


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():
       __lowerCamelCase   	=  True
       __lowerCamelCase   	=  '''p3dn.24xlarge'''
       import smdistributed.dataparallel.tensorflow as sdp
else:
       __lowerCamelCase   	=  False



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase=None			):
     a_     :  int				      =     tqdm(__UpperCamelCase			)
     for i, batch in enumerate(__UpperCamelCase			):
          with tf.GradientTape() as tape:
               a_      ,      a_     :  List[Any]				      =     batch
               a_     :  List[str]				      =     model(__UpperCamelCase			)
               a_     :  Dict				      =     loss(__UpperCamelCase	,	outputs.logits			)

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

          a_     :  Optional[Any]				      =     tape.gradient(__UpperCamelCase	,	model.trainable_variables			)
          opt.apply_gradients(zip(__UpperCamelCase	,	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

     a_     :  List[str]				      =     {"""loss""": loss_value.numpy()}
     return train_results



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     # Load dataset
     a_      ,      a_     :  int				      =     load_dataset("""imdb"""	,	split=["""train""", """test"""]			)

     # Preprocess train dataset
     a_     :  Optional[Any]				      =     train_dataset.map(
         lambda __UpperCamelCase			: tokenizer(e["""text"""]	,	truncation=__UpperCamelCase	,	padding="""max_length"""			)	,	batched=__UpperCamelCase			)
     train_dataset.set_format(type="""tensorflow"""	,	columns=["""input_ids""", """attention_mask""", """label"""]			)

     a_     :  Optional[Any]				      =     {
         x: train_dataset[x].to_tensor(default_value=0	,	shape=[None, tokenizer.model_max_length]			)
         for x in ["""input_ids""", """attention_mask"""]
     }
     a_     :  int				      =     tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])			)

     # Preprocess test dataset
     a_     :  Any				      =     test_dataset.map(
         lambda __UpperCamelCase			: tokenizer(e["""text"""]	,	truncation=__UpperCamelCase	,	padding="""max_length"""			)	,	batched=__UpperCamelCase			)
     test_dataset.set_format(type="""tensorflow"""	,	columns=["""input_ids""", """attention_mask""", """label"""]			)

     a_     :  int				      =     {
         x: test_dataset[x].to_tensor(default_value=0	,	shape=[None, tokenizer.model_max_length]			)
         for x in ["""input_ids""", """attention_mask"""]
     }
     a_     :  Optional[int]				      =     tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])			)

     if SDP_ENABLED:
          a_     :  int				      =     tf_train_dataset.shard(sdp.size()	,	sdp.rank()			)
          a_     :  Optional[Any]				      =     tf_test_dataset.shard(sdp.size()	,	sdp.rank()			)
     a_     :  str				      =     tf_train_dataset.batch(__UpperCamelCase	,	drop_remainder=__UpperCamelCase			)
     a_     :  List[str]				      =     tf_test_dataset.batch(__UpperCamelCase	,	drop_remainder=__UpperCamelCase			)

     return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
       __lowerCamelCase   	=  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'''])

       __lowerCamelCase	,							__lowerCamelCase   	=  parser.parse_known_args()

       # Set up logging
       __lowerCamelCase   	=  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()

              __lowerCamelCase   	=  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
       __lowerCamelCase   	=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       __lowerCamelCase   	=  AutoTokenizer.from_pretrained(args.model_name_or_path)

       # get datasets
       __lowerCamelCase	,							__lowerCamelCase   	=  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
       __lowerCamelCase   	=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       __lowerCamelCase   	=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       __lowerCamelCase   	=  [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)
              __lowerCamelCase   	=  time.time()
              __lowerCamelCase   	=  fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              __lowerCamelCase   	=  time.time() - start_train_time
              logger.info('''*** Train ***''')
              logger.info(f'''train_runtime = {end_train_time}''')

              __lowerCamelCase   	=  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):
              __lowerCamelCase   	=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info('''*** Evaluate ***''')

              __lowerCamelCase   	=  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
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()
UpperCamelCase			     =					logging.get_logger(__name__)

UpperCamelCase			     =					{
    '''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''',
}
UpperCamelCase			     =					[
    '''lm_head''',
]
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Any:
     for attribute in key.split('''.'''	):
          A:		Union[str, Any]          =		getattr(__lowercase		,				__lowercase	)

     if weight_type is not None:
          A:		Tuple          =		getattr(__lowercase		,				__lowercase	).shape
     else:
          A:		Optional[int]          =		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":
          A:		int          =		value
     elif weight_type == "weight_g":
          A:		Any          =		value
     elif weight_type == "weight_v":
          A:		str          =		value
     elif weight_type == "bias":
          A:		List[str]          =		value
     else:
          A:		str          =		value

     logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase	)			-> Dict:
     A:		Optional[int]          =		[]
     A:		Tuple          =		fairseq_model.state_dict()

     if not is_headless:
          A:		int          =		hf_model.dataavec_audio.feature_extractor
          A:		Dict          =		hf_model.dataavec_audio.encoder.pos_conv_embed

     else:
          A:		str          =		hf_model.feature_extractor
          A:		Tuple          =		hf_model.encoder.pos_conv_embed

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

     logger.warning(F"""Unused weights: {unused_weights}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase	)			-> str:
     A:		int          =		path.split('''.'''	)
     return reduce(__lowercase		,				__lowercase		,				__lowercase	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Tuple          =		access_by_string(__lowercase		,				__lowercase	)
     A:		Tuple          =		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."""	)
     A:		List[Any]          =		fsq_value
     logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> List[Any]:
     A:		List[Any]          =		full_name.split('''conv_layers.'''	)[-1]
     A:		List[str]          =		name.split('''.'''	)
     A:		Tuple          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

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

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Any          =		full_name.split('''pos_conv.'''	)[-1]
     A:		Any          =		name.split('''.'''	)
     A:		int          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		Optional[Any]          =		name.split('''.'''	)[-1]
     if type_id != 0:
          unused_weights.append(__lowercase	)
          return
     else:
          A:		List[str]          =		'''conv'''

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""layers.{layer_id}.{layer_type}.{weight_type}"""	)
@torch.no_grad()
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase=None		,				__lowercase=None		,				__lowercase=True	)			-> Optional[Any]:
     if config_path is not None:
          A:		Tuple          =		DataaVecAudioConfig.from_pretrained(__lowercase	)
     else:
          A:		Optional[Any]          =		DataaVecAudioConfig()

     if not is_finetuned:
          # Modify final_proj layer name
          A:		Union[str, Any]          =		DataaVecAudioModel(__lowercase	)
          A:		List[Any]          =		os.path.dirname(__lowercase	)

          A:		Union[str, Any]          =		torch.load(__lowercase	)
          A:		int          =		state_dict['''model'''].pop('''final_proj.0.weight'''	)
          A:		str          =		state_dict['''model'''].pop('''final_proj.0.bias'''	)
          A:		Any          =		os.path.join(__lowercase		,				'''converted.pt'''	)
          torch.save(__lowercase		,				__lowercase	)
     else:
          A:		Tuple          =		DataaVecAudioForCTC(__lowercase	)
          A:		Optional[int]          =		checkpoint_path

     def load_dataavec(__lowercase	):
          A     ,    A     ,    A:		List[Any]          =		fairseq.checkpoint_utils.load_model_ensemble_and_task([path]	)
          return model[0].eval()

     A:		Any          =		load_dataavec(__lowercase	)

     recursively_load_weights(__lowercase		,				__lowercase		,				not is_finetuned	)

     A:		Optional[int]          =		WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''	)

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

     A:		Dict          =		processor(__lowercase		,				return_tensors='''pt'''		,				padding=__lowercase	)

     A:		str          =		inputs.input_values
     A:		str          =		inputs.attention_mask
     #    input_values = inputs.input_values[:, :-1]
     #    attention_mask = inputs.attention_mask[:, :-1]

     hf_wavavec.eval()
     model.eval()
     if is_finetuned:
          A:		Any          =		model(source=__lowercase		,				padding_mask=(1 - attention_mask)		,				mask=__lowercase		,				features_only=__lowercase	)[
              '''encoder_out'''
          ].transpose(0		,				1	)
          A:		int          =		hf_wavavec(__lowercase		,				attention_mask=__lowercase	)['''logits''']

          A:		str          =		torch.argmax(__lowercase		,				dim=-1	)
          A:		Any          =		processor.batch_decode(__lowercase	)

          print(F"""Expected Output: {ds[:4]['text']}, Pred: {output_string}"""	)
     else:
          A:		int          =		model(source=__lowercase		,				padding_mask=(1 - attention_mask)		,				mask=__lowercase		,				features_only=__lowercase	)[
              '''layer_results'''
          ][-1][0].transpose(0		,				1	)
          A:		List[Any]          =		hf_wavavec(__lowercase		,				attention_mask=__lowercase	)['''last_hidden_state''']

     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:		Optional[int]          =		torch.allclose(__lowercase		,				__lowercase		,				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(__lowercase	)

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


if __name__ == "__main__":
     UpperCamelCase			     =					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'''
     )
     UpperCamelCase			     =					parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )






