


'''simple docstring'''

import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       LayoutLMvaForQuestionAnswering,
       LayoutLMvaForSequenceClassification,
       LayoutLMvaForTokenClassification,
       LayoutLMvaModel,
   )
   from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor





class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=2     ,							__UpperCamelCase=3     ,							__UpperCamelCase=4     ,							__UpperCamelCase=2     ,							__UpperCamelCase=7     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=99     ,							__UpperCamelCase=36     ,							__UpperCamelCase=3     ,							__UpperCamelCase=4     ,							__UpperCamelCase=37     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=16     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=6     ,							__UpperCamelCase=6     ,							__UpperCamelCase=3     ,							__UpperCamelCase=4     ,							__UpperCamelCase=None     ,							__UpperCamelCase=1_000     ,							)       ->    Tuple:
          _a						=		parent
          _a						=		batch_size
          _a						=		num_channels
          _a						=		image_size
          _a						=		patch_size
          _a						=		text_seq_length
          _a						=		is_training
          _a						=		use_input_mask
          _a						=		use_token_type_ids
          _a						=		use_labels
          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		max_position_embeddings
          _a						=		type_vocab_size
          _a						=		type_sequence_label_size
          _a						=		initializer_range
          _a						=		coordinate_size
          _a						=		shape_size
          _a						=		num_labels
          _a						=		num_choices
          _a						=		scope
          _a						=		range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          _a						=		text_seq_length
          _a						=		(image_size // patch_size) ** 2 + 1
          _a						=		self.text_seq_length + self.image_seq_length






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		ids_tensor([self.batch_size, self.text_seq_length]     ,							self.vocab_size		)

          _a						=		ids_tensor([self.batch_size, self.text_seq_length, 4]     ,							self.range_bbox		)
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]		):
               for j in range(bbox.shape[1]		):
                    if bbox[i, j, 3] < bbox[i, j, 1]:
                         _a						=		bbox[i, j, 3]
                         _a						=		bbox[i, j, 1]
                         _a						=		t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         _a						=		bbox[i, j, 2]
                         _a						=		bbox[i, j, 0]
                         _a						=		t

          _a						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          _a						=		None
          if self.use_input_mask:
               _a						=		random_attention_mask([self.batch_size, self.text_seq_length]		)

          _a						=		None
          if self.use_token_type_ids:
               _a						=		ids_tensor([self.batch_size, self.text_seq_length]     ,							self.type_vocab_size		)

          _a						=		None
          _a						=		None
          if self.use_labels:
               _a						=		ids_tensor([self.batch_size]     ,							self.type_sequence_label_size		)
               _a						=		ids_tensor([self.batch_size, self.text_seq_length]     ,							self.num_labels		)

          _a						=		LayoutLMvaConfig(
              vocab_size=self.vocab_size     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							max_position_embeddings=self.max_position_embeddings     ,							type_vocab_size=self.type_vocab_size     ,							initializer_range=self.initializer_range     ,							coordinate_size=self.coordinate_size     ,							shape_size=self.shape_size     ,							input_size=self.image_size     ,							patch_size=self.patch_size     ,							)

          return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Optional[int]:
          _a						=		LayoutLMvaModel(config=__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()

          # text + image
          _a						=		model(__UpperCamelCase     ,							pixel_values=__UpperCamelCase		)
          _a						=		model(
              __UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase		)
          _a						=		model(__UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase		)
          _a						=		model(__UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase		)

          self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)		)

          # text only
          _a						=		model(__UpperCamelCase		)
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,							(self.batch_size, self.text_seq_length, self.hidden_size)		)

          # image only
          _a						=		model(pixel_values=__UpperCamelCase		)
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,							(self.batch_size, self.image_seq_length, self.hidden_size)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[Any]:
          _a						=		self.num_labels
          _a						=		LayoutLMvaForSequenceClassification(__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(
              __UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase     ,							labels=__UpperCamelCase     ,							)
          self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		self.num_labels
          _a						=		LayoutLMvaForTokenClassification(config=__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(
              __UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase     ,							labels=__UpperCamelCase     ,							)
          self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.text_seq_length, self.num_labels)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Union[str, Any]:
          _a						=		LayoutLMvaForQuestionAnswering(config=__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(
              __UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase     ,							start_positions=__UpperCamelCase     ,							end_positions=__UpperCamelCase     ,							)
          self.parent.assertEqual(result.start_logits.shape     ,							(self.batch_size, self.seq_length)		)
          self.parent.assertEqual(result.end_logits.shape     ,							(self.batch_size, self.seq_length)		)






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		self.prepare_config_and_inputs()
          (
              (
              _a			
          )			,			(
              _a			
          )			,			(
              _a			
          )			,			(
              _a			
          )			,			(
              _a			
          )			,			(
              _a			
          )			,			(
              _a			
          )			,			(
              _a			
          )			,						
          )						=		config_and_inputs
          _a						=		{
              "input_ids": input_ids,
              "bbox": bbox,
              "pixel_values": pixel_values,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







@require_torch
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				False

     UpperCAmelCase 					=				(
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase 					=				(
         {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
         if is_torch_available()
         else {}
     )






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
          # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
          # the sequence dimension of the text embedding only.
          # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
          return True






     def  a_					(			self		)       ->    int:
          _a						=		LayoutLMvaModelTester(self		)
          _a						=		ConfigTester(self     ,							config_class=__UpperCamelCase     ,							hidden_size=37		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=False		)       ->    List[str]:
          _a						=		copy.deepcopy(__UpperCamelCase		)
          if model_class in get_values(__UpperCamelCase		):
               _a						=		{
                   k: v.unsqueeze(1		).expand(-1     ,							self.model_tester.num_choices     ,							-1		).contiguous()
                   if isinstance(__UpperCamelCase     ,							torch.Tensor		) and v.ndim > 1
                   else v
                   for k, v in inputs_dict.items()
               }
          if return_labels:
               if model_class in get_values(__UpperCamelCase		):
                    _a						=		torch.ones(self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__UpperCamelCase		)
               elif model_class in get_values(__UpperCamelCase		):
                    _a						=		torch.zeros(
                        self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__UpperCamelCase		)
                    _a						=		torch.zeros(
                        self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__UpperCamelCase		)
               elif model_class in [
                   *get_values(__UpperCamelCase		),
               ]:
                    _a						=		torch.zeros(
                        self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__UpperCamelCase		)
               elif model_class in [
                   *get_values(__UpperCamelCase		),
               ]:
                    _a						=		torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,							dtype=torch.long     ,							device=__UpperCamelCase     ,							)

          return inputs_dict






     def  a_					(			self		)       ->    Union[str, Any]:
          self.config_tester.run_common_tests()






     def  a_					(			self		)       ->    Any:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase		)






     def  a_					(			self		)       ->    str:
          _a						=		self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               _a						=		type
               self.model_tester.create_and_check_model(*__UpperCamelCase		)






     def  a_					(			self		)       ->    List[str]:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase		)






     @slow
     def  a_					(			self		)       ->    Dict:
          for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a						=		LayoutLMvaModel.from_pretrained(__UpperCamelCase		)
               self.assertIsNotNone(__UpperCamelCase		)







def __UpperCamelCase   (			)      ->     int:







     '''simple docstring'''






     _a						=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image





@require_torch
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     @cached_property
     def  a_					(			self		)       ->    Tuple:
          return LayoutLMvaImageProcessor(apply_ocr=__UpperCamelCase		) if is_vision_available() else None






     @slow
     def  a_					(			self		)       ->    Any:
          _a						=		LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		).to(__UpperCamelCase		)

          _a						=		self.default_image_processor
          _a						=		prepare_img()
          _a						=		image_processor(images=__UpperCamelCase     ,							return_tensors="pt"		).pixel_values.to(__UpperCamelCase		)

          _a						=		torch.tensor([[1, 2]]		)
          _a						=		torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

          # forward pass
          _a						=		model(
              input_ids=input_ids.to(__UpperCamelCase		)     ,							bbox=bbox.to(__UpperCamelCase		)     ,							pixel_values=pixel_values.to(__UpperCamelCase		)     ,							)

          # verify the logits
          _a						=		torch.Size((1, 199, 768)		)
          self.assertEqual(outputs.last_hidden_state.shape     ,							__UpperCamelCase		)

          _a						=		torch.tensor(
              [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]		).to(__UpperCamelCase		)

          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,							__UpperCamelCase     ,							atol=1e-4		)		)





'''simple docstring'''

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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				jnp.floataa
     UpperCAmelCase 					=				True






     def  a_					(			self		)       ->    int:
          super().setup()
          _a						=		nn.Dense(5     ,							dtype=self.dtype		)






     def __call__(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Optional[Any]:
          _a						=		super().__call__(*__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		self.cls(outputs[2]		)
          return outputs[:2] + (cls_out,)







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				FlaxBigBirdForNaturalQuestionsModule







def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Dict				)      ->     List[Any]:







     '''simple docstring'''






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

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





@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				"google/bigbird-roberta-base"
     UpperCAmelCase 					=				3_000
     UpperCAmelCase 					=				10_500

     UpperCAmelCase 					=				128
     UpperCAmelCase 					=				3

     UpperCAmelCase 					=				1
     UpperCAmelCase 					=				5

     # tx_args
     UpperCAmelCase 					=				3E-5
     UpperCAmelCase 					=				0.0
     UpperCAmelCase 					=				20_000
     UpperCAmelCase 					=				0.00_95

     UpperCAmelCase 					=				"bigbird-roberta-natural-questions"
     UpperCAmelCase 					=				"training-expt"
     UpperCAmelCase 					=				"data/nq-training.jsonl"
     UpperCAmelCase 					=				"data/nq-validation.jsonl"






     def  a_					(			self		)       ->    Tuple:
          os.makedirs(self.base_dir     ,							exist_ok=__UpperCamelCase		)
          _a						=		os.path.join(self.base_dir     ,							self.save_dir		)
          _a						=		self.batch_size_per_device * jax.device_count()







@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				4_096  # no dynamic padding on TPUs






     def __call__(			self     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		self.collate_fn(__UpperCamelCase		)
          _a						=		jax.tree_util.tree_map(__UpperCamelCase     ,							__UpperCamelCase		)
          return batch






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Tuple:
          _a			,			_a						=		self.fetch_inputs(features["input_ids"]		)
          _a						=		{
              "input_ids": jnp.array(__UpperCamelCase     ,							dtype=jnp.intaa		),
              "attention_mask": jnp.array(__UpperCamelCase     ,							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  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		[self._fetch_inputs(__UpperCamelCase		) for ids in input_ids]
          return zip(*__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		[1 for _ in range(len(__UpperCamelCase		)		)]
          while len(__UpperCamelCase		) < self.max_length:
               input_ids.append(self.pad_id		)
               attention_mask.append(0		)
          return input_ids, attention_mask







def __UpperCamelCase   (			__lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: List[Any]=None				)      ->     List[Any]:







     '''simple docstring'''






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






@partial(jax.pmap      ,  axis_name="batch"				)
def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: int      ,  **__lowerCamelCase		: Optional[int]				)      ->     Any:







     '''simple docstring'''






     def loss_fn(__lowerCamelCase		: str				):
          _a						=		model_inputs.pop("start_labels"				)
          _a						=		model_inputs.pop("end_labels"				)
          _a						=		model_inputs.pop("pooled_labels"				)

          _a						=		state.apply_fn(**__lowerCamelCase      ,  params=__lowerCamelCase      ,  dropout_rng=__lowerCamelCase      ,  train=__lowerCamelCase				)
          _a			,			_a			,			_a						=		outputs

          return state.loss_fn(
              __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  )

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

     _a						=		state.apply_gradients(grads=__lowerCamelCase				)
     return state, metrics, new_drp_rng






@partial(jax.pmap      ,  axis_name="batch"				)
def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  **__lowerCamelCase		: Tuple				)      ->     List[Any]:







     '''simple docstring'''






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

     _a						=		state.apply_fn(**__lowerCamelCase      ,  params=state.params      ,  train=__lowerCamelCase				)
     _a			,			_a			,			_a						=		outputs

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





class 						__SCREAMING_SNAKE_CASE					(      train_state.TrainState ):
     UpperCAmelCase 					=				struct.field(pytree_node=lowerCamelCase__ )







@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				None






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=None		)       ->    Union[str, Any]:
          _a						=		model.params
          _a						=		TrainState.create(
              apply_fn=model.__call__     ,							params=__UpperCamelCase     ,							tx=__UpperCamelCase     ,							loss_fn=__UpperCamelCase     ,							)
          if ckpt_dir is not None:
               _a			,			_a			,			_a			,			_a			,			_a						=		restore_checkpoint(__UpperCamelCase     ,							__UpperCamelCase		)
               _a						=		{
                   "lr": args.lr,
                   "init_lr": args.init_lr,
                   "warmup_steps": args.warmup_steps,
                   "num_train_steps": num_train_steps,
                   "weight_decay": args.weight_decay,
               }
               _a			,			_a						=		build_tx(**__UpperCamelCase		)
               _a						=		train_state.TrainState(
                   step=__UpperCamelCase     ,							apply_fn=model.__call__     ,							params=__UpperCamelCase     ,							tx=__UpperCamelCase     ,							opt_state=__UpperCamelCase     ,							)
               _a						=		args
               _a						=		data_collator
               _a						=		lr
               _a						=		params
          _a						=		jax_utils.replicate(__UpperCamelCase		)
          return state






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    str:
          _a						=		self.args
          _a						=		len(__UpperCamelCase		) // args.batch_size

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

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

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






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[Any]:
          _a						=		get_batched_dataset(__UpperCamelCase     ,							self.args.batch_size		)
          _a						=		len(__UpperCamelCase		) // self.args.batch_size
          _a						=		jnp.array(0     ,							dtype=jnp.floataa		)
          _a						=		0
          for batch in tqdm(__UpperCamelCase     ,							total=__UpperCamelCase     ,							desc="Evaluating ... "		):
               _a						=		self.data_collator(__UpperCamelCase		)
               _a						=		self.val_step_fn(__UpperCamelCase     ,							**__UpperCamelCase		)
               running_loss += jax_utils.unreplicate(metrics["loss"]		)
               i += 1
          return running_loss / i






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







def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Dict				)      ->     Optional[int]:







     '''simple docstring'''






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

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

     _a						=		joblib.load(os.path.join(__lowerCamelCase      ,  "args.joblib"				)				)
     _a						=		joblib.load(os.path.join(__lowerCamelCase      ,  "data_collator.joblib"				)				)

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

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






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: List[str]				)      ->     List[str]:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Tuple				)      ->     Any:







     '''simple docstring'''






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

     _a						=		scheduler_fn(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)

     _a						=		optax.adamw(learning_rate=__lowerCamelCase      ,  weight_decay=__lowerCamelCase      ,  mask=__lowerCamelCase				)
     return tx, lr

