




"""simple docstring"""







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 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=1_3       ,					lowerCamelCase_=7       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=9_9       ,					lowerCamelCase_=3_2       ,					lowerCamelCase_=2       ,					lowerCamelCase_=4       ,					lowerCamelCase_=3_7       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=5_1_2       ,					lowerCamelCase_=1_6       ,					lowerCamelCase_=2       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=3       ,					lowerCamelCase_=4       ,					lowerCamelCase_=None       ,					)				->   int:
       UpperCamelCase              =	parent
       UpperCamelCase              =	1_3
       UpperCamelCase              =	7
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	9_9
       UpperCamelCase              =	3_2
       UpperCamelCase              =	2
       UpperCamelCase              =	4
       UpperCamelCase              =	3_7
       UpperCamelCase              =	'''gelu'''
       UpperCamelCase              =	0.1
       UpperCamelCase              =	0.1
       UpperCamelCase              =	5_1_2
       UpperCamelCase              =	1_6
       UpperCamelCase              =	2
       UpperCamelCase              =	0.02
       UpperCamelCase              =	3
       UpperCamelCase              =	4
       UpperCamelCase              =	None







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size)

       UpperCamelCase              =	None
       if self.use_input_mask:
           UpperCamelCase              =	random_attention_mask([self.batch_size, self.seq_length])

       UpperCamelCase              =	None
       if self.use_token_type_ids:
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.type_vocab_size)

       UpperCamelCase              =	None
       UpperCamelCase              =	None
       UpperCamelCase              =	None
       if self.use_labels:
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.type_sequence_label_size)
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels)
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.num_choices)

       UpperCamelCase              =	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=lowerCamelCase_       ,					)

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







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	TFRoFormerModel(config=lowerCamelCase_)
       UpperCamelCase              =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

       UpperCamelCase              =	[input_ids, input_mask]
       UpperCamelCase              =	model(lowerCamelCase_)

       UpperCamelCase              =	model(lowerCamelCase_)

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







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Tuple:
       UpperCamelCase              =	True
       UpperCamelCase              =	TFRoFormerForCausalLM(config=lowerCamelCase_)
       UpperCamelCase              =	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       UpperCamelCase              =	model(lowerCamelCase_)['''logits''']
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape)       ,					[self.batch_size, self.seq_length, self.vocab_size])







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Optional[Any]:
       UpperCamelCase              =	TFRoFormerForMaskedLM(config=lowerCamelCase_)
       UpperCamelCase              =	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Optional[Any]:
       UpperCamelCase              =	self.num_labels
       UpperCamelCase              =	TFRoFormerForSequenceClassification(config=lowerCamelCase_)
       UpperCamelCase              =	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Dict:
       UpperCamelCase              =	self.num_choices
       UpperCamelCase              =	TFRoFormerForMultipleChoice(config=lowerCamelCase_)
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	{
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
           '''token_type_ids''': multiple_choice_token_type_ids,
       }
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	self.num_labels
       UpperCamelCase              =	TFRoFormerForTokenClassification(config=lowerCamelCase_)
       UpperCamelCase              =	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   str:
       UpperCamelCase              =	TFRoFormerForQuestionAnswering(config=lowerCamelCase_)
       UpperCamelCase              =	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       UpperCamelCase              =	model(lowerCamelCase_)
       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)				->   str:
       UpperCamelCase              =	self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,				
       )              =	config_and_inputs
       UpperCamelCase              =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict

@require_tf
class 				snake_case_			(					lowerCamelCase_	, lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       (
       (
           TFRoFormerModel,
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   A_				      =       (
       {
           '''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 {}
   )

   A_				      =       False
   A_				      =       False







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Any:
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
           return True

       return False







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	TFRoFormerModelTester(self)
       UpperCamelCase              =	ConfigTester(self       ,					config_class=lowerCamelCase_       ,					hidden_size=3_7)







   def   UpperCAmelCase__			(		self)				->   Any:
       self.config_tester.run_common_tests()







   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_)







   @slow
   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''')
       self.assertIsNotNone(lowerCamelCase_)

@require_tf
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   @slow
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''')
       UpperCamelCase              =	tf.constant([[0, 1, 2, 3, 4, 5]])
       UpperCamelCase              =	model(lowerCamelCase_)[0]

       # TODO Replace vocab size
       UpperCamelCase              =	5_0_0_0_0

       UpperCamelCase              =	[1, 6, vocab_size]
       self.assertEqual(output.shape       ,					lowerCamelCase_)

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

       # TODO Replace values below with what was printed above.
       UpperCamelCase              =	tf.constant(
           [
               [
                   [-0.1205_3341, -1.026_4901, 0.2922_1946],
                   [-1.513_3783, 0.19_7433, 0.1519_0607],
                   [-5.013_5403, -3.90_0256, -0.8403_8764],
               ]
           ])
       tf.debugging.assert_near(output[:, :3, :3]       ,					lowerCamelCase_       ,					atol=1e-4)

@require_tf
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   A_				      =       1E-4







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	tf.constant([[4, 1_0]])
       UpperCamelCase              =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6       ,					embedding_dim=6)

       UpperCamelCase              =	emba(input_ids.shape)
       UpperCamelCase              =	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(lowerCamelCase_       ,					lowerCamelCase_       ,					atol=self.tolerance)







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	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              =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2       ,					embedding_dim=5_1_2)
       emba([2, 1_6, 5_1_2])
       UpperCamelCase              =	emba.weight[:3, :5]

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

@require_tf
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   A_				      =       1E-4







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	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              =	-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              =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2       ,					embedding_dim=6_4)
       UpperCamelCase              =	embed_positions([2, 1_6, 7_6_8])[None, None, :, :]

       UpperCamelCase       ,	UpperCamelCase              =	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)

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







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       for param, grad_param in zip(model_a.parameters()   ,      model_b.parameters() ):
              if not param.requires_grad:
                     continue
              if not did_step:
                     # Grads should not be in sync
                     assert (
                         torch.allclose(param.grad   ,      grad_param.grad ) is False
                     ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
              else:
                     # Grads should be in sync
                     assert (
                         torch.allclose(param.grad   ,      grad_param.grad ) is True
                     ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=True )				->						Optional[int]:
       model.train()
       _UpperCAmelCase							=    model(__snake_case )
       _UpperCAmelCase							=    F.mse_loss(__snake_case   ,      target.to(output.device ) )
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(__snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=False )				->						Optional[int]:
       set_seed(4_2 )
       _UpperCAmelCase							=    RegressionModel()
       _UpperCAmelCase							=    deepcopy(__snake_case )
       _UpperCAmelCase							=    RegressionDataset(length=8_0 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       model.to(accelerator.device )
       if sched:
              _UpperCAmelCase							=    AdamW(params=model.parameters()   ,      lr=1E-3 )
              _UpperCAmelCase							=    AdamW(params=ddp_model.parameters()   ,      lr=1E-3 )
              _UpperCAmelCase							=    LambdaLR(__snake_case   ,      lr_lambda=lambda __snake_case : epoch**0.65 )
              _UpperCAmelCase							=    LambdaLR(__snake_case   ,      lr_lambda=lambda __snake_case : epoch**0.65 )
       # Make a copy of `model`
       if sched:
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
       else:
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case )
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       # Test when on a single CPU or GPU that the context manager does nothing
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       # Use a single batch
       _UpperCAmelCase	,			_UpperCAmelCase							=    next(iter(__snake_case ) ).values()
       for iteration in range(3 ):
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(__snake_case ):
                            step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              else:
                     # Sync grads
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     assert torch.allclose(
                         param.grad   ,      ddp_param.grad ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       # Test on distributed setup that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       # Use a single batch
       _UpperCAmelCase	,			_UpperCAmelCase							=    next(iter(__snake_case ) ).values()
       for iteration in range(3 ):
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(__snake_case ):
                            step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              else:
                     # Sync grads
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     if iteration % 2 == 0:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is False
                            ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is True
                            ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]




def       _SCREAMING_SNAKE_CASE (							__snake_case=False   ,      __snake_case=False )				->						Any:
       _UpperCAmelCase							=    Accelerator(
           split_batches=__snake_case   ,      dispatch_batches=__snake_case   ,      gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       for iteration, batch in enumerate(__snake_case ):
              _UpperCAmelCase	,			_UpperCAmelCase							=    batch.values()
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(__snake_case ):
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     if ((iteration + 1) % 2 == 0) or (iteration == len(__snake_case ) - 1):
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is True
                            ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is False
                            ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]
       GradientState._reset_state()




def       _SCREAMING_SNAKE_CASE (							__snake_case=False   ,      __snake_case=False )				->						Dict:
       _UpperCAmelCase							=    Accelerator(
           split_batches=__snake_case   ,      dispatch_batches=__snake_case   ,      gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case   ,      __snake_case )
       for iteration, batch in enumerate(__snake_case ):
              _UpperCAmelCase	,			_UpperCAmelCase							=    batch.values()
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              model.train()
              ddp_model.train()
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__snake_case )):
                     if split_batches:
                            sched.step()
                     else:
                            for _ in range(accelerator.num_processes ):
                                   sched.step()
              opt.zero_grad()
              # Perform gradient accumulation under wrapper
              with accelerator.accumulate(__snake_case ):
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
                     ddp_opt.step()
                     ddp_sched.step()
                     ddp_opt.zero_grad()

              # Learning rates should be the same
              assert (
                  opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
              ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
              _UpperCAmelCase							=    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__snake_case ))
              if accelerator.num_processes > 1:
                     check_model_parameters(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
       GradientState._reset_state()




def       _SCREAMING_SNAKE_CASE (							)				->						Union[str, Any]:
       _UpperCAmelCase							=    Accelerator()

       _UpperCAmelCase							=    RegressionDataset(length=8_0 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       _UpperCAmelCase							=    RegressionDataset(length=9_6 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case )
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(__snake_case ):
              assert id(accelerator.gradient_state.active_dataloader ) == id(__snake_case )
              if iteration < len(__snake_case ) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(__snake_case ):
                                   assert id(accelerator.gradient_state.active_dataloader ) == id(__snake_case )
                                   if batch_num < len(__snake_case ) - 1:
                                          assert not accelerator.gradient_state.end_of_dataloader
                                   else:
                                          assert accelerator.gradient_state.end_of_dataloader
              else:
                     assert accelerator.gradient_state.end_of_dataloader
       assert accelerator.gradient_state.active_dataloader is None




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       _UpperCAmelCase							=    Accelerator()
       _UpperCAmelCase							=    accelerator.state
       if state.local_process_index == 0:
              print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
       test_dataloader_break()
       if state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print("""**Test NOOP `no_sync` context manager**""" )
              test_noop_sync(__snake_case )
       if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
              if state.local_process_index == 0:
                     print("""**Test Distributed `no_sync` context manager**""" )
              test_distributed_sync(__snake_case )
       if state.distributed_type == DistributedType.MULTI_GPU:
              for split_batch in [True, False]:
                     for dispatch_batches in [True, False]:
                            if state.local_process_index == 0:
                                   print(
                                       """**Test `accumulate` gradient accumulation, """   ,      f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""   ,      )
                            test_gradient_accumulation(__snake_case   ,      __snake_case )

    # Currently will break on torch 2.0 +, need to investigate why
       if is_torch_version("""<"""   ,      """2.0""" ) or state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print(
                         """**Test `accumulate` gradient accumulation with optimizer and scheduler, """   ,      """`split_batches=False`, `dispatch_batches=False`**"""   ,      )
              test_gradient_accumulation_with_opt_and_scheduler()
              if state.distributed_type == DistributedType.MULTI_GPU:
                     for split_batch in [True, False]:
                            for dispatch_batches in [True, False]:
                                   if not split_batch and not dispatch_batches:
                                          continue
                                   if state.local_process_index == 0:
                                          print(
                                              """**Test `accumulate` gradient accumulation with optimizer and scheduler, """   ,      f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""   ,      )
                                   test_gradient_accumulation_with_opt_and_scheduler(__snake_case   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
  main()