



import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
						import tensorflow as tf
						from tensorflow.python.framework.errors_impl import ResourceExhaustedError

						from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
						import pyanvml.pyanvml as nvml

lowerCamelCase		       =      logging.get_logger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						str:
				def run_func(__UpperCamelCase ):
								@wraps(__UpperCamelCase )
								def run_in_eager_mode(*__UpperCamelCase	,				**__UpperCamelCase ):
												return func(*__UpperCamelCase	,				**__UpperCamelCase )

								@wraps(__UpperCamelCase )
								@tf.function(experimental_compile=__UpperCamelCase )
								def run_in_graph_mode(*__UpperCamelCase	,				**__UpperCamelCase ):
												return func(*__UpperCamelCase	,				**__UpperCamelCase )

								if do_eager_mode is True:
												if use_xla is not False:
																raise ValueError(
																    "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
												return run_in_eager_mode
								else:
												return run_in_graph_mode

				return run_func






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						["tf.Tensor"]:
				a__	:							Union[str, Any]      						= random.Random()
				a__	:							str      						= [rng.randint(0	,				vocab_size - 1 ) for i in range(batch_size * sequence_length )]
				return tf.constant(__UpperCamelCase	,				shape=(batch_size, sequence_length)	,				dtype=tf.intaa )






class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:TensorFlowBenchmarkArguments
							A				:PretrainedConfig
							A				:str =       "TensorFlow"



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return tf.__version__



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.args.strategy
											if strategy is None:
															raise ValueError("A device strategy has to be initialized before using TensorFlow."				)
											a__	:							Optional[int]      						= self._prepare_inference_func(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)
											return self._measure_speed(_inference				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= self.args.strategy
											if strategy is None:
															raise ValueError("A device strategy has to be initialized before using TensorFlow."				)
											a__	:							Union[str, Any]      						= self._prepare_train_func(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)
											return self._measure_speed(_train				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											if self.args.is_gpu:
															tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,					__UpperCAmelCase				)
											a__	:							Optional[Any]      						= self.args.strategy
											if strategy is None:
															raise ValueError("A device strategy has to be initialized before using TensorFlow."				)
											a__	:							Any      						= self._prepare_inference_func(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)
											return self._measure_memory(_inference				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											if self.args.is_gpu:
															tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,					__UpperCAmelCase				)
											a__	:							Any      						= self.args.strategy
											if strategy is None:
															raise ValueError("A device strategy has to be initialized before using TensorFlow."				)

											a__	:							str      						= self._prepare_train_func(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)
											return self._measure_memory(_train				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Dict      						= self.config_dict[model_name]

											if self.args.fpaa:
															raise NotImplementedError("Mixed precision is currently not supported."				)

											a__	:							Tuple      						= (
											    hasattr(__UpperCAmelCase ,					"architectures"				)
											    and isinstance(config.architectures ,					__UpperCAmelCase				)
											    and len(config.architectures				) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																			a__	:							List[str]      						= "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
																			a__	:							List[Any]      						= __import__("transformers" ,					fromlist=[model_class]				)
																			a__	:							Tuple      						= getattr(__UpperCAmelCase ,					__UpperCAmelCase				)
																			a__	:							Any      						= model_cls(__UpperCAmelCase				)
															except ImportError:
																			raise ImportError(
																			    f'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
																			    " set `--only_pretrain_model` or `args.only_pretrain_model=True`."				)
											else:
															a__	:							int      						= TF_MODEL_MAPPING[config.__class__](__UpperCAmelCase				)

											# encoder-decoder has vocab size saved differently
											a__	:							Union[str, Any]      						= config.vocab_size if hasattr(__UpperCAmelCase ,					"vocab_size"				) else config.encoder.vocab_size
											a__	:							Union[str, Any]      						= random_input_ids(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)

											@run_with_tf_optimizations(self.args.eager_mode ,					self.args.use_xla				)
											def encoder_decoder_forward():
															return model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase ,					training=__UpperCAmelCase				)

											@run_with_tf_optimizations(self.args.eager_mode ,					self.args.use_xla				)
											def encoder_forward():
															return model(__UpperCAmelCase ,					training=__UpperCAmelCase				)

											a__	:							List[Any]      						= encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

											return _inference



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Dict      						= self.config_dict[model_name]

											if self.args.eager_mode is not False:
															raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."				)

											if self.args.fpaa:
															raise NotImplementedError("Mixed precision is currently not supported."				)

											a__	:							List[Any]      						= (
											    hasattr(__UpperCAmelCase ,					"architectures"				)
											    and isinstance(config.architectures ,					__UpperCAmelCase				)
											    and len(config.architectures				) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																			a__	:							Any      						= "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
																			a__	:							Tuple      						= __import__("transformers" ,					fromlist=[model_class]				)
																			a__	:							Any      						= getattr(__UpperCAmelCase ,					__UpperCAmelCase				)
																			a__	:							Any      						= model_cls(__UpperCAmelCase				)
															except ImportError:
																			raise ImportError(
																			    f'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
																			    " set `--only_pretrain_model` or `args.only_pretrain_model=True`."				)
											else:
															a__	:							str      						= TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCAmelCase				)

											# encoder-decoder has vocab size saved differently
											a__	:							Union[str, Any]      						= config.vocab_size if hasattr(__UpperCAmelCase ,					"vocab_size"				) else config.encoder.vocab_size
											a__	:							Dict      						= random_input_ids(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)

											@run_with_tf_optimizations(self.args.eager_mode ,					self.args.use_xla				)
											def encoder_decoder_train():
															a__	:							List[Any]      						= model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase ,					training=__UpperCAmelCase				)[0]
															a__	:							List[Any]      						= tf.gradients(__UpperCAmelCase ,					model.trainable_variables				)
															return gradients

											@run_with_tf_optimizations(self.args.eager_mode ,					self.args.use_xla				)
											def encoder_train():
															a__	:							Union[str, Any]      						= model(__UpperCAmelCase ,					labels=__UpperCAmelCase ,					training=__UpperCAmelCase				)[0]
															a__	:							Any      						= tf.gradients(__UpperCAmelCase ,					model.trainable_variables				)
															return gradients

											a__	:							List[str]      						= encoder_decoder_train if config.is_encoder_decoder else encoder_train

											return _train



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											with self.args.strategy.scope():
															try:
																			if self.args.is_tpu or self.args.use_xla:
																							# run additional 10 times to stabilize compilation for tpu
																							logger.info("Do inference on TPU. Running model 5 times to stabilize compilation"				)
																							timeit.repeat(__UpperCAmelCase ,					repeat=1 ,					number=5				)

																			# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																			a__	:							Optional[Any]      						= timeit.repeat(
																			    __UpperCAmelCase ,					repeat=self.args.repeat ,					number=10 ,					)

																			return min(__UpperCAmelCase				) / 1_0.0
															except ResourceExhaustedError as e:
																			self.print_fn(f'Doesn\'t fit on GPU. {e}'				)




							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											logger.info(
											    "Note that TensorFlow allocates more memory than "
											    "it might need to speed up computation. "
											    "The memory reported here corresponds to the memory "
											    "reported by `nvidia-smi`, which can vary depending "
											    "on total available memory on the GPU that is used."				)
											with self.args.strategy.scope():
															try:
																			if self.args.trace_memory_line_by_line:
																							if not self.args.eager_mode:
																											raise ValueError(
																											    "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
																											    " consumption line by line."				)
																							a__	:							Optional[int]      						= start_memory_tracing("transformers"				)

																			if self.args.is_tpu:
																							# tpu
																							raise NotImplementedError(
																							    "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
																							    " with `args.memory=False`"				)
																			elif self.args.is_gpu:
																							# gpu
																							if not is_pyanvml_available():
																											logger.warning(
																											    "py3nvml not installed, we won't log GPU memory usage. "
																											    "Install py3nvml (pip install py3nvml) to log information about GPU."				)
																											a__	:							List[str]      						= "N/A"
																							else:
																											logger.info(
																											    "Measuring total GPU usage on GPU device. Make sure to not have additional processes"
																											    " running on the same GPU."				)
																											# init nvml
																											nvml.nvmlInit()
																											func()
																											a__	:							Tuple      						= nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx				)
																											a__	:							Dict      						= nvml.nvmlDeviceGetMemoryInfo(__UpperCAmelCase				)
																											a__	:							Optional[int]      						= meminfo.used
																											a__	:							Any      						= Memory(__UpperCAmelCase				)
																											# shutdown nvml
																											nvml.nvmlShutdown()
																			else:
																							# cpu
																							if self.args.trace_memory_line_by_line:
																											logger.info(
																											    "When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
																											    " TensorFlow."				)
																											a__	:							Union[str, Any]      						= None
																							else:
																											a__	:							Dict      						= measure_peak_memory_cpu(__UpperCAmelCase				)
																											a__	:							Dict      						= Memory(__UpperCAmelCase				) if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				) else memory_bytes
																			if self.args.trace_memory_line_by_line:
																							a__	:							Any      						= stop_memory_tracing(__UpperCAmelCase				)
																							if memory is None:
																											a__	:							str      						= summary.total
																			else:
																							a__	:							List[str]      						= None

																			return memory, summary
															except ResourceExhaustedError as e:
																			self.print_fn(f'Doesn\'t fit on GPU. {e}'				)
																			return "N/A", None







# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowerCamelCase		       =      subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
lowerCamelCase		       =      subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()

lowerCamelCase		       =      """|""".join(sys.argv[1:])
lowerCamelCase		       =      re.compile(RF'^({joined_dirs}).*?\.py$')

lowerCamelCase		       =      [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


