





import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__lowerCamelCase						: List[str]		=   'sshleifer/mar_enro_6_3_student'







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Optional[Any]:
    super().setUp()

    _UpperCamelCase       			=cached_path(
        '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''							,	extract_compressed_file=UpperCamelCase__							,	)
    _UpperCamelCase       			=F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''


  @slow
  @require_torch_gpu
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[Any]:
    MarianMTModel.from_pretrained(UpperCamelCase__  )




  @slow
  @require_torch_gpu
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			={
        '''$MAX_LEN''': 64,
        '''$BS''': 64,
        '''$GAS''': 1,
        '''$ENRO_DIR''': self.data_dir,
        '''facebook/mbart-large-cc25''': MARIAN_MODEL,
        # "val_check_interval=0.25": "val_check_interval=1.0",
        '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
        '''--num_train_epochs 6''': '''--num_train_epochs 1''',
    }

    # Clean up bash script
    _UpperCamelCase       			=(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''  )[1].strip()
    _UpperCamelCase       			=bash_script.replace('''\\\n'''							,	''''''  ).strip().replace('''"$@"'''							,	''''''  )
    for k, v in env_vars_to_replace.items():
      _UpperCamelCase       			=bash_script.replace(UpperCamelCase__							,	str(UpperCamelCase__  )  )
    _UpperCamelCase       			=self.get_auto_remove_tmp_dir()

    # bash_script = bash_script.replace("--fp16 ", "")
    _UpperCamelCase       			=F'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
    # XXX: args.gpus > 1 : handle multi_gpu in the future

    _UpperCamelCase       			=['''finetune.py'''] + bash_script.split() + args
    with patch.object(UpperCamelCase__							,	'''argv'''							,	UpperCamelCase__  ):
      _UpperCamelCase       			=argparse.ArgumentParser()
      _UpperCamelCase       			=pl.Trainer.add_argparse_args(UpperCamelCase__  )
      _UpperCamelCase       			=SummarizationModule.add_model_specific_args(UpperCamelCase__							,	os.getcwd()  )
      _UpperCamelCase       			=parser.parse_args()
      _UpperCamelCase       			=main(UpperCamelCase__  )

    # Check metrics
    _UpperCamelCase       			=load_json(model.metrics_save_path  )
    _UpperCamelCase       			=metrics['''val'''][0]
    _UpperCamelCase       			=metrics['''val'''][-1]
    self.assertEqual(len(metrics['''val''']  )							,	(args.max_epochs / args.val_check_interval)  )
    assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']							,	UpperCamelCase__  )

    self.assertGreater(last_step_stats['''val_avg_gen_time''']							,	0.01  )
    # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
    self.assertLessEqual(last_step_stats['''val_avg_gen_time''']							,	1.0  )

    # test learning requirements:

    # 1. BLEU improves over the course of training by more than 2 pts
    self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu''']							,	2  )

    # 2. BLEU finishes above 17
    self.assertGreater(last_step_stats['''val_avg_bleu''']							,	17  )

    # 3. test BLEU and val BLEU within ~1.1 pt.
    self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu''']  )							,	1.1  )

    # check lightning ckpt can be loaded and has a reasonable statedict
    _UpperCamelCase       			=os.listdir(UpperCamelCase__  )
    _UpperCamelCase       			=[x for x in contents if x.endswith('''.ckpt'''  )][0]
    _UpperCamelCase       			=os.path.join(args.output_dir							,	UpperCamelCase__  )
    _UpperCamelCase       			=torch.load(UpperCamelCase__							,	map_location='''cpu'''  )
    _UpperCamelCase       			='''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
    assert expected_key in ckpt["state_dict"]
    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

    # TODO: turn on args.do_predict when PL bug fixed.
    if args.do_predict:
      _UpperCamelCase       			={os.path.basename(UpperCamelCase__  ) for p in contents}
      assert "test_generations.txt" in contents
      assert "test_results.txt" in contents
      # assert len(metrics["val"]) ==  desired_n_evals
      assert len(metrics['''test''']  ) == 1







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @timeout_decorator.timeout(600  )
  @slow
  @require_torch_gpu
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[str]:
    _UpperCamelCase       			=F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
    _UpperCamelCase       			={
        '''--fp16_opt_level=O1''': '''''',
        '''$MAX_LEN''': 128,
        '''$BS''': 16,
        '''$GAS''': 1,
        '''$ENRO_DIR''': data_dir,
        '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
        '''val_check_interval=0.25''': '''val_check_interval=1.0''',
    }

    # Clean up bash script
    _UpperCamelCase       			=(
        (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''  )[1].strip()
    )
    _UpperCamelCase       			=bash_script.replace('''\\\n'''							,	''''''  ).strip().replace('''"$@"'''							,	''''''  )
    _UpperCamelCase       			=bash_script.replace('''--fp16 '''							,	''' '''  )

    for k, v in env_vars_to_replace.items():
      _UpperCamelCase       			=bash_script.replace(UpperCamelCase__							,	str(UpperCamelCase__  )  )
    _UpperCamelCase       			=self.get_auto_remove_tmp_dir()
    _UpperCamelCase       			=bash_script.replace('''--fp16'''							,	''''''  )
    _UpperCamelCase       			=6
    _UpperCamelCase       			=(
        ['''distillation.py''']
        + bash_script.split()
        + [
            F'''--output_dir={output_dir}''',
            '''--gpus=1''',
            '''--learning_rate=1e-3''',
            F'''--num_train_epochs={epochs}''',
            '''--warmup_steps=10''',
            '''--val_check_interval=1.0''',
            '''--do_predict''',
        ]
    )
    with patch.object(UpperCamelCase__							,	'''argv'''							,	UpperCamelCase__  ):
      _UpperCamelCase       			=argparse.ArgumentParser()
      _UpperCamelCase       			=pl.Trainer.add_argparse_args(UpperCamelCase__  )
      _UpperCamelCase       			=SummarizationDistiller.add_model_specific_args(UpperCamelCase__							,	os.getcwd()  )
      _UpperCamelCase       			=parser.parse_args()
      # assert args.gpus == gpus THIS BREAKS for multi_gpu

      _UpperCamelCase       			=distill_main(UpperCamelCase__  )

    # Check metrics
    _UpperCamelCase       			=load_json(model.metrics_save_path  )
    _UpperCamelCase       			=metrics['''val'''][0]
    _UpperCamelCase       			=metrics['''val'''][-1]
    assert len(metrics['''val''']  ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

    assert last_step_stats["val_avg_gen_time"] >= 0.01

    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
    assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
    assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']							,	UpperCamelCase__  )

    # check lightning ckpt can be loaded and has a reasonable statedict
    _UpperCamelCase       			=os.listdir(UpperCamelCase__  )
    _UpperCamelCase       			=[x for x in contents if x.endswith('''.ckpt'''  )][0]
    _UpperCamelCase       			=os.path.join(args.output_dir							,	UpperCamelCase__  )
    _UpperCamelCase       			=torch.load(UpperCamelCase__							,	map_location='''cpu'''  )
    _UpperCamelCase       			='''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
    assert expected_key in ckpt["state_dict"]
    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

    # TODO: turn on args.do_predict when PL bug fixed.
    if args.do_predict:
      _UpperCamelCase       			={os.path.basename(UpperCamelCase__  ) for p in contents}
      assert "test_generations.txt" in contents
      assert "test_results.txt" in contents
      # assert len(metrics["val"]) ==  desired_n_evals
      assert len(metrics['''test''']  ) == 1













'''simple docstring'''






from collections.abc import Generator
from math import sin


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if len(__SCREAMING_SNAKE_CASE   ) != 32:
    raise ValueError('''Input must be of length 32'''   )

  _UpperCamelCase       			=b''''''
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if i < 0:
    raise ValueError('''Input must be non-negative'''   )

  _UpperCamelCase       			=format(__SCREAMING_SNAKE_CASE  ,      '''08x'''   )[-8:]
  _UpperCamelCase       			=b''''''
  for i in [3, 2, 1, 0]:
    little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8'''   )
  return little_endian_hex


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=b''''''
  for char in message:
    bit_string += format(__SCREAMING_SNAKE_CASE  ,      '''08b'''   ).encode('''utf-8'''   )
  _UpperCamelCase       			=format(len(__SCREAMING_SNAKE_CASE   )  ,      '''064b'''   ).encode('''utf-8'''   )

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(__SCREAMING_SNAKE_CASE   ) % 512 != 448:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:]   ) + to_little_endian(start_len[:32]   )

  return bit_string


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if len(__SCREAMING_SNAKE_CASE   ) % 512 != 0:
    raise ValueError('''Input must have length that\'s a multiple of 512'''   )

  for pos in range(0  ,      len(__SCREAMING_SNAKE_CASE   )  ,      512   ):
    _UpperCamelCase       			=bit_string[pos : pos + 512]
    _UpperCamelCase       			=[]
    for i in range(0  ,      512  ,      32   ):
      block_words.append(int(to_little_endian(block[i : i + 32]   )  ,      2   )   )
    yield block_words


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if i < 0:
    raise ValueError('''Input must be non-negative'''   )

  _UpperCamelCase       			=format(__SCREAMING_SNAKE_CASE  ,      '''032b'''   )
  _UpperCamelCase       			=''''''
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(__SCREAMING_SNAKE_CASE  ,      2   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return (a + b) % 2**32


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if i < 0:
    raise ValueError('''Input must be non-negative'''   )
  if shift < 0:
    raise ValueError('''Shift must be non-negative'''   )
  return ((i << shift) ^ (i >> (32 - shift))) % 2**32


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=preprocess(__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=[int(2**32 * abs(sin(i + 1   )   )   ) for i in range(64   )]

  # Starting states
  _UpperCamelCase       			=0X6745_2301
  _UpperCamelCase       			=0Xefcd_ab89
  _UpperCamelCase       			=0X98ba_dcfe
  _UpperCamelCase       			=0X1032_5476

  _UpperCamelCase       			=[
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
  ]

  # Process bit string in chunks, each with 16 32-char words
  for block_words in get_block_words(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=aa
    _UpperCamelCase       			=ba
    _UpperCamelCase       			=ca
    _UpperCamelCase       			=da

    # Hash current chunk
    for i in range(64   ):
      if i <= 15:
        # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
        _UpperCamelCase       			=d ^ (b & (c ^ d))
        _UpperCamelCase       			=i
      elif i <= 31:
        # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
        _UpperCamelCase       			=c ^ (d & (b ^ c))
        _UpperCamelCase       			=(5 * i + 1) % 16
      elif i <= 47:
        _UpperCamelCase       			=b ^ c ^ d
        _UpperCamelCase       			=(3 * i + 5) % 16
      else:
        _UpperCamelCase       			=c ^ (b | not_aa(__SCREAMING_SNAKE_CASE   ))
        _UpperCamelCase       			=(7 * i) % 16
      _UpperCamelCase       			=(f + a + added_consts[i] + block_words[g]) % 2**32
      _UpperCamelCase       			=d
      _UpperCamelCase       			=c
      _UpperCamelCase       			=b
      _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      left_rotate_aa(__SCREAMING_SNAKE_CASE  ,      shift_amounts[i]   )   )

    # Add hashed chunk to running total
    _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=reformat_hex(__SCREAMING_SNAKE_CASE   ) + reformat_hex(__SCREAMING_SNAKE_CASE   ) + reformat_hex(__SCREAMING_SNAKE_CASE   ) + reformat_hex(__SCREAMING_SNAKE_CASE   )
  return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()






