



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


UpperCAmelCase__     = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
       from run_translation import main  # noqa


set_seed(42)
UpperCAmelCase__     = "sshleifer/student_marian_en_ro_6_1"
UpperCAmelCase__     = "sshleifer/tiny-mbart"







@require_torch
class     __lowerCAmelCase					(    A		):



   def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						Dict=False				,	A     :						str=None				,	A     :						Dict=True				,	A     :						Tuple=True				,	A     :						Tuple=True				,	A     :						List[Any]=True				,	)     ->					int:



      """simple docstring"""






      _UpperCAmelCase	 =     self.run_trainer(
          eval_steps=1				,	max_len=12				,	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				,	)
      _UpperCAmelCase	 =     TrainerState.load_from_json(os.path.join(A				,	'trainer_state.json')).log_history

      if not do_eval:
         return

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

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

         _UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						int)     ->					Tuple:



      """simple docstring"""






      self.run_seqaseq_quick()



   @require_torch_multi_gpu
   def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[int]:



      """simple docstring"""






      self.run_seqaseq_quick(distributed=A)



   @require_torch_multi_gpu
   def 					_lowerCamelCase (		self     :						Dict)     ->					str:



      """simple docstring"""






      self.run_seqaseq_quick(distributed=A)



   @unittest.skip('Requires an update of the env running those tests')
   @require_torch_multi_gpu
   @require_fairscale
   def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Union[str, Any]:



      """simple docstring"""






      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 					_lowerCamelCase (		self     :						Optional[int])     ->					List[Any]:



      """simple docstring"""






      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 					_lowerCamelCase (		self     :						List[Any])     ->					Tuple:



      """simple docstring"""






      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 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[Any]:



      """simple docstring"""






      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 					_lowerCamelCase (		self     :						Union[str, Any])     ->					List[str]:



      """simple docstring"""






      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 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						Optional[Any])     ->					Tuple:



      """simple docstring"""






      _UpperCAmelCase	 =     {
          # 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},
      }

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



   @slow
   def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[Any]:



      """simple docstring"""






      _UpperCAmelCase	 =     self.run_trainer(
          eval_steps=2				,	max_len=1_28				,	model_name=A				,	learning_rate=3E-4				,	num_train_epochs=10				,	distributed=A				,	)

      # Check metrics
      _UpperCAmelCase	 =     TrainerState.load_from_json(os.path.join(A				,	'trainer_state.json')).log_history
      _UpperCAmelCase	 =     [log for log in logs if 'eval_loss' in log.keys()]
      _UpperCAmelCase	 =     eval_metrics[0]
      _UpperCAmelCase	 =     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
      _UpperCAmelCase	 =     os.listdir(A)
      _UpperCAmelCase	 =     {os.path.basename(A) for p in contents}
      assert "generated_predictions.txt" in contents
      assert "predict_results.json" in contents



   @slow
   @require_bitsandbytes
   def 					_lowerCamelCase (		self     :						Any)     ->					List[str]:



      """simple docstring"""






      from transformers.training_args import OptimizerNames

      def train_and_return_metrics(A     :						str) -> Tuple[int, float]:
         _UpperCAmelCase	 =     '--skip_memory_metrics 0'

         _UpperCAmelCase	 =     self.run_trainer(
             max_len=1_28				,	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
         _UpperCAmelCase	 =     TrainerState.load_from_json(Path(A				,	'trainer_state.json')).log_history
         _UpperCAmelCase	 =     int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20)
         _UpperCAmelCase	 =     int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20)

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

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

      _UpperCAmelCase	 =     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

      _UpperCAmelCase	 =     gpu_peak_mem_orig + gpu_alloc_mem_orig
      _UpperCAmelCase	 =     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
      _UpperCAmelCase	 =     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
      _UpperCAmelCase	 =     1_20

      # 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 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						int				,	A     :						str				,	A     :						int				,	A     :						float = 3E-3				,	A     :						str = "adafactor"				,	A     :						bool = False				,	A     :						str = None				,	A     :						int = 0				,	A     :						bool = True				,	A     :						bool = True				,	A     :						bool = True				,	A     :						bool = True				,	A     :						int = None				,	)     ->					int:



      """simple docstring"""






      _UpperCAmelCase	 =     self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
      _UpperCAmelCase	 =     self.get_auto_remove_tmp_dir()
      _UpperCAmelCase	 =     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()

      _UpperCAmelCase	 =     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()

      _UpperCAmelCase	 =     '\n            --do_predict\n        '.split()

      _UpperCAmelCase	 =     []
      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:
            _UpperCAmelCase	 =     get_gpu_count()
         _UpperCAmelCase	 =     get_torch_dist_unique_port()
         _UpperCAmelCase	 =     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()
         _UpperCAmelCase	 =     [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:
         _UpperCAmelCase	 =     ['run_translation.py'] + args
         with patch.object(A				,	'argv'				,	A):
            main()

      return output_dir





import qiskit





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				qiskit.result.counts.Counts:







			'''simple docstring'''
			_UpperCAmelCase	 =     qiskit.Aer.get_backend('aer_simulator'					)

			# Create a Quantum Circuit acting on the q register
			_UpperCAmelCase	 =     qiskit.QuantumCircuit(_UpperCAmelCase							,     _UpperCAmelCase					)

			# Apply X (NOT) Gate to Qubits 0 & 1
			circuit.x(0					)
			circuit.x(1					)

			# Map the quantum measurement to the classical bits
			circuit.measure([0, 1]							,     [0, 1]					)

			# Execute the circuit on the qasm simulator
			_UpperCAmelCase	 =     qiskit.execute(_UpperCAmelCase							,     _UpperCAmelCase							,     shots=1_000					)

			# Return the histogram data of the results of the experiment.
			return job.result().get_counts(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = single_qubit_measure(2, 2)
							print(f"""Total count for various states are: {counts}""")
