




import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				if model.config.model_type == "gpt2":
								return model.transformer.h[0].mlp.c_fc
				return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
							import torch
							import torch.nn as nn




							class 		__A      (			nn.Module			):

													def __init__(		self   :List[Any]    , __snake_case   :nn.Module    , __snake_case   :int ):
																	'''simple docstring'''

																	super().__init__()
																	__magic_name__	:	Optional[Any]								=module
																	__magic_name__	:	Union[str, Any]								=nn.Sequential(
																	    nn.Linear(module.in_features    , __snake_case    , bias=__snake_case )    , nn.Linear(__snake_case    , module.out_features    , bias=__snake_case )    , )
																	__magic_name__	:	str								=(2.0 / (5 * min(module.in_features    , module.out_features ))) ** 0.5
																	nn.init.normal_(self.adapter[0].weight    , std=__snake_case )
																	nn.init.zeros_(self.adapter[1].weight )
																	self.adapter.to(module.weight.device )


													def     A__						(		self   :Any    , __snake_case   :List[str]    , *__snake_case   :Optional[int]    , **__snake_case   :Optional[Any] ):
																	'''simple docstring'''

																	return self.module(__snake_case    , *__snake_case    , **__snake_case ) + self.adapter(__snake_case )




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 		__A      (			unittest.TestCase			):
						# We keep the constants inside the init function and model loading inside setUp function

						# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
						# Therefore here we use only bloom-1b3 to test our module
						UpperCamelCase     						=					"""bigscience/bloom-1b7"""

						# Constant values
						UpperCamelCase     						=					2.109659552692574

						UpperCamelCase     						=					"""Hello my name is"""
						UpperCamelCase     						=					set()
						EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I"""			)
						EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n"""			)
						EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University"""			)
						UpperCamelCase     						=					10

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

										__magic_name__	:	Optional[int]								=AutoTokenizer.from_pretrained(self.model_name )




class 		__A      (			UpperCamelCase__			):

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

										super().setUp()

										# Models and tokenizer
										__magic_name__	:	Tuple								=AutoModelForCausalLM.from_pretrained(
										    self.model_name    , torch_dtype=torch.floataa    , device_map="""auto""" )
										__magic_name__	:	Union[str, Any]								=AutoModelForCausalLM.from_pretrained(self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )

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

										del self.model_fpaa
										del self.model_abit

										gc.collect()
										torch.cuda.empty_cache()

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

										__magic_name__	:	Optional[Any]								=self.model_abit.config

										self.assertTrue(hasattr(__snake_case    , """quantization_config""" ) )

										__magic_name__	:	List[str]								=config.to_dict()
										__magic_name__	:	Optional[int]								=config.to_diff_dict()

										__magic_name__	:	Dict								=config.to_json_string()

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

										from bitsandbytes.nn import Paramsabit

										__magic_name__	:	Tuple								=self.model_fpaa.get_memory_footprint()
										__magic_name__	:	Tuple								=self.model_abit.get_memory_footprint()

										self.assertAlmostEqual(mem_fpaa / mem_abit    , self.EXPECTED_RELATIVE_DIFFERENCE )
										__magic_name__	:	List[str]								=get_some_linear_layer(self.model_abit )
										self.assertTrue(linear.weight.__class__ == Paramsabit )

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

										from transformers import TaPreTrainedModel

										self.model_fpaa.get_memory_footprint()
										self.model_abit.get_memory_footprint()

										for name, module in self.model_abit.named_modules():
														if isinstance(__snake_case    , torch.nn.Linear ):
																		if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																						# 4-bit parameters are packed in uint8 variables
																						self.assertTrue(module.weight.dtype == torch.uinta )

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

										__magic_name__	:	Optional[int]								=self.tokenizer(self.input_text    , return_tensors="""pt""" )
										__magic_name__	:	Optional[Any]								=self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0 )    , max_new_tokens=10 )

										self.assertIn(self.tokenizer.decode(output_sequences[0]    , skip_special_tokens=__snake_case )    , self.EXPECTED_OUTPUTS )

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

										__magic_name__	:	str								=BitsAndBytesConfig()
										__magic_name__	:	Optional[Any]								=True

										__magic_name__	:	List[str]								=AutoModelForCausalLM.from_pretrained(
										    self.model_name    , quantization_config=__snake_case    , device_map="""auto""" )

										__magic_name__	:	Dict								=self.tokenizer(self.input_text    , return_tensors="""pt""" )
										__magic_name__	:	List[Any]								=model_abit_from_config.generate(
										    input_ids=encoded_input["""input_ids"""].to(0 )    , max_new_tokens=10 )

										self.assertIn(self.tokenizer.decode(output_sequences[0]    , skip_special_tokens=__snake_case )    , self.EXPECTED_OUTPUTS )

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

										with self.assertRaises(__snake_case ), tempfile.TemporaryDirectory() as tmpdirname:
														self.model_abit.save_pretrained(__snake_case )

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

										__magic_name__	:	int								=BitsAndBytesConfig()

										with self.assertRaises(__snake_case ):
														__magic_name__	:	Tuple								=AutoModelForCausalLM.from_pretrained(
														    self.model_name    , quantization_config=__snake_case    , load_in_abit=__snake_case    , device_map="""auto"""    , bnb_abit_quant_type="""nf4"""    , )

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

										with self.assertRaises(__snake_case ):
														# Tries with `str`
														self.model_abit.to("""cpu""" )

										with self.assertRaises(__snake_case ):
														# Tries with a `dtype``
														self.model_abit.to(torch.floataa )

										with self.assertRaises(__snake_case ):
														# Tries with a `device`
														self.model_abit.to(torch.device("""cuda:0""" ) )

										with self.assertRaises(__snake_case ):
														# Tries with a `device`
														self.model_abit.float()

										with self.assertRaises(__snake_case ):
														# Tries with a `device`
														self.model_abit.half()

										# Test if we did not break anything
										__magic_name__	:	Optional[int]								=self.tokenizer(self.input_text    , return_tensors="""pt""" )

										__magic_name__	:	List[str]								=self.model_fpaa.to(torch.floataa )
										__magic_name__	:	Any								=self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0 )    , max_new_tokens=10 )

										# Check this does not throw an error
										__magic_name__	:	Tuple								=self.model_fpaa.to("""cpu""" )

										# Check this does not throw an error
										__magic_name__	:	str								=self.model_fpaa.half()

										# Check this does not throw an error
										__magic_name__	:	Any								=self.model_fpaa.float()


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

										__magic_name__	:	Union[str, Any]								=AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""    , load_in_abit=__snake_case    , device_map="""auto""" )
										self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 		__A      (			unittest.TestCase			):

						@classmethod
						def     A__						(		cls   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								="""t5-small"""
										__magic_name__	:	Dict								="""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
										__magic_name__	:	Optional[int]								=AutoTokenizer.from_pretrained(cls.model_name )
										__magic_name__	:	int								="""Translate in German: Hello, my dog is cute"""

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

										gc.collect()
										torch.cuda.empty_cache()

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

										from transformers import TaForConditionalGeneration

										__magic_name__	:	str								=TaForConditionalGeneration._keep_in_fpaa_modules
										__magic_name__	:	int								=None

										# test with `t5-small`
										__magic_name__	:	Any								=TaForConditionalGeneration.from_pretrained(self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										__magic_name__	:	Tuple								=self.tokenizer(self.input_text    , return_tensors="""pt""" ).to(0 )
										__magic_name__	:	int								=model.generate(**__snake_case )

										# test with `flan-t5-small`
										__magic_name__	:	Optional[int]								=TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										__magic_name__	:	Union[str, Any]								=self.tokenizer(self.input_text    , return_tensors="""pt""" ).to(0 )
										__magic_name__	:	int								=model.generate(**__snake_case )
										__magic_name__	:	int								=modules


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

										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										__magic_name__	:	Optional[int]								=TaForConditionalGeneration.from_pretrained(self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )

										# there was a bug with decoders - this test checks that it is fixed
										self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q    , bnb.nn.Linearabit ) )

										__magic_name__	:	str								=self.tokenizer(self.input_text    , return_tensors="""pt""" ).to(0 )
										__magic_name__	:	str								=model.generate(**__snake_case )

										# test with `flan-t5-small`
										__magic_name__	:	Any								=TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										__magic_name__	:	List[str]								=self.tokenizer(self.input_text    , return_tensors="""pt""" ).to(0 )
										__magic_name__	:	Optional[Any]								=model.generate(**__snake_case )




class 		__A      (			UpperCamelCase__			):

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

										super().setUp()
										# model_name
										__magic_name__	:	Union[str, Any]								="""bigscience/bloom-560m"""
										__magic_name__	:	Tuple								="""t5-small"""

										# Different types of model

										__magic_name__	:	Union[str, Any]								=AutoModel.from_pretrained(self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										# Sequence classification model
										__magic_name__	:	Dict								=AutoModelForSequenceClassification.from_pretrained(
										    self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										# CausalLM model
										__magic_name__	:	Union[str, Any]								=AutoModelForCausalLM.from_pretrained(self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										# Seq2seq model
										__magic_name__	:	Dict								=AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name    , load_in_abit=__snake_case    , device_map="""auto""" )

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

										del self.base_model
										del self.sequence_model
										del self.model_abit
										del self.seq_to_seq_model

										gc.collect()
										torch.cuda.empty_cache()


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

										from bitsandbytes.nn import Paramsabit

										self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )

										# Other heads should be nn.Parameter
										self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
										self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
										self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )




class 		__A      (			UpperCamelCase__			):

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

										super().setUp()

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

										del self.pipe

										gc.collect()
										torch.cuda.empty_cache()


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

										__magic_name__	:	List[str]								=pipeline(
										    """text-generation"""    , model=self.model_name    , model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}    , max_new_tokens=self.MAX_NEW_TOKENS    , )

										# Real second forward pass
										__magic_name__	:	Optional[int]								=self.pipe(self.input_text )
										self.assertIn(pipeline_output[0]["""generated_text"""]    , self.EXPECTED_OUTPUTS )




@require_torch_multi_gpu
class 		__A      (			UpperCamelCase__			):

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

										super().setUp()


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

										__magic_name__	:	List[Any]								=AutoModelForCausalLM.from_pretrained(
										    self.model_name    , load_in_abit=__snake_case    , device_map="""balanced""" )

										# Check correct device map
										self.assertEqual(set(model_parallel.hf_device_map.values() )    , {0, 1} )

										# Check that inference pass works on the model
										__magic_name__	:	Tuple								=self.tokenizer(self.input_text    , return_tensors="""pt""" )

										# Second real batch
										__magic_name__	:	Tuple								=model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0 )    , max_new_tokens=10 )
										self.assertIn(self.tokenizer.decode(output_parallel[0]    , skip_special_tokens=__snake_case )    , self.EXPECTED_OUTPUTS )




class 		__A      (			UpperCamelCase__			):

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

										__magic_name__	:	Optional[Any]								="""facebook/opt-350m"""
										super().setUp()


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

										if version.parse(importlib.metadata.version("""bitsandbytes""" ) ) < version.parse("""0.37.0""" ):
														return

										# Step 1: freeze all parameters
										__magic_name__	:	Dict								=AutoModelForCausalLM.from_pretrained(self.model_name    , load_in_abit=__snake_case )

										self.assertEqual(set(model.hf_device_map.values() )    , {torch.cuda.current_device()} )

										for param in model.parameters():
														__magic_name__	:	List[Any]								=False  # freeze the model - train adapters later
														if param.ndim == 1:
																		# cast the small parameters (e.g. layernorm) to fp32 for stability
																		__magic_name__	:	List[Any]								=param.data.to(torch.floataa )

        # Step 2: add adapters
										for _, module in model.named_modules():
														if "OPTAttention" in repr(type(__snake_case ) ):
																		__magic_name__	:	Tuple								=LoRALayer(module.q_proj    , rank=16 )
																		__magic_name__	:	Tuple								=LoRALayer(module.k_proj    , rank=16 )
																		__magic_name__	:	List[str]								=LoRALayer(module.v_proj    , rank=16 )

        # Step 3: dummy batch
										__magic_name__	:	int								=self.tokenizer("""Test batch """    , return_tensors="""pt""" ).to(0 )

										# Step 4: Check if the gradient is not None
										with torch.cuda.amp.autocast():
														__magic_name__	:	Optional[Any]								=model.forward(**__snake_case )
														out.logits.norm().backward()

										for module in model.modules():
														if isinstance(__snake_case    , __snake_case ):
																		self.assertTrue(module.adapter[1].weight.grad is not None )
																		self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
														elif isinstance(__snake_case    , nn.Embedding ):
																		self.assertTrue(module.weight.grad is None )




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""gpt2-xl"""
						UpperCamelCase     						=					3.3191854854152187










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




