




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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=None   )							->						Tuple:

    """simple docstring"""


    __UpperCamelCase			=    tqdm(lowercase_   )
    for i, batch in enumerate(lowercase_   ):
        with tf.GradientTape() as tape:
            __UpperCamelCase		,   __UpperCamelCase			=    batch
            __UpperCamelCase			=    model(lowercase_   )
            __UpperCamelCase			=    loss(lowercase_       ,  outputs.logits   )

        if SDP_ENABLED:
            __UpperCamelCase			=    sdp.DistributedGradientTape(lowercase_       ,  sparse_as_dense=lowercase_   )

        __UpperCamelCase			=    tape.gradient(lowercase_       ,  model.trainable_variables   )
        opt.apply_gradients(zip(lowercase_       ,  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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Tuple:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    load_dataset('''imdb'''       ,  split=['''train''', '''test''']   )

    # Preprocess train dataset
    __UpperCamelCase			=    train_dataset.map(
        lambda lowercase_   : tokenizer(e['''text''']       ,  truncation=lowercase_       ,  padding='''max_length'''   )       ,  batched=lowercase_   )
    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 lowercase_   : tokenizer(e['''text''']       ,  truncation=lowercase_       ,  padding='''max_length'''   )       ,  batched=lowercase_   )
    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(lowercase_       ,  drop_remainder=lowercase_   )
    __UpperCamelCase			=    tf_test_dataset.batch(lowercase_       ,  drop_remainder=lowercase_   )

    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)











'''simple docstring'''



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()
_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="Hello world! cécé herlolip"


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						int:
				snake_case__  ,      snake_case__      :   int      =    os.path.split(UpperCamelCase__			)
				snake_case__      :   List[Any]      =    DataaVecTextModel.from_pretrained(
				    UpperCamelCase__   ,				checkpoint_file=UpperCamelCase__			)
				dataavec.eval()  # disable dropout
				snake_case__      :   Dict      =    dataavec.models[0]
				snake_case__      :   int      =    dataavec_model.encoder.sentence_encoder
				snake_case__      :   List[Any]      =    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:
								snake_case__      :   int      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase__			)

				snake_case__      :   Optional[int]      =    DataaVecTextForSequenceClassification(UpperCamelCase__			) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Optional[Any]      =    dataavec_sent_encoder.embed_tokens.weight
				snake_case__      :   Union[str, Any]      =    dataavec_sent_encoder.embed_positions.weight
				snake_case__      :   Any      =    torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				snake_case__      :   Tuple      =    dataavec_sent_encoder.layernorm_embedding.weight
				snake_case__      :   Dict      =    dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								snake_case__      :   BertLayer      =    model.dataavec_text.encoder.layer[i]
								snake_case__      :   TransformerSentenceEncoderLayer      =    dataavec_sent_encoder.layers[i]

								# self attention
								snake_case__      :   BertSelfAttention      =    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)			)}'''
								)

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

								# self-attention output
								snake_case__      :   BertSelfOutput      =    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}'''
								snake_case__      :   Optional[int]      =    dataavec_layer.self_attn.out_proj.weight
								snake_case__      :   str      =    dataavec_layer.self_attn.out_proj.bias
								snake_case__      :   int      =    dataavec_layer.self_attn_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   BertIntermediate      =    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}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   str      =    dataavec_layer.fca.bias

								# output
								snake_case__      :   BertOutput      =    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}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   List[Any]      =    dataavec_layer.fca.bias
								snake_case__      :   str      =    dataavec_layer.final_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Any      =    dataavec.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.weight
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.bias
								snake_case__      :   Union[str, Any]      =    dataavec_model.encoder.lm_head.layer_norm.weight
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    dataavec_model.encoder.lm_head.bias

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

				snake_case__      :   str      =    model(UpperCamelCase__			)[0]
				if classification_head:
								snake_case__      :   List[str]      =    dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCamelCase__			)			)
				else:
								snake_case__      :   Tuple      =    dataavec_model(UpperCamelCase__			)[0]
				print(our_output.shape   ,				their_output.shape			)
				snake_case__      :   Dict      =    torch.max(torch.abs(our_output - their_output			)			).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
				snake_case__      :   Tuple      =    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__":
				_lowercase       :						Any						=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."
				)
				_lowercase       :						Tuple						=parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)




