




from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFLEDForConditionalGeneration, TFLEDModel




@require_tf
class 		__A      :
						UpperCamelCase     						=					LEDConfig
						UpperCamelCase     						=					{}
						UpperCamelCase     						=					"""gelu"""

						def __init__(		self   :Optional[int]    , __snake_case   :Optional[int]    , __snake_case   :Optional[Any]=13    , __snake_case   :List[Any]=7    , __snake_case   :List[Any]=True    , __snake_case   :Optional[Any]=False    , __snake_case   :Optional[Any]=99    , __snake_case   :Optional[Any]=32    , __snake_case   :Tuple=2    , __snake_case   :Optional[Any]=4    , __snake_case   :Optional[int]=37    , __snake_case   :Tuple=0.1    , __snake_case   :str=0.1    , __snake_case   :Optional[int]=20    , __snake_case   :List[str]=2    , __snake_case   :List[Any]=1    , __snake_case   :List[Any]=0    , __snake_case   :Tuple=4    , ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=seq_length
										__magic_name__	:	Dict								=is_training
										__magic_name__	:	Any								=use_labels
										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=hidden_size
										__magic_name__	:	Dict								=num_hidden_layers
										__magic_name__	:	int								=num_attention_heads
										__magic_name__	:	Tuple								=intermediate_size

										__magic_name__	:	List[str]								=hidden_dropout_prob
										__magic_name__	:	Tuple								=attention_probs_dropout_prob
										__magic_name__	:	List[Any]								=max_position_embeddings
										__magic_name__	:	Optional[int]								=eos_token_id
										__magic_name__	:	Dict								=pad_token_id
										__magic_name__	:	str								=bos_token_id
										__magic_name__	:	Tuple								=attention_window

										# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
										# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
										# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
										# because its local attention only attends to `self.attention_window` and one before and one after
										__magic_name__	:	Optional[Any]								=self.attention_window + 2

										# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
										# the `test_attention_outputs` and `test_hidden_states_output` tests
										__magic_name__	:	List[Any]								=(
										    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
										)

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

										__magic_name__	:	List[Any]								=ids_tensor([self.batch_size, self.seq_length - 1]    , self.vocab_size )
										__magic_name__	:	str								=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )    , 1 )
										__magic_name__	:	int								=tf.concat([input_ids, eos_tensor]    , axis=1 )

										__magic_name__	:	List[str]								=ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

										__magic_name__	:	Optional[Any]								=self.config_cls(
										    vocab_size=self.vocab_size    , d_model=self.hidden_size    , encoder_layers=self.num_hidden_layers    , decoder_layers=self.num_hidden_layers    , encoder_attention_heads=self.num_attention_heads    , decoder_attention_heads=self.num_attention_heads    , encoder_ffn_dim=self.intermediate_size    , decoder_ffn_dim=self.intermediate_size    , dropout=self.hidden_dropout_prob    , attention_dropout=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , eos_token_ids=[2]    , bos_token_id=self.bos_token_id    , pad_token_id=self.pad_token_id    , decoder_start_token_id=self.pad_token_id    , attention_window=self.attention_window    , **self.config_updates    , )
										__magic_name__	:	Optional[int]								=prepare_led_inputs_dict(__snake_case    , __snake_case    , __snake_case )
										__magic_name__	:	str								=tf.concat(
										    [tf.zeros_like(__snake_case )[:, :-1], tf.ones_like(__snake_case )[:, -1:]]    , axis=-1    , )
										__magic_name__	:	int								=global_attention_mask
										return config, inputs_dict


						def     A__						(		self   :Dict    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=TFLEDModel(config=__snake_case ).get_decoder()
										__magic_name__	:	Tuple								=inputs_dict["""input_ids"""]

										__magic_name__	:	Union[str, Any]								=input_ids[:1, :]
										__magic_name__	:	Tuple								=inputs_dict["""attention_mask"""][:1, :]
										__magic_name__	:	Optional[int]								=1

										# first forward pass
										__magic_name__	:	Any								=model(__snake_case    , attention_mask=__snake_case    , use_cache=__snake_case )

										__magic_name__				, __magic_name__	:	str								=outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										__magic_name__	:	Any								=ids_tensor((self.batch_size, 3)    , config.vocab_size )
										__magic_name__	:	List[Any]								=tf.cast(ids_tensor((self.batch_size, 3)    , 2 )    , tf.inta )

										# append to next input_ids and
										__magic_name__	:	Tuple								=tf.concat([input_ids, next_tokens]    , axis=-1 )
										__magic_name__	:	Union[str, Any]								=tf.concat([attention_mask, next_attn_mask]    , axis=-1 )

										__magic_name__	:	Any								=model(__snake_case    , attention_mask=__snake_case )[0]
										__magic_name__	:	List[str]								=model(__snake_case    , attention_mask=__snake_case    , past_key_values=__snake_case )[0]

										self.parent.assertEqual(next_tokens.shape[1]    , output_from_past.shape[1] )

										# select random slice
										__magic_name__	:	List[Any]								=int(ids_tensor((1,)    , output_from_past.shape[-1] ) )
										__magic_name__	:	str								=output_from_no_past[:, -3:, random_slice_idx]
										__magic_name__	:	List[Any]								=output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(__snake_case    , __snake_case    , rtol=1E-3 )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=None	,     lowerCamelCase=None	,     lowerCamelCase=None	,     lowerCamelCase=None	,     ):
				if attention_mask is None:
								__magic_name__	:	List[Any]								=tf.cast(tf.math.not_equal(lowerCamelCase	,     config.pad_token_id   )	,     tf.inta   )
				if decoder_attention_mask is None:
								__magic_name__	:	Union[str, Any]								=tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape	,     dtype=tf.inta   ),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,     config.pad_token_id   )	,     tf.inta   ),
								    ]	,     axis=-1	,     )
				if head_mask is None:
								__magic_name__	:	Any								=tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
				if decoder_head_mask is None:
								__magic_name__	:	List[str]								=tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
				return {
				    "input_ids": input_ids,
				    "attention_mask": attention_mask,
				    "decoder_input_ids": decoder_input_ids,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				}




@require_tf
class 		__A      (			UpperCamelCase__					,						UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
						UpperCamelCase     						=					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
						UpperCamelCase     						=					(
						    {
						        """conversational""": TFLEDForConditionalGeneration,
						        """feature-extraction""": TFLEDModel,
						        """summarization""": TFLEDForConditionalGeneration,
						        """text2text-generation""": TFLEDForConditionalGeneration,
						        """translation""": TFLEDForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCamelCase     						=					True
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False

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

										__magic_name__	:	Union[str, Any]								=TFLEDModelTester(self )
										__magic_name__	:	Tuple								=ConfigTester(self    , config_class=__snake_case )

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

										self.config_tester.run_common_tests()

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

										__magic_name__	:	str								=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__snake_case )

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

										__magic_name__				, __magic_name__	:	Any								=self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__	:	Union[str, Any]								=tf.zeros_like(inputs_dict["""attention_mask"""] )
										__magic_name__	:	Tuple								=2
										__magic_name__	:	List[Any]								=tf.where(
										    tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices    , 1    , inputs_dict["""global_attention_mask"""]    , )

										__magic_name__	:	List[str]								=True
										__magic_name__	:	List[str]								=self.model_tester.seq_length
										__magic_name__	:	Optional[Any]								=self.model_tester.encoder_seq_length

										def check_decoder_attentions_output(__snake_case   :Optional[Any] ):
														__magic_name__	:	Optional[Any]								=outputs.decoder_attentions
														self.assertEqual(len(__snake_case )    , self.model_tester.num_hidden_layers )
														self.assertListEqual(
														    list(decoder_attentions[0].shape[-3:] )    , [self.model_tester.num_attention_heads, seq_length, seq_length]    , )

										def check_encoder_attentions_output(__snake_case   :Any ):
														__magic_name__	:	Tuple								=[t.numpy() for t in outputs.encoder_attentions]
														__magic_name__	:	Optional[Any]								=[t.numpy() for t in outputs.encoder_global_attentions]
														self.assertEqual(len(__snake_case )    , self.model_tester.num_hidden_layers )
														self.assertEqual(len(__snake_case )    , self.model_tester.num_hidden_layers )
														self.assertListEqual(
														    list(attentions[0].shape[-3:] )    , [self.model_tester.num_attention_heads, seq_length, seq_length]    , )
														self.assertListEqual(
														    list(global_attentions[0].shape[-3:] )    , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]    , )

										for model_class in self.all_model_classes:
														__magic_name__	:	int								=True
														__magic_name__	:	Tuple								=False
														__magic_name__	:	Any								=False
														__magic_name__	:	Tuple								=model_class(__snake_case )
														__magic_name__	:	List[Any]								=model(self._prepare_for_class(__snake_case    , __snake_case ) )
														__magic_name__	:	Dict								=len(__snake_case )
														self.assertEqual(config.output_hidden_states    , __snake_case )
														check_encoder_attentions_output(__snake_case )

														if self.is_encoder_decoder:
																		__magic_name__	:	Any								=model_class(__snake_case )
																		__magic_name__	:	Any								=model(self._prepare_for_class(__snake_case    , __snake_case ) )
																		self.assertEqual(config.output_hidden_states    , __snake_case )
																		check_decoder_attentions_output(__snake_case )

														# Check that output attentions can also be changed via the config
														del inputs_dict["output_attentions"]
														__magic_name__	:	Any								=True
														__magic_name__	:	str								=model_class(__snake_case )
														__magic_name__	:	str								=model(self._prepare_for_class(__snake_case    , __snake_case ) )
														self.assertEqual(config.output_hidden_states    , __snake_case )
														check_encoder_attentions_output(__snake_case )

														# Check attention is always last and order is fine
														__magic_name__	:	List[Any]								=True
														__magic_name__	:	Dict								=True
														__magic_name__	:	Tuple								=model_class(__snake_case )
														__magic_name__	:	int								=model(self._prepare_for_class(__snake_case    , __snake_case ) )

														self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)    , len(__snake_case ) )
														self.assertEqual(model.config.output_hidden_states    , __snake_case )
														check_encoder_attentions_output(__snake_case )

						@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing.""" )
						def     A__						(		self   :int ):
										'''simple docstring'''

										pass


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

										pass




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				return tf.constant(lowerCamelCase	,     dtype=tf.intaa   )


UpperCAmelCase_		:				Optional[Any]									=     1e-4




@slow
@require_tf
class 		__A      (			unittest.TestCase			):

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

										__magic_name__	:	Optional[Any]								=TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" ).led

										# change to intended input here
										__magic_name__	:	Dict								=_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
										__magic_name__	:	str								=_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
										__magic_name__	:	List[Any]								=prepare_led_inputs_dict(model.config    , __snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=model(**__snake_case )[0]
										__magic_name__	:	str								=(1, 10_24, 7_68)
										self.assertEqual(output.shape    , __snake_case )
										# change to expected output here
										__magic_name__	:	str								=tf.convert_to_tensor(
										    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]    , )
										tf.debugging.assert_near(output[:, :3, :3]    , __snake_case    , atol=1E-3 )


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

										__magic_name__	:	Dict								=TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" )

										# change to intended input here
										__magic_name__	:	Any								=_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
										__magic_name__	:	Tuple								=_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
										__magic_name__	:	Any								=prepare_led_inputs_dict(model.config    , __snake_case    , __snake_case )
										__magic_name__	:	Dict								=model(**__snake_case )[0]
										__magic_name__	:	str								=(1, 10_24, model.config.vocab_size)
										self.assertEqual(output.shape    , __snake_case )
										# change to expected output here
										__magic_name__	:	Any								=tf.convert_to_tensor(
										    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]    , )
										tf.debugging.assert_near(output[:, :3, :3]    , __snake_case    , atol=1E-3    , rtol=1E-3 )










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




