from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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 (
       TFRoFormerForCausalLM,
       TFRoFormerForMaskedLM,
       TFRoFormerForMultipleChoice,
       TFRoFormerForQuestionAnswering,
       TFRoFormerForSequenceClassification,
       TFRoFormerForTokenClassification,
       TFRoFormerModel,
   )
   from transformers.models.roformer.modeling_tf_roformer import (
       TFRoFormerSelfAttention,
       TFRoFormerSinusoidalPositionalEmbedding,
   )





class 		_lowercase						:

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

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					parent
  UpperCamelCase_					:						List[Any] =					1_3
  UpperCamelCase_					:						Any =					7
  UpperCamelCase_					:						Union[str, Any] =					True
  UpperCamelCase_					:						Dict =					True
  UpperCamelCase_					:						List[Any] =					True
  UpperCamelCase_					:						List[str] =					True
  UpperCamelCase_					:						Any =					9_9
  UpperCamelCase_					:						Tuple =					3_2
  UpperCamelCase_					:						Optional[Any] =					2
  UpperCamelCase_					:						Tuple =					4
  UpperCamelCase_					:						Tuple =					3_7
  UpperCamelCase_					:						Optional[int] =					"""gelu"""
  UpperCamelCase_					:						List[str] =					0.1
  UpperCamelCase_					:						Tuple =					0.1
  UpperCamelCase_					:						Union[str, Any] =					5_1_2
  UpperCamelCase_					:						List[str] =					1_6
  UpperCamelCase_					:						Optional[int] =					2
  UpperCamelCase_					:						str =					0.02
  UpperCamelCase_					:						int =					3
  UpperCamelCase_					:						Optional[Any] =					4
  UpperCamelCase_					:						str =					None

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)

  UpperCamelCase_					:						Tuple =					None
  if self.use_input_mask:
   UpperCamelCase_					:						Dict =					random_attention_mask([self.batch_size, self.seq_length]			)

  UpperCamelCase_					:						Any =					None
  if self.use_token_type_ids:
   UpperCamelCase_					:						Union[str, Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size			)

  UpperCamelCase_					:						List[str] =					None
  UpperCamelCase_					:						Optional[int] =					None
  UpperCamelCase_					:						Any =					None
  if self.use_labels:
   UpperCamelCase_					:						Union[str, Any] =					ids_tensor([self.batch_size]   ,      self.type_sequence_label_size			)
   UpperCamelCase_					:						Any =					ids_tensor([self.batch_size, self.seq_length]   ,      self.num_labels			)
   UpperCamelCase_					:						str =					ids_tensor([self.batch_size]   ,      self.num_choices			)

  UpperCamelCase_					:						Any =					RoFormerConfig(
      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=snake_case   ,      )

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							str   ,      snake_case	:							Dict   ,      snake_case	:							Tuple   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[int]   ,      snake_case	:							int   ,      snake_case	:							int			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					TFRoFormerModel(config=snake_case			)
  UpperCamelCase_					:						List[str] =					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

  UpperCamelCase_					:						Tuple =					[input_ids, input_mask]
  UpperCamelCase_					:						Any =					model(snake_case			)

  UpperCamelCase_					:						Any =					model(snake_case			)

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							str   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Any   ,      snake_case	:							Any   ,      snake_case	:							Optional[int]   ,      snake_case	:							Any   ,      snake_case	:							Dict			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					True
  UpperCamelCase_					:						Dict =					TFRoFormerForCausalLM(config=snake_case			)
  UpperCamelCase_					:						Dict =					{
      """input_ids""": input_ids,
      """attention_mask""": input_mask,
      """token_type_ids""": token_type_ids,
  }
  UpperCamelCase_					:						Optional[int] =					model(snake_case			)["""logits"""]
  self.parent.assertListEqual(
      list(prediction_scores.numpy().shape			)   ,      [self.batch_size, self.seq_length, self.vocab_size]			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							List[Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Tuple   ,      snake_case	:							Optional[int]   ,      snake_case	:							int   ,      snake_case	:							Optional[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					TFRoFormerForMaskedLM(config=snake_case			)
  UpperCamelCase_					:						List[Any] =					{
      """input_ids""": input_ids,
      """attention_mask""": input_mask,
      """token_type_ids""": token_type_ids,
  }
  UpperCamelCase_					:						Dict =					model(snake_case			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Tuple   ,      snake_case	:							Optional[int]   ,      snake_case	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Any   ,      snake_case	:							Any   ,      snake_case	:							Any			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.num_labels
  UpperCamelCase_					:						Any =					TFRoFormerForSequenceClassification(config=snake_case			)
  UpperCamelCase_					:						int =					{
      """input_ids""": input_ids,
      """attention_mask""": input_mask,
      """token_type_ids""": token_type_ids,
  }

  UpperCamelCase_					:						Tuple =					model(snake_case			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							Optional[int]   ,      snake_case	:							str   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[int]   ,      snake_case	:							List[str]			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.num_choices
  UpperCamelCase_					:						Tuple =					TFRoFormerForMultipleChoice(config=snake_case			)
  UpperCamelCase_					:						Any =					tf.tile(tf.expand_dims(snake_case   ,      1			)   ,      (1, self.num_choices, 1)			)
  UpperCamelCase_					:						Union[str, Any] =					tf.tile(tf.expand_dims(snake_case   ,      1			)   ,      (1, self.num_choices, 1)			)
  UpperCamelCase_					:						Optional[Any] =					tf.tile(tf.expand_dims(snake_case   ,      1			)   ,      (1, self.num_choices, 1)			)
  UpperCamelCase_					:						Optional[Any] =					{
      """input_ids""": multiple_choice_inputs_ids,
      """attention_mask""": multiple_choice_input_mask,
      """token_type_ids""": multiple_choice_token_type_ids,
  }
  UpperCamelCase_					:						Dict =					model(snake_case			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_choices)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Optional[int]   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Dict   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							Any			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					self.num_labels
  UpperCamelCase_					:						List[str] =					TFRoFormerForTokenClassification(config=snake_case			)
  UpperCamelCase_					:						int =					{
      """input_ids""": input_ids,
      """attention_mask""": input_mask,
      """token_type_ids""": token_type_ids,
  }
  UpperCamelCase_					:						List[str] =					model(snake_case			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.num_labels)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Dict   ,      snake_case	:							Tuple   ,      snake_case	:							List[Any]   ,      snake_case	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Dict   ,      snake_case	:							int			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					TFRoFormerForQuestionAnswering(config=snake_case			)
  UpperCamelCase_					:						Optional[Any] =					{
      """input_ids""": input_ids,
      """attention_mask""": input_mask,
      """token_type_ids""": token_type_ids,
  }

  UpperCamelCase_					:						Dict =					model(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 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Any:

  """simple docstring"""





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





@require_tf
class 		_lowercase						(					snake_case_						,      snake_case_						,      unittest.TestCase       ):
 lowercase				     =   (
     (
         TFRoFormerModel,
         TFRoFormerForCausalLM,
         TFRoFormerForMaskedLM,
         TFRoFormerForQuestionAnswering,
         TFRoFormerForSequenceClassification,
         TFRoFormerForTokenClassification,
         TFRoFormerForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 lowercase				     =   (
     {
         "feature-extraction": TFRoFormerModel,
         "fill-mask": TFRoFormerForMaskedLM,
         "question-answering": TFRoFormerForQuestionAnswering,
         "text-classification": TFRoFormerForSequenceClassification,
         "text-generation": TFRoFormerForCausalLM,
         "token-classification": TFRoFormerForTokenClassification,
         "zero-shot": TFRoFormerForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 lowercase				     =   False
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							str   ,      snake_case	:							Any   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Tuple   ,      snake_case	:							Any			)   ->						List[str]:

  """simple docstring"""





  if pipeline_test_casse_name == "TextGenerationPipelineTests":
   return True

  return False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					TFRoFormerModelTester(self			)
  UpperCamelCase_					:						Optional[Any] =					ConfigTester(self   ,      config_class=snake_case   ,      hidden_size=3_7			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Optional[Any]:

  """simple docstring"""





  self.config_tester.run_common_tests()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_lm_head(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_multiple_choice(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*snake_case			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Any =					TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'			)
  self.assertIsNotNone(snake_case			)





@require_tf
class 		_lowercase						(					unittest.TestCase       ):

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Any =					TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'			)
  UpperCamelCase_					:						Optional[int] =					tf.constant([[0, 1, 2, 3, 4, 5]]			)
  UpperCamelCase_					:						Tuple =					model(snake_case			)[0]

  # TODO Replace vocab size
  UpperCamelCase_					:						int =					5_0_0_0_0

  UpperCamelCase_					:						Optional[Any] =					[1, 6, vocab_size]
  self.assertEqual(output.shape   ,      snake_case			)

  print(output[:, :3, :3]			)

  # TODO Replace values below with what was printed above.
  UpperCamelCase_					:						Any =					tf.constant(
      [
          [
              [-0.12053341, -1.0264901, 0.29221946],
              [-1.5133783, 0.197433, 0.15190607],
              [-5.0135403, -3.900256, -0.84038764],
          ]
      ]			)
  tf.debugging.assert_near(output[:, :3, :3]   ,      snake_case   ,      atol=1e-4			)





@require_tf
class 		_lowercase						(					unittest.TestCase       ):
 lowercase				     =   1E-4

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					tf.constant([[4, 1_0]]			)
  UpperCamelCase_					:						int =					TFRoFormerSinusoidalPositionalEmbedding(num_positions=6   ,      embedding_dim=6			)

  UpperCamelCase_					:						Optional[Any] =					emba(input_ids.shape			)
  UpperCamelCase_					:						Optional[int] =					tf.constant(
      [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]			)

  tf.debugging.assert_near(snake_case   ,      snake_case   ,      atol=self.tolerance			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					tf.constant(
      [
          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
          [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
          [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
      ]			)
  UpperCamelCase_					:						str =					TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2   ,      embedding_dim=5_1_2			)
  emba([2, 1_6, 5_1_2]			)
  UpperCamelCase_					:						Union[str, Any] =					emba.weight[:3, :5]

  tf.debugging.assert_near(snake_case   ,      snake_case   ,      atol=self.tolerance			)





@require_tf
class 		_lowercase						(					unittest.TestCase       ):
 lowercase				     =   1E-4

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4   ,      dtype=tf.floataa			)   ,      shape=(2, 1_2, 1_6, 6_4)			) / 1_0_0

  UpperCamelCase_					:						Tuple =					-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4   ,      dtype=tf.floataa			)   ,      shape=(2, 1_2, 1_6, 6_4)			) / 1_0_0

  UpperCamelCase_					:						Tuple =					TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2   ,      embedding_dim=6_4			)
  UpperCamelCase_					:						Union[str, Any] =					embed_positions([2, 1_6, 7_6_8]			)[None, None, :, :]

  UpperCamelCase_					:						Union[str, Any] =					TFRoFormerSelfAttention.apply_rotary_position_embeddings(
      snake_case   ,      snake_case   ,      snake_case			)

  UpperCamelCase_					:						Optional[Any] =					tf.constant(
      [
          [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
          [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
          [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
          [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
          [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
          [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
      ]			)
  UpperCamelCase_					:						Optional[Any] =					tf.constant(
      [
          [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
          [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
          [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
          [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
          [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
          [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
      ]			)

  tf.debugging.assert_near(query_layer[0, 0, :6, :8]   ,      snake_case   ,      atol=self.tolerance			)
  tf.debugging.assert_near(key_layer[0, 0, :6, :8]   ,      snake_case   ,      atol=self.tolerance			)





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		(      lowerCAmelCase     ):
    _a		:						BigBirdConfig
    _a		:						jnp.dtype=				jnp.floataa
    _a		:						bool=				True
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().setup()
           lowercase							:     List[str]       	=      nn.Dense(5		,dtype=self.dtype      )
    def __call__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      super().__call__(*snake_case		,**snake_case      )
           lowercase							:     Any       	=      self.cls(outputs[2]      )
           return outputs[:2] + (cls_out,)






class __snake_case		(      lowerCAmelCase     ):
    _a		:						List[Any]=				FlaxBigBirdForNaturalQuestionsModule







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

       lowercase							:     Optional[Any]       	=      partial(SCREAMING_SNAKE_CASE__					,	reduction=jnp.mean				)
       lowercase							:     Optional[int]       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     int       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class __snake_case		:
    _a		:						str=				"google/bigbird-roberta-base"
    _a		:						int=				3000
    _a		:						int=				1_0500

    _a		:						int=				128
    _a		:						int=				3

    _a		:						int=				1
    _a		:						int=				5

    # tx_args
    _a		:						float=				3E-5
    _a		:						float=				0.0
    _a		:						int=				2_0000
    _a		:						float=				0.00_95

    _a		:						str=				"bigbird-roberta-natural-questions"
    _a		:						str=				"training-expt"
    _a		:						str=				"data/nq-training.jsonl"
    _a		:						str=				"data/nq-validation.jsonl"
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           os.makedirs(self.base_dir		,exist_ok=snake_case      )
           lowercase							:     Optional[int]       	=      os.path.join(self.base_dir		,self.save_dir      )
           lowercase							:     Optional[int]       	=      self.batch_size_per_device * jax.device_count()






@dataclass
class __snake_case		:
    _a		:						int
    _a		:						int=				4096  # no dynamic padding on TPUs
    def __call__(  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.collate_fn(snake_case      )
           lowercase							:     Union[str, Any]       	=      jax.tree_util.tree_map(snake_case		,snake_case      )
           return batch
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     Union[str, Any]       	=      self.fetch_inputs(features["""input_ids"""]      )
           lowercase							:     Tuple       	=      {
               """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       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      [self._fetch_inputs(snake_case      ) for ids in input_ids]
           return zip(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      [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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				) ->    Any:
       if seed is not None:
              lowercase							:     Optional[int]       	=      dataset.shuffle(seed=SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				) // batch_size				):
              lowercase							:     Optional[Any]       	=      dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(SCREAMING_SNAKE_CASE__				)







@partial(jax.pmap					,	axis_name="""batch"""				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       def loss_fn(SCREAMING_SNAKE_CASE__				):
              lowercase							:     List[str]       	=      model_inputs.pop("""start_labels"""				)
              lowercase							:     Optional[int]       	=      model_inputs.pop("""end_labels"""				)
              lowercase							:     str       	=      model_inputs.pop("""pooled_labels"""				)

              lowercase							:     Union[str, Any]       	=      state.apply_fn(**SCREAMING_SNAKE_CASE__					,	params=SCREAMING_SNAKE_CASE__					,	dropout_rng=SCREAMING_SNAKE_CASE__					,	train=SCREAMING_SNAKE_CASE__				)
              lowercase	,			lowercase	,			lowercase							:     List[str]       	=      outputs

              return state.loss_fn(
                  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	)

       lowercase	,			lowercase							:     int       	=      jax.random.split(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      jax.value_and_grad(SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase							:     Union[str, Any]       	=      grad_fn(state.params				)
       lowercase							:     List[Any]       	=      jax.lax.pmean({"""loss""": loss}					,	axis_name="""batch"""				)
       lowercase							:     List[Any]       	=      jax.lax.pmean(SCREAMING_SNAKE_CASE__					,	"""batch"""				)

       lowercase							:     str       	=      state.apply_gradients(grads=SCREAMING_SNAKE_CASE__				)
       return state, metrics, new_drp_rng







@partial(jax.pmap					,	axis_name="""batch"""				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     int       	=      model_inputs.pop("""start_labels"""				)
       lowercase							:     Dict       	=      model_inputs.pop("""end_labels"""				)
       lowercase							:     Optional[Any]       	=      model_inputs.pop("""pooled_labels"""				)

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

       lowercase							:     Dict       	=      state.loss_fn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      jax.lax.pmean({"""loss""": loss}					,	axis_name="""batch"""				)
       return metrics

class __snake_case		(      train_state.TrainState     ):
    _a		:						Callable=				struct.field(pytree_node=lowerCAmelCase     )






@dataclass
class __snake_case		:
    _a		:						Args
    _a		:						Callable
    _a		:						Callable
    _a		:						Callable
    _a		:						Callable
    _a		:						wandb
    _a		:						Callable=				None
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      model.params
           lowercase							:     Any       	=      TrainState.create(
               apply_fn=model.__call__		,params=snake_case		,tx=snake_case		,loss_fn=snake_case		,)
           if ckpt_dir is not None:
                  lowercase	,			lowercase	,			lowercase	,			lowercase	,			lowercase							:     Tuple       	=      restore_checkpoint(snake_case		,snake_case      )
                  lowercase							:     List[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							:     Tuple       	=      build_tx(**snake_case      )
                  lowercase							:     str       	=      train_state.TrainState(
                      step=snake_case		,apply_fn=model.__call__		,params=snake_case		,tx=snake_case		,opt_state=snake_case		,)
                  lowercase							:     Any       	=      args
                  lowercase							:     Optional[Any]       	=      data_collator
                  lowercase							:     List[str]       	=      lr
                  lowercase							:     str       	=      params
           lowercase							:     Tuple       	=      jax_utils.replicate(snake_case      )
           return state
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.args
           lowercase							:     Optional[Any]       	=      len(snake_case      ) // args.batch_size

           lowercase							:     int       	=      jax.random.PRNGKey(0      )
           lowercase							:     List[str]       	=      jax.random.split(snake_case		,jax.device_count()      )
           for epoch in range(args.max_epochs      ):
                  lowercase							:     List[Any]       	=      jnp.array(0		,dtype=jnp.floataa      )
                  lowercase							:     List[str]       	=      get_batched_dataset(snake_case		,args.batch_size		,seed=snake_case      )
                  lowercase							:     int       	=      0
                  for batch in tqdm(snake_case		,total=snake_case		,desc=f"Running EPOCH-{epoch}"      ):
                         lowercase							:     Dict       	=      self.data_collator(snake_case      )
                         lowercase	,			lowercase	,			lowercase							:     Optional[int]       	=      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:
                                lowercase							:     Optional[Any]       	=      jax_utils.unreplicate(state.step      )
                                lowercase							:     List[str]       	=      running_loss.item() / i
                                lowercase							:     List[str]       	=      self.scheduler_fn(state_step - 1      )

                                lowercase							:     int       	=      self.evaluate(snake_case		,snake_case      )
                                lowercase							:     Tuple       	=      {
                                    """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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     str       	=      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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       print(f"RESTORING CHECKPOINT FROM {save_dir}"					,	end=""" ... """				)
       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""flax_model.msgpack"""				)					,	"""rb"""				) as f:
              lowercase							:     str       	=      from_bytes(state.params					,	f.read()				)

       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""opt_state.msgpack"""				)					,	"""rb"""				) as f:
              lowercase							:     Optional[int]       	=      from_bytes(state.opt_state					,	f.read()				)

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

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

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     List[str]       	=      num_train_steps - warmup_steps
       lowercase							:     Dict       	=      optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE__					,	end_value=SCREAMING_SNAKE_CASE__					,	transition_steps=SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE__					,	end_value=1e-7					,	transition_steps=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      optax.join_schedules(schedules=[warmup_fn, decay_fn]					,	boundaries=[warmup_steps]				)
       return lr







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

       lowercase							:     List[str]       	=      scheduler_fn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      optax.adamw(learning_rate=SCREAMING_SNAKE_CASE__					,	weight_decay=SCREAMING_SNAKE_CASE__					,	mask=SCREAMING_SNAKE_CASE__				)
       return tx, lr
