




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch

							from transformers.generation import DisjunctiveConstraint




@require_torch
class 		__A      (			unittest.TestCase			):

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=[[1, 2, 4], [1, 2, 3, 4]]
										__magic_name__	:	Optional[int]								=DisjunctiveConstraint(__snake_case )
										self.assertTrue(isinstance(dc.token_ids    , __snake_case ) )

										with self.assertRaises(__snake_case ):
														DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

										with self.assertRaises(__snake_case ):
														DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=[[1, 2], [1, 2, 3, 4]]

										with self.assertRaises(__snake_case ):
														DisjunctiveConstraint(__snake_case )  # fails here

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=[[1, 2, 3], [1, 2, 4]]

										__magic_name__	:	Any								=DisjunctiveConstraint(__snake_case )

										__magic_name__				, __magic_name__				, __magic_name__	:	int								=dc.update(1 )
										__magic_name__	:	Optional[int]								=stepped is True and completed is False and reset is False
										self.assertTrue(__snake_case )
										self.assertTrue(not dc.completed )
										self.assertTrue(dc.current_seq == [1] )

										__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, Any]								=dc.update(2 )
										__magic_name__	:	Tuple								=stepped is True and completed is False and reset is False
										self.assertTrue(__snake_case )
										self.assertTrue(not dc.completed )
										self.assertTrue(dc.current_seq == [1, 2] )

										__magic_name__				, __magic_name__				, __magic_name__	:	List[str]								=dc.update(3 )
										__magic_name__	:	Dict								=stepped is True and completed is True and reset is False
										self.assertTrue(__snake_case )
										self.assertTrue(dc.completed )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 3] )


						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

										__magic_name__	:	List[str]								=DisjunctiveConstraint(__snake_case )

										__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, Any]								=dc.update(1 )
										self.assertTrue(not dc.completed )
										self.assertTrue(dc.current_seq == [1] )

										__magic_name__				, __magic_name__				, __magic_name__	:	List[str]								=dc.update(2 )
										self.assertTrue(not dc.completed )
										self.assertTrue(dc.current_seq == [1, 2] )

										__magic_name__				, __magic_name__				, __magic_name__	:	Tuple								=dc.update(4 )
										self.assertTrue(not dc.completed )
										self.assertTrue(dc.current_seq == [1, 2, 4] )

										__magic_name__				, __magic_name__				, __magic_name__	:	Tuple								=dc.update(5 )
										self.assertTrue(dc.completed )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

										dc.reset()

										__magic_name__				, __magic_name__				, __magic_name__	:	str								=dc.update(1 )
										self.assertTrue(not dc.completed )
										self.assertTrue(dc.remaining() == 3 )
										self.assertTrue(dc.current_seq == [1] )

										__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, Any]								=dc.update(2 )
										self.assertTrue(not dc.completed )
										self.assertTrue(dc.remaining() == 2 )
										self.assertTrue(dc.current_seq == [1, 2] )

										__magic_name__				, __magic_name__				, __magic_name__	:	Dict								=dc.update(5 )
										self.assertTrue(dc.completed )  # Completed!
										self.assertTrue(dc.remaining() == 0 )
										self.assertTrue(dc.current_seq == [1, 2, 5] )










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_		:				Optional[Any]									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				def run_func(lowerCamelCase   ):
								@wraps(lowerCamelCase   )
								def run_in_eager_mode(*lowerCamelCase	,     **lowerCamelCase   ):
												return func(*lowerCamelCase	,     **lowerCamelCase   )

								@wraps(lowerCamelCase   )
								@tf.function(experimental_compile=lowerCamelCase   )
								def run_in_graph_mode(*lowerCamelCase	,     **lowerCamelCase   ):
												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	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Dict								=random.Random()
				__magic_name__	:	Union[str, Any]								=[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 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					42
						UpperCamelCase     						=					42
						UpperCamelCase     						=					"TensorFlow"

						@property
						def     A__						(		self   :str ):
										'''simple docstring'''

										return tf.__version__

						def     A__						(		self   :str    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
										__magic_name__	:	Union[str, Any]								=self._prepare_inference_func(__snake_case    , __snake_case    , __snake_case )
										return self._measure_speed(_inference )

						def     A__						(		self   :int    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

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

						def     A__						(		self   :str    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

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

						def     A__						(		self   :str    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

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

										__magic_name__	:	Any								=self._prepare_train_func(__snake_case    , __snake_case    , __snake_case )
										return self._measure_memory(_train )

						def     A__						(		self   :int    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	int								=self.config_dict[model_name]

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

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

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

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

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

										__magic_name__	:	Tuple								=encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

										return _inference

						def     A__						(		self   :int    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=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.""" )

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

										# encoder-decoder has vocab size saved differently
										__magic_name__	:	int								=config.vocab_size if hasattr(__snake_case    , """vocab_size""" ) else config.encoder.vocab_size
										__magic_name__	:	Optional[Any]								=random_input_ids(__snake_case    , __snake_case    , __snake_case )

										@run_with_tf_optimizations(self.args.eager_mode    , self.args.use_xla )
										def encoder_decoder_train():
														__magic_name__	:	List[str]								=model(__snake_case    , decoder_input_ids=__snake_case    , labels=__snake_case    , training=__snake_case )[0]
														__magic_name__	:	int								=tf.gradients(__snake_case    , model.trainable_variables )
														return gradients

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

										__magic_name__	:	Union[str, Any]								=encoder_decoder_train if config.is_encoder_decoder else encoder_train

										return _train

						def     A__						(		self   :Any    , __snake_case   :Union[str, Any] ):
										'''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(__snake_case    , 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
																		__magic_name__	:	Union[str, Any]								=timeit.repeat(
																		    __snake_case    , repeat=self.args.repeat    , number=10    , )

																		return min(__snake_case ) / 10.0
														except ResourceExhaustedError as e:
																		self.print_fn(f"Doesn't fit on GPU. {e}" )


						def     A__						(		self   :Any    , __snake_case   :Callable[[], None] ):
										'''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.""" )
																						__magic_name__	:	Union[str, Any]								=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.""" )
																										__magic_name__	:	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()
																										__magic_name__	:	List[str]								=nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
																										__magic_name__	:	Tuple								=nvml.nvmlDeviceGetMemoryInfo(__snake_case )
																										__magic_name__	:	Any								=meminfo.used
																										__magic_name__	:	str								=Memory(__snake_case )
																										# 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.""" )
																										__magic_name__	:	List[str]								=None
																						else:
																										__magic_name__	:	List[Any]								=measure_peak_memory_cpu(__snake_case )
																										__magic_name__	:	str								=Memory(__snake_case ) if isinstance(__snake_case    , __snake_case ) else memory_bytes
																		if self.args.trace_memory_line_by_line:
																						__magic_name__	:	List[Any]								=stop_memory_tracing(__snake_case )
																						if memory is None:
																										__magic_name__	:	Any								=summary.total
																		else:
																						__magic_name__	:	Optional[Any]								=None

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




