
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


lowercase__		:List[str]   =    os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
							from run_translation import main  # noqa


set_seed(42)
lowercase__		:Any   =    "sshleifer/student_marian_en_ro_6_1"
lowercase__		:Optional[Any]   =    "sshleifer/tiny-mbart"







@require_torch
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self   ,A__=False   ,A__=None   ,A__=True   ,A__=True   ,A__=True   ,A__=True   ,):
												lowercase							=						self.run_trainer(
												    eval_steps=1   ,max_len=1_2   ,model_name=A__   ,num_train_epochs=1   ,distributed=A__   ,extra_args_str=A__   ,predict_with_generate=A__   ,do_train=A__   ,do_eval=A__   ,do_predict=A__   ,)
												lowercase							=						TrainerState.load_from_json(os.path.join(A__   ,'''trainer_state.json''')).log_history

												if not do_eval:
																	return

												lowercase							=						[log for log in logs if '''eval_loss''' in log.keys()]

												lowercase							=						eval_metrics[0]
												if predict_with_generate:
																	assert "eval_bleu" in first_step_stats

																	lowercase							=						eval_metrics[-1]
																	assert isinstance(last_step_stats['''eval_bleu''']   ,A__)
																	assert not math.isnan(float(last_step_stats['''eval_loss'''])), "eval_loss must not be `nan`"




							@require_torch_non_multi_gpu
							def  A__      (				self):
												self.run_seqaseq_quick()




							@require_torch_multi_gpu
							def  A__      (				self):
												self.run_seqaseq_quick(distributed=A__)




							@require_torch_multi_gpu
							def  A__      (				self):
												self.run_seqaseq_quick(distributed=A__)




							@unittest.skip('''Requires an update of the env running those tests''')
							@require_torch_multi_gpu
							@require_fairscale
							def  A__      (				self):
												self.run_seqaseq_quick(distributed=A__   ,extra_args_str='''--sharded_ddp simple''')




							@unittest.skip('''Requires an update of the env running those tests''')
							@require_torch_multi_gpu
							@require_fairscale
							def  A__      (				self):
												self.run_seqaseq_quick(distributed=A__   ,extra_args_str='''--sharded_ddp simple --fp16''')




							@unittest.skip('''Requires an update of the env running those tests''')
							@require_torch_multi_gpu
							@require_fairscale
							def  A__      (				self):
												self.run_seqaseq_quick(distributed=A__   ,extra_args_str='''--sharded_ddp zero_dp_2'''   ,predict_with_generate=A__)




							@unittest.skip('''Requires an update of the env running those tests''')
							@require_torch_multi_gpu
							@require_fairscale
							def  A__      (				self):
												self.run_seqaseq_quick(
												    distributed=A__   ,extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''   ,predict_with_generate=A__)




							@require_apex
							@require_torch_gpu
							def  A__      (				self):
												# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
												# program and it breaks other tests that run from the same pytest worker, therefore until this is
												# sorted out it must be run only in an external program, that is distributed=True in this
												# test and only under one or more gpus - if we want cpu will need to make a special test
												#
												# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
												# 2nd main() call it botches the future eval.
												#
												self.run_seqaseq_quick(distributed=A__   ,extra_args_str='''--fp16 --fp16_backend=apex''')
												# test 2nd time - was getting eval_loss': nan'
												# to reproduce the problem set distributed=False
												self.run_seqaseq_quick(distributed=A__   ,extra_args_str='''--fp16 --fp16_backend=apex''')




							@parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''])
							@require_torch_multi_gpu
							def  A__      (				self   ,A__):
												# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
												lowercase							=						{
												    # test with the default log_level - should be info and thus log info once
												    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
												    # test with low log_level and log_level_replica - should be noisy on all processes
												    # now the info string should appear twice on 2 processes
												    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
												    # test with high log_level and low log_level_replica
												    # now the info string should appear once only on the replica
												    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
												    # test with high log_level and log_level_replica - should be quiet on all processes
												    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
												}

												lowercase							=						experiments[experiment_id]
												lowercase							=						{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
												lowercase							=						'''Running training'''
												with CaptureStderr() as cl:
																	self.run_seqaseq_quick(**A__   ,extra_args_str=data['''extra_args_str'''])
												lowercase							=						len(re.findall(A__   ,cl.err))
												self.assertEqual(A__   ,data['''n_matches'''])




							@slow
							def  A__      (				self):
												lowercase							=						self.run_trainer(
												    eval_steps=2   ,max_len=1_2_8   ,model_name=A__   ,learning_rate=3E-4   ,num_train_epochs=1_0   ,distributed=A__   ,)

												# Check metrics
												lowercase							=						TrainerState.load_from_json(os.path.join(A__   ,'''trainer_state.json''')).log_history
												lowercase							=						[log for log in logs if '''eval_loss''' in log.keys()]
												lowercase							=						eval_metrics[0]
												lowercase							=						eval_metrics[-1]

												assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
												assert isinstance(last_step_stats['''eval_bleu''']   ,A__)

												# test if do_predict saves generations and metrics
												lowercase							=						os.listdir(A__)
												lowercase							=						{os.path.basename(A__) for p in contents}
												assert "generated_predictions.txt" in contents
												assert "predict_results.json" in contents




							@slow
							@require_bitsandbytes
							def  A__      (				self):
												from transformers.training_args import OptimizerNames

												def train_and_return_metrics(A__) -> Tuple[int, float]:
																	lowercase							=						'''--skip_memory_metrics 0'''

																	lowercase							=						self.run_trainer(
																	    max_len=1_2_8   ,model_name=A__   ,learning_rate=3E-4   ,num_train_epochs=1   ,optim=A__   ,distributed=A__   ,extra_args_str=A__   ,do_eval=A__   ,do_predict=A__   ,n_gpus_to_use=1   ,)

																	# Check metrics
																	lowercase							=						TrainerState.load_from_json(Path(A__   ,'''trainer_state.json''')).log_history
																	lowercase							=						int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0)
																	lowercase							=						int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0)

																	lowercase							=						logs[0]['''train_loss''']
																	return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

												lowercase	,						lowercase	,						lowercase							=						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value)
												lowercase	,						lowercase	,						lowercase							=						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value)

												lowercase							=						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

												lowercase							=						gpu_peak_mem_orig + gpu_alloc_mem_orig
												lowercase							=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
												lowercase							=						gpu_total_mem_orig - gpu_total_mem_bnb

												# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
												# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
												# in 2 bytes and the diff in optim memory usage is derived as so:
												#
												# - normal 25*8=~200MB (8 bytes per param)
												# - bnb    25*2= ~50MB (2 bytes per param)
												#
												# Thus we should expect ~150MB total memory saved.
												#
												# Peak memory should be the same - the total should be different by about that same margin
												#
												# After leaving a small margin to accommodate for differences between gpus let's check
												# that we have at least 120MB in savings
												lowercase							=						1_2_0

												# uncomment the following if this test starts failing - requires py38 for a new print feature
												# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
												# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
												# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
												# print(f"{gpu_alloc_mem_diff=}MB")
												# print(f"{gpu_peak_mem_diff=}MB")
												# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
												# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

												self.assertGreater(
												    A__   ,A__   ,'''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
												    f' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'
												    f' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'   ,)

												self.assertGreater(
												    A__   ,A__   ,'''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
												    f' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'
												    f' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'   ,)

												self.assertEqual(
												    A__   ,A__   ,f'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}')





							def  A__      (				self   ,A__   ,A__   ,A__   ,A__ = 3E-3   ,A__ = "adafactor"   ,A__ = False   ,A__ = None   ,A__ = 0   ,A__ = True   ,A__ = True   ,A__ = True   ,A__ = True   ,A__ = None   ,):
												lowercase							=						self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						f'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(A__)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(A__)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

												lowercase							=						f'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(A__)}\n        '.split()

												lowercase							=						'''
            --do_predict
        '''.split()

												lowercase							=						[]
												if do_train:
																	args += args_train

												if do_eval:
																	args += args_eval

												if do_predict:
																	args += args_predict

												if predict_with_generate:
																	args += "--predict_with_generate".split()

												if do_train:
																	if optim == "adafactor":
																						args += "--adafactor".split()
																	else:
																						args += f'--optim {optim}'.split()

												if extra_args_str is not None:
																	args += extra_args_str.split()

												if distributed:
																	if n_gpus_to_use is None:
																						lowercase							=						get_gpu_count()
																	lowercase							=						get_torch_dist_unique_port()
																	lowercase							=						f'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
																	lowercase							=						[sys.executable] + distributed_args + args
																	# keep for quick debug
																	# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
																	execute_subprocess_async(A__   ,env=self.get_env())
												else:
																	lowercase							=						['''run_translation.py'''] + args
																	with patch.object(A__   ,'''argv'''   ,A__):
																						main()

												return output_dir










"""simple docstring"""


import argparse
import hashlib  # hashlib is only used inside the Test class
import struct






class 					lowercase:







 '''simple docstring'''





 def __init__(				self:		List[Any],      a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		int								=  data
   _snake_case							:		Dict								=  [0X67452301, 0Xefcdab89, 0X98badcfe, 0X10325476, 0Xc3d2e1f0]


 @staticmethod
 def  UpperCamelCase_   (				a_:		Optional[Any],      a_:		Dict		):




   '''simple docstring'''



   return ((n << b) | (n >> (32 - b))) & 0Xffffffff


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  B"""\x80""" + B"""\x00""" * (63 - (len(self.data		) + 8) % 64)
   _snake_case							:		Optional[int]								=  self.data + padding + struct.pack(""">Q""",      8 * len(self.data		)		)
   return padded_data


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   return [
       self.padded_data[i : i + 64] for i in range(0,      len(self.padded_data		),      64		)
   ]


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  list(struct.unpack(""">16L""",      a_		)		) + [0] * 64
   for i in range(16,      80		):
     _snake_case							:		List[Any]								=  self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),      1		)
   return w


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.padding()
   _snake_case							:		str								=  self.split_blocks()
   for block in self.blocks:
     _snake_case							:		Any								=  self.expand_block(a_		)
     _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  self.h
     for i in range(0,      80		):
       if 0 <= i < 20:
         _snake_case							:		int								=  (b & c) | ((~b) & d)
         _snake_case							:		str								=  0X5a827999
       elif 20 <= i < 40:
         _snake_case							:		Optional[int]								=  b ^ c ^ d
         _snake_case							:		str								=  0X6ed9eba1
       elif 40 <= i < 60:
         _snake_case							:		List[Any]								=  (b & c) | (b & d) | (c & d)
         _snake_case							:		List[Any]								=  0X8f1bbcdc
       elif 60 <= i < 80:
         _snake_case							:		List[Any]								=  b ^ c ^ d
         _snake_case							:		int								=  0Xca62c1d6
       _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  (
           self.rotate(a_,      5		) + f + e + k + expanded_block[i] & 0Xffffffff,
           a,
           self.rotate(a_,      30		),
           c,
           d,
       )
     _snake_case							:		Union[str, Any]								=  (
         self.h[0] + a & 0Xffffffff,
         self.h[1] + b & 0Xffffffff,
         self.h[2] + c & 0Xffffffff,
         self.h[3] + d & 0Xffffffff,
         self.h[4] + e & 0Xffffffff,
     )
   return ("{:08x}" * 5).format(*self.h		)







def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  B"""Test String"""
  assert SHAaHash(snake_case__    ).final_hash() == hashlib.shaa(snake_case__    ).hexdigest()  # noqa: S324


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		List[Any]								=  argparse.ArgumentParser(description="""Process some strings or files"""    )
  parser.add_argument(
      """--string"""		,   dest="""input_string"""		,   default="""Hello World!! Welcome to Cryptography"""		,   help="""Hash the string"""		,   )
  parser.add_argument("""--file"""		,   dest="""input_file"""		,   help="""Hash contents of a file"""    )
  _snake_case							:		Union[str, Any]								=  parser.parse_args()
  _snake_case							:		List[Any]								=  args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file		,   """rb"""    ) as f:
      _snake_case							:		str								=  f.read()
  else:
    _snake_case							:		int								=  bytes(snake_case__		,   """utf-8"""    )
  print(SHAaHash(snake_case__    ).final_hash()    )


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod()


