





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:
								__lowercase							: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)]
																)
																__lowercase							:Optional[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  _a  :






							"""simple docstring"""

							snake_case_							=    None
							snake_case_							=    ()
							snake_case_							=    ()
							snake_case_							=    True
							snake_case_							=    True
							snake_case_							=    True
							snake_case_							=    False

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

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

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

							@slow
							def   A_	(   self					:  str     )						->List[Any]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       self._prepare_for_class(a				,						a     )
																			SCREAMING_SNAKE_CASE__  :					Dict =       model_class(a     )

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

																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       run_in_graph_mode()
																			self.assertIsNotNone(a     )

							@slow
							def   A_	(   self					:  Optional[int]     )						->int:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			SCREAMING_SNAKE_CASE__  :					str =       self._prepare_for_class(a				,						a     )
																			SCREAMING_SNAKE_CASE__  :					Tuple =       model_class(a     )

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

																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       run_in_graph_mode()
																			self.assertIsNotNone(a     )

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

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

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

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

																									SCREAMING_SNAKE_CASE__  :					List[Any] =       history.history["loss"][0]
																									self.assertTrue(not isnan(a     )     )
																									SCREAMING_SNAKE_CASE__  :					str =       history.history["val_loss"][0]
																									self.assertTrue(not isnan(a     )     )

							@slow
							def   A_	(   self					:  str     )						->List[str]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       True
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       True

													if hasattr(a				,						"use_cache"     ):
																			SCREAMING_SNAKE_CASE__  :					Dict =       True

													SCREAMING_SNAKE_CASE__  :					int =       getattr(self.model_tester				,						"encoder_seq_length"				,						self.model_tester.seq_length     )
													SCREAMING_SNAKE_CASE__  :					str =       getattr(self.model_tester				,						"key_length"				,						a     )

													for model_class in self.all_model_classes[:2]:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       self._prepare_for_class(a				,						a     )
																			SCREAMING_SNAKE_CASE__  :					int =       model_class(a     )
																			model.build()
																			SCREAMING_SNAKE_CASE__  :					str =       len(model(a     )     )

																			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:
																															SCREAMING_SNAKE_CASE__  :					Dict =       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(a				,						saved_model=a     )
																									SCREAMING_SNAKE_CASE__  :					Any =       os.path.join(a				,						"saved_model"				,						"1"     )
																									SCREAMING_SNAKE_CASE__  :					Dict =       tf.keras.models.load_model(a     )
																									SCREAMING_SNAKE_CASE__  :					Dict =       model(a     )

																									if self.is_encoder_decoder:
																															SCREAMING_SNAKE_CASE__  :					List[Any] =       outputs["encoder_hidden_states"]
																															SCREAMING_SNAKE_CASE__  :					Optional[int] =       outputs["encoder_attentions"]
																									else:
																															SCREAMING_SNAKE_CASE__  :					Dict =       outputs["hidden_states"]
																															SCREAMING_SNAKE_CASE__  :					Dict =       outputs["attentions"]

																									self.assertEqual(len(a     )				,						a     )

																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       getattr(
																									    self.model_tester				,						"expected_num_hidden_layers"				,						self.model_tester.num_hidden_layers + 1     )

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

																									self.assertEqual(len(a     )				,						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					:  int     )						->int:
													tf.keras.mixed_precision.set_global_policy("mixed_float16"     )

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

																									self.assertIsNotNone(a     )
													finally:
																			tf.keras.mixed_precision.set_global_policy("float32"     )

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

													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
																									SCREAMING_SNAKE_CASE__  :					List[Any] =       TFSharedEmbeddings(self.model_tester.vocab_size				,						self.model_tester.hidden_size				,						name="shared"     )
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       False
																									SCREAMING_SNAKE_CASE__  :					Any =       main_layer_class(a				,						embed_tokens=a     )
																			else:
																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       main_layer_class(a     )

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

																			if hasattr(self.model_tester				,						"num_labels"     ):
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.model_tester.num_labels
																			else:
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       2

																			SCREAMING_SNAKE_CASE__  :					Union[str, 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     )

																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       main_layer(a     )[0]
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       tf.keras.layers.Dense(a				,						activation="softmax"				,						name="outputs"     )(a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       tf.keras.models.Model(inputs=a				,						outputs=[outputs]     )

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

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

							@slow
							def   A_	(   self					:  Union[str, Any]     )						->int:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       self.model_tester.prepare_config_and_inputs_for_common()

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

																			SCREAMING_SNAKE_CASE__  :					Any =       copy.deepcopy(a     )

																			if not self.is_encoder_decoder:
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"]
																									del inputs["input_ids"]
																			else:
																									SCREAMING_SNAKE_CASE__  :					str =       inputs["input_ids"]
																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       inputs.get("decoder_input_ids"				,						a     )
																									del inputs["input_ids"]
																									inputs.pop("decoder_input_ids"				,						a     )

																			if not self.is_encoder_decoder:
																									SCREAMING_SNAKE_CASE__  :					str =       model.get_input_embeddings()(a     )
																			else:
																									SCREAMING_SNAKE_CASE__  :					Any =       model.get_input_embeddings()(a     )
																									SCREAMING_SNAKE_CASE__  :					Dict =       model.get_input_embeddings()(a     )

																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self._prepare_for_class(a				,						a     )

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

																			SCREAMING_SNAKE_CASE__  :					Dict =       run_in_graph_mode()
																			self.assertIsNotNone(a     )

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

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






							def   A_	(   self					:  List[str]				,						a					:  Tuple				,						a					:  Any     )						->Any:
													# 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(a     )				,						len(a     )     ):
																															# if tokens match
																															if generated_ids_slice[i - len(a     ) : 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  _a  (					lowercase__				):






							"""simple docstring"""

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

													if self.tokenized:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       seq_length * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Dict =       "input_ids"
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       seq_length * chars_per_token * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Any =       "content"

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






							def   A_	(   self					:  Optional[Any]				,						a					:  Optional[Any]=10_00     )						->Union[str, Any]:
													return ShufflerIterDataPipe(self				,						buffer_size=a     )







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       args.model_ckpt.split("/"  )[-1]
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       logging.getLogger(__name__  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       Path(args.save_dir  ) / "log/"
						log_dir.mkdir(exist_ok=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       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  )  )
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO  )
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ""
												logger.setLevel(logging.ERROR  )
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       {"streaming": True}
						SCREAMING_SNAKE_CASE__  :					str =       load_dataset(args.dataset_name_train      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       train_data.shuffle(buffer_size=args.shuffle_buffer      , seed=args.seed  )
						SCREAMING_SNAKE_CASE__  :					Any =       load_dataset(args.dataset_name_valid      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Any =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					str =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					Any =       train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
						SCREAMING_SNAKE_CASE__  :					Dict =       DataLoader(_lowerCamelCase      , batch_size=args.train_batch_size      , shuffle=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       DataLoader(_lowerCamelCase      , batch_size=args.valid_batch_size  )
						return train_dataloader, eval_dataloader







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       [], []
						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    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						logger.info(f"""Step {step}: {metrics}"""  )
						if accelerator.is_main_process:
												accelerator.log(_lowerCamelCase      , _lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.unwrap_model(_lowerCamelCase  ).config
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       4 if args.gradient_checkpointing else 3
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       factor * (
						    1.0
						    + (args.seq_length / (6.0 * config_model.n_embd))
						    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
						)
						SCREAMING_SNAKE_CASE__  :					Tuple =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						model.eval()
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for step, batch in enumerate(_lowerCamelCase  ):
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE__  :					List[str] =       model(_lowerCamelCase      , labels=_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					Any =       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
						SCREAMING_SNAKE_CASE__  :					str =       torch.cat(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       losses[: eval_dataloader.dataset.current_size].mean()
						try:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.exp(_lowerCamelCase  )
						except OverflowError:
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       float("inf"  )
						return loss.item(), perplexity.item()


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

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

__lowercase							:List[Any]							=	Namespace(**vars(args), **acc_state)
__lowercase							:Optional[Any]							=	accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
				__lowercase							:Dict							=	Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowercase							,  __lowercase							:Union[str, Any]							=	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
__lowercase							:Tuple							=	AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
__lowercase							:Union[str, Any]							=	AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowercase							,  __lowercase							:Any							=	create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowercase							:int							=	AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase							:str							=	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    UpperCAmelCase				(  ):





						'''simple docstring'''


						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowercase							,  __lowercase							,  __lowercase							,  __lowercase							:Dict							=	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)
								__lowercase							:List[str]							=	os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								__lowercase							:Union[str, Any]							=	[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)
								__lowercase							:Dict							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract the step of the checkpoint to continue from there
				__lowercase							:Any							=	os.path.splitext(path)[0]
				__lowercase							:str							=	int(training_difference.replace("step_", ""))

# Train model
model.train()
__lowercase							:int							=	0
__lowercase							:List[Any]							=	time.time()
__lowercase							:Any							=	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
				__lowercase							:List[str]							=	model(batch, labels=batch, use_cache=False).loss
				__lowercase							:Union[str, Any]							=	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()})
				__lowercase							:List[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:
								__lowercase							:List[Any]							=	get_lr()
								accelerator.backward(loss)
								accelerator.clip_grad_norm_(model.parameters(), 1.0)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
								__lowercase							:str							=	time.time() - t_start
								__lowercase							:List[str]							=	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,
								    },
								)
								__lowercase							:str							=	time.time()
								__lowercase							:Optional[int]							=	0
								completed_steps += 1
				if step % args.save_checkpoint_steps == 0:
								logger.info("Evaluating and saving model checkpoint")
								__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
								log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
								accelerator.wait_for_everyone()
								__lowercase							: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")
__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
__lowercase							:List[str]							=	accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowercase							: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")