import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


UpperCAmelCase							=       torch.cuda.is_available()
UpperCAmelCase							=       [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCAmelCase							=       ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCAmelCase							=       {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 4_2,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 1_2,
    '''max_target_length''': 1_2,
    '''val_max_target_length''': 1_2,
    '''test_max_target_length''': 1_2,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Path ,	lowerCAmelCase_: list     ):
      snake_case_     :			Union[str, Any]       =       '\n'.join(lowerCAmelCase_     )
      Path(lowerCAmelCase_     ).open("w"     ).writelines(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[int]     ):







      for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(lowerCAmelCase_ ,	f"{split}.source"     ) ,	lowerCAmelCase_     )
            _dump_articles(os.path.join(lowerCAmelCase_ ,	f"{split}.target"     ) ,	lowerCAmelCase_     )
      return tmp_dir





class 					snake_case__       (				lowercase__							):



   @classmethod
   def 					UpperCAmelCase__       (				cls   :   Any   )							->     List[str]:




         '''simple docstring'''




         return cls



   @require_torch_multi_gpu
   def 					UpperCAmelCase__       (				self   :   List[Any]   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       {
             'no_teacher': True,
             'freeze_encoder': True,
             'gpus': 2,
             'overwrite_output_dir': True,
             'sortish_sampler': True,
         }
         self._test_distiller_cli_fork(_A			,	check_contents=_A   )



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   Optional[Any]			,	A__   :   Dict=True   )							->     str:




         '''simple docstring'''




         snake_case_     :			Tuple       =       {
             'label_smoothing': 0.0,
             'early_stopping_patience': -1,
             'train_batch_size': 1,
             'eval_batch_size': 2,
             'max_epochs': 2,
             'alpha_mlm': 0.2,
             'alpha_ce': 0.8,
             'do_predict': True,
             'model_name_or_path': 'sshleifer/tinier_bart',
             'teacher': CHEAP_ARGS['model_name_or_path'],
             'val_check_interval': 0.5,
         }
         default_updates.update(_A   )
         snake_case_     :			dict       =       CHEAP_ARGS.copy()
         snake_case_     :			Tuple       =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
         snake_case_     :			Optional[int]       =       self.get_auto_remove_tmp_dir()
         args_d.update(data_dir=_A			,	output_dir=_A			,	**_A   )

         def convert(A__   :   int			,	A__   :   List[Any]   ):
               if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                     return ""
               if v is False or v is None:
                     return ""
               if v is True:  # or len(str(v))==0:
                     return f"--{k}"
               return f"--{k}={v}"

         snake_case_     :			List[Any]       =       [x for x in (convert(_A			,	_A   ) for k, v in args_d.items()) if len(_A   )]
         snake_case_     :			Union[str, Any]       =       [sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
         execute_subprocess_async(_A			,	env=self.get_env()   )

         snake_case_     :			Optional[Any]       =       os.listdir(_A   )
         snake_case_     :			Tuple       =       {os.path.basename(_A   ) for p in contents}
         snake_case_     :			Union[str, Any]       =       [p for p in contents if p.endswith("ckpt"   )]
         assert len(_A   ) > 0

         self.assertIn("test_generations.txt"			,	_A   )
         self.assertIn("test_results.txt"			,	_A   )

         # get the following from the module, (we don't have access to `model` here)
         snake_case_     :			str       =       os.path.join(_A			,	"metrics.json"   )
         snake_case_     :			int       =       'rouge2'

         snake_case_     :			Optional[Any]       =       load_json(_A   )
         # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
         print(_A   )
         snake_case_     :			Any       =       metrics['val'][-1]
         self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]			,	0.01   )
         self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"]			,	_A   )
         self.assertEqual(len(metrics["test"]   )			,	1   )
         snake_case_     :			str       =       int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1   )
         self.assertEqual(len(metrics["val"]   )			,	_A   )
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: bool = False     ):
      if device_specific:
            seed += AcceleratorState().process_index
      random.seed(lowerCAmelCase_     )
      np.random.seed(lowerCAmelCase_     )
      torch.manual_seed(lowerCAmelCase_     )
      if is_xpu_available():
            torch.xpu.manual_seed_all(lowerCAmelCase_     )
      elif is_npu_available():
            torch.npu.manual_seed_all(lowerCAmelCase_     )
      else:
            torch.cuda.manual_seed_all(lowerCAmelCase_     )
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
            xm.set_rng_state(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[RNGType] = None ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      # Get the proper rng state
      if rng_type == RNGType.TORCH:
            snake_case_     :			List[Any]       =       torch.get_rng_state()
      elif rng_type == RNGType.CUDA:
            snake_case_     :			Dict       =       torch.cuda.get_rng_state()
      elif rng_type == RNGType.XLA:
            assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
            snake_case_     :			Tuple       =       torch.tensor(xm.get_rng_state()     )
      elif rng_type == RNGType.NPU:
            assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
            snake_case_     :			List[str]       =       torch.npu.get_rng_state()
      elif rng_type == RNGType.XPU:
            assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
            snake_case_     :			List[str]       =       torch.xpu.get_rng_state()
      elif rng_type == RNGType.GENERATOR:
            assert generator is not None, "Need a generator to synchronize its seed."
            snake_case_     :			Optional[Any]       =       generator.get_state()

      # Broadcast the rng state from device 0 to other devices
      snake_case_     :			List[Any]       =       AcceleratorState()
      if state.distributed_type == DistributedType.TPU:
            snake_case_     :			int       =       rng_state.to(xm.xla_device()     )
            xm.collective_broadcast([rng_state]     )
            xm.mark_step()
            snake_case_     :			List[str]       =       rng_state.cpu()
      elif (
          state.distributed_type in CUDA_DISTRIBUTED_TYPES
          or state.distributed_type == DistributedType.MULTI_NPU
          or state.distributed_type == DistributedType.MULTI_XPU
      ):
            snake_case_     :			Dict       =       rng_state.to(state.device     )
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )
            snake_case_     :			Dict       =       rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )

      # Set the broadcast rng state
      if rng_type == RNGType.TORCH:
            torch.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.CUDA:
            torch.cuda.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.NPU:
            torch.npu.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.XPU:
            torch.xpu.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.XLA:
            xm.set_rng_state(rng_state.item()     )
      elif rng_type == RNGType.GENERATOR:
            generator.set_state(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Union[str, RNGType]] ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      for rng_type in rng_types:
            synchronize_rng_state(RNGType(lowerCAmelCase_     ) ,	generator=lowerCAmelCase_     )



