

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def 						lowerCamelCase__      ( __A    :Tuple		):


       """simple docstring"""




       __snake_case       	=							[
           """encoder.version""",
           """decoder.version""",
           """model.encoder.version""",
           """model.decoder.version""",
           """decoder.output_projection.weight""",
           """_float_tensor""",
           """encoder.embed_positions._float_tensor""",
           """decoder.embed_positions._float_tensor""",
       ]
       for k in ignore_keys:
              state_dict.pop(__A	,__A		)







def 						lowerCamelCase__      ( __A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							list(s_dict.keys()		)
       for key in keys:
              if "transformer_layers" in key:
                     __snake_case       	=							s_dict.pop(__A		)
              elif "subsample" in key:
                     __snake_case       	=							s_dict.pop(__A		)







def 						lowerCamelCase__      ( __A    :List[Any]		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							emb.weight.shape
       __snake_case       	=							nn.Linear(__A	,__A	,bias=__A		)
       __snake_case       	=							emb.weight.data
       return lin_layer







def 						lowerCamelCase__      ( __A    :Any	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							torch.load(__A	,map_location="""cpu"""		)
       __snake_case       	=							mam_aaa["""args"""]
       __snake_case       	=							mam_aaa["""model"""]
       __snake_case       	=							state_dict["""decoder.output_projection.weight"""]

       remove_ignore_keys_(__A		)
       rename_keys(__A		)

       __snake_case       	=							state_dict["""decoder.embed_tokens.weight"""].shape[0]

       __snake_case       	=							args.share_decoder_input_output_embed

       __snake_case       	=							[int(__A		) for i in args.conv_kernel_sizes.split(""","""		)]
       __snake_case       	=							SpeechaTextConfig(
           vocab_size=__A	,max_source_positions=args.max_source_positions	,max_target_positions=args.max_target_positions	,encoder_layers=args.encoder_layers	,decoder_layers=args.decoder_layers	,encoder_attention_heads=args.encoder_attention_heads	,decoder_attention_heads=args.decoder_attention_heads	,encoder_ffn_dim=args.encoder_ffn_embed_dim	,decoder_ffn_dim=args.decoder_ffn_embed_dim	,d_model=args.encoder_embed_dim	,dropout=args.dropout	,attention_dropout=args.attention_dropout	,activation_dropout=args.activation_dropout	,activation_function="""relu"""	,num_conv_layers=len(__A		)	,conv_channels=args.conv_channels	,conv_kernel_sizes=__A	,input_feat_per_channel=args.input_feat_per_channel	,input_channels=args.input_channels	,tie_word_embeddings=__A	,num_beams=5	,max_length=2_0_0	,use_cache=__A	,decoder_start_token_id=2	,early_stopping=__A	,)

       __snake_case       	=							SpeechaTextForConditionalGeneration(__A		)
       __snake_case	,    __snake_case       	=							model.model.load_state_dict(__A	,strict=__A		)
       if len(__A		) > 0 and not set(__A		) <= {
           "encoder.embed_positions.weights",
           "decoder.embed_positions.weights",
       }:
              raise ValueError(
                  """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                  F' but all the following weights are missing {missing}'		)

       if tie_embeds:
              __snake_case       	=							make_linear_from_emb(model.model.decoder.embed_tokens		)
       else:
              __snake_case       	=							lm_head_weights

       model.save_pretrained(__A		)


if __name__ == "__main__":
    UpperCamelCase__		 =     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    UpperCamelCase__		 =     parser.parse_args()
    convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)




import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


UpperCamelCase__		 =     re.compile(r'''\s+''')







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




       return {"hash": hashlib.mda(re.sub(__A	,""""""	,example["""content"""]		).encode("""utf-8"""		)		).hexdigest()}







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




       __snake_case       	=							[len(__A		) for line in example["""content"""].splitlines()]
       return {"line_mean": np.mean(__A		), "line_max": max(__A		)}







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




       __snake_case       	=							np.mean([c.isalnum() for c in example["""content"""]]		)
       return {"alpha_frac": alpha_frac}







def 						lowerCamelCase__      ( __A    :str	,__A    :int		):


       """simple docstring"""




       if example["hash"] in uniques:
              uniques.remove(example["""hash"""]		)
              return True
       else:
              return False







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :int=5		):


       """simple docstring"""




       __snake_case       	=							["""auto-generated""", """autogenerated""", """automatically generated"""]
       __snake_case       	=							example["""content"""].splitlines()
       for _, line in zip(range(__A		)	,__A		):
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"autogenerated": True}
       else:
              return {"autogenerated": False}







def 						lowerCamelCase__      ( __A    :int	,__A    :int=5	,__A    :str=0.05		):


       """simple docstring"""




       __snake_case       	=							["""unit tests""", """test file""", """configuration file"""]
       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							0
       __snake_case       	=							0
       # first test
       for _, line in zip(range(__A		)	,__A		):
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"config_or_test": True}
    # second test
       __snake_case       	=							example["""content"""].count("""\n"""		)
       __snake_case       	=							int(coeff * nlines		)
       for line in lines:
              count_config += line.lower().count("""config"""		)
              count_test += line.lower().count("""test"""		)
              if count_config > threshold or count_test > threshold:
                     return {"config_or_test": True}
       return {"config_or_test": False}







def 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       __snake_case       	=							["""def """, """class """, """for """, """while """]
       __snake_case       	=							example["""content"""].splitlines()
       for line in lines:
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"has_no_keywords": False}
       return {"has_no_keywords": True}







def 						lowerCamelCase__      ( __A    :Dict	,__A    :List[str]=4		):


       """simple docstring"""




       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							0
       for line in lines:
              counter += line.lower().count("""="""		)
              if counter > minimum:
                     return {"has_few_assignments": False}
       return {"has_few_assignments": True}







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       __snake_case       	=							tokenizer(example["""content"""]	,truncation=__A		)["""input_ids"""]
       __snake_case       	=							len(example["""content"""]		) / len(__A		)
       return {"ratio": ratio}







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							{}
       results.update(get_hash(__A		)		)
       results.update(line_stats(__A		)		)
       results.update(alpha_stats(__A		)		)
       results.update(char_token_ratio(__A		)		)
       results.update(is_autogenerated(__A		)		)
       results.update(is_config_or_test(__A		)		)
       results.update(has_no_keywords(__A		)		)
       results.update(has_few_assignments(__A		)		)
       return results







def 						lowerCamelCase__      ( __A    :int	,__A    :str	,__A    :Dict		):


       """simple docstring"""




       if not check_uniques(__A	,__A		):
              return False
       elif example["autogenerated"]:
              return False
       elif example["line_max"] > args.line_max:
              return False
       elif example["line_mean"] > args.line_mean:
              return False
       elif example["alpha_frac"] < args.alpha_frac:
              return False
       elif example["ratio"] < args.min_token_ratio:
              return False
       elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
              return False
       elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
              return False
       elif example["has_few_assignments"]:
              return False
       else:
              return True







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       with open(__A	,"""rb"""		) as f_in:
              with gzip.open(str(__A		) + """.gz"""	,"""wb"""	,compresslevel=6		) as f_out:
                     shutil.copyfileobj(__A	,__A		)
       os.unlink(__A		)


# Settings
UpperCamelCase__		 =     HfArgumentParser(PreprocessingArguments)
UpperCamelCase__		 =     parser.parse_args()
if args.num_workers is None:
    UpperCamelCase__		 =     multiprocessing.cpu_count()
UpperCamelCase__		 =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
UpperCamelCase__		 =     time.time()
UpperCamelCase__		 =     load_dataset(args.dataset_name, split='''train''')
print(F'Time to load dataset: {time.time()-t_start:.2f}')

# Run preprocessing
UpperCamelCase__		 =     time.time()
UpperCamelCase__		 =     ds.map(preprocess, num_proc=args.num_workers)
print(F'Time to preprocess dataset: {time.time()-t_start:.2f}')

# Deduplicate hashes
UpperCamelCase__		 =     set(ds.unique('''hash'''))
UpperCamelCase__		 =     len(uniques) / len(ds)
print(F'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
UpperCamelCase__		 =     time.time()
UpperCamelCase__		 =     ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'Time to filter dataset: {time.time()-t_start:.2f}')
print(F'Size of filtered dataset: {len(ds_filter)}')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
    UpperCamelCase__		 =     time.time()
    UpperCamelCase__ ,UpperCamelCase__		 =     deduplicate_dataset(ds_filter, args.jaccard_threshold)
    print(F'Time to deduplicate dataset: {time.time()-t_start:.2f}')
    print(F'Size of deduplicate dataset: {len(ds_filter)}')

# Save data in batches of samples_per_file
UpperCamelCase__		 =     Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
    with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
        json.dump(duplicate_clusters, f)

UpperCamelCase__		 =     output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

UpperCamelCase__		 =     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
    UpperCamelCase__		 =     str(data_dir / F'file-{file_number+1:012}.json')
    UpperCamelCase__		 =     min(len(ds_filter), index + args.samples_per_file)
    ds_filter.select(list(range(index, end_index))).to_json(file_path)
    compress_file(file_path)




print(F'Time to save dataset: {time.time()-t_start:.2f}')

