										A_			:  Optional[Any]			=				roberta_layer.final_layer_norm.bias
										# end of layer

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

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

					A_			:  Optional[int]			=				model(lowerCamelCase__      )[0]
					if classification_head:
										A_			:  Union[str, Any]			=				roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCamelCase__      )      )
					else:
										A_			:  Union[str, Any]			=				roberta.model(lowerCamelCase__      )[0]
					print(our_output.shape      ,		their_output.shape      )
					A_			:  Tuple			=				torch.max(torch.abs(our_output - their_output      )      ).item()
					print(f'max_absolute_diff = {max_absolute_diff}'      )  # ~ 1e-7
					A_			:  int			=				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_roberta_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_flax_available, is_tf_available, is_torch_available


snake_case__ 							=    {
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """Constraint""",
							    """ConstraintListState""",
							    """DisjunctiveConstraint""",
							    """PhrasalConstraint""",
							]
							snake_case__ 							=    [
							    """BeamHypotheses""",
							    """BeamScorer""",
							    """BeamSearchScorer""",
							    """ConstrainedBeamSearchScorer""",
							]
							snake_case__ 							=    [
							    """EpsilonLogitsWarper""",
							    """EtaLogitsWarper""",
							    """ForcedBOSTokenLogitsProcessor""",
							    """ForcedEOSTokenLogitsProcessor""",
							    """HammingDiversityLogitsProcessor""",
							    """InfNanRemoveLogitsProcessor""",
							    """LogitsProcessor""",
							    """LogitsProcessorList""",
							    """LogitsWarper""",
							    """MinLengthLogitsProcessor""",
							    """MinNewTokensLengthLogitsProcessor""",
							    """NoBadWordsLogitsProcessor""",
							    """NoRepeatNGramLogitsProcessor""",
							    """PrefixConstrainedLogitsProcessor""",
							    """RepetitionPenaltyLogitsProcessor""",
							    """SequenceBiasLogitsProcessor""",
							    """EncoderRepetitionPenaltyLogitsProcessor""",
							    """TemperatureLogitsWarper""",
							    """TopKLogitsWarper""",
							    """TopPLogitsWarper""",
							    """TypicalLogitsWarper""",
							    """EncoderNoRepeatNGramLogitsProcessor""",
							    """ExponentialDecayLengthPenalty""",
							    """LogitNormalization""",
							]
							snake_case__ 							=    [
							    """MaxNewTokensCriteria""",
							    """MaxLengthCriteria""",
							    """MaxTimeCriteria""",
							    """StoppingCriteria""",
							    """StoppingCriteriaList""",
							    """validate_stopping_criteria""",
							]
							snake_case__ 							=    [
							    """GenerationMixin""",
							    """top_k_top_p_filtering""",
							    """GreedySearchEncoderDecoderOutput""",
							    """GreedySearchDecoderOnlyOutput""",
							    """SampleEncoderDecoderOutput""",
							    """SampleDecoderOnlyOutput""",
							    """BeamSearchEncoderDecoderOutput""",
							    """BeamSearchDecoderOnlyOutput""",
							    """BeamSampleEncoderDecoderOutput""",
							    """BeamSampleDecoderOnlyOutput""",
							    """ContrastiveSearchEncoderDecoderOutput""",
							    """ContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """TFForcedBOSTokenLogitsProcessor""",
							    """TFForcedEOSTokenLogitsProcessor""",
							    """TFLogitsProcessor""",
							    """TFLogitsProcessorList""",
							    """TFLogitsWarper""",
							    """TFMinLengthLogitsProcessor""",
							    """TFNoBadWordsLogitsProcessor""",
							    """TFNoRepeatNGramLogitsProcessor""",
							    """TFRepetitionPenaltyLogitsProcessor""",
							    """TFTemperatureLogitsWarper""",
							    """TFTopKLogitsWarper""",
							    """TFTopPLogitsWarper""",
							    """TFForceTokensLogitsProcessor""",
							    """TFSuppressTokensAtBeginLogitsProcessor""",
							    """TFSuppressTokensLogitsProcessor""",
							]
							snake_case__ 							=    [
							    """TFGenerationMixin""",
							    """tf_top_k_top_p_filtering""",
							    """TFGreedySearchDecoderOnlyOutput""",
							    """TFGreedySearchEncoderDecoderOutput""",
							    """TFSampleEncoderDecoderOutput""",
							    """TFSampleDecoderOnlyOutput""",
							    """TFBeamSearchEncoderDecoderOutput""",
							    """TFBeamSearchDecoderOnlyOutput""",
							    """TFBeamSampleEncoderDecoderOutput""",
							    """TFBeamSampleDecoderOnlyOutput""",
							    """TFContrastiveSearchEncoderDecoderOutput""",
							    """TFContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """FlaxForcedBOSTokenLogitsProcessor""",
							    """FlaxForcedEOSTokenLogitsProcessor""",
							    """FlaxLogitsProcessor""",
							    """FlaxLogitsProcessorList""",
							    """FlaxLogitsWarper""",
							    """FlaxMinLengthLogitsProcessor""",
							    """FlaxTemperatureLogitsWarper""",
							    """FlaxTopKLogitsWarper""",
							    """FlaxTopPLogitsWarper""",
							]
							snake_case__ 							=    [
							    """FlaxGenerationMixin""",
							    """FlaxGreedySearchOutput""",
							    """FlaxSampleOutput""",
							    """FlaxBeamSearchOutput""",
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

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



