





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				__lowercase							:Any							=	"platform"

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	int      , _lowerCamelCase   :	Optional[int]=None      , _lowerCamelCase   :	Union[str, Any]=None  ):





						'''simple docstring'''


						if attention_mask is None:
												SCREAMING_SNAKE_CASE__  :					str =       np.where(input_ids != config.pad_token_id      , 1      , 0  )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						}



@require_flax
class  _a  :






							"""simple docstring"""

							def __init__(   self					:  int				,						a					:  Any				,						a					:  Optional[Any]=13				,						a					:  Optional[int]=7				,						a					:  List[Any]=True				,						a					:  List[Any]=False				,						a					:  Any=99				,						a					:  Optional[Any]=16				,						a					:  Dict=2				,						a					:  str=4				,						a					:  Tuple=4				,						a					:  List[Any]="gelu"				,						a					:  Dict=0.1				,						a					:  Any=0.1				,						a					:  int=20				,						a					:  str=2				,						a					:  Union[str, Any]=1				,						a					:  Optional[Any]=0				,						a					:  int=16				,						a					:  Tuple=16				,						a					:  Optional[int]=0.02				,						)						->List[str]:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       parent
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       batch_size
													SCREAMING_SNAKE_CASE__  :					Any =       seq_length
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       is_training
													SCREAMING_SNAKE_CASE__  :					List[str] =       use_labels
													SCREAMING_SNAKE_CASE__  :					Dict =       vocab_size
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       hidden_size
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       num_hidden_layers
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       num_attention_heads
													SCREAMING_SNAKE_CASE__  :					List[str] =       intermediate_size
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       hidden_act
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       hidden_dropout_prob
													SCREAMING_SNAKE_CASE__  :					List[Any] =       attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__  :					int =       max_position_embeddings
													SCREAMING_SNAKE_CASE__  :					int =       eos_token_id
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       pad_token_id
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       bos_token_id
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       embed_dim
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       word_embed_proj_dim
													SCREAMING_SNAKE_CASE__  :					Dict =       initializer_range
													SCREAMING_SNAKE_CASE__  :					Tuple =       False

							def   A_	(   self					:  int     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,						self.vocab_size     )				,						3				,						self.vocab_size     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,						dtype=np.intaa     ))				,						-1     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       OPTConfig(
													    vocab_size=self.vocab_size				,						hidden_size=self.hidden_size				,						num_hidden_layers=self.num_hidden_layers				,						num_attention_heads=self.num_attention_heads				,						ffn_dim=self.intermediate_size				,						dropout=self.hidden_dropout_prob				,						attention_dropout=self.attention_probs_dropout_prob				,						max_position_embeddings=self.max_position_embeddings				,						eos_token_id=self.eos_token_id				,						bos_token_id=self.bos_token_id				,						pad_token_id=self.pad_token_id				,						embed_dim=self.embed_dim				,						is_encoder_decoder=a				,						word_embed_proj_dim=self.word_embed_proj_dim				,						initializer_range=self.initializer_range				,						use_cache=a				,						)
													SCREAMING_SNAKE_CASE__  :					List[Any] =       prepare_opt_inputs_dict(a				,						a     )
													return config, inputs_dict

							def   A_	(   self					:  Union[str, Any]     )						->Dict:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.prepare_config_and_inputs()
													return config, inputs_dict

							def   A_	(   self					:  List[Any]				,						a					:  Tuple				,						a					:  Union[str, Any]				,						a					:  Optional[int]     )						->str:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       20
													SCREAMING_SNAKE_CASE__  :					str =       model_class_name(a     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       inputs_dict["input_ids"]
													SCREAMING_SNAKE_CASE__  :					str =       inputs_dict["attention_mask"]

													SCREAMING_SNAKE_CASE__  :					List[str] =       model.init_cache(input_ids.shape[0]				,						a     )
													SCREAMING_SNAKE_CASE__  :					int =       jnp.ones((input_ids.shape[0], max_length)				,						dtype="i4"     )

													SCREAMING_SNAKE_CASE__  :					Any =       jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1     )[None, :]				,						(input_ids.shape[0], input_ids.shape[-1] - 1)				,						)
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       model(
													    input_ids[:, :-1]				,						attention_mask=a				,						past_key_values=a				,						position_ids=a				,						)

													SCREAMING_SNAKE_CASE__  :					str =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,						dtype="i4"     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model(
													    input_ids[:, -1:]				,						attention_mask=a				,						past_key_values=outputs_cache.past_key_values				,						position_ids=a				,						)

													SCREAMING_SNAKE_CASE__  :					Dict =       model(a     )

													SCREAMING_SNAKE_CASE__  :					Dict =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
													self.parent.assertTrue(diff < 1E-3				,						msg=f"""Max diff is {diff}"""     )






							def   A_	(   self					:  str				,						a					:  Any				,						a					:  Dict				,						a					:  int     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					str =       20
													SCREAMING_SNAKE_CASE__  :					List[Any] =       model_class_name(a     )

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       (
													    inputs_dict["input_ids"],
													    inputs_dict["attention_mask"],
													)

													SCREAMING_SNAKE_CASE__  :					int =       jnp.concatenate(
													    [
													        attention_mask,
													        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
													    ]				,						axis=-1				,						)

													SCREAMING_SNAKE_CASE__  :					Tuple =       model.init_cache(input_ids.shape[0]				,						a     )
													SCREAMING_SNAKE_CASE__  :					Dict =       jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1     )[None, :]				,						(input_ids.shape[0], input_ids.shape[-1] - 1)				,						)

													SCREAMING_SNAKE_CASE__  :					Any =       model(
													    input_ids[:, :-1]				,						attention_mask=a				,						past_key_values=a				,						position_ids=a				,						)
													SCREAMING_SNAKE_CASE__  :					str =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,						dtype="i4"     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       model(
													    input_ids[:, -1:]				,						past_key_values=outputs_cache.past_key_values				,						attention_mask=a				,						position_ids=a				,						)

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model(a				,						attention_mask=a     )

													SCREAMING_SNAKE_CASE__  :					str =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
													self.parent.assertTrue(diff < 1E-3				,						msg=f"""Max diff is {diff}"""     )







@require_flax
class  _a  (					lowercase__       ,				unittest.TestCase       ,				lowercase__				):






							"""simple docstring"""

							snake_case_							=    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
							snake_case_							=    () if is_flax_available() else ()

							def   A_	(   self					:  str     )						->int:
													SCREAMING_SNAKE_CASE__  :					Any =       FlaxOPTModelTester(self     )

							def   A_	(   self					:  Any     )						->List[Any]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(a				,						a				,						a     )

							def   A_	(   self					:  Union[str, Any]     )						->Any:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward_with_attn_mask(a				,						a				,						a     )






							@slow
							def   A_	(   self					:  List[Any]     )						->Dict:
													for model_class_name in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__  :					Dict =       model_class_name.from_pretrained("facebook/opt-125m"     )
																			SCREAMING_SNAKE_CASE__  :					Any =       np.ones((1, 1)     ) * model.config.eos_token_id
																			SCREAMING_SNAKE_CASE__  :					List[str] =       model(a     )
																			self.assertIsNotNone(a     )







@require_sentencepiece
@require_flax
class  _a  (					unittest.TestCase				):






							"""simple docstring"""

							@slow
							def   A_	(   self					:  int     )						->Optional[Any]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       FlaxOPTModel.from_pretrained("facebook/opt-350m"     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       model(input_ids=a     ).last_hidden_state
													SCREAMING_SNAKE_CASE__  :					int =       (1, 11, 5_12)
													self.assertEqual(output.shape				,						a     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       jnp.array(
													    [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]     )
													self.assertTrue(jnp.allclose(output[:, :3, :3]				,						a				,						atol=4E-2     )     )







@require_flax
@slow
class  _a  (					unittest.TestCase				):






							"""simple docstring"""

							def   A_	(   self					:  List[str]     )						->int:
													super().setUp()
													SCREAMING_SNAKE_CASE__  :					List[str] =       "facebook/opt-350m"






							def   A_	(   self					:  Dict     )						->List[Any]:
													SCREAMING_SNAKE_CASE__  :					Dict =       FlaxOPTForCausalLM.from_pretrained(self.path_model     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       GPTaTokenizer.from_pretrained(self.path_model     )

													SCREAMING_SNAKE_CASE__  :					Dict =       [
													    "Today is a beautiful day and I want to",
													    "In the city of",
													    "Paris is the capital of France and",
													    "Computers and mobile phones have taken",
													]
													# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
													SCREAMING_SNAKE_CASE__  :					Dict =       tokenizer(a				,						return_tensors="jax"				,						padding=a				,						add_special_tokens=a     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       model(inputs.input_ids				,						attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       jnp.array(
													    [
													        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
													        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
													        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
													        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
													    ]     )
													self.assertTrue(jnp.allclose(a				,						a				,						atol=4E-2     )     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       jax.jit(a     )
													SCREAMING_SNAKE_CASE__  :					str =       model(inputs.input_ids				,						attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
													self.assertTrue(jnp.allclose(a				,						a				,						atol=4E-2     )     )







@require_flax
@slow
class  _a  (					unittest.TestCase				):






							"""simple docstring"""

							@property
							def   A_	(   self					:  Any     )						->str:
													return [
													    "Today is a beautiful day and I want",
													    "In the city of",
													    "Paris is the capital of France and",
													    "Computers and mobile phones have taken",
													]

							def   A_	(   self					:  Dict     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					str =       "facebook/opt-125m"

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [
													    "Today is a beautiful day and I want to",
													    "In the city of New York, the city",
													    "Paris is the capital of France and the capital",
													    "Computers and mobile phones have taken over the",
													]

													SCREAMING_SNAKE_CASE__  :					Dict =       []

													SCREAMING_SNAKE_CASE__  :					Tuple =       FlaxOPTForCausalLM.from_pretrained(a     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       GPTaTokenizer.from_pretrained(a     )

													for prompt in self.prompts:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       tokenizer(a				,						return_tensors="jax"     ).input_ids

																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model.generate(a				,						max_length=10     )
																			SCREAMING_SNAKE_CASE__  :					str =       generated_ids[0]

																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tokenizer.batch_decode(a				,						skip_special_tokens=a     )
																			predicted_outputs += generated_string

													self.assertListEqual(a				,						a     )

							def   A_	(   self					:  Optional[Any]     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       "facebook/opt-350m"

													SCREAMING_SNAKE_CASE__  :					List[str] =       [
													    "Today is a beautiful day and I want to",
													    "In the city of San Francisco, the city",
													    "Paris is the capital of France and the capital",
													    "Computers and mobile phones have taken over the",
													]

													SCREAMING_SNAKE_CASE__  :					str =       []
													SCREAMING_SNAKE_CASE__  :					str =       FlaxOPTForCausalLM.from_pretrained(a     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       GPTaTokenizer.from_pretrained(a     )

													for prompt in self.prompts:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       tokenizer(a				,						return_tensors="jax"     ).input_ids

																			SCREAMING_SNAKE_CASE__  :					int =       model.generate(a				,						max_length=10     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       generated_ids[0]

																			SCREAMING_SNAKE_CASE__  :					List[str] =       tokenizer.batch_decode(a				,						skip_special_tokens=a     )
																			predicted_outputs += generated_string

													self.assertListEqual(a				,						a     )

							def   A_	(   self					:  List[str]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					str =       "facebook/opt-125m"
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [
													    "Today is a beautiful day and I want to thank",
													    "In the city of Rome Canaver Canaver Canaver Canaver",
													]
													SCREAMING_SNAKE_CASE__  :					Dict =       FlaxOPTForCausalLM.from_pretrained(a     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       GPTaTokenizer.from_pretrained(a     )
													SCREAMING_SNAKE_CASE__  :					Any =       tokenizer(
													    [
													        "Today is a beautiful day and I want to",
													        "In the city of",
													    ]				,						return_tensors="jax"				,						padding=a				,						)

													SCREAMING_SNAKE_CASE__  :					int =       jax.jit(model.generate     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       jit_generate(inputs["input_ids"]				,						attention_mask=inputs["attention_mask"]     ).sequences

													SCREAMING_SNAKE_CASE__  :					Any =       tokenizer.batch_decode(a				,						skip_special_tokens=a     )

													self.assertIsNotNone(a				,						a     )






							def   A_	(   self					:  Optional[Any]     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					Tuple =       "facebook/opt-350m"

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       GPTaTokenizer.from_pretrained(a     )
													SCREAMING_SNAKE_CASE__  :					str =       FlaxOPTForCausalLM.from_pretrained(a     )

													SCREAMING_SNAKE_CASE__  :					Any =       "left"

													# use different length sentences to test batching
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [
													    "Hello, my dog is a little",
													    "Today, I",
													]

													SCREAMING_SNAKE_CASE__  :					List[str] =       tokenizer(a				,						return_tensors="jax"				,						padding=a     )
													SCREAMING_SNAKE_CASE__  :					str =       inputs["input_ids"]

													SCREAMING_SNAKE_CASE__  :					List[Any] =       model.generate(input_ids=a				,						attention_mask=inputs["attention_mask"]				,						trace=a     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       tokenizer(sentences[0]				,						return_tensors="jax"     ).input_ids
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model.generate(input_ids=a     )

													SCREAMING_SNAKE_CASE__  :					Dict =       inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
													SCREAMING_SNAKE_CASE__  :					List[Any] =       tokenizer(sentences[1]				,						return_tensors="jax"     ).input_ids
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       model.generate(input_ids=a				,						max_length=model.config.max_length - num_paddings     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       tokenizer.batch_decode(outputs[0]				,						skip_special_tokens=a     )
													SCREAMING_SNAKE_CASE__  :					int =       tokenizer.decode(output_non_padded[0][0]				,						skip_special_tokens=a     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       tokenizer.decode(output_padded[0][0]				,						skip_special_tokens=a     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       [
													    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
													    "Today, I was in the middle of a conversation with a friend about the",
													]
													self.assertListEqual(a				,						a     )
													self.assertListEqual(a				,						[non_padded_sentence, padded_sentence]     )






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")