
"""simple docstring"""





import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_a									=				logging.get_logger(__name__)

_a									=				{
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
_a									=				[
    'lm_head',
]







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   for attribute in key.split("."    ):
      UpperCAmelCase_   :   Any     	=       getattr(__lowerCamelCase,						__lowerCamelCase    )

   if weight_type is not None:
      UpperCAmelCase_   :   int     	=       getattr(__lowerCamelCase,						__lowerCamelCase    ).shape
   else:
      UpperCAmelCase_   :   Union[str, Any]     	=       hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
          f""" {value.shape} for {full_name}"""    )

   if weight_type == "weight":
      UpperCAmelCase_   :   Tuple     	=       value
   elif weight_type == "weight_g":
      UpperCAmelCase_   :   Optional[int]     	=       value
   elif weight_type == "weight_v":
      UpperCAmelCase_   :   str     	=       value
   elif weight_type == "bias":
      UpperCAmelCase_   :   Any     	=       value
   else:
      UpperCAmelCase_   :   List[str]     	=       value

   logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[Any]     	=       []
   UpperCAmelCase_   :   Any     	=       fairseq_model.state_dict()

   if not is_headless:
      UpperCAmelCase_   :   Any     	=       hf_model.dataavec_audio.feature_extractor
      UpperCAmelCase_   :   int     	=       hf_model.dataavec_audio.encoder.pos_conv_embed

   else:
      UpperCAmelCase_   :   Optional[Any]     	=       hf_model.feature_extractor
      UpperCAmelCase_   :   List[Any]     	=       hf_model.encoder.pos_conv_embed

   for name, value in fairseq_dict.items():
      UpperCAmelCase_   :   Any     	=       False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						)
         UpperCAmelCase_   :   Tuple     	=       True
      elif "pos_conv" in name:
         load_pos_conv_layer(
             __lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						)
         UpperCAmelCase_   :   Dict     	=       True
      else:
         for key, mapped_key in MAPPING.items():
            if not is_headless:
               UpperCAmelCase_   :   List[Any]     	=       "data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
               UpperCAmelCase_   :   Optional[Any]     	=       True
               if "*" in mapped_key:
                  UpperCAmelCase_   :   Any     	=       name.split(__lowerCamelCase    )[0].split("."    )[-2]
                  UpperCAmelCase_   :   Tuple     	=       mapped_key.replace("*",						__lowerCamelCase    )
               if "weight_g" in name:
                  UpperCAmelCase_   :   Optional[int]     	=       "weight_g"
               elif "weight_v" in name:
                  UpperCAmelCase_   :   str     	=       "weight_v"
               elif "bias" in name:
                  UpperCAmelCase_   :   Optional[int]     	=       "bias"
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  UpperCAmelCase_   :   Any     	=       "weight"
               else:
                  UpperCAmelCase_   :   Union[str, Any]     	=       None
               set_recursively(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
            continue
      if not is_used:
         unused_weights.append(__lowerCamelCase    )

   logger.warning(f"""Unused weights: {unused_weights}"""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       path.split("."    )
   return reduce(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Any     	=       access_by_string(__lowerCamelCase,						__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       hf_weight.data

   if fsq_value.shape != hf_value.shape:
      raise ValueError(f"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""    )
   UpperCAmelCase_   :   str     	=       fsq_value
   logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       full_name.split("conv_layers."    )[-1]
   UpperCAmelCase_   :   Union[str, Any]     	=       name.split("."    )
   UpperCAmelCase_   :   Tuple     	=       int(items[0]    )
   UpperCAmelCase_   :   Union[str, Any]     	=       int(items[1]    )

   UpperCAmelCase_   :   Optional[Any]     	=       name.split("."    )[-1]
   if type_id == 0:
      UpperCAmelCase_   :   List[Any]     	=       "conv"
   elif type_id == 2:
      UpperCAmelCase_   :   str     	=       "layer_norm"
   else:
      unused_weights.append(__lowerCamelCase    )
      return

   set_weights(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						f"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       full_name.split("pos_conv."    )[-1]
   UpperCAmelCase_   :   Any     	=       name.split("."    )
   UpperCAmelCase_   :   List[Any]     	=       int(items[0]    )
   UpperCAmelCase_   :   str     	=       int(items[1]    )

   UpperCAmelCase_   :   Tuple     	=       name.split("."    )[-1]
   if type_id != 0:
      unused_weights.append(__lowerCamelCase    )
      return
   else:
      UpperCAmelCase_   :   Tuple     	=       "conv"

   set_weights(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						f"""layers.{layer_id}.{layer_type}.{weight_type}"""    )







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=None,						__lowerCamelCase=None,						__lowerCamelCase=True    ):
   if config_path is not None:
      UpperCAmelCase_   :   Dict     	=       DataaVecAudioConfig.from_pretrained(__lowerCamelCase    )
   else:
      UpperCAmelCase_   :   Dict     	=       DataaVecAudioConfig()

   if not is_finetuned:
      # Modify final_proj layer name
      UpperCAmelCase_   :   int     	=       DataaVecAudioModel(__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       os.path.dirname(__lowerCamelCase    )

      UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       state_dict["model"].pop("final_proj.0.weight"    )
      UpperCAmelCase_   :   List[str]     	=       state_dict["model"].pop("final_proj.0.bias"    )
      UpperCAmelCase_   :   List[str]     	=       os.path.join(__lowerCamelCase,						"converted.pt"    )
      torch.save(__lowerCamelCase,						__lowerCamelCase    )
   else:
      UpperCAmelCase_   :   str     	=       DataaVecAudioForCTC(__lowerCamelCase    )
      UpperCAmelCase_   :   str     	=       checkpoint_path

   def load_dataavec(__lowerCamelCase    ):
      UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
      return model[0].eval()

   UpperCAmelCase_   :   Union[str, Any]     	=       load_dataavec(__lowerCamelCase    )

   recursively_load_weights(__lowerCamelCase,						__lowerCamelCase,						not is_finetuned    )

   UpperCAmelCase_   :   int     	=       WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"    )

   UpperCAmelCase_   :   Any     	=       load_dataset("patrickvonplaten/librispeech_asr_dummy",						"clean",						split="validation"    )
   UpperCAmelCase_   :   Optional[Any]     	=       [x["array"] for x in ds[:4]["audio"]]

   UpperCAmelCase_   :   str     	=       processor(__lowerCamelCase,						return_tensors="pt",						padding=__lowerCamelCase    )

   UpperCAmelCase_   :   List[str]     	=       inputs.input_values
   UpperCAmelCase_   :   Union[str, Any]     	=       inputs.attention_mask
   #    input_values = inputs.input_values[:, :-1]
   #    attention_mask = inputs.attention_mask[:, :-1]

   hf_wavavec.eval()
   model.eval()
   if is_finetuned:
      UpperCAmelCase_   :   List[str]     	=       model(source=__lowerCamelCase,						padding_mask=(1 - attention_mask),						mask=__lowerCamelCase,						features_only=__lowerCamelCase    )[
          "encoder_out"
      ].transpose(0,						1    )
      UpperCAmelCase_   :   Dict     	=       hf_wavavec(__lowerCamelCase,						attention_mask=__lowerCamelCase    )["logits"]

      UpperCAmelCase_   :   int     	=       torch.argmax(__lowerCamelCase,						dim=-1    )
      UpperCAmelCase_   :   Any     	=       processor.batch_decode(__lowerCamelCase    )

      print(f"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""    )
   else:
      UpperCAmelCase_   :   Tuple     	=       model(source=__lowerCamelCase,						padding_mask=(1 - attention_mask),						mask=__lowerCamelCase,						features_only=__lowerCamelCase    )[
          "layer_results"
      ][-1][0].transpose(0,						1    )
      UpperCAmelCase_   :   Union[str, Any]     	=       hf_wavavec(__lowerCamelCase,						attention_mask=__lowerCamelCase    )["last_hidden_state"]

   print(our_output.shape,						their_output.shape    )
   UpperCAmelCase_   :   Tuple     	=       torch.max(torch.abs(our_output - their_output    )    ).item()
   print(f"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
   UpperCAmelCase_   :   Optional[Any]     	=       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"    )

   hf_wavavec.save_pretrained(__lowerCamelCase    )

   if is_finetuned:
      processor.save_pretrained(__lowerCamelCase    )
   else:
      processor.feature_extractor.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
    _a									=				argparse.ArgumentParser()
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    _a									=				parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )







'''simple docstring'''







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():
      lowercase__  :	Any            =      True
      lowercase__  :	Tuple            =      'p3dn.24xlarge'
      import smdistributed.dataparallel.tensorflow as sdp
else:
      lowercase__  :	Dict            =      False





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=None						):
    UpperCamelCase__	     =      tqdm(lowerCAmelCase_						)
    for i, batch in enumerate(lowerCAmelCase_						):
        with tf.GradientTape() as tape:
            UpperCamelCase__     ,	UpperCamelCase__	     =      batch
            UpperCamelCase__	     =      model(lowerCAmelCase_						)
            UpperCamelCase__	     =      loss(lowerCAmelCase_       ,	outputs.logits						)

        if SDP_ENABLED:
            UpperCamelCase__	     =      sdp.DistributedGradientTape(lowerCAmelCase_       ,	sparse_as_dense=lowerCAmelCase_						)

        UpperCamelCase__	     =      tape.gradient(lowerCAmelCase_       ,	model.trainable_variables						)
        opt.apply_gradients(zip(lowerCAmelCase_       ,	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     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    # Load dataset
    UpperCamelCase__     ,	UpperCamelCase__	     =      load_dataset("imdb"       ,	split=["train", "test"]						)

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

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
      lowercase__  :	Any            =      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'])

      lowercase__       ,	lowercase__  :	Union[str, Any]            =      parser.parse_known_args()

      # Set up logging
      lowercase__  :	int            =      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()

            lowercase__  :	Union[str, Any]            =      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
      lowercase__  :	int            =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      lowercase__  :	Any            =      AutoTokenizer.from_pretrained(args.model_name_or_path)

      # get datasets
      lowercase__       ,	lowercase__  :	Dict            =      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
      lowercase__  :	Any            =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      lowercase__  :	Tuple            =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      lowercase__  :	Tuple            =      [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)
            lowercase__  :	List[str]            =      time.time()
            lowercase__  :	Union[str, Any]            =      fit(
                model,
                loss,
                optimizer,
                tf_train_dataset,
                args.epochs,
                args.per_device_train_batch_size,
                max_steps=args.max_steps,
            )
            lowercase__  :	Dict            =      time.time() - start_train_time
            logger.info('*** Train ***')
            logger.info(F'''train_runtime = {end_train_time}''')

            lowercase__  :	List[str]            =      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):
            lowercase__  :	Tuple            =      model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
            logger.info('*** Evaluate ***')

            lowercase__  :	Any            =      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)




