


"""simple docstring"""
import copy
import unittest

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

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


if is_torch_available():
 import torch

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

if is_vision_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=99      ,   SCREAMING_SNAKE_CASE=36      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=37      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=16      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=6      ,   SCREAMING_SNAKE_CASE=6      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=1000      ,   )  ->  Tuple:
      _lowerCamelCase	:					Dict   =		parent
      _lowerCamelCase	:					Union[str, Any]   =		batch_size
      _lowerCamelCase	:					Dict   =		num_channels
      _lowerCamelCase	:					int   =		image_size
      _lowerCamelCase	:					Optional[Any]   =		patch_size
      _lowerCamelCase	:					str   =		text_seq_length
      _lowerCamelCase	:					int   =		is_training
      _lowerCamelCase	:					Optional[Any]   =		use_input_mask
      _lowerCamelCase	:					List[str]   =		use_token_type_ids
      _lowerCamelCase	:					Dict   =		use_labels
      _lowerCamelCase	:					Dict   =		vocab_size
      _lowerCamelCase	:					Optional[int]   =		hidden_size
      _lowerCamelCase	:					List[Any]   =		num_hidden_layers
      _lowerCamelCase	:					Optional[int]   =		num_attention_heads
      _lowerCamelCase	:					List[Any]   =		intermediate_size
      _lowerCamelCase	:					Tuple   =		hidden_act
      _lowerCamelCase	:					int   =		hidden_dropout_prob
      _lowerCamelCase	:					Tuple   =		attention_probs_dropout_prob
      _lowerCamelCase	:					List[str]   =		max_position_embeddings
      _lowerCamelCase	:					Optional[int]   =		type_vocab_size
      _lowerCamelCase	:					int   =		type_sequence_label_size
      _lowerCamelCase	:					str   =		initializer_range
      _lowerCamelCase	:					Optional[Any]   =		coordinate_size
      _lowerCamelCase	:					Dict   =		shape_size
      _lowerCamelCase	:					Tuple   =		num_labels
      _lowerCamelCase	:					Union[str, Any]   =		num_choices
      _lowerCamelCase	:					str   =		scope
      _lowerCamelCase	:					int   =		range_bbox

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





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					List[Any]   =		ids_tensor([self.batch_size, self.text_seq_length]      ,   self.vocab_size)

      _lowerCamelCase	:					Union[str, Any]   =		ids_tensor([self.batch_size, self.text_seq_length, 4]      ,   self.range_bbox)
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]):
         for j in range(bbox.shape[1]):
            if bbox[i, j, 3] < bbox[i, j, 1]:
               _lowerCamelCase	:					Optional[int]   =		bbox[i, j, 3]
               _lowerCamelCase	:					Tuple   =		bbox[i, j, 1]
               _lowerCamelCase	:					Optional[Any]   =		t
            if bbox[i, j, 2] < bbox[i, j, 0]:
               _lowerCamelCase	:					Any   =		bbox[i, j, 2]
               _lowerCamelCase	:					str   =		bbox[i, j, 0]
               _lowerCamelCase	:					Dict   =		t

      _lowerCamelCase	:					Optional[Any]   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      _lowerCamelCase	:					Any   =		None
      if self.use_input_mask:
         _lowerCamelCase	:					Tuple   =		random_attention_mask([self.batch_size, self.text_seq_length])

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

      _lowerCamelCase	:					Tuple   =		None
      _lowerCamelCase	:					Optional[int]   =		None
      if self.use_labels:
         _lowerCamelCase	:					Optional[int]   =		ids_tensor([self.batch_size]      ,   self.type_sequence_label_size)
         _lowerCamelCase	:					List[str]   =		ids_tensor([self.batch_size, self.text_seq_length]      ,   self.num_labels)

      _lowerCamelCase	:					Optional[int]   =		LayoutLMvaConfig(
          vocab_size=self.vocab_size      ,   hidden_size=self.hidden_size      ,   num_hidden_layers=self.num_hidden_layers      ,   num_attention_heads=self.num_attention_heads      ,   intermediate_size=self.intermediate_size      ,   hidden_act=self.hidden_act      ,   hidden_dropout_prob=self.hidden_dropout_prob      ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,   max_position_embeddings=self.max_position_embeddings      ,   type_vocab_size=self.type_vocab_size      ,   initializer_range=self.initializer_range      ,   coordinate_size=self.coordinate_size      ,   shape_size=self.shape_size      ,   input_size=self.image_size      ,   patch_size=self.patch_size      ,   )

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					Tuple   =		LayoutLMvaModel(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()

      # text + image
      _lowerCamelCase	:					List[str]   =		model(SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		model(
          SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		model(SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		model(SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE)

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

      # text only
      _lowerCamelCase	:					Tuple   =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,   (self.batch_size, self.text_seq_length, self.hidden_size))

      # image only
      _lowerCamelCase	:					List[str]   =		model(pixel_values=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,   (self.batch_size, self.image_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      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      _lowerCamelCase	:					int   =		self.num_labels
      _lowerCamelCase	:					List[str]   =		LayoutLMvaForSequenceClassification(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Tuple   =		model(
          SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   labels=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      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					int   =		self.num_labels
      _lowerCamelCase	:					int   =		LayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Dict   =		model(
          SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE      ,   )
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.text_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      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					List[str]   =		LayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Optional[Any]   =		model(
          SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   start_positions=SCREAMING_SNAKE_CASE      ,   end_positions=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)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		self.prepare_config_and_inputs()
      (
          (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    
      )	:					Any   =		config_and_inputs
      _lowerCamelCase	:					Optional[int]   =		{
          """input_ids""": input_ids,
          """bbox""": bbox,
          """pixel_values""": pixel_values,
          """token_type_ids""": token_type_ids,
          """attention_mask""": input_mask,
      }
      return config, inputs_dict







@require_torch
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False

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





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





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Optional[int]   =		LayoutLMvaModelTester(self)
      _lowerCamelCase	:					Optional[Any]   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   hidden_size=37)





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

      return inputs_dict





   def 		UpperCamelCase_  (							self)  ->  str:
      self.config_tester.run_common_tests()





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					List[str]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Dict   =		self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowerCamelCase	:					Dict   =		type
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Any   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					List[str]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					Any   =		LayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)







def 				_snake_case      (   ):




   """simple docstring"""







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





@require_torch
class 	lowercase__				(					unittest.TestCase				):





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  int:
      return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE) if is_vision_available() else None





   @slow
   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					str   =		LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""").to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		self.default_image_processor
      _lowerCamelCase	:					List[Any]   =		prepare_img()
      _lowerCamelCase	:					List[str]   =		image_processor(images=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values.to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		torch.tensor([[1, 2]])
      _lowerCamelCase	:					Optional[Any]   =		torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)

      # forward pass
      _lowerCamelCase	:					Tuple   =		model(
          input_ids=input_ids.to(SCREAMING_SNAKE_CASE)      ,   bbox=bbox.to(SCREAMING_SNAKE_CASE)      ,   pixel_values=pixel_values.to(SCREAMING_SNAKE_CASE)      ,   )

      # verify the logits
      _lowerCamelCase	:					Union[str, Any]   =		torch.Size((1, 199, 768))
      self.assertEqual(outputs.last_hidden_state.shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		torch.tensor(
          [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]).to(SCREAMING_SNAKE_CASE)

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









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()