



'''simple docstring'''
from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import (
					    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_MASKED_LM_MAPPING,
					    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    TFSharedEmbeddings,
					)

					if _tf_gpu_memory_limit is not None:
										snake_case_				:			Union[str, Any]						 =						tf.config.list_physical_devices('GPU')
										for gpu in gpus:
															# Restrict TensorFlow to only allocate x GB of memory on the GPUs
															try:
																				tf.config.set_logical_device_configuration(
																				    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
																				)
																				snake_case_				:			int						 =						tf.config.list_logical_devices('GPU')
																				print('Logical GPUs', logical_gpus)
															except RuntimeError as e:
																				# Virtual devices must be set before GPUs have been initialized
																				print(e)






@require_tf
class 							lowercase__			:
	lowercase__						       =    None
	lowercase__						       =    ()
	lowercase__						       =    ()
	lowercase__						       =    True
	lowercase__						       =    True
	lowercase__						       =    True
	lowercase__						       =    False





	def 			UpperCamelCase_						(				self      :       str				,lowerCamelCase__      :       List[Any]				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       Tuple=False				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[Any]				       =     copy.deepcopy(_lowerCAmelCase				)

							if model_class in get_values(_lowerCAmelCase				):
													_UpperCamelCase      :	Optional[Any]				       =     {
													    k: tf.tile(tf.expand_dims(_lowerCAmelCase				,1				)				,(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)				)
													    if isinstance(_lowerCAmelCase				,tf.Tensor				) and v.ndim > 0
													    else v
													    for k, v in inputs_dict.items()
													}

							if return_labels:
													if model_class in get_values(_lowerCAmelCase				):
																			_UpperCamelCase      :	int				       =     tf.ones(self.model_tester.batch_size				,dtype=tf.intaa				)
													elif model_class in get_values(_lowerCAmelCase				):
																			_UpperCamelCase      :	Optional[Any]				       =     tf.zeros(self.model_tester.batch_size				,dtype=tf.intaa				)
																			_UpperCamelCase      :	int				       =     tf.zeros(self.model_tester.batch_size				,dtype=tf.intaa				)
													elif model_class in [
													    *get_values(_lowerCAmelCase				),
													    *get_values(_lowerCAmelCase				),
													]:
																			_UpperCamelCase      :	Optional[int]				       =     tf.zeros(self.model_tester.batch_size				,dtype=tf.intaa				)
													elif model_class in get_values(_lowerCAmelCase				):
																			_UpperCamelCase      :	Optional[int]				       =     tf.zeros(self.model_tester.batch_size				,dtype=tf.intaa				)
													elif model_class in [
													    *get_values(_lowerCAmelCase				),
													    *get_values(_lowerCAmelCase				),
													    *get_values(_lowerCAmelCase				),
													    *get_values(_lowerCAmelCase				),
													    *get_values(_lowerCAmelCase				),
													]:
																			_UpperCamelCase      :	str				       =     tf.zeros(
																			    (self.model_tester.batch_size, self.model_tester.seq_length)				,dtype=tf.intaa				)
							return inputs_dict





	@slow
	def 			UpperCamelCase_						(				self      :       Optional[Any]				):







							'''simple docstring'''
							_UpperCamelCase       ,    _UpperCamelCase      :	Optional[Any]				       =     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
													_UpperCamelCase      :	List[Any]				       =     self._prepare_for_class(_lowerCAmelCase				,_lowerCAmelCase				)
													_UpperCamelCase      :	List[str]				       =     model_class(_lowerCAmelCase				)

													@tf.function
													def run_in_graph_mode():
																			return model(_lowerCAmelCase				)

													_UpperCamelCase      :	Dict				       =     run_in_graph_mode()
													self.assertIsNotNone(_lowerCAmelCase				)





	@slow
	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							_UpperCamelCase       ,    _UpperCamelCase      :	Optional[Any]				       =     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
													_UpperCamelCase      :	Optional[Any]				       =     self._prepare_for_class(_lowerCAmelCase				,_lowerCAmelCase				)
													_UpperCamelCase      :	int				       =     model_class(_lowerCAmelCase				)

													@tf.function(experimental_compile=_lowerCAmelCase				)
													def run_in_graph_mode():
																			return model(_lowerCAmelCase				)

													_UpperCamelCase      :	Optional[int]				       =     run_in_graph_mode()
													self.assertIsNotNone(_lowerCAmelCase				)





	@slow
	def 			UpperCamelCase_						(				self      :       Tuple				):







							'''simple docstring'''
							# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
							_UpperCamelCase       ,    _UpperCamelCase      :	List[Any]				       =     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
													_UpperCamelCase      :	Optional[Any]				       =     model_class(_lowerCAmelCase				)
													if getattr(_lowerCAmelCase				,'hf_compute_loss'				,_lowerCAmelCase				):
																			# Test that model correctly compute the loss with kwargs
																			_UpperCamelCase      :	str				       =     self._prepare_for_class(inputs_dict.copy()				,_lowerCAmelCase				,return_labels=_lowerCAmelCase				)
																			# Is there a better way to remove these decoder inputs?
																			_UpperCamelCase      :	int				       =     {
																			    key: val
																			    for key, val in prepared_for_class.items()
																			    if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
																			}

																			_UpperCamelCase      :	Tuple				       =     {
																			    'labels',
																			    'label',
																			    'label_ids',
																			    'start_positions',
																			    'start_position',
																			    'end_positions',
																			    'end_position',
																			    'next_sentence_label',
																			}
																			_UpperCamelCase      :	Union[str, Any]				       =     possible_label_cols.intersection(set(_lowerCAmelCase				)				)
																			self.assertGreater(len(_lowerCAmelCase				)				,0				,msg='No matching label names found!'				)
																			_UpperCamelCase      :	int				       =     {key: val for key, val in prepared_for_class.items() if key in label_names}
																			_UpperCamelCase      :	Tuple				       =     {key: val for key, val in prepared_for_class.items() if key not in label_names}
																			self.assertGreater(len(_lowerCAmelCase				)				,0				)

																			# Make sure it works with XLA!
																			model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)				,jit_compile=_lowerCAmelCase				)
																			# Make sure the model fits without crashing regardless of where we pass the labels
																			_UpperCamelCase      :	Optional[int]				       =     model.fit(
																			    _lowerCAmelCase				,validation_data=_lowerCAmelCase				,steps_per_epoch=1				,validation_steps=1				,shuffle=_lowerCAmelCase				,verbose=0				,)
																			_UpperCamelCase      :	List[Any]				       =     history.history['loss'][0]
																			self.assertTrue(not isnan(_lowerCAmelCase				)				)
																			_UpperCamelCase      :	Any				       =     history.history['val_loss'][0]
																			self.assertTrue(not isnan(_lowerCAmelCase				)				)

																			# Now test it with separate labels, to make sure that path works in XLA too.
																			_UpperCamelCase      :	Any				       =     model_class(_lowerCAmelCase				)
																			model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)				,jit_compile=_lowerCAmelCase				)
																			_UpperCamelCase      :	str				       =     model.fit(
																			    _lowerCAmelCase				,_lowerCAmelCase				,validation_data=(inputs_minus_labels, labels)				,steps_per_epoch=1				,validation_steps=1				,shuffle=_lowerCAmelCase				,verbose=0				,)

																			_UpperCamelCase      :	Optional[int]				       =     history.history['loss'][0]
																			self.assertTrue(not isnan(_lowerCAmelCase				)				)
																			_UpperCamelCase      :	Optional[Any]				       =     history.history['val_loss'][0]
																			self.assertTrue(not isnan(_lowerCAmelCase				)				)





	@slow
	def 			UpperCamelCase_						(				self      :       Union[str, Any]				):







							'''simple docstring'''
							_UpperCamelCase       ,    _UpperCamelCase      :	Dict				       =     self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase      :	str				       =     True
							_UpperCamelCase      :	List[str]				       =     True

							if hasattr(_lowerCAmelCase				,'use_cache'				):
													_UpperCamelCase      :	Tuple				       =     True

							_UpperCamelCase      :	Optional[Any]				       =     getattr(self.model_tester				,'encoder_seq_length'				,self.model_tester.seq_length				)
							_UpperCamelCase      :	Optional[int]				       =     getattr(self.model_tester				,'key_length'				,_lowerCAmelCase				)

							for model_class in self.all_model_classes[:2]:
													_UpperCamelCase      :	Dict				       =     self._prepare_for_class(_lowerCAmelCase				,_lowerCAmelCase				)
													_UpperCamelCase      :	Tuple				       =     model_class(_lowerCAmelCase				)
													model.build()
													_UpperCamelCase      :	List[str]				       =     len(model(_lowerCAmelCase				)				)

													for key in list(class_inputs_dict.keys()				):
																			# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																			if key not in model.input_signature:
																									del class_inputs_dict[key]
																			# Check it's a tensor, in case the inputs dict has some bools in it too
																			elif isinstance(class_inputs_dict[key]				,tf.Tensor				) and class_inputs_dict[key].dtype.is_integer:
																									_UpperCamelCase      :	List[str]				       =     tf.cast(class_inputs_dict[key]				,tf.intaa				)

													if set(class_inputs_dict.keys()				) != set(model.input_signature.keys()				):
																			continue  # Some models have inputs that the preparation functions don't create, we skip those

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model.save_pretrained(_lowerCAmelCase				,saved_model=_lowerCAmelCase				)
																			_UpperCamelCase      :	str				       =     os.path.join(_lowerCAmelCase				,'saved_model'				,'1'				)
																			_UpperCamelCase      :	Optional[Any]				       =     tf.keras.models.load_model(_lowerCAmelCase				)
																			_UpperCamelCase      :	Optional[Any]				       =     model(_lowerCAmelCase				)

																			if self.is_encoder_decoder:
																									_UpperCamelCase      :	str				       =     outputs['encoder_hidden_states']
																									_UpperCamelCase      :	Optional[int]				       =     outputs['encoder_attentions']
																			else:
																									_UpperCamelCase      :	Optional[Any]				       =     outputs['hidden_states']
																									_UpperCamelCase      :	Tuple				       =     outputs['attentions']

																			self.assertEqual(len(_lowerCAmelCase				)				,_lowerCAmelCase				)

																			_UpperCamelCase      :	Any				       =     getattr(
																			    self.model_tester				,'expected_num_hidden_layers'				,self.model_tester.num_hidden_layers + 1				)

																			self.assertEqual(len(_lowerCAmelCase				)				,_lowerCAmelCase				)
																			self.assertListEqual(
																			    list(output_hidden_states[0].shape[-2:]				)				,[self.model_tester.seq_length, self.model_tester.hidden_size]				,)

																			self.assertEqual(len(_lowerCAmelCase				)				,self.model_tester.num_hidden_layers				)
																			self.assertListEqual(
																			    list(output_attentions[0].shape[-3:]				)				,[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]				,)





	@slow
	def 			UpperCamelCase_						(				self      :       Dict				):







							'''simple docstring'''
							tf.keras.mixed_precision.set_global_policy('mixed_float16'				)

							# try/finally block to ensure subsequent tests run in float32
							try:
													_UpperCamelCase       ,    _UpperCamelCase      :	List[Any]				       =     self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			_UpperCamelCase      :	Any				       =     self._prepare_for_class(_lowerCAmelCase				,_lowerCAmelCase				)
																			_UpperCamelCase      :	Union[str, Any]				       =     model_class(_lowerCAmelCase				)
																			_UpperCamelCase      :	List[Any]				       =     model(_lowerCAmelCase				)

																			self.assertIsNotNone(_lowerCAmelCase				)
							finally:
													tf.keras.mixed_precision.set_global_policy('float32'				)





	@slow
	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							_UpperCamelCase       ,    _UpperCamelCase      :	List[str]				       =     self.model_tester.prepare_config_and_inputs_for_common()
							# head_mask and decoder_head_mask has different shapes than other input args
							if "head_mask" in inputs_dict:
													del inputs_dict["head_mask"]
							if "decoder_head_mask" in inputs_dict:
													del inputs_dict["decoder_head_mask"]
							if "cross_attn_head_mask" in inputs_dict:
													del inputs_dict["cross_attn_head_mask"]
							_UpperCamelCase      :	int				       =     {
							    module_member
							    for model_class in self.all_model_classes
							    for module in (import_module(model_class.__module__				),)
							    for module_member_name in dir(_lowerCAmelCase				)
							    if module_member_name.endswith('MainLayer'				)
							    for module_member in (getattr(_lowerCAmelCase				,_lowerCAmelCase				),)
							    if isinstance(_lowerCAmelCase				,_lowerCAmelCase				)
							    and tf.keras.layers.Layer in module_member.__bases__
							    and getattr(_lowerCAmelCase				,'_keras_serializable'				,_lowerCAmelCase				)
							}

							for main_layer_class in tf_main_layer_classes:
													# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
													if "T5" in main_layer_class.__name__:
																			# Take the same values than in TFT5ModelTester for this shared layer
																			_UpperCamelCase      :	Optional[int]				       =     TFSharedEmbeddings(self.model_tester.vocab_size				,self.model_tester.hidden_size				,name='shared'				)
																			_UpperCamelCase      :	Optional[int]				       =     False
																			_UpperCamelCase      :	Optional[Any]				       =     main_layer_class(_lowerCAmelCase				,embed_tokens=_lowerCAmelCase				)
													else:
																			_UpperCamelCase      :	List[str]				       =     main_layer_class(_lowerCAmelCase				)

													_UpperCamelCase      :	Dict				       =     {
													    name: tf.keras.Input(tensor.shape[1:]				,dtype=tensor.dtype				) for name, tensor in inputs_dict.items()
													}

													if hasattr(self.model_tester				,'num_labels'				):
																			_UpperCamelCase      :	List[Any]				       =     self.model_tester.num_labels
													else:
																			_UpperCamelCase      :	Any				       =     2

													_UpperCamelCase      :	Any				       =     tf.data.Dataset.from_tensor_slices(
													    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)				))				).batch(1				)

													_UpperCamelCase      :	Optional[Any]				       =     main_layer(_lowerCAmelCase				)[0]
													_UpperCamelCase      :	Any				       =     tf.keras.layers.Dense(_lowerCAmelCase				,activation='softmax'				,name='outputs'				)(_lowerCAmelCase				)
													_UpperCamelCase      :	Tuple				       =     tf.keras.models.Model(inputs=_lowerCAmelCase				,outputs=[outputs]				)

													model.compile(loss='binary_crossentropy'				,optimizer='adam'				,metrics=['binary_accuracy']				)
													model.fit(_lowerCAmelCase				,epochs=1				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																			_UpperCamelCase      :	str				       =     os.path.join(_lowerCAmelCase				,'keras_model.h5'				)
																			model.save(_lowerCAmelCase				)
																			if "T5" in main_layer_class.__name__:
																									_UpperCamelCase      :	Optional[int]				       =     tf.keras.models.load_model(
																									    _lowerCAmelCase				,custom_objects={
																									        main_layer_class.__name__: main_layer_class,
																									        'TFSharedEmbeddings': TFSharedEmbeddings,
																									    }				,)
																			else:
																									_UpperCamelCase      :	List[Any]				       =     tf.keras.models.load_model(
																									    _lowerCAmelCase				,custom_objects={main_layer_class.__name__: main_layer_class}				)
																			assert isinstance(_lowerCAmelCase				,tf.keras.Model				)
																			model(_lowerCAmelCase				)





	@slow
	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							_UpperCamelCase       ,    _UpperCamelCase      :	Optional[Any]				       =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:2]:
													_UpperCamelCase      :	List[str]				       =     model_class(_lowerCAmelCase				)

													_UpperCamelCase      :	List[str]				       =     copy.deepcopy(_lowerCAmelCase				)

													if not self.is_encoder_decoder:
																			_UpperCamelCase      :	Dict				       =     inputs['input_ids']
																			del inputs["input_ids"]
													else:
																			_UpperCamelCase      :	List[Any]				       =     inputs['input_ids']
																			_UpperCamelCase      :	str				       =     inputs.get('decoder_input_ids'				,_lowerCAmelCase				)
																			del inputs["input_ids"]
																			inputs.pop('decoder_input_ids'				,_lowerCAmelCase				)

													if not self.is_encoder_decoder:
																			_UpperCamelCase      :	List[str]				       =     model.get_input_embeddings()(_lowerCAmelCase				)
													else:
																			_UpperCamelCase      :	List[Any]				       =     model.get_input_embeddings()(_lowerCAmelCase				)
																			_UpperCamelCase      :	Optional[Any]				       =     model.get_input_embeddings()(_lowerCAmelCase				)

													_UpperCamelCase      :	List[str]				       =     self._prepare_for_class(_lowerCAmelCase				,_lowerCAmelCase				)

													@tf.function
													def run_in_graph_mode():
																			return model(_lowerCAmelCase				)

													_UpperCamelCase      :	Optional[int]				       =     run_in_graph_mode()
													self.assertIsNotNone(_lowerCAmelCase				)





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       str				,lowerCamelCase__      :       Tuple				):







							'''simple docstring'''
							# special tokens cannot be bad tokens
							_UpperCamelCase      :	List[Any]				       =     []
							if model.config.bos_token_id is not None:
													special_tokens.append(model.config.bos_token_id				)
							if model.config.pad_token_id is not None:
													special_tokens.append(model.config.pad_token_id				)
							if model.config.eos_token_id is not None:
													special_tokens.append(model.config.eos_token_id				)

							# create random bad tokens that are not special tokens
							_UpperCamelCase      :	Dict				       =     []
							while len(_lowerCAmelCase				) < num_bad_tokens:
													_UpperCamelCase      :	str				       =     tf.squeeze(ids_tensor((1, 1)				,self.model_tester.vocab_size				)				,0				).numpy()[0]
													if token not in special_tokens:
																			bad_tokens.append(_lowerCAmelCase				)
							return bad_tokens





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''
							for token_id in output_ids[0].numpy().tolist():
													self.assertGreaterEqual(_lowerCAmelCase				,0				)
													self.assertLess(_lowerCAmelCase				,self.model_tester.vocab_size				)





	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       Optional[int]				,lowerCamelCase__      :       List[Any]				):







							'''simple docstring'''
							# for all bad word tokens
							for bad_word_ids in bad_words_ids:
													# for all slices in batch
													for generated_ids_slice in generated_ids:
																			# for all word idx
																			for i in range(len(_lowerCAmelCase				)				,len(_lowerCAmelCase				)				):
																									# if tokens match
																									if generated_ids_slice[i - len(_lowerCAmelCase				) : i] == bad_word_ids:
																															return True
							return False







'''simple docstring'''
import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


snake_case_				:			str						 =						{
    'gpt-j-6b': {'is_causal': True, 'model': 'sgugger/sharded-gpt-j-6B', 'tokenizer': 'EleutherAI/gpt-j-6B'},
    'gpt-neox': {'is_causal': True, 'model': 'EleutherAI/gpt-neox-20b'},
    'opt': {'is_causal': True, 'model': 'facebook/opt-30b'},
    'T0pp': {'is_causal': False, 'model': 'bigscience/T0pp', 'model_revision': 'sharded'},
}

snake_case_				:			int						 =						[
    'Hello, my name is',
    'Are unicorns real? Unicorns are',
    'For the first time in several years,',
    'My name is Julien and I am',
    'The goal of life is',
    'Whenever I\'m sad, I like to',
]






def A__					(  ):
						_UpperCamelCase      :	Tuple				       =     argparse.ArgumentParser(description='Run and time generations on a big model using Accelerate.'							)
						parser.add_argument('model_name'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='The name of the model to try.'							)
						parser.add_argument(
						    '--tokenizer_name'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='The name of the tokenizer (if different from the model.'							)
						parser.add_argument('--is_causal'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='Whether or not the model is causal.'							)
						parser.add_argument(
						    '--model_revision'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='The revision to use for the model checkpoint.'							)
						parser.add_argument('--torch_dtype'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='The dtype for the model.'							)
						parser.add_argument('--disk_offload'						,						action='store_true'							)

						_UpperCamelCase      :	Dict				       =     parser.parse_args()

						# Sanitize args
						if args.model_name in DEFAULT_MODELS:
												_UpperCamelCase      :	Optional[Any]				       =     DEFAULT_MODELS[args.model_name]
												_UpperCamelCase      :	int				       =     defaults['model']
												if args.tokenizer_name is None:
																		_UpperCamelCase      :	List[str]				       =     defaults.get('tokenizer'						,						args.model_name							)
												if args.is_causal is None:
																		_UpperCamelCase      :	List[Any]				       =     defaults['is_causal']
												if args.model_revision is None:
																		_UpperCamelCase      :	Optional[int]				       =     defaults.get('model_revision'						,						'main'							)

						if args.is_causal is None:
												raise ValueError('Could not infer the default for `--is_causal`, pass either True or False for it.'							)
						if args.tokenizer_name is None:
												_UpperCamelCase      :	List[str]				       =     args.model_name
						if args.model_revision is None:
												_UpperCamelCase      :	List[str]				       =     'main'

						return args






def A__					(  ):
						transformers.utils.logging.set_verbosity_error()
						_UpperCamelCase      :	Optional[Any]				       =     parse_args()

						if args.torch_dtype is None:
												_UpperCamelCase      :	Tuple				       =     AutoConfig.from_pretrained(args.model_name							)
												_UpperCamelCase      :	str				       =     getattr(UpperCAmelCase_						,						'torch_dtype'						,						torch.floataa							)
						else:
												_UpperCamelCase      :	Union[str, Any]				       =     getattr(UpperCAmelCase_						,						args.torch_dtype							)
						_UpperCamelCase      :	int				       =     AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
						_UpperCamelCase      :	int				       =     {
						    'torch_dtype': torch_dtype,
						    'revision': args.model_revision,
						}
						if args.disk_offload:
												_UpperCamelCase      :	List[str]				       =     'tmp_offload'
												_UpperCamelCase      :	Union[str, Any]				       =     True

						_UpperCamelCase      :	Dict				       =     start_measure()
						_UpperCamelCase      :	Optional[int]				       =     model_cls.from_pretrained(args.model_name						,						device_map='auto'						,						**UpperCAmelCase_							)
						_UpperCamelCase      :	List[str]				       =     end_measure(UpperCAmelCase_							)
						log_measures(UpperCAmelCase_						,						'Model loading'							)

						_UpperCamelCase      :	Optional[int]				       =     compute_module_sizes(UpperCAmelCase_							)
						_UpperCamelCase      :	str				       =     {v: 0 for v in model.hf_device_map.values()}
						for module, device in model.hf_device_map.items():
												device_size[device] += module_sizes[module]
						_UpperCamelCase      :	Tuple				       =     '\n'.join([f'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]							)
						print(f'\nTheoretical use:\n{message}'							)

						_UpperCamelCase      :	List[str]				       =     AutoTokenizer.from_pretrained(args.tokenizer_name							)

						_UpperCamelCase      :	List[str]				       =     start_measure()
						_UpperCamelCase      :	Optional[Any]				       =     []
						_UpperCamelCase      :	Dict				       =     []
						_UpperCamelCase      :	List[str]				       =     []
						for prompt in PROMPTS:
												_UpperCamelCase      :	List[Any]				       =     tokenizer(UpperCAmelCase_						,						return_tensors='pt'							).to(0							)
												_UpperCamelCase      :	List[str]				       =     inputs['input_ids'][0].tolist()
												_UpperCamelCase      :	int				       =     time.time()
												_UpperCamelCase      :	int				       =     model.generate(inputs['input_ids']							)
												_UpperCamelCase      :	int				       =     time.time()
												_UpperCamelCase      :	Optional[Any]				       =     outputs[0].tolist()
												_UpperCamelCase      :	List[Any]				       =     len(UpperCAmelCase_							) if outputs[: len(UpperCAmelCase_							)] != tokens else len(UpperCAmelCase_							) - len(UpperCAmelCase_							)
												_UpperCamelCase      :	Any				       =     after_generate - before_generate

												_UpperCamelCase      :	Optional[int]				       =     tokenizer.decode(UpperCAmelCase_						,						skip_special_tokens=UpperCAmelCase_							)
												texts_outs.append(UpperCAmelCase_							)
												generation_times.append(UpperCAmelCase_							)
												gen_tokens.append(UpperCAmelCase_							)
												print(f'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'							)

						_UpperCamelCase      :	Union[str, Any]				       =     end_measure(UpperCAmelCase_							)
						log_measures(UpperCAmelCase_						,						'Model generation'							)

						_UpperCamelCase      :	str				       =     [gen / tok for gen, tok in zip(UpperCAmelCase_						,						UpperCAmelCase_							)]
						_UpperCamelCase      :	List[Any]				       =     sum(UpperCAmelCase_							) / len(UpperCAmelCase_							)
						print(f'Average time of generation per token: {avg_gen:.2f}s'							)
						print(f'First generation (avg time per token): {generation_times_per_token[0]:.2f}s'							)
						_UpperCamelCase      :	List[Any]				       =     sum(generation_times_per_token[1:]							) / (len(UpperCAmelCase_							) - 1)
						print(f'Average time of generation per token (excluding the first): {avg_gen:.2f}s'							)


if __name__ == "__main__":
					main()


