


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

UpperCAmelCase   :    List[str]     =		logging.get_logger(__name__)






def 	__lowerCamelCase						(							lowerCamelCase__		:						bool  ,							lowerCamelCase__		:						bool      ):





			'''simple docstring'''




			def run_func(lowerCamelCase__		:						Tuple      ):
						@wraps(lowerCamelCase__      )
						def run_in_eager_mode(*lowerCamelCase__		:						Any  ,							**lowerCamelCase__		:						Optional[int]      ):
									return func(*lowerCamelCase__  ,							**lowerCamelCase__      )

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

						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 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  random.Random()
			lowerCamelCase    		=  [rng.randint(0  ,							vocab_size - 1      ) for i in range(batch_size * sequence_length      )]
			return tf.constant(lowerCamelCase__  ,							shape=(batch_size, sequence_length)  ,							dtype=tf.intaa      )







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					TensorFlowBenchmarkArguments
		UpperCamelCase     :					PretrainedConfig
		UpperCamelCase     :					str			       =		"TensorFlow"







		@property
		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					return tf.__version__







		def 					__A   ( self		,      A		,      A		,      A	)  -> float:







					'''simple docstring'''







					lowerCamelCase    		=  self.args.strategy
					if strategy is None:
								raise ValueError("""A device strategy has to be initialized before using TensorFlow."""	)
					lowerCamelCase    		=  self._prepare_inference_func(A		,      A		,      A	)
					return self._measure_speed(_inference	)







		def 					__A   ( self		,      A		,      A		,      A	)  -> float:







					'''simple docstring'''







					lowerCamelCase    		=  self.args.strategy
					if strategy is None:
								raise ValueError("""A device strategy has to be initialized before using TensorFlow."""	)
					lowerCamelCase    		=  self._prepare_train_func(A		,      A		,      A	)
					return self._measure_speed(_train	)







		def 					__A   ( self		,      A		,      A		,      A	)  -> [Memory, Optional[MemorySummary]]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A		,      A	)  -> [Memory, Optional[MemorySummary]]:







					'''simple docstring'''







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

					lowerCamelCase    		=  self._prepare_train_func(A		,      A		,      A	)
					return self._measure_memory(_train	)







		def 					__A   ( self		,      A		,      A		,      A	)  -> Callable[[], None]:







					'''simple docstring'''







					lowerCamelCase    		=  self.config_dict[model_name]

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

					lowerCamelCase    		=  (
					    hasattr(A		,      """architectures"""	)
					    and isinstance(config.architectures		,      A	)
					    and len(config.architectures	) > 0
					)
					if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
											lowerCamelCase    		=  """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
											lowerCamelCase    		=  __import__("""transformers"""		,      fromlist=[model_class]	)
											lowerCamelCase    		=  getattr(A		,      A	)
											lowerCamelCase    		=  model_cls(A	)
								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:
								lowerCamelCase    		=  TF_MODEL_MAPPING[config.__class__](A	)

					# encoder-decoder has vocab size saved differently
					lowerCamelCase    		=  config.vocab_size if hasattr(A		,      """vocab_size"""	) else config.encoder.vocab_size
					lowerCamelCase    		=  random_input_ids(A		,      A		,      A	)

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

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

					lowerCamelCase    		=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

					return _inference







		def 					__A   ( self		,      A		,      A		,      A	)  -> Callable[[], None]:







					'''simple docstring'''







					lowerCamelCase    		=  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."""	)

					lowerCamelCase    		=  (
					    hasattr(A		,      """architectures"""	)
					    and isinstance(config.architectures		,      A	)
					    and len(config.architectures	) > 0
					)
					if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
											lowerCamelCase    		=  """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
											lowerCamelCase    		=  __import__("""transformers"""		,      fromlist=[model_class]	)
											lowerCamelCase    		=  getattr(A		,      A	)
											lowerCamelCase    		=  model_cls(A	)
								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:
								lowerCamelCase    		=  TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A	)

					# encoder-decoder has vocab size saved differently
					lowerCamelCase    		=  config.vocab_size if hasattr(A		,      """vocab_size"""	) else config.encoder.vocab_size
					lowerCamelCase    		=  random_input_ids(A		,      A		,      A	)

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

					@run_with_tf_optimizations(self.args.eager_mode		,      self.args.use_xla	)
					def encoder_train():
								lowerCamelCase    		=  model(A		,      labels=A		,      training=A	)[0]
								lowerCamelCase    		=  tf.gradients(A		,      model.trainable_variables	)
								return gradients

					lowerCamelCase    		=  encoder_decoder_train if config.is_encoder_decoder else encoder_train

					return _train







		def 					__A   ( self		,      A	)  -> float:







					'''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(A		,      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
											lowerCamelCase    		=  timeit.repeat(
											    A		,      repeat=self.args.repeat		,      number=10		,      )

											return min(A	) / 10.0
								except ResourceExhaustedError as e:
											self.print_fn(F'Doesn\'t fit on GPU. {e}'	)







		def 					__A   ( self		,      A	)  -> [Memory, MemorySummary]:







					'''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."""	)
														lowerCamelCase    		=  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."""	)
																	lowerCamelCase    		=  """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()
																	lowerCamelCase    		=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
																	lowerCamelCase    		=  nvml.nvmlDeviceGetMemoryInfo(A	)
																	lowerCamelCase    		=  meminfo.used
																	lowerCamelCase    		=  Memory(A	)
																	# 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."""	)
																	lowerCamelCase    		=  None
														else:
																	lowerCamelCase    		=  measure_peak_memory_cpu(A	)
																	lowerCamelCase    		=  Memory(A	) if isinstance(A		,      A	) else memory_bytes
											if self.args.trace_memory_line_by_line:
														lowerCamelCase    		=  stop_memory_tracing(A	)
														if memory is None:
																	lowerCamelCase    		=  summary.total
											else:
														lowerCamelCase    		=  None

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





import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase   :    str     =		[
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  Github(os.environ["""GITHUB_TOKEN"""]      )
			lowerCamelCase    		=  g.get_repo("""huggingface/diffusers"""      )
			lowerCamelCase    		=  repo.get_issues(state="""open"""      )

			for issue in open_issues:
						lowerCamelCase    		=  sorted(issue.get_comments()  ,							key=lambda lowerCamelCase__      : i.created_at  ,							reverse=lowerCamelCase__      )
						lowerCamelCase    		=  comments[0] if len(lowerCamelCase__      ) > 0 else None
						if (
						    last_comment is not None
						    and last_comment.user.login == "github-actions[bot]"
						    and (dt.utcnow() - issue.updated_at).days > 7
						    and (dt.utcnow() - issue.created_at).days >= 30
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
						):
									# Closes the issue after 7 days of inactivity since the Stalebot notification.
									issue.edit(state="""closed"""      )
						elif (
						    "stale" in issue.get_labels()
						    and last_comment is not None
						    and last_comment.user.login != "github-actions[bot]"
						):
									# Opens the issue if someone other than Stalebot commented.
									issue.edit(state="""open"""      )
									issue.remove_from_labels("""stale"""      )
						elif (
						    (dt.utcnow() - issue.updated_at).days > 23
						    and (dt.utcnow() - issue.created_at).days >= 30
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
						):
									# Post a Stalebot notification after 23 days of inactivity.
									issue.create_comment(
									    """This issue has been automatically marked as stale because it has not had """
									    """recent activity. If you think this still needs to be addressed """
									    """please comment on this thread.\n\nPlease note that issues that do not follow the """
									    """[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
									    """are likely to be ignored."""      )
									issue.add_to_labels("""stale"""      )


if __name__ == "__main__":
		main()

