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


__lowerCamelCase    :				Optional[int]							     =	os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
   from run_translation import main  # noqa


set_seed(42)
__lowerCamelCase    :				Dict							     =	'''sshleifer/student_marian_en_ro_6_1'''
__lowerCamelCase    :				Optional[Any]							     =	'''sshleifer/tiny-mbart'''







@require_torch
class __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       Dict ,			_lowercase    :       int=False ,			_lowercase    :       str=None ,			_lowercase    :       Optional[Any]=True ,			_lowercase    :       int=True ,			_lowercase    :       str=True ,			_lowercase    :       Optional[Any]=True ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.run_trainer(
         eval_steps=1 ,			max_len=12 ,			model_name=_lowercase ,			num_train_epochs=1 ,			distributed=_lowercase ,			extra_args_str=_lowercase ,			predict_with_generate=_lowercase ,			do_train=_lowercase ,			do_eval=_lowercase ,			do_predict=_lowercase ,			)
     SCREAMING_SNAKE_CASE__    							=   TrainerState.load_from_json(os.path.join(_lowercase ,			"""trainer_state.json"""   )   ).log_history

     if not do_eval:
       return

     SCREAMING_SNAKE_CASE__    							=   [log for log in logs if """eval_loss""" in log.keys()]

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

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




   @require_torch_non_multi_gpu
   def 			__a		(					self    :       int   ):



     """simple docstring"""






     self.run_seqaseq_quick()




   @require_torch_multi_gpu
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     self.run_seqaseq_quick(distributed=_lowercase   )




   @require_torch_multi_gpu
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     self.run_seqaseq_quick(distributed=_lowercase   )




   @unittest.skip("""Requires an update of the env running those tests"""   )
   @require_torch_multi_gpu
   @require_fairscale
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     self.run_seqaseq_quick(distributed=_lowercase ,			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    :       List[str]   ):



     """simple docstring"""






     self.run_seqaseq_quick(distributed=_lowercase ,			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    :       Any   ):



     """simple docstring"""






     self.run_seqaseq_quick(distributed=_lowercase ,			extra_args_str="""--sharded_ddp zero_dp_2""" ,			predict_with_generate=_lowercase   )




   @unittest.skip("""Requires an update of the env running those tests"""   )
   @require_torch_multi_gpu
   @require_fairscale
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     self.run_seqaseq_quick(
         distributed=_lowercase ,			extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" ,			predict_with_generate=_lowercase   )




   @require_apex
   @require_torch_gpu
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     self.run_seqaseq_quick(distributed=_lowercase ,			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=_lowercase ,			extra_args_str="""--fp16 --fp16_backend=apex"""   )




   @parameterized.expand(["""base""", """low""", """high""", """mixed"""]   )
   @require_torch_multi_gpu
   def 			__a		(					self    :       Dict ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {
         # 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},
     }

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




   @slow
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






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

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

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

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




   @slow
   @require_bitsandbytes
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     from transformers.training_args import OptimizerNames

     def train_and_return_metrics(_lowercase    :       str   ) -> Tuple[int, float]:
       SCREAMING_SNAKE_CASE__    							=   """--skip_memory_metrics 0"""

       SCREAMING_SNAKE_CASE__    							=   self.run_trainer(
           max_len=1_28 ,			model_name=_lowercase ,			learning_rate=3E-4 ,			num_train_epochs=1 ,			optim=_lowercase ,			distributed=_lowercase ,			extra_args_str=_lowercase ,			do_eval=_lowercase ,			do_predict=_lowercase ,			n_gpus_to_use=1 ,			)

       # Check metrics
       SCREAMING_SNAKE_CASE__    							=   TrainerState.load_from_json(Path(_lowercase ,			"""trainer_state.json"""   )   ).log_history
       SCREAMING_SNAKE_CASE__    							=   int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20   )
       SCREAMING_SNAKE_CASE__    							=   int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20   )

       SCREAMING_SNAKE_CASE__    							=   logs[0]["""train_loss"""]
       return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

     SCREAMING_SNAKE_CASE__    							=   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

     SCREAMING_SNAKE_CASE__    							=   gpu_peak_mem_orig + gpu_alloc_mem_orig
     SCREAMING_SNAKE_CASE__    							=   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
     SCREAMING_SNAKE_CASE__    							=   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
     SCREAMING_SNAKE_CASE__    							=   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(
         _lowercase ,			_lowercase ,			"""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(
         _lowercase ,			_lowercase ,			"""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(
         _lowercase ,			_lowercase ,			f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       int ,			_lowercase    :       str ,			_lowercase    :       int ,			_lowercase    :       float = 3E-3 ,			_lowercase    :       str = "adafactor" ,			_lowercase    :       bool = False ,			_lowercase    :       str = None ,			_lowercase    :       int = 0 ,			_lowercase    :       bool = True ,			_lowercase    :       bool = True ,			_lowercase    :       bool = True ,			_lowercase    :       bool = True ,			_lowercase    :       int = None ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
     SCREAMING_SNAKE_CASE__    							=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__    							=   f"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(_lowercase   )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(_lowercase   )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

     SCREAMING_SNAKE_CASE__    							=   f"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(_lowercase   )}
        """.split()

     SCREAMING_SNAKE_CASE__    							=   """
            --do_predict
        """.split()

     SCREAMING_SNAKE_CASE__    							=   []
     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:
         SCREAMING_SNAKE_CASE__    							=   get_gpu_count()
       SCREAMING_SNAKE_CASE__    							=   get_torch_dist_unique_port()
       SCREAMING_SNAKE_CASE__    							=   f"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
       SCREAMING_SNAKE_CASE__    							=   [sys.executable] + distributed_args + args
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       execute_subprocess_async(_lowercase ,			env=self.get_env()   )
     else:
       SCREAMING_SNAKE_CASE__    							=   ["""run_translation.py"""] + args
       with patch.object(_lowercase ,			"""argv""" ,			_lowercase   ):
         main()

     return output_dir

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__lowerCamelCase    :				Tuple							     =	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
   import torch

   print('''Torch version:''', torch.__version__)
   print('''Cuda available:''', torch.cuda.is_available())
   print('''Cuda version:''', torch.version.cuda)
   print('''CuDNN version:''', torch.backends.cudnn.version())
   print('''Number of GPUs available:''', torch.cuda.device_count())
   print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
   print('''Torch version:''', None)

try:
   import deepspeed

   print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
   print('''DeepSpeed version:''', None)

try:
   import tensorflow as tf

   print('''TensorFlow version:''', tf.__version__)
   print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
   print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
   print('''TensorFlow version:''', None)
