
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFTransfoXLForSequenceClassification,
					    TFTransfoXLLMHeadModel,
					    TFTransfoXLModel,
					)

class       a		:







							def __init__(						self  :  Union[str, Any]      ,				snake_case__  :  List[Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							13
								__lowerCAmelCase         =							7
								__lowerCAmelCase         =							30
								__lowerCAmelCase         =							self.seq_length + self.mem_len
								__lowerCAmelCase         =							15
								__lowerCAmelCase         =							True
								__lowerCAmelCase         =							True
								__lowerCAmelCase         =							99
								__lowerCAmelCase         =							[10, 50, 80]
								__lowerCAmelCase         =							32
								__lowerCAmelCase         =							32
								__lowerCAmelCase         =							4
								__lowerCAmelCase         =							8
								__lowerCAmelCase         =							128
								__lowerCAmelCase         =							2
								__lowerCAmelCase         =							2
								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							1
								__lowerCAmelCase         =							0
								__lowerCAmelCase         =							3
								__lowerCAmelCase         =							self.vocab_size - 1
								__lowerCAmelCase         =							0.0_1







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size )
								__lowerCAmelCase         =							ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size )

								__lowerCAmelCase         =							None
								if self.use_labels:
									__lowerCAmelCase         =							ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size )

								__lowerCAmelCase         =							TransfoXLConfig(
								    vocab_size=self.vocab_size      ,				mem_len=self.mem_len      ,				clamp_len=self.clamp_len      ,				cutoffs=self.cutoffs      ,				d_model=self.hidden_size      ,				d_embed=self.d_embed      ,				n_head=self.num_attention_heads      ,				d_head=self.d_head      ,				d_inner=self.d_inner      ,				div_val=self.div_val      ,				n_layer=self.num_hidden_layers      ,				eos_token_id=self.eos_token_id      ,				pad_token_id=self.vocab_size - 1      ,				init_range=self.init_range      ,				num_labels=self.num_labels      ,				)

								return (config, input_ids_a, input_ids_a, lm_labels)







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								random.seed(self.seed )
								tf.random.set_seed(self.seed )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Any      ,				snake_case__  :  Any      ,				snake_case__  :  Tuple      ,				snake_case__  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFTransfoXLModel(snake_case__ )

								__lowerCAmelCase         =							model(snake_case__ ).to_tuple()

								__lowerCAmelCase         =							{"input_ids": input_ids_a, "mems": mems_a}

								__lowerCAmelCase         =							model(snake_case__ ).to_tuple()

								self.parent.assertEqual(hidden_states_a.shape      ,				(self.batch_size, self.seq_length, self.hidden_size) )
								self.parent.assertEqual(hidden_states_a.shape      ,				(self.batch_size, self.seq_length, self.hidden_size) )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]      ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,				)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]      ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,				)







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  int      ,				snake_case__  :  Tuple      ,				snake_case__  :  int      ,				snake_case__  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFTransfoXLLMHeadModel(snake_case__ )

								__lowerCAmelCase         =							model(snake_case__ ).to_tuple()

								__lowerCAmelCase         =							{"input_ids": input_ids_a, "labels": lm_labels}
								__lowerCAmelCase         =							model(snake_case__ ).to_tuple()

								__lowerCAmelCase         =							model([input_ids_a, mems_a] ).to_tuple()

								__lowerCAmelCase         =							{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

								__lowerCAmelCase         =							model(snake_case__ ).to_tuple()

								self.parent.assertEqual(lm_logits_a.shape      ,				(self.batch_size, self.seq_length, self.vocab_size) )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]      ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,				)

								self.parent.assertEqual(lm_logits_a.shape      ,				(self.batch_size, self.seq_length, self.vocab_size) )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]      ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,				)







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  List[Any]      ,				snake_case__  :  str      ,				snake_case__  :  str      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFTransfoXLForSequenceClassification(snake_case__ )
								__lowerCAmelCase         =							model(snake_case__ )
								self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels) )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.prepare_config_and_inputs()
								(__lowerCAmelCase)         =							config_and_inputs
								__lowerCAmelCase         =							{"input_ids": input_ids_a}
								return config, inputs_dict

@require_tf
class       a		(   _UpperCamelCase      ,	_UpperCamelCase      ,	unittest.TestCase					):
							lowercase_		:						Dict   							=  (
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							lowercase_		:						int   							=  () if is_tf_available() else ()
							lowercase_		:						Any   							=  (
							    {
							        'feature-extraction': TFTransfoXLModel,
							        'text-classification': TFTransfoXLForSequenceClassification,
							        'text-generation': TFTransfoXLLMHeadModel,
							        'zero-shot': TFTransfoXLForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
							lowercase_		:						Union[str, Any]   							=  False
							lowercase_		:						Union[str, Any]   							=  False
							lowercase_		:						Dict   							=  False
							lowercase_		:						Optional[Any]   							=  False







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  str      ,				snake_case__  :  Any      ,				snake_case__  :  Tuple      ,				snake_case__  :  str      ,				snake_case__  :  str ):
								"""simple docstring"""







								if pipeline_test_casse_name == "TextGenerationPipelineTests":
									# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
									# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
									# tokenizer.
									return True

								return False







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFTransfoXLModelTester(self )
								__lowerCAmelCase         =							ConfigTester(self      ,				config_class=snake_case__      ,				d_embed=37 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								self.config_tester.run_common_tests()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								self.model_tester.set_seed()
								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*snake_case__ )







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								self.model_tester.set_seed()
								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*snake_case__ )







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCAmelCase         =							[TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
									__lowerCAmelCase         =							model_class(snake_case__ )
									assert isinstance(model.get_input_embeddings()      ,				tf.keras.layers.Layer )
									if model_class in list_other_models_with_output_ebd:
										__lowerCAmelCase         =							model.get_output_embeddings()
										assert isinstance(snake_case__      ,				tf.keras.layers.Layer )
										__lowerCAmelCase         =							model.get_bias()
										assert name is None
									else:
										__lowerCAmelCase         =							model.get_output_embeddings()
										assert x is None
										__lowerCAmelCase         =							model.get_bias()
										assert name is None







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								pass







							@slow
							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCAmelCase         =							TFTransfoXLModel.from_pretrained(snake_case__ )
									self.assertIsNotNone(snake_case__ )







							@unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss." )
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								pass

@require_tf
class       a		(   unittest.TestCase					):







							@unittest.skip("Skip test until #12651 is resolved." )
							@slow
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103" )
								# fmt: off
								__lowerCAmelCase         =							tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]      ,				dtype=tf.intaa )  # noqa: E231
								# fmt: on
								#  In 1991 , the remains of Russian Tsar Nicholas II and his family
								#  ( except for Alexei and Maria ) are discovered .
								#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
								#  remainder of the story . 1883 Western Siberia ,
								#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
								#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
								#  father initially slaps him for making such an accusation , Rasputin watches as the
								#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
								#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
								#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

								# fmt: off
								__lowerCAmelCase         =							[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
								# fmt: on
								#  In 1991, the remains of Russian Tsar Nicholas II and his family (
								#  except for Alexei and Maria ) are discovered. The voice of young son,
								#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
								#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
								#  and a group of men to perform magic. Rasputin has a vision and
								#  denounces one of the men as a horse thief. Although his father initially
								#  slaps him for making such an accusation, Rasputin watches as the man
								#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
								#  of the Virgin Mary, prompting him to become a priest.
								#  Rasputin quickly becomes famous, with people, even a bishop, begging for
								#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
								# Nicholas II and his family were discovered. The voice of <unk> young son,
								# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

								__lowerCAmelCase         =							model.generate(snake_case__      ,				max_length=200      ,				do_sample=snake_case__ )
								self.assertListEqual(output_ids[0].numpy().tolist()      ,				snake_case__ )








import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule

class       a		(   __UpperCAmelCase					):
							lowercase_		:						BigBirdConfig
							lowercase_		:						jnp.dtype   							=  jnp.floataa
							lowercase_		:						bool   							=  True







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								super().setup()
								__lowerCAmelCase         =							nn.Dense(5      ,				dtype=self.dtype )







							def __call__(						self  :  Optional[Any]      ,				*snake_case__  :  List[str]      ,				**snake_case__  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							super().__call__(*snake_case__      ,				**snake_case__ )
								__lowerCAmelCase         =							self.cls(outputs[2] )
								return outputs[:2] + (cls_out,)

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  FlaxBigBirdForNaturalQuestionsModule






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     int  ):






	"""simple docstring"""


	def cross_entropy(UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[int]=None  ):
		__lowerCAmelCase         =							logits.shape[-1]
		__lowerCAmelCase         =							(labels[..., None] == jnp.arange(UpperCamelCase  )[None]).astype("f4"  )
		__lowerCAmelCase         =							jax.nn.log_softmax(UpperCamelCase			,							axis=-1  )
		__lowerCAmelCase         =							-jnp.sum(labels * logits			,							axis=-1  )
		if reduction is not None:
			__lowerCAmelCase         =							reduction(UpperCamelCase  )
		return loss

	__lowerCAmelCase         =							partial(UpperCamelCase			,							reduction=jnp.mean  )
	__lowerCAmelCase         =							cross_entropy(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							cross_entropy(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							cross_entropy(UpperCamelCase			,							UpperCamelCase  )
	return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class       a		:
							lowercase_		:						str   							=  "google/bigbird-roberta-base"
							lowercase_		:						int   							=  3_000
							lowercase_		:						int   							=  10_500

							lowercase_		:						int   							=  128
							lowercase_		:						int   							=  3

							lowercase_		:						int   							=  1
							lowercase_		:						int   							=  5

							# tx_args
							lowercase_		:						float   							=  3e-5
							lowercase_		:						float   							=  0.0
							lowercase_		:						int   							=  20_000
							lowercase_		:						float   							=  0.0095

							lowercase_		:						str   							=  "bigbird-roberta-natural-questions"
							lowercase_		:						str   							=  "training-expt"
							lowercase_		:						str   							=  "data/nq-training.jsonl"
							lowercase_		:						str   							=  "data/nq-validation.jsonl"







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								os.makedirs(self.base_dir      ,				exist_ok=snake_case__ )
								__lowerCAmelCase         =							os.path.join(self.base_dir      ,				self.save_dir )
								__lowerCAmelCase         =							self.batch_size_per_device * jax.device_count()

@dataclass
class       a		:
							lowercase_		:						int
							lowercase_		:						int   							=  4_096  # no dynamic padding on TPUs







							def __call__(						self  :  List[Any]      ,				snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.collate_fn(snake_case__ )
								__lowerCAmelCase         =							jax.tree_util.tree_map(snake_case__      ,				snake_case__ )
								return batch







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.fetch_inputs(features["input_ids"] )
								__lowerCAmelCase         =							{
								    "input_ids": jnp.array(snake_case__      ,				dtype=jnp.intaa ),
								    "attention_mask": jnp.array(snake_case__      ,				dtype=jnp.intaa ),
								    "start_labels": jnp.array(features["start_token"]      ,				dtype=jnp.intaa ),
								    "end_labels": jnp.array(features["end_token"]      ,				dtype=jnp.intaa ),
								    "pooled_labels": jnp.array(features["category"]      ,				dtype=jnp.intaa ),
								}
								return batch







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  list ):
								"""simple docstring"""







								__lowerCAmelCase         =							[self._fetch_inputs(snake_case__ ) for ids in input_ids]
								return zip(*snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  list ):
								"""simple docstring"""







								__lowerCAmelCase         =							[1 for _ in range(len(snake_case__ ) )]
								while len(snake_case__ ) < self.max_length:
									input_ids.append(self.pad_id )
									attention_mask.append(0 )
								return input_ids, attention_mask






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Optional[Any]=None  ):






	"""simple docstring"""


	if seed is not None:
		__lowerCAmelCase         =							dataset.shuffle(seed=UpperCamelCase  )
	for i in range(len(UpperCamelCase  ) // batch_size  ):
		__lowerCAmelCase         =							dataset[i * batch_size : (i + 1) * batch_size]
		yield dict(UpperCamelCase  )






@partial(jax.pmap			,							axis_name="batch"  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Union[str, Any]			,							**UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	def loss_fn(UpperCamelCase:     Dict  ):
		__lowerCAmelCase         =							model_inputs.pop("start_labels"  )
		__lowerCAmelCase         =							model_inputs.pop("end_labels"  )
		__lowerCAmelCase         =							model_inputs.pop("pooled_labels"  )

		__lowerCAmelCase         =							state.apply_fn(**UpperCamelCase			,							params=UpperCamelCase			,							dropout_rng=UpperCamelCase			,							train=UpperCamelCase  )
		__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							outputs

		return state.loss_fn(
		    UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							)

	__lowerCAmelCase   ,						__lowerCAmelCase         =							jax.random.split(UpperCamelCase  )
	__lowerCAmelCase         =							jax.value_and_grad(UpperCamelCase  )
	__lowerCAmelCase   ,						__lowerCAmelCase         =							grad_fn(state.params  )
	__lowerCAmelCase         =							jax.lax.pmean({"loss": loss}			,							axis_name="batch"  )
	__lowerCAmelCase         =							jax.lax.pmean(UpperCamelCase			,							"batch"  )

	__lowerCAmelCase         =							state.apply_gradients(grads=UpperCamelCase  )
	return state, metrics, new_drp_rng






@partial(jax.pmap			,							axis_name="batch"  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							**UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							model_inputs.pop("start_labels"  )
	__lowerCAmelCase         =							model_inputs.pop("end_labels"  )
	__lowerCAmelCase         =							model_inputs.pop("pooled_labels"  )

	__lowerCAmelCase         =							state.apply_fn(**UpperCamelCase			,							params=state.params			,							train=UpperCamelCase  )
	__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							outputs

	__lowerCAmelCase         =							state.loss_fn(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							jax.lax.pmean({"loss": loss}			,							axis_name="batch"  )
	return metrics

class       a		(   train_state.TrainState					):
							lowercase_		:						Callable   							=  struct.field(pytree_node=__UpperCAmelCase					)

@dataclass
class       a		:
							lowercase_		:						Args
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						wandb
							lowercase_		:						Callable   							=  None







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  int      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Any      ,				snake_case__  :  str=None ):
								"""simple docstring"""







								__lowerCAmelCase         =							model.params
								__lowerCAmelCase         =							TrainState.create(
								    apply_fn=model.__call__      ,				params=snake_case__      ,				tx=snake_case__      ,				loss_fn=snake_case__      ,				)
								if ckpt_dir is not None:
									__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							restore_checkpoint(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							{
									    "lr": args.lr,
									    "init_lr": args.init_lr,
									    "warmup_steps": args.warmup_steps,
									    "num_train_steps": num_train_steps,
									    "weight_decay": args.weight_decay,
									}
									__lowerCAmelCase   ,						__lowerCAmelCase         =							build_tx(**snake_case__ )
									__lowerCAmelCase         =							train_state.TrainState(
									    step=snake_case__      ,				apply_fn=model.__call__      ,				params=snake_case__      ,				tx=snake_case__      ,				opt_state=snake_case__      ,				)
									__lowerCAmelCase         =							args
									__lowerCAmelCase         =							data_collator
									__lowerCAmelCase         =							lr
									__lowerCAmelCase         =							params
								__lowerCAmelCase         =							jax_utils.replicate(snake_case__ )
								return state







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.args
								__lowerCAmelCase         =							len(snake_case__ ) // args.batch_size

								__lowerCAmelCase         =							jax.random.PRNGKey(0 )
								__lowerCAmelCase         =							jax.random.split(snake_case__      ,				jax.device_count() )
								for epoch in range(args.max_epochs ):
									__lowerCAmelCase         =							jnp.array(0      ,				dtype=jnp.floataa )
									__lowerCAmelCase         =							get_batched_dataset(snake_case__      ,				args.batch_size      ,				seed=snake_case__ )
									__lowerCAmelCase         =							0
									for batch in tqdm(snake_case__      ,				total=snake_case__      ,				desc=F"Running EPOCH-{epoch}" ):
										__lowerCAmelCase         =							self.data_collator(snake_case__ )
										__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							self.train_step_fn(snake_case__      ,				snake_case__      ,				**snake_case__ )
										running_loss += jax_utils.unreplicate(metrics["loss"] )
										i += 1
										if i % args.logging_steps == 0:
											__lowerCAmelCase         =							jax_utils.unreplicate(state.step )
											__lowerCAmelCase         =							running_loss.item() / i
											__lowerCAmelCase         =							self.scheduler_fn(state_step - 1 )

											__lowerCAmelCase         =							self.evaluate(snake_case__      ,				snake_case__ )
											__lowerCAmelCase         =							{
											    "step": state_step.item(),
											    "eval_loss": eval_loss.item(),
											    "tr_loss": tr_loss,
											    "lr": lr.item(),
											}
											tqdm.write(str(snake_case__ ) )
											self.logger.log(snake_case__      ,				commit=snake_case__ )

										if i % args.save_steps == 0:
											self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}"      ,				state=snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Any      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							get_batched_dataset(snake_case__      ,				self.args.batch_size )
								__lowerCAmelCase         =							len(snake_case__ ) // self.args.batch_size
								__lowerCAmelCase         =							jnp.array(0      ,				dtype=jnp.floataa )
								__lowerCAmelCase         =							0
								for batch in tqdm(snake_case__      ,				total=snake_case__      ,				desc="Evaluating ... " ):
									__lowerCAmelCase         =							self.data_collator(snake_case__ )
									__lowerCAmelCase         =							self.val_step_fn(snake_case__      ,				**snake_case__ )
									running_loss += jax_utils.unreplicate(metrics["loss"] )
									i += 1
								return running_loss / i







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							jax_utils.unreplicate(snake_case__ )
								print(F"SAVING CHECKPOINT IN {save_dir}"      ,				end=" ... " )
								self.model_save_fn(snake_case__      ,				params=state.params )
								with open(os.path.join(snake_case__      ,				"opt_state.msgpack" )      ,				"wb" ) as f:
									f.write(to_bytes(state.opt_state ) )
								joblib.dump(self.args      ,				os.path.join(snake_case__      ,				"args.joblib" ) )
								joblib.dump(self.data_collator      ,				os.path.join(snake_case__      ,				"data_collator.joblib" ) )
								with open(os.path.join(snake_case__      ,				"training_state.json" )      ,				"w" ) as f:
									json.dump({"step": state.step.item()}      ,				snake_case__ )
								print("DONE" )






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	print(F"RESTORING CHECKPOINT FROM {save_dir}"			,							end=" ... "  )
	with open(os.path.join(UpperCamelCase			,							"flax_model.msgpack"  )			,							"rb"  ) as f:
		__lowerCAmelCase         =							from_bytes(state.params			,							f.read()  )

	with open(os.path.join(UpperCamelCase			,							"opt_state.msgpack"  )			,							"rb"  ) as f:
		__lowerCAmelCase         =							from_bytes(state.opt_state			,							f.read()  )

	__lowerCAmelCase         =							joblib.load(os.path.join(UpperCamelCase			,							"args.joblib"  )  )
	__lowerCAmelCase         =							joblib.load(os.path.join(UpperCamelCase			,							"data_collator.joblib"  )  )

	with open(os.path.join(UpperCamelCase			,							"training_state.json"  )			,							"r"  ) as f:
		__lowerCAmelCase         =							json.load(UpperCamelCase  )
	__lowerCAmelCase         =							training_state["step"]

	print("DONE"  )
	return params, opt_state, step, args, data_collator






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


	__lowerCAmelCase         =							num_train_steps - warmup_steps
	__lowerCAmelCase         =							optax.linear_schedule(init_value=UpperCamelCase			,							end_value=UpperCamelCase			,							transition_steps=UpperCamelCase  )
	__lowerCAmelCase         =							optax.linear_schedule(init_value=UpperCamelCase			,							end_value=1e-7			,							transition_steps=UpperCamelCase  )
	__lowerCAmelCase         =							optax.join_schedules(schedules=[warmup_fn, decay_fn]			,							boundaries=[warmup_steps]  )
	return lr







def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     str			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Union[str, Any]  ):






	"""simple docstring"""


	def weight_decay_mask(UpperCamelCase:     int  ):
		__lowerCAmelCase         =							traverse_util.flatten_dict(UpperCamelCase  )
		__lowerCAmelCase         =							{k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
		return traverse_util.unflatten_dict(UpperCamelCase  )

	__lowerCAmelCase         =							scheduler_fn(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )

	__lowerCAmelCase         =							optax.adamw(learning_rate=UpperCamelCase			,							weight_decay=UpperCamelCase			,							mask=UpperCamelCase  )
	return tx, lr






