
"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.xglm.modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
    )


@require_tf
class A_	:




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		XGLMConfig
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		{}
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		"""gelu"""







  def __init__(		self				,   lowercase_				,   lowercase_=14				,   lowercase_=7				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   lowercase_=99				,   lowercase_=32				,   lowercase_=2				,   lowercase_=4				,   lowercase_=37				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=512				,   lowercase_=0.02				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    parent
     UpperCAmelCase_					:					Tuple			     =    batch_size
     UpperCAmelCase_					:					Optional[int]			     =    seq_length
     UpperCAmelCase_					:					Union[str, Any]			     =    is_training
     UpperCAmelCase_					:					List[Any]			     =    use_input_mask
     UpperCAmelCase_					:					Tuple			     =    use_labels
     UpperCAmelCase_					:					List[Any]			     =    vocab_size
     UpperCAmelCase_					:					Union[str, Any]			     =    d_model
     UpperCAmelCase_					:					Optional[int]			     =    num_hidden_layers
     UpperCAmelCase_					:					List[Any]			     =    num_attention_heads
     UpperCAmelCase_					:					List[Any]			     =    ffn_dim
     UpperCAmelCase_					:					int			     =    activation_function
     UpperCAmelCase_					:					List[str]			     =    activation_dropout
     UpperCAmelCase_					:					List[Any]			     =    attention_dropout
     UpperCAmelCase_					:					List[str]			     =    max_position_embeddings
     UpperCAmelCase_					:					Optional[Any]			     =    initializer_range
     UpperCAmelCase_					:					Optional[Any]			     =    None
     UpperCAmelCase_					:					Dict			     =    0
     UpperCAmelCase_					:					List[str]			     =    2
     UpperCAmelCase_					:					Tuple			     =    1







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return XGLMConfig.from_pretrained("facebook/xglm-564M"  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    tf.clip_by_value(
         ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size  )				,   clip_value_min=0				,   clip_value_max=3  )

     UpperCAmelCase_					:					Optional[int]			     =    None
     if self.use_input_mask:
        UpperCAmelCase_					:					str			     =    random_attention_mask([self.batch_size, self.seq_length]  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_config()

     UpperCAmelCase_					:					List[Any]			     =    floats_tensor([self.num_hidden_layers, self.num_attention_heads]				,   2  )

     return (
         config,
         input_ids,
         input_mask,
         head_mask,
     )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return XGLMConfig(
         vocab_size=self.vocab_size				,   d_model=self.hidden_size				,   num_layers=self.num_hidden_layers				,   attention_heads=self.num_attention_heads				,   ffn_dim=self.ffn_dim				,   activation_function=self.activation_function				,   activation_dropout=self.activation_dropout				,   attention_dropout=self.attention_dropout				,   max_position_embeddings=self.max_position_embeddings				,   initializer_range=self.initializer_range				,   use_cache=lowercase_				,   bos_token_id=self.bos_token_id				,   eos_token_id=self.eos_token_id				,   pad_token_id=self.pad_token_id				,   return_dict=lowercase_				,   )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.prepare_config_and_inputs()

     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Any			     =    config_and_inputs

     UpperCAmelCase_					:					List[str]			     =    {
         "input_ids": input_ids,
         "head_mask": head_mask,
     }

     return config, inputs_dict




@require_tf
class A_	(lowercase__				,lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__			:						Tuple       =		(TFXGLMForCausalLM,) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__			:						List[str]       =		(
      {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
  )
  SCREAMING_SNAKE_CASE__			:						List[str]       =		False
  SCREAMING_SNAKE_CASE__			:						List[str]       =		False
  SCREAMING_SNAKE_CASE__			:						Tuple       =		False







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    TFXGLMModelTester(self  )
     UpperCAmelCase_					:					Dict			     =    ConfigTester(self				,   config_class=lowercase_				,   n_embd=37  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_					:					Union[str, Any]			     =    TFXGLMModel.from_pretrained(lowercase_  )
        self.assertIsNotNone(lowercase_  )







  @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().test_resize_token_embeddings()




@require_tf
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  @slow
  def        UpperCamelCase__					(		self				,   lowercase_=True  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"  )
     UpperCAmelCase_					:					Union[str, Any]			     =    tf.convert_to_tensor([[2, 268, 9865]]				,   dtype=tf.intaa  )  # The dog
     # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
     # fmt: off
     UpperCAmelCase_					:					Tuple			     =    [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
     # fmt: on
     UpperCAmelCase_					:					List[str]			     =    model.generate(lowercase_				,   do_sample=lowercase_				,   num_beams=1  )
     if verify_outputs:
        self.assertListEqual(output_ids[0].numpy().tolist()				,   lowercase_  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    XGLMTokenizer.from_pretrained("facebook/xglm-564M"  )
     UpperCAmelCase_					:					Any			     =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"  )

     tf.random.set_seed(0  )
     UpperCAmelCase_					:					Optional[Any]			     =    tokenizer("Today is a nice day and"				,   return_tensors="tf"  )
     UpperCAmelCase_					:					Optional[Any]			     =    tokenized.input_ids
     # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
     with tf.device(":/CPU:0"  ):
        UpperCAmelCase_					:					List[Any]			     =    model.generate(lowercase_				,   do_sample=lowercase_				,   seed=[7, 0]  )
     UpperCAmelCase_					:					Dict			     =    tokenizer.decode(output_ids[0]				,   skip_special_tokens=lowercase_  )

     UpperCAmelCase_					:					Dict			     =    (
         "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
     )
     self.assertEqual(lowercase_				,   lowercase_  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"  )
     UpperCAmelCase_					:					Tuple			     =    XGLMTokenizer.from_pretrained("facebook/xglm-564M"  )

     UpperCAmelCase_					:					Any			     =    "left"

     # use different length sentences to test batching
     UpperCAmelCase_					:					List[str]			     =    [
         "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
         "left-padding, such as in batched generation. The output for the sequence below should be the same "
         "regardless of whether left padding is applied or not. When",
         "Hello, my dog is a little",
     ]

     UpperCAmelCase_					:					List[Any]			     =    tokenizer(lowercase_				,   return_tensors="tf"				,   padding=lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    inputs["input_ids"]

     UpperCAmelCase_					:					Union[str, Any]			     =    model.generate(input_ids=lowercase_				,   attention_mask=inputs["attention_mask"]				,   max_new_tokens=12  )

     UpperCAmelCase_					:					Optional[int]			     =    tokenizer(sentences[0]				,   return_tensors="tf"  ).input_ids
     UpperCAmelCase_					:					Optional[Any]			     =    model.generate(input_ids=lowercase_				,   max_new_tokens=12  )

     UpperCAmelCase_					:					List[str]			     =    tokenizer(sentences[1]				,   return_tensors="tf"  ).input_ids
     UpperCAmelCase_					:					List[Any]			     =    model.generate(input_ids=lowercase_				,   max_new_tokens=12  )

     UpperCAmelCase_					:					Optional[int]			     =    tokenizer.batch_decode(lowercase_				,   skip_special_tokens=lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    tokenizer.decode(output_non_padded[0]				,   skip_special_tokens=lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    tokenizer.decode(output_padded[0]				,   skip_special_tokens=lowercase_  )

     UpperCAmelCase_					:					Tuple			     =    [
         "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
         "left-padding, such as in batched generation. The output for the sequence below should be the same "
         "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
         "a single",
         "Hello, my dog is a little bit of a shy one, but he is very friendly",
     ]
     self.assertListEqual(lowercase_				,   lowercase_  )
     self.assertListEqual(lowercase_				,   [non_padded_sentence, padded_sentence]  )









"""simple docstring"""




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


lowerCamelCase_     :						Any						=					re.compile(r'\s+')





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  [len(_UpperCAmelCase					) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(_UpperCAmelCase					), "line_max": max(_UpperCAmelCase					)}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5					):






  """simple docstring"""





  A_  :       Optional[int]										=  ['auto-generated', 'autogenerated', 'automatically generated']
  A_  :       List[str]										=  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5 ,					_UpperCAmelCase=0.05					):






  """simple docstring"""





  A_  :       Any										=  ['unit tests', 'test file', 'configuration file']
  A_  :       Dict										=  example['content'].splitlines()
  A_  :       List[Any]										=  0
  A_  :       str										=  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
  A_  :       Tuple										=  example['content'].count('\n'					)
  A_  :       Tuple										=  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 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  ['def ', 'class ', 'for ', 'while ']
  A_  :       Tuple										=  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=4					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  tokenizer(example['content'] ,					truncation=_UpperCAmelCase					)['input_ids']
  A_  :       Dict										=  len(example['content']					) / len(_UpperCAmelCase					)
  return {"ratio": ratio}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  {}
  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  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 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  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
lowerCamelCase_     :						Optional[int]						=					HfArgumentParser(PreprocessingArguments)
lowerCamelCase_     :						Optional[Any]						=					parser.parse_args()
if args.num_workers is None:
   lowerCamelCase_     :						int						=					multiprocessing.cpu_count()
lowerCamelCase_     :						Tuple						=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
lowerCamelCase_     :						List[str]						=					time.time()
lowerCamelCase_     :						Optional[int]						=					ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")

# Deduplicate hashes
lowerCamelCase_     :						int						=					set(ds.unique('hash'))
lowerCamelCase_     :						Union[str, Any]						=					len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

# Deduplicate data and apply heuristics
lowerCamelCase_     :						Optional[int]						=					time.time()
lowerCamelCase_     :						Tuple						=					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:
   lowerCamelCase_     :						Union[str, Any]						=					time.time()
   lowerCamelCase_					,					lowerCamelCase_     :						str						=					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
lowerCamelCase_     :						Tuple						=					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)

lowerCamelCase_     :						Optional[Any]						=					output_dir / 'data'
data_dir.mkdir(exist_ok=True)

lowerCamelCase_     :						List[str]						=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   lowerCamelCase_     :						Optional[int]						=					str(data_dir / F"file-{file_number+1:012}.json")
   lowerCamelCase_     :						List[str]						=					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}")