code: 
  
  
  
  
  
  
  import argparse
  import logging
  import os
  import sys
  import time
  
  import tensorflow as tf
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
  from transformers.utils import is_sagemaker_dp_enabled
  
  
  if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
     __A  							=  True
     __A  							=  "p3dn.24xlarge"
     import smdistributed.dataparallel.tensorflow as sdp
  else:
     __A  							=  False
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	List[Any]     ,			A_:	str     ,			A_:	int     ,			A_:	str     ,			A_:	Optional[Any]     ,			A_:	List[Any]     ,			A_:	Tuple=None		)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tqdm(A_		)
         for i, batch in enumerate(A_		):
                with tf.GradientTape() as tape:
                       __UpperCAmelCase							,   __UpperCAmelCase    							=batch
                       __UpperCAmelCase    							=model(A_		)
                       __UpperCAmelCase    							=loss(A_     ,			outputs.logits		)
  
                if SDP_ENABLED:
                       __UpperCAmelCase    							=sdp.DistributedGradientTape(A_     ,			sparse_as_dense=A_		)
  
                __UpperCAmelCase    							=tape.gradient(A_     ,			model.trainable_variables		)
                opt.apply_gradients(zip(A_     ,			model.trainable_variables		)		)
  
                pbar.set_description(F'''Loss: {loss_value:.4f}'''		)
  
                if SDP_ENABLED and i == 0:
                       sdp.broadcast_variables(model.variables     ,			root_rank=0		)
                       sdp.broadcast_variables(opt.variables()     ,			root_rank=0		)
  
                if max_steps and i >= max_steps:
                       break
  
         __UpperCAmelCase    							={"""loss""": loss_value.numpy()}
         return train_results
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	List[str]     ,			A_:	str     ,			A_:	List[Any]		)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=load_dataset("""imdb"""     ,			split=["""train""", """test"""]		)
  
         # Preprocess train dataset
         __UpperCAmelCase    							=train_dataset.map(
             lambda A_		: tokenizer(e["""text"""]     ,			truncation=A_     ,			padding="""max_length"""		)     ,			batched=A_		)
         train_dataset.set_format(type="""tensorflow"""     ,			columns=["""input_ids""", """attention_mask""", """label"""]		)
  
         __UpperCAmelCase    							={
             x: train_dataset[x].to_tensor(default_value=0     ,			shape=[None, tokenizer.model_max_length]		)
             for x in ["""input_ids""", """attention_mask"""]
         }
         __UpperCAmelCase    							=tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])		)
  
         # Preprocess test dataset
         __UpperCAmelCase    							=test_dataset.map(
             lambda A_		: tokenizer(e["""text"""]     ,			truncation=A_     ,			padding="""max_length"""		)     ,			batched=A_		)
         test_dataset.set_format(type="""tensorflow"""     ,			columns=["""input_ids""", """attention_mask""", """label"""]		)
  
         __UpperCAmelCase    							={
             x: test_dataset[x].to_tensor(default_value=0     ,			shape=[None, tokenizer.model_max_length]		)
             for x in ["""input_ids""", """attention_mask"""]
         }
         __UpperCAmelCase    							=tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])		)
  
         if SDP_ENABLED:
                __UpperCAmelCase    							=tf_train_dataset.shard(sdp.size()     ,			sdp.rank()		)
                __UpperCAmelCase    							=tf_test_dataset.shard(sdp.size()     ,			sdp.rank()		)
         __UpperCAmelCase    							=tf_train_dataset.batch(A_     ,			drop_remainder=A_		)
         __UpperCAmelCase    							=tf_test_dataset.batch(A_     ,			drop_remainder=A_		)
  
         return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
     __A  							=  argparse.ArgumentParser()
  
     # Hyperparameters sent by the client are passed as command-line arguments to the script.
     parser.add_argument("--epochs", type=int, default=3)
     parser.add_argument("--per_device_train_batch_size", type=int, default=16)
     parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
     parser.add_argument("--model_name_or_path", type=str)
     parser.add_argument("--learning_rate", type=str, default=5E-5)
     parser.add_argument("--do_train", type=bool, default=True)
     parser.add_argument("--do_eval", type=bool, default=True)
     parser.add_argument("--output_dir", type=str)
     parser.add_argument("--max_steps", type=int, default=None)
  
     # Data, model, and output directories
     parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
     parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
     parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])
  
     __A							,    __A  							=  parser.parse_known_args()
  
     # Set up logging
     __A  							=  logging.getLogger(__name__)
  
     logging.basicConfig(
         level=logging.getLevelName("INFO"),
         handlers=[logging.StreamHandler(sys.stdout)],
         format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
     )
  
     if SDP_ENABLED:
        sdp.init()
  
        __A  							=  tf.config.experimental.list_physical_devices("GPU")
        for gpu in gpus:
           tf.config.experimental.set_memory_growth(gpu, True)
        if gpus:
           tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")
  
      # Load model and tokenizer
     __A  							=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
     __A  							=  AutoTokenizer.from_pretrained(args.model_name_or_path)
  
     # get datasets
     __A							,    __A  							=  get_datasets(
         tokenizer=tokenizer,
         train_batch_size=args.per_device_train_batch_size,
         eval_batch_size=args.per_device_eval_batch_size,
     )
  
     # fine optimizer and loss
     __A  							=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
     __A  							=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
     __A  							=  [tf.keras.metrics.SparseCategoricalAccuracy()]
     model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
     # Training
     if args.do_train:
        # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
        __A  							=  time.time()
        __A  							=  fit(
            model,
            loss,
            optimizer,
            tf_train_dataset,
            args.epochs,
            args.per_device_train_batch_size,
            max_steps=args.max_steps,
        )
        __A  							=  time.time() - start_train_time
        logger.info("*** Train ***")
        logger.info(F"""train_runtime = {end_train_time}""")
  
        __A  							=  os.path.join(args.output_dir, "train_results.txt")
  
        if not SDP_ENABLED or sdp.rank() == 0:
           with open(output_eval_file, "w") as writer:
              logger.info("***** Train results *****")
              logger.info(train_results)
              for key, value in train_results.items():
                 logger.info(F"""  {key} = {value}""")
                 writer.write(F"""{key} = {value}\n""")
  
      # Evaluation
     if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
        __A  							=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
        logger.info("*** Evaluate ***")
  
        __A  							=  os.path.join(args.output_dir, "eval_results.txt")
  
        with open(output_eval_file, "w") as writer:
           logger.info("***** Eval results *****")
           logger.info(result)
           for key, value in result.items():
              logger.info(F"""  {key} = {value}""")
              writer.write(F"""{key} = {value}\n""")
  
      # Save result
     if SDP_ENABLED:
        if sdp.rank() == 0:
           model.save_pretrained(args.output_dir)
           tokenizer.save_pretrained(args.output_dir)
     else:
        model.save_pretrained(args.output_dir)
        tokenizer.save_pretrained(args.output_dir)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import argparse
  import os
  import pathlib
  
  import fairseq
  import torch
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import (
      DataaVecTextConfig,
      DataaVecTextForMaskedLM,
      DataaVecTextForSequenceClassification,
      DataaVecTextModel,
  )
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  
  # IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
  # File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
  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 			lowercase__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
         __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
             A_     ,			checkpoint_file=A_		)
         dataavec.eval()  # disable dropout
         __UpperCAmelCase    							=dataavec.models[0]
         __UpperCAmelCase    							=dataavec_model.encoder.sentence_encoder
         __UpperCAmelCase    							=DataaVecTextConfig(
             vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings     ,			hidden_size=dataavec_model.args.encoder_embed_dim     ,			num_hidden_layers=dataavec_model.args.encoder_layers     ,			num_attention_heads=dataavec_model.args.encoder_attention_heads     ,			intermediate_size=dataavec_model.args.encoder_ffn_embed_dim     ,			max_position_embeddings=514     ,			type_vocab_size=1     ,			layer_norm_eps=1e-5     ,			)
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
         print("""Our BERT config:"""     ,			A_		)
  
         __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_tokens.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_positions.weight
         __UpperCAmelCase    							=torch.zeros_like(
             model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.bias
  
         for i in range(config.num_hidden_layers		):
                # Encoder: start of layer
                __UpperCAmelCase    							=model.dataavec_text.encoder.layer[i]
                __UpperCAmelCase    							=dataavec_sent_encoder.layers[i]
  
                # self attention
                __UpperCAmelCase    							=layer.attention.self
                assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
                assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
                assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
  
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.bias
  
                # self-attention output
                __UpperCAmelCase    							=layer.attention.output
                assert (
                    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
                ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.bias
  
                # intermediate
                __UpperCAmelCase    							=layer.intermediate
                assert (
                    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
                ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
  
                # output
                __UpperCAmelCase    							=layer.output
                assert (
                    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
                ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.bias
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.bias
         else:
                # LM Head
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         __UpperCAmelCase    							=dataavec.encode(A_		).unsqueeze(0		)  # batch of size 1
  
         __UpperCAmelCase    							=model(A_		)[0]
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(A_		)		)
         else:
                __UpperCAmelCase    							=dataavec_model(A_		)[0]
         print(our_output.shape     ,			their_output.shape		)
         __UpperCAmelCase    							=torch.max(torch.abs(our_output - their_output		)		).item()
         print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
         __UpperCAmelCase    							=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(
         "--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_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )
  
  
  
  
  