


# Copyright 2021 The HuggingFace 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


SCREAMING_SNAKE_CASE		:str     						=   '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''



def 	_lowerCAmelCase  (							)->Optional[int]:
   '''simple docstring'''






   snake_case_    			=    _ask_options(
       "In which compute environment are you running?"						,  ["This machine", "AWS (Amazon SageMaker)"]						,  _convert_compute_environment						,  )
   if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
      snake_case_    			=    get_sagemaker_input()
   else:
      snake_case_    			=    get_cluster_input()
   return config



def 	_lowerCAmelCase  (							lowerCAmelCase_							:Dict=None							)->Optional[int]:
   '''simple docstring'''






   if subparsers is not None:
      snake_case_    			=    subparsers.add_parser("config"						,  description=lowerCamelCase__							)
   else:
      snake_case_    			=    argparse.ArgumentParser("Accelerate config command"						,  description=lowerCamelCase__							)

   parser.add_argument(
       "--config_file"						,  default=lowerCamelCase__						,  help=(
           "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
           "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
           "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
           "with 'huggingface'."
       )						,  )

   if subparsers is not None:
      parser.set_defaults(func=lowerCamelCase__							)
   return parser



def 	_lowerCAmelCase  (							lowerCAmelCase_							:List[str]							)->int:
   '''simple docstring'''






   snake_case_    			=    get_user_input()
   if args.config_file is not None:
      snake_case_    			=    args.config_file
   else:
      if not os.path.isdir(lowerCamelCase__							):
         os.makedirs(lowerCamelCase__							)
      snake_case_    			=    default_yaml_config_file

   if config_file.endswith(".json"							):
      config.to_json_file(lowerCamelCase__							)
   else:
      config.to_yaml_file(lowerCamelCase__							)
   print(F'''accelerate configuration saved at {config_file}'''							)



def 	_lowerCAmelCase  (							)->Tuple:
   '''simple docstring'''






   snake_case_    			=    config_command_parser()
   snake_case_    			=    parser.parse_args()
   config_command(lowerCamelCase__							)


if __name__ == "__main__":
   main()






import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm
			import torch_xla.debug.metrics as met




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  *lowercase						,  lowercase=None						,  lowercase=None						,  **lowercase )				->       List[str]:
					super().__init__(*lowercase						,  **lowercase )
					lowerCamelCase_						=			eval_examples
					lowerCamelCase_						=			post_process_function



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase = None						,  lowercase=None						,  lowercase = None						,  lowercase = "eval"						,  **lowercase						,  )				->       Dict[str, float]:
					lowerCamelCase_						=			gen_kwargs.copy()
					lowerCamelCase_						=			(
					    gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
					)
					lowerCamelCase_						=			(
					    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
					)
					lowerCamelCase_						=			gen_kwargs

					lowerCamelCase_						=			self.eval_dataset if eval_dataset is None else eval_dataset
					lowerCamelCase_						=			self.get_eval_dataloader(lowercase )
					lowerCamelCase_						=			self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCamelCase_						=			self.compute_metrics
					lowerCamelCase_						=			None
					lowerCamelCase_						=			time.time()
					lowerCamelCase_						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									lowerCamelCase_						=			eval_loop(
									    lowercase						,  description="Evaluation"						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase						,  metric_key_prefix=lowercase						,  )
					finally:
									lowerCamelCase_						=			compute_metrics
					lowerCamelCase_						=			self.args.eval_batch_size * self.args.world_size
					if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
					output.metrics.update(
					    speed_metrics(
					        lowercase						,  lowercase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size )						,  ) )

					if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
									# Only the main node write the results by default
									lowerCamelCase_						=			self.post_process_function(lowercase						,  lowercase						,  lowercase )
									lowerCamelCase_						=			self.compute_metrics(lowercase )

									# Prefix all keys with metric_key_prefix + '_'
									for key in list(metrics.keys() ):
													if not key.startswith(f'{metric_key_prefix}_' ):
																	lowerCamelCase_						=			metrics.pop(lowercase )

									metrics.update(output.metrics )
					else:
									lowerCamelCase_						=			output.metrics

					if self.args.should_log:
									# Only the main node log the results by default
									self.log(lowercase )

					if self.args.tpu_metrics_debug or self.args.debug:
									# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
									xm.master_print(met.metrics_report() )

					lowerCamelCase_						=			self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  lowercase )
					return metrics



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase=None						,  lowercase = "test"						,  **lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			gen_kwargs.copy()

					lowerCamelCase_						=			self.get_test_dataloader(lowercase )

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCamelCase_						=			self.compute_metrics
					lowerCamelCase_						=			None
					lowerCamelCase_						=			time.time()
					lowerCamelCase_						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									lowerCamelCase_						=			eval_loop(
									    lowercase						,  description="Prediction"						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase						,  metric_key_prefix=lowercase						,  )
					finally:
									lowerCamelCase_						=			compute_metrics

					lowerCamelCase_						=			self.args.eval_batch_size * self.args.world_size
					if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
					output.metrics.update(
					    speed_metrics(
					        lowercase						,  lowercase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size )						,  ) )
					if self.post_process_function is None or self.compute_metrics is None:
									return output

					lowerCamelCase_						=			self.post_process_function(lowercase						,  lowercase						,  lowercase						,  "predict" )
					lowerCamelCase_						=			self.compute_metrics(lowercase )

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys() ):
									if not key.startswith(f'{metric_key_prefix}_' ):
													lowerCamelCase_						=			metrics.pop(lowercase )
					metrics.update(output.metrics )
					return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=lowercase )


