






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

     if is_vision_available():
          from transformers import MaskaFormerImageProcessor

if is_vision_available():
     from PIL import Image




class    lowercase_    :






      def __init__(    self     ,     a_     ,     a_=2     ,     a_=True     ,     a_=False     ,     a_=1_0     ,     a_=3     ,     a_=3_2 * 8     ,     a_=3_2 * 8     ,     a_=4     ,     a_=6_4     ,     )	->Tuple:







        '''simple docstring'''


        _a							=	parent
        _a							=	batch_size
        _a							=	is_training
        _a							=	use_auxiliary_loss
        _a							=	num_queries
        _a							=	num_channels
        _a							=	min_size
        _a							=	max_size
        _a							=	num_labels
        _a							=	hidden_dim
        _a							=	hidden_dim






      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        _a							=	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
            a_	)

        _a							=	torch.ones([self.batch_size, self.min_size, self.max_size]     ,     device=a_	)

        _a							=	(
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,     device=a_	) > 0.5
        ).float()
        _a							=	(torch.rand((self.batch_size, self.num_labels)     ,     device=a_	) > 0.5).long()

        _a							=	self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	MaskaFormerConfig(
            hidden_size=self.hidden_dim     ,     )
        _a							=	self.num_queries
        _a							=	self.num_labels

        _a							=	[1, 1, 1, 1]
        _a							=	self.num_channels

        _a							=	6_4
        _a							=	1_2_8
        _a							=	self.hidden_dim
        _a							=	self.hidden_dim
        _a							=	self.hidden_dim
        return config






      def 			lowerCamelCase__    (    self	)	->List[str]:







        '''simple docstring'''


        _a			,	_a			,	_a			,	_a			,	_a							=	self.prepare_config_and_inputs()
        _a							=	{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
        return config, inputs_dict






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->int:







        '''simple docstring'''


        _a							=	output.encoder_hidden_states
        _a							=	output.pixel_decoder_hidden_states
        _a							=	output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(a_	)     ,     len(config.backbone_config.depths	)	)
        self.parent.assertTrue(len(a_	)     ,     len(config.backbone_config.depths	)	)
        self.parent.assertTrue(len(a_	)     ,     config.decoder_layers	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_     ,     a_=False	)	->Optional[Any]:







        '''simple docstring'''


        with torch.no_grad():
          _a							=	MaskaFormerModel(config=a_	)
          model.to(a_	)
          model.eval()

          _a							=	model(pixel_values=a_     ,     pixel_mask=a_	)
          _a							=	model(a_     ,     output_hidden_states=a_	)

        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape     ,     (self.batch_size, self.num_queries, self.hidden_dim)     ,     )
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None	)
        self.parent.assertTrue(output.encoder_last_hidden_state is not None	)

        if output_hidden_states:
          self.check_output_hidden_state(a_     ,     a_	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_	)	->Tuple:







        '''simple docstring'''


        _a							=	MaskaFormerForUniversalSegmentation(config=a_	)
        model.to(a_	)
        model.eval()

        def comm_check_on_output(a_	):
          # let's still check that all the required stuff is there
          self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None	)
          self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None	)
          self.parent.assertTrue(result.encoder_last_hidden_state is not None	)
          # okay, now we need to check the logits shape
          # due to the encoder compression, masks have a //4 spatial size
          self.parent.assertEqual(
              result.masks_queries_logits.shape     ,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)     ,     )
          # + 1 for null class
          self.parent.assertEqual(
              result.class_queries_logits.shape     ,     (self.batch_size, self.num_queries, self.num_labels + 1)	)

        with torch.no_grad():
          _a							=	model(pixel_values=a_     ,     pixel_mask=a_	)
          _a							=	model(a_	)

          comm_check_on_output(a_	)

          _a							=	model(
              pixel_values=a_     ,     pixel_mask=a_     ,     mask_labels=a_     ,     class_labels=a_	)

        comm_check_on_output(a_	)

        self.parent.assertTrue(result.loss is not None	)
        self.parent.assertEqual(result.loss.shape     ,     torch.Size([1]	)	)







@require_torch
class    lowercase_    (_UpperCAmelCase,    _UpperCAmelCase,    unittest.TestCase     ):
      A__	:		List[Any]       			=				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
      A__	:		Tuple       			=				{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

      A__	:		Dict       			=				False
      A__	:		List[Any]       			=				False
      A__	:		List[str]       			=				False
      A__	:		Optional[int]       			=				False






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	MaskaFormerModelTester(self	)
        _a							=	ConfigTester(self     ,     config_class=a_     ,     has_text_modality=a_	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        self.config_tester.run_common_tests()






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







        '''simple docstring'''


        _a			,	_a							=	self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(a_     ,     **a_     ,     output_hidden_states=a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*a_	)






      @unittest.skip(reason="Mask2Former does not use inputs_embeds"	)
      def 			lowerCamelCase__    (    self	)	->Optional[int]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"	)
      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Mask2Former is not a generative model"	)
      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Mask2Former does not use token embeddings"	)
      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        pass






      @require_torch_multi_gpu
      @unittest.skip(
          reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"	)
      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        pass






      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        pass






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a			,	_a							=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _a							=	model_class(a_	)
          _a							=	inspect.signature(model.forward	)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          _a							=	[*signature.parameters.keys()]

          _a							=	["pixel_values"]
          self.assertListEqual(arg_names[:1]     ,     a_	)






      @slow
      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
          _a							=	MaskaFormerModel.from_pretrained(a_	)
          self.assertIsNotNone(a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	(self.model_tester.min_size,) * 2
        _a							=	{
            "pixel_values": torch.randn((2, 3, *size)     ,     device=a_	),
            "mask_labels": torch.randn((2, 1_0, *size)     ,     device=a_	),
            "class_labels": torch.zeros(2     ,     1_0     ,     device=a_	).long(),
        }
        _a							=	self.model_tester.get_config()

        _a							=	MaskaFormerForUniversalSegmentation(a_	).to(a_	)
        _a							=	model(**a_	)
        self.assertTrue(outputs.loss is not None	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a			,	_a							=	self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(a_     ,     **a_     ,     output_hidden_states=a_	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a			,	_a							=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _a							=	model_class(a_	).to(a_	)
          _a							=	model(**a_     ,     output_attentions=a_	)
          self.assertTrue(outputs.attentions is not None	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        if not self.model_tester.is_training:
          return

        _a							=	self.all_model_classes[1]
        _a			,	_a			,	_a			,	_a			,	_a							=	self.model_tester.prepare_config_and_inputs()

        _a							=	model_class(a_	)
        model.to(a_	)
        model.train()

        _a							=	model(a_     ,     mask_labels=a_     ,     class_labels=a_	).loss
        loss.backward()






      def 			lowerCamelCase__    (    self	)	->List[str]:







        '''simple docstring'''


        _a							=	self.all_model_classes[1]
        _a			,	_a			,	_a			,	_a			,	_a							=	self.model_tester.prepare_config_and_inputs()
        _a							=	True
        _a							=	True

        _a							=	model_class(a_	).to(a_	)
        model.train()

        _a							=	model(a_     ,     mask_labels=a_     ,     class_labels=a_	)

        _a							=	outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        _a							=	outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        _a							=	outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        _a							=	outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=a_	)

        self.assertIsNotNone(encoder_hidden_states.grad	)
        self.assertIsNotNone(pixel_decoder_hidden_states.grad	)
        self.assertIsNotNone(transformer_decoder_hidden_states.grad	)
        self.assertIsNotNone(attentions.grad	)







UpperCamelCase							       =    1e-4






def        lowerCAmelCase (      )       ->							str:







  '''simple docstring'''







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




@require_vision
@slow
class    lowercase_    (unittest.TestCase     ):






      @cached_property
      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        return "facebook/mask2former-swin-small-coco-instance"






      @cached_property
      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints	) if is_vision_available() else None






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	MaskaFormerModel.from_pretrained(self.model_checkpoints	).to(a_	)
        _a							=	self.default_image_processor
        _a							=	prepare_img()
        _a							=	image_processor(a_     ,     return_tensors="pt"	).to(a_	)
        _a							=	inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0	)
        # check size
        self.assertEqual(a_     ,     (1, 3, 3_8_4, 3_8_4)	)

        with torch.no_grad():
          _a							=	model(**a_	)

        _a							=	torch.tensor(
            [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]	).to(a_	)
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,     a_     ,     atol=a_	)	)

        _a							=	torch.tensor(
            [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]	).to(a_	)
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,     a_     ,     atol=a_	)	)

        _a							=	torch.tensor(
            [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]	).to(a_	)
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,     a_     ,     atol=a_	)	)






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        _a							=	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(a_	).eval()
        _a							=	self.default_image_processor
        _a							=	prepare_img()
        _a							=	image_processor(a_     ,     return_tensors="pt"	).to(a_	)
        _a							=	inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0	)
        # check size
        self.assertEqual(a_     ,     (1, 3, 3_8_4, 3_8_4)	)

        with torch.no_grad():
          _a							=	model(**a_	)
        # masks_queries_logits
        _a							=	outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape     ,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	)
        _a							=	[
            [-8.7_839, -9.0_056, -8.8_121],
            [-7.4_104, -7.0_313, -6.5_401],
            [-6.6_105, -6.3_427, -6.4_675],
        ]
        _a							=	torch.tensor(a_	).to(a_	)
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,     a_     ,     atol=a_	)	)
        # class_queries_logits
        _a							=	outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape     ,     (1, model.config.num_queries, model.config.num_labels + 1)	)
        _a							=	torch.tensor(
            [
                [1.8_324, -8.0_835, -4.1_922],
                [0.8_450, -9.0_050, -3.6_053],
                [0.3_045, -7.7_293, -3.0_275],
            ]	).to(a_	)
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,     a_     ,     atol=a_	)	)






      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        _a							=	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(a_	).eval()
        _a							=	self.default_image_processor

        _a							=	image_processor(
            [np.zeros((3, 8_0_0, 1_3_3_3)	), np.zeros((3, 8_0_0, 1_3_3_3)	)]     ,     segmentation_maps=[np.zeros((3_8_4, 3_8_4)	).astype(np.floataa	), np.zeros((3_8_4, 3_8_4)	).astype(np.floataa	)]     ,     return_tensors="pt"     ,     )

        _a							=	inputs["pixel_values"].to(a_	)
        _a							=	[el.to(a_	) for el in inputs["mask_labels"]]
        _a							=	[el.to(a_	) for el in inputs["class_labels"]]

        with torch.no_grad():
          _a							=	model(**a_	)

        self.assertTrue(outputs.loss is not None	)










"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     a_	)	->Tuple:







        '''simple docstring'''


        _a							=	data






      def __iter__(    self	)	->Optional[int]:







        '''simple docstring'''


        for element in self.data:
          yield element







def        lowerCAmelCase (      UpperCamelCase_:							Optional[int]=True					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	Accelerator(even_batches=UpperCamelCase_					)
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator






def        lowerCAmelCase (      UpperCamelCase_:							Accelerator     , UpperCamelCase_:							int     , UpperCamelCase_:							int     , UpperCamelCase_:							bool = False					)       ->							Tuple:







  '''simple docstring'''







  if iterable:
    _a							=	DummyIterableDataset(torch.as_tensor(range(UpperCamelCase_					)					)					)
  else:
    _a							=	TensorDataset(torch.as_tensor(range(UpperCamelCase_					)					)					)

  _a							=	DataLoader(UpperCamelCase_     , batch_size=UpperCamelCase_					)
  _a							=	accelerator.prepare(UpperCamelCase_					)

  return dl






def        lowerCAmelCase (      UpperCamelCase_:							Accelerator     , UpperCamelCase_:							int     , UpperCamelCase_:							int     , UpperCamelCase_:							List[int]     , UpperCamelCase_:							List[int]     , )       ->							List[Any]:







  '''simple docstring'''







  _a							=	create_dataloader(accelerator=UpperCamelCase_     , dataset_size=UpperCamelCase_     , batch_size=UpperCamelCase_					)

  _a							=	[len(batch[0]					) for batch in dl]

  if accelerator.process_index == 0:
    assert batch_sizes == process_0_expected_batch_sizes
  elif accelerator.process_index == 1:
    assert batch_sizes == process_1_expected_batch_sizes






def        lowerCAmelCase (      )       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	create_accelerator()

  # without padding, we would expect a different number of batches
  verify_dataloader_batch_sizes(
      UpperCamelCase_     , dataset_size=3     , batch_size=1     , process_0_expected_batch_sizes=[1, 1]     , process_1_expected_batch_sizes=[1, 1]     , )

  # without padding, we would expect the same number of batches, but different sizes
  verify_dataloader_batch_sizes(
      UpperCamelCase_     , dataset_size=7     , batch_size=2     , process_0_expected_batch_sizes=[2, 2]     , process_1_expected_batch_sizes=[2, 2]     , )






def        lowerCAmelCase (      )       ->							str:







  '''simple docstring'''







  _a							=	create_accelerator(even_batches=UpperCamelCase_					)

  verify_dataloader_batch_sizes(
      UpperCamelCase_     , dataset_size=3     , batch_size=1     , process_0_expected_batch_sizes=[1, 1]     , process_1_expected_batch_sizes=[1]     , )

  verify_dataloader_batch_sizes(
      UpperCamelCase_     , dataset_size=7     , batch_size=2     , process_0_expected_batch_sizes=[2, 2]     , process_1_expected_batch_sizes=[2, 1]     , )






def        lowerCAmelCase (      )       ->							int:







  '''simple docstring'''







  _a							=	create_accelerator(even_batches=UpperCamelCase_					)

  _a							=	torch.nn.Linear(1     , 1					)
  _a							=	accelerator.prepare(UpperCamelCase_					)

  _a							=	create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1					)

  _a							=	[]
  with accelerator.join_uneven_inputs([ddp_model]					):
    for batch_idx, batch in enumerate(UpperCamelCase_					):
      _a							=	ddp_model(batch[0].float()					)
      _a							=	output.sum()
      loss.backward()
      batch_idxs.append(UpperCamelCase_					)

  accelerator.wait_for_everyone()

  if accelerator.process_index == 0:
    assert batch_idxs == [0, 1]
  elif accelerator.process_index == 1:
    assert batch_idxs == [0]






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							Tuple:







  '''simple docstring'''







  with warnings.catch_warnings(record=UpperCamelCase_					) as w:
    with accelerator.join_uneven_inputs([Mock()]					):
      pass

    assert issubclass(w[-1].category     , UpperCamelCase_					)
    assert "only supported for multi-GPU" in str(w[-1].message					)






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	True
  _a							=	False
  _a							=	create_accelerator(even_batches=UpperCamelCase_					)
  _a							=	torch.nn.Linear(1     , 1					)
  _a							=	accelerator.prepare(UpperCamelCase_					)
  _a							=	create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1					)
  _a							=	create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1					)

  with accelerator.join_uneven_inputs([ddp_model]     , even_batches=UpperCamelCase_					):
    _a							=	train_dl.batch_sampler.even_batches
    _a							=	valid_dl.batch_sampler.even_batches

  assert train_dl_overridden_value == overridden_even_batches
  assert valid_dl_overridden_value == overridden_even_batches
  assert train_dl.batch_sampler.even_batches == default_even_batches
  assert valid_dl.batch_sampler.even_batches == default_even_batches






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	True
  _a							=	False
  _a							=	create_accelerator(even_batches=UpperCamelCase_					)
  _a							=	torch.nn.Linear(1     , 1					)
  _a							=	accelerator.prepare(UpperCamelCase_					)
  create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1     , iterable=UpperCamelCase_					)
  _a							=	create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1					)

  with warnings.catch_warnings():
    warnings.filterwarnings("ignore"					)
    try:
      with accelerator.join_uneven_inputs([ddp_model]     , even_batches=UpperCamelCase_					):
        _a							=	batch_dl.batch_sampler.even_batches
    except AttributeError:
      # ensure attribute error is not raised when processing iterable dl
      raise AssertionError

  assert batch_dl_overridden_value == overridden_even_batches
  assert batch_dl.batch_sampler.even_batches == default_even_batches






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	create_accelerator()
  _a							=	torch.nn.Linear(1     , 1					)
  _a							=	accelerator.prepare(UpperCamelCase_					)
  create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1     , iterable=UpperCamelCase_					)

  with warnings.catch_warnings(record=UpperCamelCase_					) as w:
    with accelerator.join_uneven_inputs([ddp_model]     , even_batches=UpperCamelCase_					):
      pass

    assert issubclass(w[-1].category     , UpperCamelCase_					)
    assert "only supported for map-style datasets" in str(w[-1].message					)






def        lowerCAmelCase (      )       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	create_accelerator()

  accelerator.print("Test that even_batches variable ensures uniform batches across processes"					)
  test_default_ensures_even_batch_sizes()

  accelerator.print("Run tests with even_batches disabled"					)
  test_can_disable_even_batches()

  accelerator.print("Test joining uneven inputs"					)
  test_can_join_uneven_inputs()

  accelerator.print("Test overriding even_batches when joining uneven inputs"					)
  test_join_can_override_even_batches()

  accelerator.print("Test overriding even_batches for mixed dataloader types"					)
  test_join_can_override_for_mixed_type_dataloaders()

  accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"					)
  test_join_raises_warning_for_iterable_when_overriding_even_batches()

  accelerator.print("Test join with non DDP distributed raises warning"					)
  _a							=	accelerator.state.distributed_type
  _a							=	DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(UpperCamelCase_					)
  _a							=	original_state


if __name__ == "__main__":
     main()


