


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 	_a							(      UpperCamelCase__      ):

							def __init__(							self:     int   ,						*UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Tuple=None   ,						UpperCamelCase_:     List[str]=None   ,						**UpperCamelCase_:     Tuple							)      ->       Optional[int]:





									"""simple docstring"""







									super().__init__(*UpperCamelCase_   ,						**UpperCamelCase_							)
									lowercase__						=				eval_examples
									lowercase__						=				post_process_function

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     Optional[Dataset] = None   ,						UpperCamelCase_:     str=None   ,						UpperCamelCase_:     Optional[List[str]] = None   ,						UpperCamelCase_:     str = "eval"   ,						**UpperCamelCase_:     Dict   ,						)      ->       Dict[str, float]:





									"""simple docstring"""







									lowercase__						=				gen_kwargs.copy()
									lowercase__						=				(
									    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''							) is not None else self.args.generation_max_length
									)
									lowercase__						=				(
									    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''							) is not None else self.args.generation_num_beams
									)
									lowercase__						=				gen_kwargs

									lowercase__						=				self.eval_dataset if eval_dataset is None else eval_dataset
									lowercase__						=				self.get_eval_dataloader(UpperCamelCase_							)
									lowercase__						=				self.eval_examples if eval_examples is None else eval_examples

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

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

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

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

									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()							)

									lowercase__						=				self.callback_handler.on_evaluate(self.args   ,						self.state   ,						self.control   ,						UpperCamelCase_							)
									return metrics


							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Any=None   ,						UpperCamelCase_:     str = "test"   ,						**UpperCamelCase_:     str							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				gen_kwargs.copy()

									lowercase__						=				self.get_test_dataloader(UpperCamelCase_							)

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

									lowercase__						=				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(
									        UpperCamelCase_   ,						UpperCamelCase_   ,						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

									lowercase__						=				self.post_process_function(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						'''predict'''							)
									lowercase__						=				self.compute_metrics(UpperCamelCase_							)

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






# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCAmelCase	=							'Create a default config file for Accelerate with only a few flags set.'
def       _a			(				SCREAMING_SNAKE_CASE="no"						,    SCREAMING_SNAKE_CASE = default_json_config_file						,    SCREAMING_SNAKE_CASE = False					):


		"""simple docstring"""




		lowercase__						=				Path(SCREAMING_SNAKE_CASE					)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE						,    exist_ok=SCREAMING_SNAKE_CASE					)
		if path.exists():
				print(
				    f'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'					)
				return False
		lowercase__						=				mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    f'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'					)
		lowercase__						=				{
		    '''compute_environment''': '''LOCAL_MACHINE''',
		    '''mixed_precision''': mixed_precision,
		}
		if torch.cuda.is_available():
				lowercase__						=				torch.cuda.device_count()
				lowercase__						=				num_gpus
				lowercase__						=				False
				if num_gpus > 1:
						lowercase__						=				'''MULTI_GPU'''
				else:
						lowercase__						=				'''NO'''
		elif is_xpu_available() and use_xpu:
				lowercase__						=				torch.xpu.device_count()
				lowercase__						=				num_xpus
				lowercase__						=				False
				if num_xpus > 1:
						lowercase__						=				'''MULTI_XPU'''
				else:
						lowercase__						=				'''NO'''
		elif is_npu_available():
				lowercase__						=				torch.npu.device_count()
				lowercase__						=				num_npus
				lowercase__						=				False
				if num_npus > 1:
						lowercase__						=				'''MULTI_NPU'''
				else:
						lowercase__						=				'''NO'''
		else:
				lowercase__						=				0
				lowercase__						=				True
				lowercase__						=				1
				lowercase__						=				'''NO'''
		lowercase__						=				ClusterConfig(**SCREAMING_SNAKE_CASE					)
		config.to_json_file(SCREAMING_SNAKE_CASE					)
		return path
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				parser.add_parser('''default'''						,    parents=SCREAMING_SNAKE_CASE						,    help=SCREAMING_SNAKE_CASE						,    formatter_class=SCREAMING_SNAKE_CASE					)
		parser.add_argument(
		    '''--config_file'''						,    default=SCREAMING_SNAKE_CASE						,    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\'.'''
		    )						,    dest='''save_location'''						,    )

		parser.add_argument(
		    '''--mixed_precision'''						,    choices=['''no''', '''fp16''', '''bf16''']						,    type=SCREAMING_SNAKE_CASE						,    help='''Whether or not to use mixed precision training. '''
		    '''Choose between FP16 and BF16 (bfloat16) training. '''
		    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''						,    default='''no'''						,    )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE					)
		return parser
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				write_basic_config(args.mixed_precision						,    args.save_location					)
		if config_file:
				print(f'accelerate configuration saved at {config_file}'					)


