
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:
										SCREAMING_SNAKE_CASE__     :    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)]
																				)
																				SCREAMING_SNAKE_CASE__     :    Dict      							=					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      lowerCAmelCase__  :
		a__ :		Optional[int]   						=	None
		a__ :		Tuple   						=	()
		a__ :		Tuple   						=	()
		a__ :		str   						=	True
		a__ :		Optional[int]   						=	True
		a__ :		str   						=	True
		a__ :		Optional[Any]   						=	False






		def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Any							,    SCREAMING_SNAKE_CASE__	:							str=False						)  ->							dict:
						__lowerCamelCase					      =			copy.deepcopy(SCREAMING_SNAKE_CASE__						)

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

						if return_labels:
										if model_class in get_values(SCREAMING_SNAKE_CASE__						):
														__lowerCamelCase					      =			tf.ones(self.model_tester.batch_size							,    dtype=tf.intaa						)
										elif model_class in get_values(SCREAMING_SNAKE_CASE__						):
														__lowerCamelCase					      =			tf.zeros(self.model_tester.batch_size							,    dtype=tf.intaa						)
														__lowerCamelCase					      =			tf.zeros(self.model_tester.batch_size							,    dtype=tf.intaa						)
										elif model_class in [
										    *get_values(SCREAMING_SNAKE_CASE__						),
										    *get_values(SCREAMING_SNAKE_CASE__						),
										]:
														__lowerCamelCase					      =			tf.zeros(self.model_tester.batch_size							,    dtype=tf.intaa						)
										elif model_class in get_values(SCREAMING_SNAKE_CASE__						):
														__lowerCamelCase					      =			tf.zeros(self.model_tester.batch_size							,    dtype=tf.intaa						)
										elif model_class in [
										    *get_values(SCREAMING_SNAKE_CASE__						),
										    *get_values(SCREAMING_SNAKE_CASE__						),
										    *get_values(SCREAMING_SNAKE_CASE__						),
										    *get_values(SCREAMING_SNAKE_CASE__						),
										    *get_values(SCREAMING_SNAKE_CASE__						),
										]:
														__lowerCamelCase					      =			tf.zeros(
														    (self.model_tester.batch_size, self.model_tester.seq_length)							,    dtype=tf.intaa						)
						return inputs_dict






		@slow
		def 				__A (	self	:							Dict						)  ->							List[Any]:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes[:2]:
										__lowerCamelCase					      =			self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)

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

										__lowerCamelCase					      =			run_in_graph_mode()
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)






		@slow
		def 				__A (	self	:							Union[str, Any]						)  ->							Tuple:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes[:2]:
										__lowerCamelCase					      =			self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)

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

										__lowerCamelCase					      =			run_in_graph_mode()
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)






		@slow
		def 				__A (	self	:							Tuple						)  ->							Optional[int]:
						# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes[:2]:
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										if getattr(SCREAMING_SNAKE_CASE__							,    '''hf_compute_loss'''							,    SCREAMING_SNAKE_CASE__						):
														# Test that model correctly compute the loss with kwargs
														__lowerCamelCase					      =			self._prepare_for_class(inputs_dict.copy()							,    SCREAMING_SNAKE_CASE__							,    return_labels=SCREAMING_SNAKE_CASE__						)
														# Is there a better way to remove these decoder inputs?
														__lowerCamelCase					      =			{
														    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''')
														}

														__lowerCamelCase					      =			{
														    '''labels''',
														    '''label''',
														    '''label_ids''',
														    '''start_positions''',
														    '''start_position''',
														    '''end_positions''',
														    '''end_position''',
														    '''next_sentence_label''',
														}
														__lowerCamelCase					      =			possible_label_cols.intersection(set(SCREAMING_SNAKE_CASE__						)						)
														self.assertGreater(len(SCREAMING_SNAKE_CASE__						)							,    0							,    msg='''No matching label names found!'''						)
														__lowerCamelCase					      =			{key: val for key, val in prepared_for_class.items() if key in label_names}
														__lowerCamelCase					      =			{key: val for key, val in prepared_for_class.items() if key not in label_names}
														self.assertGreater(len(SCREAMING_SNAKE_CASE__						)							,    0						)

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

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

														__lowerCamelCase					      =			history.history['''loss'''][0]
														self.assertTrue(not isnan(SCREAMING_SNAKE_CASE__						)						)
														__lowerCamelCase					      =			history.history['''val_loss'''][0]
														self.assertTrue(not isnan(SCREAMING_SNAKE_CASE__						)						)






		@slow
		def 				__A (	self	:							Dict						)  ->							str:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCamelCase					      =			True
						__lowerCamelCase					      =			True

						if hasattr(SCREAMING_SNAKE_CASE__							,    '''use_cache'''						):
										__lowerCamelCase					      =			True

						__lowerCamelCase					      =			getattr(self.model_tester							,    '''encoder_seq_length'''							,    self.model_tester.seq_length						)
						__lowerCamelCase					      =			getattr(self.model_tester							,    '''key_length'''							,    SCREAMING_SNAKE_CASE__						)

						for model_class in self.all_model_classes[:2]:
										__lowerCamelCase					      =			self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										model.build()
										__lowerCamelCase					      =			len(model(SCREAMING_SNAKE_CASE__						)						)

										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:
																		__lowerCamelCase					      =			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(SCREAMING_SNAKE_CASE__							,    saved_model=SCREAMING_SNAKE_CASE__						)
														__lowerCamelCase					      =			os.path.join(SCREAMING_SNAKE_CASE__							,    '''saved_model'''							,    '''1'''						)
														__lowerCamelCase					      =			tf.keras.models.load_model(SCREAMING_SNAKE_CASE__						)
														__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

														if self.is_encoder_decoder:
																		__lowerCamelCase					      =			outputs['''encoder_hidden_states''']
																		__lowerCamelCase					      =			outputs['''encoder_attentions''']
														else:
																		__lowerCamelCase					      =			outputs['''hidden_states''']
																		__lowerCamelCase					      =			outputs['''attentions''']

														self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

														__lowerCamelCase					      =			getattr(
														    self.model_tester							,    '''expected_num_hidden_layers'''							,    self.model_tester.num_hidden_layers + 1						)

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

														self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    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 				__A (	self	:							List[Any]						)  ->							Tuple:
						tf.keras.mixed_precision.set_global_policy('''mixed_float16'''						)

						# try/finally block to ensure subsequent tests run in float32
						try:
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
														__lowerCamelCase					      =			self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
														__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
														__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

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






		@slow
		def 				__A (	self	:							Optional[Any]						)  ->							int:
						__lowerCamelCase      ,					__lowerCamelCase					      =			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"]
						__lowerCamelCase					      =			{
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__						),)
						    for module_member_name in dir(SCREAMING_SNAKE_CASE__						)
						    if module_member_name.endswith('''MainLayer'''						)
						    for module_member in (getattr(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						),)
						    if isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(SCREAMING_SNAKE_CASE__							,    '''_keras_serializable'''							,    SCREAMING_SNAKE_CASE__						)
						}

						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
														__lowerCamelCase					      =			TFSharedEmbeddings(self.model_tester.vocab_size							,    self.model_tester.hidden_size							,    name='''shared'''						)
														__lowerCamelCase					      =			False
														__lowerCamelCase					      =			main_layer_class(SCREAMING_SNAKE_CASE__							,    embed_tokens=SCREAMING_SNAKE_CASE__						)
										else:
														__lowerCamelCase					      =			main_layer_class(SCREAMING_SNAKE_CASE__						)

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

										if hasattr(self.model_tester							,    '''num_labels'''						):
														__lowerCamelCase					      =			self.model_tester.num_labels
										else:
														__lowerCamelCase					      =			2

										__lowerCamelCase					      =			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						)

										__lowerCamelCase					      =			main_layer(SCREAMING_SNAKE_CASE__						)[0]
										__lowerCamelCase					      =			tf.keras.layers.Dense(SCREAMING_SNAKE_CASE__							,    activation='''softmax'''							,    name='''outputs'''						)(SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			tf.keras.models.Model(inputs=SCREAMING_SNAKE_CASE__							,    outputs=[outputs]						)

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

										with tempfile.TemporaryDirectory() as tmpdirname:
														__lowerCamelCase					      =			os.path.join(SCREAMING_SNAKE_CASE__							,    '''keras_model.h5'''						)
														model.save(SCREAMING_SNAKE_CASE__						)
														if "T5" in main_layer_class.__name__:
																		__lowerCamelCase					      =			tf.keras.models.load_model(
																		    SCREAMING_SNAKE_CASE__							,    custom_objects={
																		        main_layer_class.__name__: main_layer_class,
																		        '''TFSharedEmbeddings''': TFSharedEmbeddings,
																		    }							,    )
														else:
																		__lowerCamelCase					      =			tf.keras.models.load_model(
																		    SCREAMING_SNAKE_CASE__							,    custom_objects={main_layer_class.__name__: main_layer_class}						)
														assert isinstance(SCREAMING_SNAKE_CASE__							,    tf.keras.Model						)
														model(SCREAMING_SNAKE_CASE__						)






		@slow
		def 				__A (	self	:							Optional[int]						)  ->							Dict:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes[:2]:
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)

										__lowerCamelCase					      =			copy.deepcopy(SCREAMING_SNAKE_CASE__						)

										if not self.is_encoder_decoder:
														__lowerCamelCase					      =			inputs['''input_ids''']
														del inputs["input_ids"]
										else:
														__lowerCamelCase					      =			inputs['''input_ids''']
														__lowerCamelCase					      =			inputs.get('''decoder_input_ids'''							,    SCREAMING_SNAKE_CASE__						)
														del inputs["input_ids"]
														inputs.pop('''decoder_input_ids'''							,    SCREAMING_SNAKE_CASE__						)

										if not self.is_encoder_decoder:
														__lowerCamelCase					      =			model.get_input_embeddings()(SCREAMING_SNAKE_CASE__						)
										else:
														__lowerCamelCase					      =			model.get_input_embeddings()(SCREAMING_SNAKE_CASE__						)
														__lowerCamelCase					      =			model.get_input_embeddings()(SCREAMING_SNAKE_CASE__						)

										__lowerCamelCase					      =			self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

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

										__lowerCamelCase					      =			run_in_graph_mode()
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							Any						)  ->							Any:
						# special tokens cannot be bad tokens
						__lowerCamelCase					      =			[]
						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
						__lowerCamelCase					      =			[]
						while len(SCREAMING_SNAKE_CASE__						) < num_bad_tokens:
										__lowerCamelCase					      =			tf.squeeze(ids_tensor((1, 1)							,    self.model_tester.vocab_size						)							,    0						).numpy()[0]
										if token not in special_tokens:
														bad_tokens.append(SCREAMING_SNAKE_CASE__						)
						return bad_tokens






		def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							List[Any]						)  ->							Union[str, Any]:
						for token_id in output_ids[0].numpy().tolist():
										self.assertGreaterEqual(SCREAMING_SNAKE_CASE__							,    0						)
										self.assertLess(SCREAMING_SNAKE_CASE__							,    self.model_tester.vocab_size						)







		def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							int:
						# 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(SCREAMING_SNAKE_CASE__						)							,    len(SCREAMING_SNAKE_CASE__						)						):
																		# if tokens match
																		if generated_ids_slice[i - len(SCREAMING_SNAKE_CASE__						) : i] == bad_word_ids:
																						return True
						return False





import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed

class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Any=False							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=10_24							,    SCREAMING_SNAKE_CASE__	:							Dict=10_24							,    SCREAMING_SNAKE_CASE__	:							str=3.6							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=False							,    )  ->							Optional[int]:
						__lowerCamelCase					      =			tokenizer
						__lowerCamelCase					      =			tokenizer.bos_token_id
						__lowerCamelCase					      =			dataset
						__lowerCamelCase					      =			seq_length
						__lowerCamelCase					      =			0
						__lowerCamelCase					      =			infinite
						__lowerCamelCase					      =			0
						__lowerCamelCase					      =			tokenized

						if self.tokenized:
										__lowerCamelCase					      =			seq_length * num_of_sequences
										__lowerCamelCase					      =			'''input_ids'''
						else:
										__lowerCamelCase					      =			seq_length * chars_per_token * num_of_sequences
										__lowerCamelCase					      =			'''content'''






		def __iter__(	self	:							Tuple						)  ->							Dict:
						__lowerCamelCase					      =			iter(self.dataset						)
						__lowerCamelCase					      =			True
						while more_examples:
										__lowerCamelCase      ,					__lowerCamelCase					      =			[], 0
										while True:
														if buffer_len >= self.max_buffer_size:
																		break
														try:
																		buffer.append(next(SCREAMING_SNAKE_CASE__						)[self.content_field]						)
																		buffer_len += len(buffer[-1]						)
														except StopIteration:
																		if self.infinite:
																						__lowerCamelCase					      =			iter(self.dataset						)
																						self.epoch += 1
																						logger.info(f'''Dataset epoch: {self.epoch}'''						)
																		else:
																						__lowerCamelCase					      =			False
																						break
										if self.tokenized:
														__lowerCamelCase					      =			buffer
										else:
														__lowerCamelCase					      =			self.tokenizer(SCREAMING_SNAKE_CASE__							,    truncation=SCREAMING_SNAKE_CASE__						)['''input_ids''']
										__lowerCamelCase					      =			[]
										for tokenized_input in tokenized_inputs:
														all_token_ids.extend(tokenized_input + [self.concat_token_id]						)
										for i in range(0							,    len(SCREAMING_SNAKE_CASE__						)							,    self.seq_length						):
														__lowerCamelCase					      =			all_token_ids[i : i + self.seq_length]
														if len(SCREAMING_SNAKE_CASE__						) == self.seq_length:
																		self.current_size += 1
																		yield torch.tensor(SCREAMING_SNAKE_CASE__						)







		def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							str=10_00						)  ->							Union[str, Any]:
						return ShufflerIterDataPipe(self							,    buffer_size=SCREAMING_SNAKE_CASE__						)





def 				__magic_name__				(     __lowerCAmelCase			:		Tuple							)    ->     Dict:
				__lowerCamelCase					      =			args.model_ckpt.split('''/'''							)[-1]
				__lowerCamelCase					      =			logging.getLogger(__name__							)
				__lowerCamelCase					      =			Path(args.save_dir							) / '''log/'''
				log_dir.mkdir(exist_ok=__lowerCAmelCase							)
				__lowerCamelCase					      =			f'''debug_{accelerator.process_index}.log'''
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,						datefmt='''%m/%d/%Y %H:%M:%S'''	,						level=logging.INFO	,						handlers=[logging.FileHandler(log_dir / filename							), logging.StreamHandler()]	,						)
				if accelerator.is_main_process:  # we only want to setup logging once
								accelerator.init_trackers(__lowerCAmelCase	,						vars(__lowerCAmelCase							)							)
								__lowerCamelCase					      =			accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO							)
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								__lowerCamelCase					      =			''''''
								logger.setLevel(logging.ERROR							)
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]							)    ->     Tuple:
				__lowerCamelCase					      =			{'''streaming''': True}
				__lowerCamelCase					      =			load_dataset(args.dataset_name_train	,						split='''train'''	,						**__lowerCAmelCase							)
				__lowerCamelCase					      =			train_data.shuffle(buffer_size=args.shuffle_buffer	,						seed=args.seed							)
				__lowerCamelCase					      =			load_dataset(args.dataset_name_valid	,						split='''train'''	,						**__lowerCAmelCase							)
				__lowerCamelCase					      =			ConstantLengthDataset(
				    __lowerCAmelCase	,						__lowerCAmelCase	,						infinite=__lowerCAmelCase	,						seq_length=args.seq_length	,						tokenized=args.tokenized							)
				__lowerCamelCase					      =			ConstantLengthDataset(
				    __lowerCAmelCase	,						__lowerCAmelCase	,						infinite=__lowerCAmelCase	,						seq_length=args.seq_length	,						tokenized=args.tokenized							)
				__lowerCamelCase					      =			train_dataset.shuffle(buffer_size=args.shuffle_buffer							)
				__lowerCamelCase					      =			DataLoader(__lowerCAmelCase	,						batch_size=args.train_batch_size	,						shuffle=__lowerCAmelCase							)
				__lowerCamelCase					      =			DataLoader(__lowerCAmelCase	,						batch_size=args.valid_batch_size							)
				return train_dataloader, eval_dataloader




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							)    ->     List[str]:
				__lowerCamelCase      ,					__lowerCamelCase					      =			[], []
				for n, p in model.named_parameters():
								if any(nd in n for nd in no_decay							):
												params_without_wd.append(__lowerCAmelCase							)
								else:
												params_with_wd.append(__lowerCAmelCase							)
				return [
				    {"params": params_with_wd, "weight_decay": args.weight_decay},
				    {"params": params_without_wd, "weight_decay": 0.0},
				]




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Union[str, Any]							)    ->     str:
				logger.info(f'''Step {step}: {metrics}'''							)
				if accelerator.is_main_process:
								accelerator.log(__lowerCAmelCase	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		Any							)    ->     Dict:
				# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
				__lowerCamelCase					      =			accelerator.unwrap_model(__lowerCAmelCase							).config
				__lowerCamelCase					      =			4 if args.gradient_checkpointing else 3
				__lowerCamelCase					      =			args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				__lowerCamelCase					      =			24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				__lowerCamelCase					      =			factor * (
				    1.0
				    + (args.seq_length / (6.0 * config_model.n_embd))
				    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
				)
				__lowerCamelCase					      =			flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops




def 				__magic_name__				(     __lowerCAmelCase			:		Dict							)    ->     Optional[Any]:
				model.eval()
				__lowerCamelCase					      =			[]
				for step, batch in enumerate(__lowerCAmelCase							):
								with torch.no_grad():
												__lowerCamelCase					      =			model(__lowerCAmelCase	,						labels=__lowerCAmelCase							)
								__lowerCamelCase					      =			outputs.loss.repeat(args.valid_batch_size							)
								losses.append(accelerator.gather(__lowerCAmelCase							)							)
								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				__lowerCamelCase					      =			torch.cat(__lowerCAmelCase							)
				__lowerCamelCase					      =			losses[: eval_dataloader.dataset.current_size].mean()
				try:
								__lowerCamelCase					      =			torch.exp(__lowerCAmelCase							)
				except OverflowError:
								__lowerCamelCase					      =			float('''inf'''							)
				return loss.item(), perplexity.item()


# Settings
SCREAMING_SNAKE_CASE__     :    str      							=					HfArgumentParser(TrainingArguments)
SCREAMING_SNAKE_CASE__     :    List[Any]      							=					parser.parse_args()

# Accelerator
SCREAMING_SNAKE_CASE__     :    str      							=					ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
SCREAMING_SNAKE_CASE__     :    Any      							=					{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

SCREAMING_SNAKE_CASE__     :    Tuple      							=					Namespace(**vars(args), **acc_state)
SCREAMING_SNAKE_CASE__     :    int      							=					accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
					SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[str]      							=					setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
					hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
					model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__     :    Tuple      							=					AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[str]      							=					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE__     :    Tuple      							=					get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)




def 				__magic_name__				(     )    ->     Any:
				return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    Tuple      							=					accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
					if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
										accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}')
										accelerator.load_state(args.resume_from_checkpoint)
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					os.path.basename(args.resume_from_checkpoint)
					else:
										# Get the most recent checkpoint
										SCREAMING_SNAKE_CASE__     :    str      							=					[f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
										dirs.sort(key=os.path.getctime)
										SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
					# Extract the step of the checkpoint to continue from there
					SCREAMING_SNAKE_CASE__     :    str      							=					os.path.splitext(path)[0]
					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					int(training_difference.replace("step_", ""))

# Train model
model.train()
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					0
SCREAMING_SNAKE_CASE__     :    Any      							=					time.time()
SCREAMING_SNAKE_CASE__     :    int      							=					0
for step, batch in enumerate(train_dataloader, start=1):
					if args.resume_from_checkpoint and step < resume_step:
										continue  # we need to skip steps until we reach the resumed step
					SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					model(batch, labels=batch, use_cache=False).loss
					SCREAMING_SNAKE_CASE__     :    str      							=					accelerator.gather(loss.repeat(args.train_batch_size)).mean()
					loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
					log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					loss / args.gradient_accumulation_steps
					if step % args.gradient_accumulation_steps != 0:
										# Prevent backward from doing gradient all_reduce in every step
										if accelerator.distributed_type == DistributedType.MULTI_GPU:
															with model.no_sync():
																				accelerator.backward(loss)
										else:
															accelerator.backward(loss)
					else:
										SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					get_lr()
										accelerator.backward(loss)
										accelerator.clip_grad_norm_(model.parameters(), 1.0)
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					time.time() - t_start
										SCREAMING_SNAKE_CASE__     :    Any      							=					compute_tflops(elapsed_time, accelerator, args)
										log_metrics(
										    step,
										    {
										        "steps": completed_steps,
										        "loss/train": loss_tracking,
										        "lr": lr,
										        "tflops": tflops,
										        "time_per_iteration": elapsed_time,
										    },
										)
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					time.time()
										SCREAMING_SNAKE_CASE__     :    str      							=					0
										completed_steps += 1
					if step % args.save_checkpoint_steps == 0:
										logger.info("Evaluating and saving model checkpoint")
										SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					evaluate(args)
										log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
										accelerator.wait_for_everyone()
										SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					os.path.join(args.save_dir, F'step_{step}')
										accelerator.save_state(save_dir)
										if accelerator.is_main_process:
															hf_repo.push_to_hub(commit_message=F'step {step}')
										model.train()
					if completed_steps >= args.max_train_steps:
										break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[Any]      							=					evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
SCREAMING_SNAKE_CASE__     :    List[Any]      							=					os.path.join(args.save_dir, F'step_{step}')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
					hf_repo.push_to_hub(commit_message="final model")



