





'''simple docstring'''






from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertModel,
    )
class 					__snake_case  :






   '''simple docstring'''





   def __init__(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE=1_3	,		__SCREAMING_SNAKE_CASE=7	,		__SCREAMING_SNAKE_CASE=True	,		__SCREAMING_SNAKE_CASE=True	,		__SCREAMING_SNAKE_CASE=True	,		__SCREAMING_SNAKE_CASE=True	,		__SCREAMING_SNAKE_CASE=9_9	,		__SCREAMING_SNAKE_CASE=3_2	,		__SCREAMING_SNAKE_CASE=2	,		__SCREAMING_SNAKE_CASE=4	,		__SCREAMING_SNAKE_CASE=3_7	,		__SCREAMING_SNAKE_CASE="gelu"	,		__SCREAMING_SNAKE_CASE=0.1	,		__SCREAMING_SNAKE_CASE=0.1	,		__SCREAMING_SNAKE_CASE=5_1_2	,		__SCREAMING_SNAKE_CASE=1_6	,		__SCREAMING_SNAKE_CASE=2	,		__SCREAMING_SNAKE_CASE=0.02	,		__SCREAMING_SNAKE_CASE=3	,		__SCREAMING_SNAKE_CASE=4	,		__SCREAMING_SNAKE_CASE=None	,		):
    snake_case__     :					str    =  parent
    snake_case__     :					Dict    =  1_3
    snake_case__     :					Any    =  7
    snake_case__     :					Optional[int]    =  True
    snake_case__     :					Optional[Any]    =  True
    snake_case__     :					Optional[int]    =  True
    snake_case__     :					str    =  True
    snake_case__     :					Optional[Any]    =  9_9
    snake_case__     :					Dict    =  3_8_4
    snake_case__     :					Optional[Any]    =  2
    snake_case__     :					List[str]    =  4
    snake_case__     :					Union[str, Any]    =  3_7
    snake_case__     :					Optional[Any]    =  """gelu"""
    snake_case__     :					Optional[Any]    =  0.1
    snake_case__     :					Optional[Any]    =  0.1
    snake_case__     :					List[Any]    =  5_1_2
    snake_case__     :					Tuple    =  1_6
    snake_case__     :					Tuple    =  2
    snake_case__     :					List[str]    =  0.02
    snake_case__     :					Tuple    =  3
    snake_case__     :					List[str]    =  4
    snake_case__     :					Optional[Any]    =  1_2_8
    snake_case__     :					str    =  2
    snake_case__     :					Union[str, Any]    =  9
    snake_case__     :					Optional[int]    =  1
    snake_case__     :					List[Any]    =  None

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  ids_tensor([self.batch_size, self.seq_length]	,		self.vocab_size       )

    snake_case__     :					int    =  None
    if self.use_input_mask:
     snake_case__     :					Any    =  random_attention_mask([self.batch_size, self.seq_length]       )

    snake_case__     :					List[Any]    =  None
    if self.use_token_type_ids:
     snake_case__     :					Dict    =  ids_tensor([self.batch_size, self.seq_length]	,		self.type_vocab_size       )

    snake_case__     :					List[str]    =  None
    snake_case__     :					Dict    =  None
    snake_case__     :					Tuple    =  None
    if self.use_labels:
     snake_case__     :					Tuple    =  ids_tensor([self.batch_size]	,		self.type_sequence_label_size       )
     snake_case__     :					Tuple    =  ids_tensor([self.batch_size, self.seq_length]	,		self.num_labels       )
     snake_case__     :					List[str]    =  ids_tensor([self.batch_size]	,		self.num_choices       )

    snake_case__     :					Union[str, Any]    =  ConvBertConfig(
        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	,		return_dict=__SCREAMING_SNAKE_CASE	,		)

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					int    =  TFConvBertModel(config=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

    snake_case__     :					int    =  [input_ids, input_mask]
    snake_case__     :					Any    =  model(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[Any]    =  model(__SCREAMING_SNAKE_CASE       )

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

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					str    =  TFConvBertForMaskedLM(config=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					str    =  {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    snake_case__     :					Tuple    =  model(__SCREAMING_SNAKE_CASE       )
    self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.seq_length, self.vocab_size)       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Dict    =  self.num_labels
    snake_case__     :					int    =  TFConvBertForSequenceClassification(config=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					List[str]    =  {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }

    snake_case__     :					List[Any]    =  model(__SCREAMING_SNAKE_CASE       )
    self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.num_labels)       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					str    =  self.num_choices
    snake_case__     :					List[Any]    =  TFConvBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					List[str]    =  tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE	,		1       )	,		(1, self.num_choices, 1)       )
    snake_case__     :					List[Any]    =  tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE	,		1       )	,		(1, self.num_choices, 1)       )
    snake_case__     :					List[Any]    =  tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE	,		1       )	,		(1, self.num_choices, 1)       )
    snake_case__     :					Tuple    =  {
        """input_ids""": multiple_choice_inputs_ids,
        """attention_mask""": multiple_choice_input_mask,
        """token_type_ids""": multiple_choice_token_type_ids,
    }
    snake_case__     :					Optional[int]    =  model(__SCREAMING_SNAKE_CASE       )
    self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.num_choices)       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					str    =  self.num_labels
    snake_case__     :					List[Any]    =  TFConvBertForTokenClassification(config=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    snake_case__     :					str    =  model(__SCREAMING_SNAKE_CASE       )
    self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.seq_length, self.num_labels)       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					List[Any]    =  TFConvBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }

    snake_case__     :					Any    =  model(__SCREAMING_SNAKE_CASE       )
    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 		__UpperCamelCase						(							self       ):
    snake_case__     :					Union[str, Any]    =  self.prepare_config_and_inputs()
    (
        (
        snake_case__
    )  ,							(
        snake_case__
    )  ,							(
        snake_case__
    )  ,							(
        snake_case__
    )  ,							(
        snake_case__
    )  ,							(
        snake_case__
    )  ,							(
        snake_case__
    )  ,							
    )     :					Union[str, Any]    =  config_and_inputs
    snake_case__     :					Any    =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
    return config, inputs_dict



@require_tf
class 					__snake_case  (					__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				unittest.TestCase							):






   '''simple docstring'''





   lowerCamelCase__    							=      (
       (
           TFConvBertModel,
           TFConvBertForMaskedLM,
           TFConvBertForQuestionAnswering,
           TFConvBertForSequenceClassification,
           TFConvBertForTokenClassification,
           TFConvBertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   lowerCamelCase__    							=      (
       {
           '''feature-extraction''': TFConvBertModel,
           '''fill-mask''': TFConvBertForMaskedLM,
           '''question-answering''': TFConvBertForQuestionAnswering,
           '''text-classification''': TFConvBertForSequenceClassification,
           '''token-classification''': TFConvBertForTokenClassification,
           '''zero-shot''': TFConvBertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   lowerCamelCase__    							=      False
   lowerCamelCase__    							=      False
   lowerCamelCase__    							=      False

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Dict    =  TFConvBertModelTester(self       )
    snake_case__     :					Dict    =  ConfigTester(self	,		config_class=__SCREAMING_SNAKE_CASE	,		hidden_size=3_7       )

   def 		__UpperCamelCase						(							self       ):
    self.config_tester.run_common_tests()

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Dict    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					str    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Dict    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[Any]    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE       )

   @slow
   def 		__UpperCamelCase						(							self       ):
    snake_case__  ,							snake_case__     :					List[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()
    snake_case__     :					Union[str, Any]    =  True
    snake_case__     :					List[Any]    =  True

    if hasattr(__SCREAMING_SNAKE_CASE	,		"""use_cache"""       ):
     snake_case__     :					Optional[Any]    =  True

    snake_case__     :					str    =  getattr(self.model_tester	,		"""encoder_seq_length"""	,		self.model_tester.seq_length       )
    snake_case__     :					Dict    =  getattr(self.model_tester	,		"""key_length"""	,		__SCREAMING_SNAKE_CASE       )

    for model_class in self.all_model_classes:
     snake_case__     :					Tuple    =  self._prepare_for_class(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Dict    =  model_class(__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Any    =  len(model(__SCREAMING_SNAKE_CASE       )       )

     with tempfile.TemporaryDirectory() as tmpdirname:
      model.save_pretrained(__SCREAMING_SNAKE_CASE	,		saved_model=__SCREAMING_SNAKE_CASE       )
      snake_case__     :					List[str]    =  os.path.join(__SCREAMING_SNAKE_CASE	,		"""saved_model"""	,		"""1"""       )
      snake_case__     :					List[str]    =  tf.keras.models.load_model(__SCREAMING_SNAKE_CASE       )
      snake_case__     :					List[Any]    =  model(__SCREAMING_SNAKE_CASE       )

      if self.is_encoder_decoder:
       snake_case__     :					Optional[int]    =  outputs["""encoder_hidden_states"""]
       snake_case__     :					str    =  outputs["""encoder_attentions"""]
      else:
       snake_case__     :					List[str]    =  outputs["""hidden_states"""]
       snake_case__     :					Tuple    =  outputs["""attentions"""]

      self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,		__SCREAMING_SNAKE_CASE       )

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

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

      self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,		self.model_tester.num_hidden_layers       )
      self.assertListEqual(
          list(output_attentions[0].shape[-3:]       )	,		[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]	,		)

   @slow
   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Tuple    =  TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )


   def 		__UpperCamelCase						(							self       ):
    snake_case__  ,							snake_case__     :					List[str]    =  self.model_tester.prepare_config_and_inputs_for_common()
    snake_case__     :					Optional[Any]    =  True
    snake_case__     :					Dict    =  getattr(self.model_tester	,		"""decoder_seq_length"""	,		self.model_tester.seq_length       )
    snake_case__     :					Optional[Any]    =  getattr(self.model_tester	,		"""encoder_seq_length"""	,		self.model_tester.seq_length       )
    snake_case__     :					List[str]    =  getattr(self.model_tester	,		"""key_length"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Union[str, Any]    =  getattr(self.model_tester	,		"""key_length"""	,		__SCREAMING_SNAKE_CASE       )

    def check_decoder_attentions_output(__SCREAMING_SNAKE_CASE       ):
     snake_case__     :					Union[str, Any]    =  len(__SCREAMING_SNAKE_CASE       )
     self.assertEqual(out_len % 2	,		0       )
     snake_case__     :					int    =  outputs.decoder_attentions
     self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,		self.model_tester.num_hidden_layers       )
     self.assertListEqual(
         list(decoder_attentions[0].shape[-3:]       )	,		[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]	,		)

    def check_encoder_attentions_output(__SCREAMING_SNAKE_CASE       ):
     snake_case__     :					List[str]    =  [
         t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
     ]
     self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,		self.model_tester.num_hidden_layers       )
     self.assertListEqual(
         list(attentions[0].shape[-3:]       )	,		[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]	,		)

    for model_class in self.all_model_classes:
     snake_case__     :					Optional[Any]    =  True
     snake_case__     :					Dict    =  False
     snake_case__     :					Tuple    =  model_class(__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Any    =  model(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )       )
     snake_case__     :					Optional[Any]    =  len(__SCREAMING_SNAKE_CASE       )
     self.assertEqual(config.output_hidden_states	,		__SCREAMING_SNAKE_CASE       )
     check_encoder_attentions_output(__SCREAMING_SNAKE_CASE       )

     if self.is_encoder_decoder:
      snake_case__     :					str    =  model_class(__SCREAMING_SNAKE_CASE       )
      snake_case__     :					List[Any]    =  model(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )       )
      self.assertEqual(config.output_hidden_states	,		__SCREAMING_SNAKE_CASE       )
      check_decoder_attentions_output(__SCREAMING_SNAKE_CASE       )

     # Check that output attentions can also be changed via the config
     del inputs_dict["output_attentions"]
     snake_case__     :					str    =  True
     snake_case__     :					List[str]    =  model_class(__SCREAMING_SNAKE_CASE       )
     snake_case__     :					str    =  model(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )       )
     self.assertEqual(config.output_hidden_states	,		__SCREAMING_SNAKE_CASE       )
     check_encoder_attentions_output(__SCREAMING_SNAKE_CASE       )

     # Check attention is always last and order is fine
     snake_case__     :					List[Any]    =  True
     snake_case__     :					Any    =  True
     snake_case__     :					List[Any]    =  model_class(__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Union[str, Any]    =  model(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )       )

     self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)	,		len(__SCREAMING_SNAKE_CASE       )       )
     self.assertEqual(model.config.output_hidden_states	,		__SCREAMING_SNAKE_CASE       )
     check_encoder_attentions_output(__SCREAMING_SNAKE_CASE       )



@require_tf
class 					__snake_case  (					unittest.TestCase							):






   '''simple docstring'''





   @slow
   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""       )
    snake_case__     :					Any    =  tf.constant([[0, 1, 2, 3, 4, 5]]       )
    snake_case__     :					int    =  model(__SCREAMING_SNAKE_CASE       )[0]

    snake_case__     :					Any    =  [1, 6, 7_6_8]
    self.assertEqual(output.shape	,		__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Any    =  tf.constant(
        [
            [
                [-0.0347_5493, -0.468_6034, -0.3063_8832],
                [0.2263_7248, -0.2698_8646, -0.742_3424],
                [0.1032_4868, -0.4501_3508, -0.5828_0784],
            ]
        ]       )
    tf.debugging.assert_near(output[:, :3, :3]	,		__SCREAMING_SNAKE_CASE	,		atol=1e-4       )






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    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  BigBirdConfig
 __lowerCAmelCase	:  jnp.dtype	   =      jnp.floataa
 __lowerCAmelCase	:  bool	   =      True


 def   lowercase__  (       self):


   '''simple docstring'''




   super().setup()
   lowercase__	: Dict     		=   nn.Dense(5       ,	dtype=self.dtype)



 def __call__(       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[str]     		=   super().__call__(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   self.cls(outputs[2])
   return outputs[:2] + (cls_out,)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[int]	   =      FlaxBigBirdForNaturalQuestionsModule

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






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

  lowercase__	: int     		=   partial(lowercase_			,			reduction=jnp.mean )
  lowercase__	: Tuple     		=   cross_entropy(lowercase_			,			lowercase_ )
  lowercase__	: List[Any]     		=   cross_entropy(lowercase_			,			lowercase_ )
  lowercase__	: Union[str, Any]     		=   cross_entropy(lowercase_			,			lowercase_ )
  return (start_loss + end_loss + pooled_loss) / 3



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

 __lowerCAmelCase	:  int	   =      128
 __lowerCAmelCase	:  int	   =      3

 __lowerCAmelCase	:  int	   =      1
 __lowerCAmelCase	:  int	   =      5

 # tx_args
 __lowerCAmelCase	:  float	   =      3e-5
 __lowerCAmelCase	:  float	   =      0.0
 __lowerCAmelCase	:  int	   =      20_000
 __lowerCAmelCase	:  float	   =      0.0_095

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


 def   lowercase__  (       self):


   '''simple docstring'''




   os.makedirs(self.base_dir       ,	exist_ok=SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   os.path.join(self.base_dir       ,	self.save_dir)
   lowercase__	: str     		=   self.batch_size_per_device * jax.device_count()



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  int
 __lowerCAmelCase	:  int	   =      4_096  # no dynamic padding on TPUs


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.collate_fn(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   jax.tree_util.tree_map(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   return batch


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   self.fetch_inputs(features["""input_ids"""])
   lowercase__	: str     		=   {
       """input_ids""": jnp.array(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.intaa),
       """attention_mask""": jnp.array(SCREAMING_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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   [self._fetch_inputs(SCREAMING_SNAKE_CASE_) for ids in input_ids]
   return zip(*SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






  if seed is not None:
    lowercase__	: Any     		=   dataset.shuffle(seed=lowercase_ )
  for i in range(len(lowercase_ ) // batch_size ):
    lowercase__	: List[str]     		=   dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(lowercase_ )

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			**lowercase_ )		->						int:


  '''simple docstring'''






  def loss_fn(lowercase_ ):
    lowercase__	: Dict     		=   model_inputs.pop("""start_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""end_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""pooled_labels""" )

    lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=lowercase_			,			dropout_rng=lowercase_			,			train=lowercase_ )
    lowercase__		,     lowercase__		,     lowercase__	: Any     		=   outputs

    return state.loss_fn(
        lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)

  lowercase__		,     lowercase__	: Optional[int]     		=   jax.random.split(lowercase_ )
  lowercase__	: Tuple     		=   jax.value_and_grad(lowercase_ )
  lowercase__		,     lowercase__	: Optional[int]     		=   grad_fn(state.params )
  lowercase__	: Tuple     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  lowercase__	: Any     		=   jax.lax.pmean(lowercase_			,			"""batch""" )

  lowercase__	: str     		=   state.apply_gradients(grads=lowercase_ )
  return state, metrics, new_drp_rng

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			**lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Tuple     		=   model_inputs.pop("""start_labels""" )
  lowercase__	: List[str]     		=   model_inputs.pop("""end_labels""" )
  lowercase__	: int     		=   model_inputs.pop("""pooled_labels""" )

  lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=state.params			,			train=lowercase_ )
  lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   outputs

  lowercase__	: Optional[Any]     		=   state.loss_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: List[str]     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  return metrics



class    _snake_case (						train_state.TrainState				):
 __lowerCAmelCase	:  Callable	   =      struct.field(pytree_node=UpperCAmelCase_				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Args
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  wandb
 __lowerCAmelCase	:  Callable	   =      None


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: List[str]     		=   model.params
   lowercase__	: Dict     		=   TrainState.create(
       apply_fn=model.__call__       ,	params=SCREAMING_SNAKE_CASE_       ,	tx=SCREAMING_SNAKE_CASE_       ,	loss_fn=SCREAMING_SNAKE_CASE_       ,	)
   if ckpt_dir is not None:
     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: str     		=   restore_checkpoint(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
     lowercase__	: str     		=   {
         """lr""": args.lr,
         """init_lr""": args.init_lr,
         """warmup_steps""": args.warmup_steps,
         """num_train_steps""": num_train_steps,
         """weight_decay""": args.weight_decay,
     }
     lowercase__		,     lowercase__	: Any     		=   build_tx(**SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   train_state.TrainState(
         step=SCREAMING_SNAKE_CASE_       ,	apply_fn=model.__call__       ,	params=SCREAMING_SNAKE_CASE_       ,	tx=SCREAMING_SNAKE_CASE_       ,	opt_state=SCREAMING_SNAKE_CASE_       ,	)
     lowercase__	: Optional[Any]     		=   args
     lowercase__	: Union[str, Any]     		=   data_collator
     lowercase__	: str     		=   lr
     lowercase__	: Union[str, Any]     		=   params
   lowercase__	: Dict     		=   jax_utils.replicate(SCREAMING_SNAKE_CASE_)
   return state


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.args
   lowercase__	: List[str]     		=   len(SCREAMING_SNAKE_CASE_) // args.batch_size

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

         lowercase__	: Tuple     		=   self.evaluate(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
         lowercase__	: List[Any]     		=   {
             """step""": state_step.item(),
             """eval_loss""": eval_loss.item(),
             """tr_loss""": tr_loss,
             """lr""": lr.item(),
         }
         tqdm.write(str(SCREAMING_SNAKE_CASE_))
         self.logger.log(SCREAMING_SNAKE_CASE_       ,	commit=SCREAMING_SNAKE_CASE_)

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


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   get_batched_dataset(SCREAMING_SNAKE_CASE_       ,	self.args.batch_size)
   lowercase__	: Tuple     		=   len(SCREAMING_SNAKE_CASE_) // self.args.batch_size
   lowercase__	: Union[str, Any]     		=   jnp.array(0       ,	dtype=jnp.floataa)
   lowercase__	: Optional[Any]     		=   0
   for batch in tqdm(SCREAMING_SNAKE_CASE_       ,	total=SCREAMING_SNAKE_CASE_       ,	desc="""Evaluating ... """):
     lowercase__	: Tuple     		=   self.data_collator(SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   self.val_step_fn(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
     running_loss += jax_utils.unreplicate(metrics["""loss"""])
     i += 1
   return running_loss / i



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  print(F'RESTORING CHECKPOINT FROM {save_dir}'			,			end=""" ... """ )
  with open(os.path.join(lowercase_			,			"""flax_model.msgpack""" )			,			"""rb""" ) as f:
    lowercase__	: Optional[Any]     		=   from_bytes(state.params			,			f.read() )

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

  lowercase__	: Any     		=   joblib.load(os.path.join(lowercase_			,			"""args.joblib""" ) )
  lowercase__	: Optional[int]     		=   joblib.load(os.path.join(lowercase_			,			"""data_collator.joblib""" ) )

  with open(os.path.join(lowercase_			,			"""training_state.json""" )			,			"""r""" ) as f:
    lowercase__	: int     		=   json.load(lowercase_ )
  lowercase__	: Optional[Any]     		=   training_state["""step"""]

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   num_train_steps - warmup_steps
  lowercase__	: int     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=lowercase_			,			transition_steps=lowercase_ )
  lowercase__	: Optional[int]     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=1E-7			,			transition_steps=lowercase_ )
  lowercase__	: Any     		=   optax.join_schedules(schedules=[warmup_fn, decay_fn]			,			boundaries=[warmup_steps] )
  return lr



def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[int]:


  '''simple docstring'''






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

  lowercase__	: Optional[int]     		=   scheduler_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  lowercase__	: int     		=   optax.adamw(learning_rate=lowercase_			,			weight_decay=lowercase_			,			mask=lowercase_ )
  return tx, lr






