code: 
  
  
  import argparse
  import copy
  import json
  import multiprocessing
  import os
  import time
  
  from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
  from huggingface_hub import ModelFilter, hf_api
  
  import transformers
  from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
  from transformers.image_processing_utils import BaseImageProcessor
  def 							_UpperCAmelCase       ():
         _A   : Optional[int]       					=				set()
         # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
         for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
                _A   : Any       					=				getattr(transformers.models.auto       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                if module is None:
                       continue
                # all mappings in a single auto modeling file
                _A   : Tuple       					=				[
                    x
                    for x in dir(UpperCamelCase__		)
                    if x.endswith("_MAPPING_NAMES"		)
                    and (x.startswith("MODEL_"		) or x.startswith("TF_MODEL_"		) or x.startswith("FLAX_MODEL_"		))
                ]
                for name in mapping_names:
                       _A   : Any       					=				getattr(UpperCamelCase__       ,							UpperCamelCase__		)
                       if mapping is not None:
                              for v in mapping.values():
                                     if isinstance(UpperCamelCase__       ,							(list, tuple)		):
                                            model_names.update(UpperCamelCase__		)
                                     elif isinstance(UpperCamelCase__       ,							UpperCamelCase__		):
                                            model_names.add(UpperCamelCase__		)
  
         return sorted(UpperCamelCase__		)
  def 							_UpperCAmelCase       ():
         # All model names defined in auto mappings
         _A   : Dict       					=				set(get_all_model_names()		)
  
         with open("tests/utils/tiny_model_summary.json"		) as fp:
                _A   : str       					=				json.load(UpperCamelCase__		)
         _A   : int       					=				set()
         for model_base_name in tiny_model_info:
                tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"]		)
  
         # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
         _A   : Union[str, Any]       					=				model_names.difference(UpperCamelCase__		)
         for model_name in copy.copy(UpperCamelCase__		):
                if not model_name.startswith("TF"		) and f"TF{model_name}" in not_on_hub:
                       tiny_models_names.remove(UpperCamelCase__		)
                elif model_name.startswith("TF"		) and model_name[2:] in not_on_hub:
                       tiny_models_names.remove(UpperCamelCase__		)
  
         return sorted(UpperCamelCase__		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[Any]		):
         _A   : Optional[int]       					=				COMPOSITE_MODELS.values()
  
         # All tiny model base names on Hub
         _A   : Union[str, Any]       					=				get_all_model_names()
         _A   : List[str]       					=				hf_api.list_models(
             filter=ModelFilter(
                 author="hf-internal-testing"       ,							)		)
         _A   : int       					=				set()
         for x in models:
                _A   : Tuple       					=				x.modelId
                _A					,    _A   : str       					=				model.split("/"		)
                if not model.startswith("tiny-random-"		):
                       continue
                _A   : int       					=				model.replace("tiny-random-"       ,							""		)
                if not model[0].isupper():
                       continue
                if model not in model_names and model not in special_models:
                       continue
                _models.add(UpperCamelCase__		)
  
         _A   : str       					=				sorted(_models		)
         # All tiny model names on Hub
         _A   : List[Any]       					=				{}
         for model in models:
                _A   : Optional[Any]       					=				f"hf-internal-testing/tiny-random-{model}"
                _A   : Union[str, Any]       					=				model.split("-"		)[0]
                try:
                       _A   : str       					=				hf_api.repo_info(UpperCamelCase__		)
                       _A   : Tuple       					=				{
                           "tokenizer_classes": set(),
                           "processor_classes": set(),
                           "model_classes": set(),
                           "sha": repo_info.sha,
                       }
                except Exception:
                       continue
                try:
                       time.sleep(1		)
                       _A   : Union[str, Any]       					=				AutoTokenizer.from_pretrained(UpperCamelCase__		)
                       content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__		)
                except Exception:
                       pass
                try:
                       time.sleep(1		)
                       _A   : Any       					=				AutoTokenizer.from_pretrained(UpperCamelCase__       ,							use_fast=UpperCamelCase__		)
                       content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__		)
                except Exception:
                       pass
                try:
                       time.sleep(1		)
                       _A   : List[Any]       					=				AutoImageProcessor.from_pretrained(UpperCamelCase__		)
                       content["processor_classes"].add(img_p.__class__.__name__		)
                except Exception:
                       pass
                try:
                       time.sleep(1		)
                       _A   : Any       					=				AutoFeatureExtractor.from_pretrained(UpperCamelCase__		)
                       if not isinstance(UpperCamelCase__       ,							UpperCamelCase__		):
                              content["processor_classes"].add(feat_p.__class__.__name__		)
                except Exception:
                       pass
                try:
                       time.sleep(1		)
                       _A   : Union[str, Any]       					=				getattr(UpperCamelCase__       ,							UpperCamelCase__		)
                       _A   : int       					=				model_class.from_pretrained(UpperCamelCase__		)
                       content["model_classes"].add(m.__class__.__name__		)
                except Exception:
                       pass
                try:
                       time.sleep(1		)
                       _A   : List[Any]       					=				getattr(UpperCamelCase__       ,							f"TF{model}"		)
                       _A   : List[str]       					=				model_class.from_pretrained(UpperCamelCase__		)
                       content["model_classes"].add(m.__class__.__name__		)
                except Exception:
                       pass
  
                _A   : Dict       					=				sorted(content["tokenizer_classes"]		)
                _A   : str       					=				sorted(content["processor_classes"]		)
                _A   : Tuple       					=				sorted(content["model_classes"]		)
  
                _A   : Union[str, Any]       					=				content
                with open(os.path.join(UpperCamelCase__       ,							"hub_tiny_model_summary.json"		)       ,							"w"		) as fp:
                       json.dump(UpperCamelCase__       ,							UpperCamelCase__       ,							ensure_ascii=UpperCamelCase__       ,							indent=4		)
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				argparse.ArgumentParser()
      parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
      lowerCAmelCase__   	=				parser.parse_args()
  
      # This has to be `spawn` to avoid hanging forever!
      multiprocessing.set_start_method('spawn')
  
      lowerCAmelCase__   	=				'tiny_models'
      lowerCAmelCase__   	=				True
      lowerCAmelCase__   	=				None
      lowerCAmelCase__   	=				get_tiny_model_names_from_repo()
      lowerCAmelCase__   	=				True
      lowerCAmelCase__   	=				True
      lowerCAmelCase__   	=				'hf-internal-testing'
  
      create_tiny_models(
          output_path,
          all,
          model_types,
          models_to_skip,
          no_check,
          upload,
          organization,
          token=os.environ.get('TOKEN', None),
          num_workers=args.num_workers,
      )
  
  
  
  
  
  
style_context: 
  
  
  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()
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  lowerCAmelCase__   	=				'Hello world! cécé herlolip'
  def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						bool		):
         _A   : Union[str, Any]       					=				FairseqRobertaModel.from_pretrained(UpperCamelCase__		)
         roberta.eval()  # disable dropout
         _A   : Dict       					=				roberta.model.encoder.sentence_encoder
         _A   : 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=514       ,							type_vocab_size=1       ,							layer_norm_eps=1E-5       ,							)
         if classification_head:
                _A   : List[Any]       					=				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
         print("Our BERT config:"       ,							UpperCamelCase__		)
  
         _A   : Dict       					=				RobertaForSequenceClassification(UpperCamelCase__		) if classification_head else RobertaForMaskedLM(UpperCamelCase__		)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         _A   : Optional[int]       					=				roberta_sent_encoder.embed_tokens.weight
         _A   : List[str]       					=				roberta_sent_encoder.embed_positions.weight
         _A   : Optional[int]       					=				torch.zeros_like(
             model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.
         _A   : List[Any]       					=				roberta_sent_encoder.emb_layer_norm.weight
         _A   : Tuple       					=				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   : List[str]       					=				roberta_layer.self_attn.q_proj.weight
                _A   : Optional[Any]       					=				roberta_layer.self_attn.q_proj.bias
                _A   : Union[str, Any]       					=				roberta_layer.self_attn.k_proj.weight
                _A   : Tuple       					=				roberta_layer.self_attn.k_proj.bias
                _A   : List[str]       					=				roberta_layer.self_attn.v_proj.weight
                _A   : Optional[Any]       					=				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   : Tuple       					=				roberta_layer.self_attn.out_proj.weight
                _A   : List[str]       					=				roberta_layer.self_attn.out_proj.bias
                _A   : Dict       					=				roberta_layer.self_attn_layer_norm.weight
                _A   : Optional[int]       					=				roberta_layer.self_attn_layer_norm.bias
  
                # intermediate
                _A   : BertIntermediate       					=				layer.intermediate
                assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
                _A   : Tuple       					=				roberta_layer.fca.weight
                _A   : List[str]       					=				roberta_layer.fca.bias
  
                # output
                _A   : BertOutput       					=				layer.output
                assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
                _A   : int       					=				roberta_layer.fca.weight
                _A   : List[str]       					=				roberta_layer.fca.bias
                _A   : List[str]       					=				roberta_layer.final_layer_norm.weight
                _A   : Any       					=				roberta_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                _A   : int       					=				roberta.model.classification_heads["mnli"].dense.weight
                _A   : List[Any]       					=				roberta.model.classification_heads["mnli"].dense.bias
                _A   : Optional[Any]       					=				roberta.model.classification_heads["mnli"].out_proj.weight
                _A   : Optional[int]       					=				roberta.model.classification_heads["mnli"].out_proj.bias
         else:
                # LM Head
                _A   : Any       					=				roberta.model.encoder.lm_head.dense.weight
                _A   : Dict       					=				roberta.model.encoder.lm_head.dense.bias
                _A   : List[str]       					=				roberta.model.encoder.lm_head.layer_norm.weight
                _A   : List[str]       					=				roberta.model.encoder.lm_head.layer_norm.bias
                _A   : List[Any]       					=				roberta.model.encoder.lm_head.weight
                _A   : Optional[Any]       					=				roberta.model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         _A   : torch.Tensor       					=				roberta.encode(UpperCamelCase__		).unsqueeze(0		)  # batch of size 1
  
         _A   : int       					=				model(UpperCamelCase__		)[0]
         if classification_head:
                _A   : str       					=				roberta.model.classification_heads["mnli"](roberta.extract_features(UpperCamelCase__		)		)
         else:
                _A   : int       					=				roberta.model(UpperCamelCase__		)[0]
         print(our_output.shape       ,							their_output.shape		)
         _A   : List[Any]       					=				torch.max(torch.abs(our_output - their_output		)		).item()
         print(f"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
         _A   : List[Any]       					=				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__":
      lowerCAmelCase__   	=				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.'
      )
      lowerCAmelCase__   	=				parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
  