

"""simple docstring"""

from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


a__			:						List[str]							=			{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Tuple							=			[
							    """Constraint""",
							    """ConstraintListState""",
							    """DisjunctiveConstraint""",
							    """PhrasalConstraint""",
							]
							a__			:						Optional[Any]							=			[
							    """BeamHypotheses""",
							    """BeamScorer""",
							    """BeamSearchScorer""",
							    """ConstrainedBeamSearchScorer""",
							]
							a__			:						Optional[Any]							=			[
							    """EpsilonLogitsWarper""",
							    """EtaLogitsWarper""",
							    """ForcedBOSTokenLogitsProcessor""",
							    """ForcedEOSTokenLogitsProcessor""",
							    """HammingDiversityLogitsProcessor""",
							    """InfNanRemoveLogitsProcessor""",
							    """LogitsProcessor""",
							    """LogitsProcessorList""",
							    """LogitsWarper""",
							    """MinLengthLogitsProcessor""",
							    """MinNewTokensLengthLogitsProcessor""",
							    """NoBadWordsLogitsProcessor""",
							    """NoRepeatNGramLogitsProcessor""",
							    """PrefixConstrainedLogitsProcessor""",
							    """RepetitionPenaltyLogitsProcessor""",
							    """SequenceBiasLogitsProcessor""",
							    """EncoderRepetitionPenaltyLogitsProcessor""",
							    """TemperatureLogitsWarper""",
							    """TopKLogitsWarper""",
							    """TopPLogitsWarper""",
							    """TypicalLogitsWarper""",
							    """EncoderNoRepeatNGramLogitsProcessor""",
							    """ExponentialDecayLengthPenalty""",
							    """LogitNormalization""",
							]
							a__			:						Optional[int]							=			[
							    """MaxNewTokensCriteria""",
							    """MaxLengthCriteria""",
							    """MaxTimeCriteria""",
							    """StoppingCriteria""",
							    """StoppingCriteriaList""",
							    """validate_stopping_criteria""",
							]
							a__			:						Optional[Any]							=			[
							    """GenerationMixin""",
							    """top_k_top_p_filtering""",
							    """GreedySearchEncoderDecoderOutput""",
							    """GreedySearchDecoderOnlyOutput""",
							    """SampleEncoderDecoderOutput""",
							    """SampleDecoderOnlyOutput""",
							    """BeamSearchEncoderDecoderOutput""",
							    """BeamSearchDecoderOnlyOutput""",
							    """BeamSampleEncoderDecoderOutput""",
							    """BeamSampleDecoderOnlyOutput""",
							    """ContrastiveSearchEncoderDecoderOutput""",
							    """ContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[Any]							=			[
							    """TFForcedBOSTokenLogitsProcessor""",
							    """TFForcedEOSTokenLogitsProcessor""",
							    """TFLogitsProcessor""",
							    """TFLogitsProcessorList""",
							    """TFLogitsWarper""",
							    """TFMinLengthLogitsProcessor""",
							    """TFNoBadWordsLogitsProcessor""",
							    """TFNoRepeatNGramLogitsProcessor""",
							    """TFRepetitionPenaltyLogitsProcessor""",
							    """TFTemperatureLogitsWarper""",
							    """TFTopKLogitsWarper""",
							    """TFTopPLogitsWarper""",
							    """TFForceTokensLogitsProcessor""",
							    """TFSuppressTokensAtBeginLogitsProcessor""",
							    """TFSuppressTokensLogitsProcessor""",
							]
							a__			:						int							=			[
							    """TFGenerationMixin""",
							    """tf_top_k_top_p_filtering""",
							    """TFGreedySearchDecoderOnlyOutput""",
							    """TFGreedySearchEncoderDecoderOutput""",
							    """TFSampleEncoderDecoderOutput""",
							    """TFSampleDecoderOnlyOutput""",
							    """TFBeamSearchEncoderDecoderOutput""",
							    """TFBeamSearchDecoderOnlyOutput""",
							    """TFBeamSampleEncoderDecoderOutput""",
							    """TFBeamSampleDecoderOnlyOutput""",
							    """TFContrastiveSearchEncoderDecoderOutput""",
							    """TFContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Any							=			[
							    """FlaxForcedBOSTokenLogitsProcessor""",
							    """FlaxForcedEOSTokenLogitsProcessor""",
							    """FlaxLogitsProcessor""",
							    """FlaxLogitsProcessorList""",
							    """FlaxLogitsWarper""",
							    """FlaxMinLengthLogitsProcessor""",
							    """FlaxTemperatureLogitsWarper""",
							    """FlaxTopKLogitsWarper""",
							    """FlaxTopPLogitsWarper""",
							]
							a__			:						int							=			[
							    """FlaxGenerationMixin""",
							    """FlaxGreedySearchOutput""",
							    """FlaxSampleOutput""",
							    """FlaxBeamSearchOutput""",
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

							a__			:						int							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""

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


a__			:						Union[str, Any]							=			torch.cuda.is_available()
a__			:						Union[str, Any]							=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__			:						List[Any]							=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__			:						str							=			{
    """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""": 42,
    """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""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """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 					__UpperCamelCase (     a__:			Path			,						a__:			list      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      '''\n'''.join(a__      )
					Path(a__      ).open('''w'''      ).writelines(a__      )





def 					__UpperCamelCase (     a__:			int      )					->      List[str]:




					"""simple docstring"""



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


class       _snake_case  (    UpperCamelCase							):







						@classmethod
						def 							_UpperCamelCase     (cls				)      ->    List[str]:
											return cls







						@require_torch_multi_gpu
						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      {
											    '''no_teacher''': True,
											    '''freeze_encoder''': True,
											    '''gpus''': 2,
											    '''overwrite_output_dir''': True,
											    '''sortish_sampler''': True,
											}
											self._test_distiller_cli_fork(UpperCAmelCase_  ,				check_contents=UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=True				)      ->    Tuple:
											__lowercase        =      {
											    '''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(UpperCAmelCase_				)
											__lowercase        =      CHEAP_ARGS.copy()
											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase        =      self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				**UpperCAmelCase_				)

											def convert(UpperCAmelCase_  ,				UpperCAmelCase_				):
																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}'''

											__lowercase        =      [x for x in (convert(UpperCAmelCase_  ,				UpperCAmelCase_				) for k, v in args_d.items()) if len(UpperCAmelCase_				)]
											__lowercase        =      [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
											execute_subprocess_async(UpperCAmelCase_  ,				env=self.get_env()				)

											__lowercase        =      os.listdir(UpperCAmelCase_				)
											__lowercase        =      {os.path.basename(UpperCAmelCase_				) for p in contents}
											__lowercase        =      [p for p in contents if p.endswith('''ckpt'''				)]
											assert len(UpperCAmelCase_				) > 0

											self.assertIn('''test_generations.txt'''  ,				UpperCAmelCase_				)
											self.assertIn('''test_results.txt'''  ,				UpperCAmelCase_				)

											# get the following from the module, (we don't have access to `model` here)
											__lowercase        =      os.path.join(UpperCAmelCase_  ,				'''metrics.json'''				)
											__lowercase        =      '''rouge2'''

											__lowercase        =      load_json(UpperCAmelCase_				)
											# {'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(UpperCAmelCase_				)
											__lowercase        =      metrics['''val'''][-1]
											self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']  ,				0.0_1				)
											self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']  ,				UpperCAmelCase_				)
											self.assertEqual(len(metrics['''test''']				)  ,				1				)
											__lowercase        =      int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1				)
											self.assertEqual(len(metrics['''val''']				)  ,				UpperCAmelCase_				)


