code: 
  
  
  '''simple docstring'''
  
  
  
  from ..utils import (
      OptionalDependencyNotAvailable,
      is_flax_available,
      is_invisible_watermark_available,
      is_k_diffusion_available,
      is_librosa_available,
      is_note_seq_available,
      is_onnx_available,
      is_torch_available,
      is_transformers_available,
  )
  
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_pt_objects import *  # noqa F403
  else:
        from .consistency_models import ConsistencyModelPipeline
        from .dance_diffusion import DanceDiffusionPipeline
        from .ddim import DDIMPipeline
        from .ddpm import DDPMPipeline
        from .dit import DiTPipeline
        from .latent_diffusion import LDMSuperResolutionPipeline
        from .latent_diffusion_uncond import LDMPipeline
        from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
        from .pndm import PNDMPipeline
        from .repaint import RePaintPipeline
        from .score_sde_ve import ScoreSdeVePipeline
        from .stochastic_karras_ve import KarrasVePipeline
  
  try:
        if not (is_torch_available() and is_librosa_available()):
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
  else:
        from .audio_diffusion import AudioDiffusionPipeline, Mel
  
  try:
        if not (is_torch_available() and is_transformers_available()):
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
  else:
        from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
        from .audioldm import AudioLDMPipeline
        from .controlnet import (
            StableDiffusionControlNetImgaImgPipeline,
            StableDiffusionControlNetInpaintPipeline,
            StableDiffusionControlNetPipeline,
        )
        from .deepfloyd_if import (
            IFImgaImgPipeline,
            IFImgaImgSuperResolutionPipeline,
            IFInpaintingPipeline,
            IFInpaintingSuperResolutionPipeline,
            IFPipeline,
            IFSuperResolutionPipeline,
        )
        from .kandinsky import (
            KandinskyImgaImgPipeline,
            KandinskyInpaintPipeline,
            KandinskyPipeline,
            KandinskyPriorPipeline,
        )
        from .kandinskya_a import (
            KandinskyVaaControlnetImgaImgPipeline,
            KandinskyVaaControlnetPipeline,
            KandinskyVaaImgaImgPipeline,
            KandinskyVaaInpaintPipeline,
            KandinskyVaaPipeline,
            KandinskyVaaPriorEmbaEmbPipeline,
            KandinskyVaaPriorPipeline,
        )
        from .latent_diffusion import LDMTextToImagePipeline
        from .paint_by_example import PaintByExamplePipeline
        from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
        from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
        from .stable_diffusion import (
            CycleDiffusionPipeline,
            StableDiffusionAttendAndExcitePipeline,
            StableDiffusionDepthaImgPipeline,
            StableDiffusionDiffEditPipeline,
            StableDiffusionImageVariationPipeline,
            StableDiffusionImgaImgPipeline,
            StableDiffusionInpaintPipeline,
            StableDiffusionInpaintPipelineLegacy,
            StableDiffusionInstructPixaPixPipeline,
            StableDiffusionLatentUpscalePipeline,
            StableDiffusionLDMaDPipeline,
            StableDiffusionModelEditingPipeline,
            StableDiffusionPanoramaPipeline,
            StableDiffusionParadigmsPipeline,
            StableDiffusionPipeline,
            StableDiffusionPixaPixZeroPipeline,
            StableDiffusionSAGPipeline,
            StableDiffusionUpscalePipeline,
            StableUnCLIPImgaImgPipeline,
            StableUnCLIPPipeline,
        )
        from .stable_diffusion_safe import StableDiffusionPipelineSafe
        from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
        from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
        from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
        from .versatile_diffusion import (
            VersatileDiffusionDualGuidedPipeline,
            VersatileDiffusionImageVariationPipeline,
            VersatileDiffusionPipeline,
            VersatileDiffusionTextToImagePipeline,
        )
        from .vq_diffusion import VQDiffusionPipeline
  
  
  try:
        if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
  else:
        from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
  
  try:
        if not is_onnx_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_onnx_objects import *  # noqa F403
  else:
        from .onnx_utils import OnnxRuntimeModel
  
  try:
        if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
  else:
        from .stable_diffusion import (
            OnnxStableDiffusionImgaImgPipeline,
            OnnxStableDiffusionInpaintPipeline,
            OnnxStableDiffusionInpaintPipelineLegacy,
            OnnxStableDiffusionPipeline,
            OnnxStableDiffusionUpscalePipeline,
            StableDiffusionOnnxPipeline,
        )
  
  try:
        if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
  else:
        from .stable_diffusion import StableDiffusionKDiffusionPipeline
  
  try:
        if not is_flax_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_flax_objects import *  # noqa F403
  else:
        from .pipeline_flax_utils import FlaxDiffusionPipeline
  
  
  try:
        if not (is_flax_available() and is_transformers_available()):
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
  else:
        from .controlnet import FlaxStableDiffusionControlNetPipeline
        from .stable_diffusion import (
            FlaxStableDiffusionImgaImgPipeline,
            FlaxStableDiffusionInpaintPipeline,
            FlaxStableDiffusionPipeline,
        )
  try:
        if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
  else:
        from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline
  
  
  
  
  
  
  
style_context: 
  
  
  '''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__	:      List[str]				=						logging.get_logger(__name__)
  
  A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
       __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
       roberta.eval()  # disable dropout
       __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
       __lowerCamelCase     :			Optional[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=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
       if classification_head:
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'   ,					UpperCAmelCase_							)
  
       __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
       __lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
       __lowerCamelCase     :			Tuple              =	torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
       __lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
       __lowerCamelCase     :			Tuple              =	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     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
            __lowerCamelCase     :			List[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     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
            __lowerCamelCase     :			List[str]              =	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     :			Optional[Any]              =	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     :			Tuple              =	roberta_layer.fca.bias
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
            # end of layer
  
       if classification_head:
            __lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
            __lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
            __lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
            __lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
       else:
            # LM Head
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
            __lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
            __lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
            __lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias
  
       # Let's check that we get the same results.
       __lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1
  
       __lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
       if classification_head:
            __lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
       else:
            __lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
       print(our_output.shape   ,					their_output.shape							)
       __lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
       print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
       __lowerCamelCase     :			List[str]              =	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__":
        A__	:      Optional[Any]				=						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
        )
  
  
  
  
  
  
  