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


def       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	[len(UpperCAmelCase_		) for line in example['content'].splitlines()]
   return {"line_mean": np.mean(UpperCAmelCase_		), "line_max": max(UpperCAmelCase_		)}


def       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	np.mean([c.isalnum() for c in example['''content''']]		)
   return {"alpha_frac": alpha_frac}


def       A__      (    __lowerCamelCase,   __lowerCamelCase		):
   if example["hash"] in uniques:
      uniques.remove(example['''hash''']		)
      return True
   else:
      return False


def       A__      (    __lowerCamelCase,   __lowerCamelCase=5		):
   SCREAMING_SNAKE_CASE_     =	['auto-generated', 'autogenerated', 'automatically generated']
   SCREAMING_SNAKE_CASE_     =	example['content'].splitlines()
   for _, line in zip(range(UpperCAmelCase_		),   UpperCAmelCase_		):
      for keyword in keywords:
         if keyword in line.lower():
            return {"autogenerated": True}
   else:
      return {"autogenerated": False}


def       A__      (    __lowerCamelCase,   __lowerCamelCase=5,   __lowerCamelCase=0.05		):
   SCREAMING_SNAKE_CASE_     =	['unit tests', 'test file', 'configuration file']
   SCREAMING_SNAKE_CASE_     =	example['content'].splitlines()
   SCREAMING_SNAKE_CASE_     =	0
   SCREAMING_SNAKE_CASE_     =	0
   # first test
   for _, line in zip(range(UpperCAmelCase_		),   UpperCAmelCase_		):
      for keyword in keywords:
         if keyword in line.lower():
            return {"config_or_test": True}
    # second test
   SCREAMING_SNAKE_CASE_     =	example['content'].count('''\n'''		)
   SCREAMING_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       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	['def ', 'class ', 'for ', 'while ']
   SCREAMING_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       A__      (    __lowerCamelCase,   __lowerCamelCase=4		):
   SCREAMING_SNAKE_CASE_     =	example['content'].splitlines()
   SCREAMING_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       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	tokenizer(example['''content'''],   truncation=UpperCAmelCase_		)['input_ids']
   SCREAMING_SNAKE_CASE_     =	len(example['''content''']		) / len(UpperCAmelCase_		)
   return {"ratio": ratio}


def       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	{}
   results.update(get_hash(UpperCAmelCase_		)		)
   results.update(line_stats(UpperCAmelCase_		)		)
   results.update(alpha_stats(UpperCAmelCase_		)		)
   results.update(char_token_ratio(UpperCAmelCase_		)		)
   results.update(is_autogenerated(UpperCAmelCase_		)		)
   results.update(is_config_or_test(UpperCAmelCase_		)		)
   results.update(has_no_keywords(UpperCAmelCase_		)		)
   results.update(has_few_assignments(UpperCAmelCase_		)		)
   return results


def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase		):
   if not check_uniques(UpperCAmelCase_,   UpperCAmelCase_		):
      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       A__      (    __lowerCamelCase		):
   with open(UpperCAmelCase_,   '''rb'''		) as f_in:
      with gzip.open(str(UpperCAmelCase_		) + '''.gz''',   '''wb''',   compresslevel=6		) as f_out:
         shutil.copyfileobj(UpperCAmelCase_,   UpperCAmelCase_		)
   os.unlink(UpperCAmelCase_		)


# 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							=			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}""")






'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


A__		:					str												=  [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
A__		:					Dict												=  [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

A__		:					int												=  (
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  (
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  [
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any							)     ->		List[str]:
     for tf_name, hf_name in patterns:
          __lowerCamelCase   :	Optional[int]								=		k.replace(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return k



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				dict							)     ->		BigBirdPegasusForConditionalGeneration:
     __lowerCamelCase   :	int								=		BigBirdPegasusConfig(**UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		BigBirdPegasusForConditionalGeneration(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		torch_model.state_dict()
     __lowerCamelCase   :	Tuple								=		{}

     # separating decoder weights
     __lowerCamelCase   :	Dict								=		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'							)}
     __lowerCamelCase   :	str								=		{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'							)}

     for k, v in tqdm(decoder_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Tuple								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Tuple								=		DECODER_PATTERNS
          __lowerCamelCase   :	Optional[int]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict:
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	Union[str, Any]								=		v.T
          __lowerCamelCase   :	str								=		torch.from_numpy(UpperCAmelCase_							)
          assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     for k, v in tqdm(remaining_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Optional[Any]								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Dict								=		REMAINING_PATTERNS
          __lowerCamelCase   :	List[str]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	List[str]								=		v.T
          __lowerCamelCase   :	List[str]								=		torch.from_numpy(UpperCAmelCase_							)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     __lowerCamelCase   :	Any								=		mapping['model.embed_positions.weight']
     __lowerCamelCase   :	Union[str, Any]								=		mapping.pop('model.embed_positions.weight'							)
     __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		torch_model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		[
         k
         for k in missing
         if k
         not in [
             'final_logits_bias',
             'model.encoder.embed_tokens.weight',
             'model.decoder.embed_tokens.weight',
             'lm_head.weight',
         ]
     ]
     assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], F'no matches found for the following tf keys {extra}'
     return torch_model



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	int								=		tf.train.list_variables(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		{}
     __lowerCamelCase   :	List[Any]								=		['global_step']
     for name, shape in tqdm(UpperCAmelCase_      ,    desc='converting tf checkpoint to dict'							):
          __lowerCamelCase   :	Any								=		any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          __lowerCamelCase   :	Dict								=		tf.train.load_variable(UpperCAmelCase_      ,    UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		array
     return tf_weights



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				dict							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		get_tf_weights_as_numpy(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		convert_bigbird_pegasus(UpperCAmelCase_      ,    UpperCAmelCase_							)
     torch_model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					Any												=  argparse.ArgumentParser()
      parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
      parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
      A__		:					List[str]												=  parser.parse_args()
      A__		:					Optional[int]												=  {}
      convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






