





'''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


snake_case_  :							Tuple            =	torch.cuda.is_available()
snake_case_  :							Any            =	[" Sam ate lunch today.", "Sams lunch ingredients."]
snake_case_  :							Tuple            =	["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
snake_case_  :							Optional[int]            =	{
    "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 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Path,      SCREAMING_SNAKE_CASE__   :      list )			->						Tuple:
						UpperCAmelCase_      :   int      =	'''\n'''.join(SCREAMING_SNAKE_CASE__ )
						Path(SCREAMING_SNAKE_CASE__ ).open('''w''' ).writelines(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any] )			->						Dict:
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__,      F"""{split}.source""" ),      SCREAMING_SNAKE_CASE__ )
												_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__,      F"""{split}.target""" ),      SCREAMING_SNAKE_CASE__ )
						return tmp_dir


class        __a   (lowerCamelCase  ):

							@classmethod
							def      UpperCAmelCase__						(				cls				:  Union[str, Any] )       ->							Tuple:







													"""simple docstring"""
													return cls

							@require_torch_multi_gpu
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	{
													    '''no_teacher''': True,
													    '''freeze_encoder''': True,
													    '''gpus''': 2,
													    '''overwrite_output_dir''': True,
													    '''sortish_sampler''': True,
													}
													self._test_distiller_cli_fork(__magic_name__							,  check_contents=__magic_name__ )



							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  int=True )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	{
													    '''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(__magic_name__ )
													UpperCAmelCase_      :   dict      =	CHEAP_ARGS.copy()
													UpperCAmelCase_      :   List[Any]      =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
													UpperCAmelCase_      :   List[str]      =	self.get_auto_remove_tmp_dir()
													args_d.update(data_dir=__magic_name__							,  output_dir=__magic_name__							,  **__magic_name__ )

													def convert(__magic_name__				:  str							,  __magic_name__				:  Dict ):
																			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}"""

													UpperCAmelCase_      :   Dict      =	[x for x in (convert(__magic_name__							,  __magic_name__ ) for k, v in args_d.items()) if len(__magic_name__ )]
													UpperCAmelCase_      :   List[Any]      =	[sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
													execute_subprocess_async(__magic_name__							,  env=self.get_env() )

													UpperCAmelCase_      :   int      =	os.listdir(__magic_name__ )
													UpperCAmelCase_      :   Optional[Any]      =	{os.path.basename(__magic_name__ ) for p in contents}
													UpperCAmelCase_      :   Dict      =	[p for p in contents if p.endswith('''ckpt''' )]
													assert len(__magic_name__ ) > 0

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

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

													UpperCAmelCase_      :   int      =	load_json(__magic_name__ )
													# {'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(__magic_name__ )
													UpperCAmelCase_      :   Union[str, Any]      =	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}"""]							,  __magic_name__ )
													self.assertEqual(len(metrics['''test'''] )							,  1 )
													UpperCAmelCase_      :   Union[str, Any]      =	int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1 )
													self.assertEqual(len(metrics['''val'''] )							,  __magic_name__ )











# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_    :       int       =							{
    '''agents''': ['''Agent''', '''AzureOpenAiAgent''', '''HfAgent''', '''LocalAgent''', '''OpenAiAgent'''],
    '''base''': ['''PipelineTool''', '''RemoteTool''', '''Tool''', '''launch_gradio_demo''', '''load_tool'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_    :       int       =							['''DocumentQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''ImageCaptioningTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''ImageQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       str       =							['''ImageSegmentationTool''']
 SCREAMING_SNAKE_CASE_    :       Any       =							['''SpeechToTextTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''TextClassificationTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''TextQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TextSummarizationTool''']
 SCREAMING_SNAKE_CASE_    :       Optional[int]       =							['''TextToSpeechTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TranslationTool''']

if TYPE_CHECKING:
 from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
 from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .document_question_answering import DocumentQuestionAnsweringTool
  from .image_captioning import ImageCaptioningTool
  from .image_question_answering import ImageQuestionAnsweringTool
  from .image_segmentation import ImageSegmentationTool
  from .speech_to_text import SpeechToTextTool
  from .text_classification import TextClassificationTool
  from .text_question_answering import TextQuestionAnsweringTool
  from .text_summarization import TextSummarizationTool
  from .text_to_speech import TextToSpeechTool
  from .translation import TranslationTool
else:
 import sys

 SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






