




"""simple docstring"""



import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          FocalNetBackbone,
          FocalNetForImageClassification,
          FocalNetForMaskedImageModeling,
          FocalNetModel,
      )
      from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor




class 	UpperCAmelCase_  :




    def __init__(    self						:       Optional[Any]	, snake_case_						:       Tuple	, snake_case_						:       List[Any]=13	, snake_case_						:       Tuple=32	, snake_case_						:       Dict=2	, snake_case_						:       Optional[int]=3	, snake_case_						:       Tuple=16	, snake_case_						:       Tuple=[32, 64, 128]	, snake_case_						:       List[Any]=[1, 2, 1]	, snake_case_						:       int=[2, 2, 4]	, snake_case_						:       Optional[int]=2	, snake_case_						:       Dict=2.0	, snake_case_						:       Union[str, Any]=True	, snake_case_						:       List[Any]=0.0	, snake_case_						:       Union[str, Any]=0.0	, snake_case_						:       Any=0.1	, snake_case_						:       int="gelu"	, snake_case_						:       Union[str, Any]=False	, snake_case_						:       Optional[int]=True	, snake_case_						:       List[str]=0.02	, snake_case_						:       List[str]=1e-5	, snake_case_						:       List[Any]=True	, snake_case_						:       Tuple=None	, snake_case_						:       Union[str, Any]=True	, snake_case_						:       Dict=10	, snake_case_						:       str=8	, snake_case_						:       List[Any]=["stage1", "stage2"]	, snake_case_						:       Tuple=[1, 2]	, )      ->							Union[str, Any]:




     '''simple docstring'''

     A__       =				parent
     A__       =				batch_size
     A__       =				image_size
     A__       =				patch_size
     A__       =				num_channels
     A__       =				embed_dim
     A__       =				hidden_sizes
     A__       =				depths
     A__       =				num_heads
     A__       =				window_size
     A__       =				mlp_ratio
     A__       =				qkv_bias
     A__       =				hidden_dropout_prob
     A__       =				attention_probs_dropout_prob
     A__       =				drop_path_rate
     A__       =				hidden_act
     A__       =				use_absolute_embeddings
     A__       =				patch_norm
     A__       =				layer_norm_eps
     A__       =				initializer_range
     A__       =				is_training
     A__       =				scope
     A__       =				use_labels
     A__       =				type_sequence_label_size
     A__       =				encoder_stride
     A__       =				out_features
     A__       =				out_indices




    def 							__magic_name__							(    self						:       Tuple      )      ->							Tuple:




     '''simple docstring'''

     A__       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

     A__       =				None
     if self.use_labels:
      A__       =				ids_tensor([self.batch_size]	, self.type_sequence_label_size      )

     A__       =				self.get_config()

     return config, pixel_values, labels




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




     '''simple docstring'''

     return FocalNetConfig(
         image_size=self.image_size	, patch_size=self.patch_size	, num_channels=self.num_channels	, embed_dim=self.embed_dim	, hidden_sizes=self.hidden_sizes	, depths=self.depths	, num_heads=self.num_heads	, window_size=self.window_size	, mlp_ratio=self.mlp_ratio	, qkv_bias=self.qkv_bias	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, drop_path_rate=self.drop_path_rate	, hidden_act=self.hidden_act	, use_absolute_embeddings=self.use_absolute_embeddings	, path_norm=self.patch_norm	, layer_norm_eps=self.layer_norm_eps	, initializer_range=self.initializer_range	, encoder_stride=self.encoder_stride	, out_features=self.out_features	, out_indices=self.out_indices	, )




    def 							__magic_name__							(    self						:       int	, snake_case_						:       str	, snake_case_						:       Optional[int]	, snake_case_						:       int      )      ->							List[Any]:




     '''simple docstring'''

     A__       =				FocalNetModel(config=lowercase_      )
     model.to(lowercase_      )
     model.eval()
     A__       =				model(lowercase_      )

     A__       =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
     A__       =				int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

     self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, expected_seq_len, expected_dim)      )




    def 							__magic_name__							(    self						:       Union[str, Any]	, snake_case_						:       List[str]	, snake_case_						:       Optional[Any]	, snake_case_						:       int      )      ->							Optional[int]:




     '''simple docstring'''

     A__       =				FocalNetBackbone(config=lowercase_      )
     model.to(lowercase_      )
     model.eval()
     A__       =				model(lowercase_      )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps      )	, len(config.out_features      )      )
     self.parent.assertListEqual(list(result.feature_maps[0].shape      )	, [self.batch_size, self.image_size, 8, 8]      )

     # verify channels
     self.parent.assertEqual(len(model.channels      )	, len(config.out_features      )      )
     self.parent.assertListEqual(model.channels	, config.hidden_sizes[:-1]      )

     # verify backbone works with out_features=None
     A__       =				None
     A__       =				FocalNetBackbone(config=lowercase_      )
     model.to(lowercase_      )
     model.eval()
     A__       =				model(lowercase_      )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps      )	, 1      )
     self.parent.assertListEqual(list(result.feature_maps[0].shape      )	, [self.batch_size, self.image_size * 2, 4, 4]      )

     # verify channels
     self.parent.assertEqual(len(model.channels      )	, 1      )
     self.parent.assertListEqual(model.channels	, [config.hidden_sizes[-1]]      )




    def 							__magic_name__							(    self						:       str	, snake_case_						:       List[Any]	, snake_case_						:       List[str]	, snake_case_						:       Any      )      ->							List[Any]:




     '''simple docstring'''

     A__       =				FocalNetForMaskedImageModeling(config=lowercase_      )
     model.to(lowercase_      )
     model.eval()
     A__       =				model(lowercase_      )
     self.parent.assertEqual(
         result.reconstruction.shape	, (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

     # test greyscale images
     A__       =				1
     A__       =				FocalNetForMaskedImageModeling(lowercase_      )
     model.to(lowercase_      )
     model.eval()

     A__       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
     A__       =				model(lowercase_      )
     self.parent.assertEqual(result.reconstruction.shape	, (self.batch_size, 1, self.image_size, self.image_size)      )




    def 							__magic_name__							(    self						:       int	, snake_case_						:       List[Any]	, snake_case_						:       Optional[int]	, snake_case_						:       Union[str, Any]      )      ->							Optional[int]:




     '''simple docstring'''

     A__       =				self.type_sequence_label_size
     A__       =				FocalNetForImageClassification(lowercase_      )
     model.to(lowercase_      )
     model.eval()
     A__       =				model(lowercase_	, labels=lowercase_      )
     self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)      )

     # test greyscale images
     A__       =				1
     A__       =				FocalNetForImageClassification(lowercase_      )
     model.to(lowercase_      )
     model.eval()

     A__       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
     A__       =				model(lowercase_      )
     self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)      )




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




     '''simple docstring'''

     A__       =				self.prepare_config_and_inputs()

     A__       =				config_and_inputs
     A__       =				{"""pixel_values""": pixel_values}
     return config, inputs_dict






@require_torch
class 	UpperCAmelCase_  (						_UpperCAmelCase,   _UpperCAmelCase,   unittest.TestCase							):
    lowercase__											=      (
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    lowercase__											=      (
        {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    lowercase__											=      False

    lowercase__											=      False
    lowercase__											=      False
    lowercase__											=      False
    lowercase__											=      False




    def 							__magic_name__							(    self						:       Dict      )      ->							str:




     '''simple docstring'''

     A__       =				FocalNetModelTester(self      )
     A__       =				ConfigTester(self	, config_class=lowercase_	, embed_dim=37	, has_text_modality=lowercase_      )




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




     '''simple docstring'''

     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()




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




     '''simple docstring'''

     return




    def 							__magic_name__							(    self						:       Dict      )      ->							List[str]:




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_      )




    def 							__magic_name__							(    self						:       Any      )      ->							int:




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*lowercase_      )




    def 							__magic_name__							(    self						:       Dict      )      ->							Tuple:




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_      )




    def 							__magic_name__							(    self						:       Any      )      ->							Tuple:




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_      )




    @unittest.skip(reason="FocalNet does not use inputs_embeds"      )
    def 							__magic_name__							(    self						:       Any      )      ->							int:




     '''simple docstring'''

     pass




    @unittest.skip(reason="FocalNet does not use feedforward chunking"      )
    def 							__magic_name__							(    self						:       Any      )      ->							Optional[int]:




     '''simple docstring'''

     pass




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




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
      A__       =				model_class(lowercase_      )
      self.assertIsInstance(model.get_input_embeddings()	, (nn.Module)      )
      A__       =				model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(lowercase_	, nn.Linear      )      )




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




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
      A__       =				model_class(lowercase_      )
      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]	, lowercase_      )




    def 							__magic_name__							(    self						:       Dict	, snake_case_						:       Any	, snake_case_						:       Union[str, Any]	, snake_case_						:       str	, snake_case_						:       List[str]      )      ->							Dict:




     '''simple docstring'''

     A__       =				model_class(lowercase_      )
     model.to(lowercase_      )
     model.eval()

     with torch.no_grad():
      A__       =				model(**self._prepare_for_class(lowercase_	, lowercase_      )      )

     A__       =				outputs.hidden_states

     A__       =				getattr(
         self.model_tester	, "expected_num_hidden_layers"	, len(self.model_tester.depths      ) + 1      )
     self.assertEqual(len(lowercase_      )	, lowercase_      )

     # FocalNet has a different seq_length
     A__       =				(
         config.patch_size
         if isinstance(config.patch_size	, collections.abc.Iterable      )
         else (config.patch_size, config.patch_size)
     )

     A__       =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

     self.assertListEqual(
         list(hidden_states[0].shape[-2:]      )	, [num_patches, self.model_tester.embed_dim]	, )

     A__       =				outputs.reshaped_hidden_states
     self.assertEqual(len(lowercase_      )	, lowercase_      )

     A__       =				reshaped_hidden_states[0].shape
     A__       =				(
         reshaped_hidden_states[0].view(lowercase_	, lowercase_	, height * width      ).permute(0	, 2	, 1      )
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:]      )	, [num_patches, self.model_tester.embed_dim]	, )




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




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs_for_common()

     A__       =				(
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size	, collections.abc.Iterable      )
         else (self.model_tester.image_size, self.model_tester.image_size)
     )

     for model_class in self.all_model_classes[:-1]:
      A__       =				True
      self.check_hidden_states_output(lowercase_	, lowercase_	, lowercase_	, lowercase_      )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      A__       =				True

      self.check_hidden_states_output(lowercase_	, lowercase_	, lowercase_	, lowercase_      )




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




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs_for_common()
     A__       =				3

     A__       =				(
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size	, collections.abc.Iterable      )
         else (self.model_tester.image_size, self.model_tester.image_size)
     )
     A__       =				(
         config.patch_size
         if isinstance(config.patch_size	, collections.abc.Iterable      )
         else (config.patch_size, config.patch_size)
     )

     A__       =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     A__       =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes[:-1]:
      A__       =				True
      self.check_hidden_states_output(lowercase_	, lowercase_	, lowercase_	, (padded_height, padded_width)      )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      A__       =				True
      self.check_hidden_states_output(lowercase_	, lowercase_	, lowercase_	, (padded_height, padded_width)      )




    @slow
    def 							__magic_name__							(    self						:       int      )      ->							List[Any]:




     '''simple docstring'''

     for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__       =				FocalNetModel.from_pretrained(lowercase_      )
      self.assertIsNotNone(lowercase_      )




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




     '''simple docstring'''

     A__       =				self.model_tester.prepare_config_and_inputs_for_common()

     A__       =				_config_zero_init(lowercase_      )
     for model_class in self.all_model_classes:
      A__       =				model_class(config=lowercase_      )
      for name, param in model.named_parameters():
       if "embeddings" not in name and param.requires_grad:
        self.assertIn(
            ((param.data.mean() * 1e9).round() / 1e9).item()	, [0.0, 1.0]	, msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""	, )






@require_vision
@require_torch
class 	UpperCAmelCase_  (						unittest.TestCase							):




    @cached_property
    def 							__magic_name__							(    self						:       List[str]      )      ->							List[Any]:




     '''simple docstring'''

     return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"      ) if is_vision_available() else None




    @slow
    def 							__magic_name__							(    self						:       List[str]      )      ->							Dict:




     '''simple docstring'''

     A__       =				FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"      ).to(lowercase_      )
     A__       =				self.default_image_processor

     A__       =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
     A__       =				image_processor(images=lowercase_	, return_tensors="pt"      ).to(lowercase_      )

     # forward pass
     with torch.no_grad():
      A__       =				model(**lowercase_      )

     # verify the logits
     A__       =				torch.Size((1, 1_000)      )
     self.assertEqual(outputs.logits.shape	, lowercase_      )
     A__       =				torch.tensor([0.2166, -0.4368, 0.2191]      ).to(lowercase_      )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]	, lowercase_	, atol=1e-4      )      )
     self.assertTrue(outputs.logits.argmax(dim=-1      ).item()	, 281      )






@require_torch
class 	UpperCAmelCase_  (						_UpperCAmelCase,   unittest.TestCase							):
    lowercase__											=      (FocalNetBackbone,) if is_torch_available() else ()
    lowercase__											=      FocalNetConfig

    lowercase__											=      False




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




     '''simple docstring'''

     A__       =				FocalNetModelTester(self      )






'''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     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  str    ):
										lowercase_					:			int										=       data







			def __iter__(	self				:  int    ):
										for element in self.data:
																	yield element




def 							lowerCamelCase	(      UpperCAmelCase__       :					Any=True )			->      Any:
							lowercase_					:			Optional[int]										=       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 )			->      Optional[Any]:
							if iterable:
														lowercase_					:			Dict										=       DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )
							else:
														lowercase_					:			Union[str, Any]										=       TensorDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )

							lowercase_					:			Any										=       DataLoader(UpperCAmelCase__      ,      batch_size=UpperCAmelCase__ )
							lowercase_					:			Any										=       accelerator.prepare(UpperCAmelCase__ )

							return dl




def 							lowerCamelCase	(      UpperCAmelCase__       :					Accelerator      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					List[int]      ,      UpperCAmelCase__       :					List[int]      ,      )			->      int:
							lowercase_					:			List[str]										=       create_dataloader(accelerator=UpperCAmelCase__      ,      dataset_size=UpperCAmelCase__      ,      batch_size=UpperCAmelCase__ )

							lowercase_					:			Tuple										=       [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	(      )			->      int:
							lowercase_					:			Tuple										=       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	(      )			->      Optional[int]:
							lowercase_					:			Optional[int]										=       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	(      )			->      List[str]:
							lowercase_					:			str										=       create_accelerator(even_batches=UpperCAmelCase__ )

							lowercase_					:			Dict										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Optional[Any]										=       accelerator.prepare(UpperCAmelCase__ )

							lowercase_					:			List[Any]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							lowercase_					:			Optional[Any]										=       []
							with accelerator.join_uneven_inputs([ddp_model] ):
														for batch_idx, batch in enumerate(UpperCAmelCase__ ):
																					lowercase_					:			Any										=       ddp_model(batch[0].float() )
																					lowercase_					:			List[str]										=       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__       :					List[str] )			->      List[str]:
							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	(      )			->      Any:
							lowercase_					:			str										=       True
							lowercase_					:			Tuple										=       False
							lowercase_					:			str										=       create_accelerator(even_batches=UpperCAmelCase__ )
							lowercase_					:			Union[str, Any]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Any										=       accelerator.prepare(UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )
							lowercase_					:			List[Any]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							with accelerator.join_uneven_inputs([ddp_model]      ,      even_batches=UpperCAmelCase__ ):
														lowercase_					:			Union[str, Any]										=       train_dl.batch_sampler.even_batches
														lowercase_					:			List[str]										=       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	(      )			->      Dict:
							lowercase_					:			str										=       True
							lowercase_					:			Optional[Any]										=       False
							lowercase_					:			Union[str, Any]										=       create_accelerator(even_batches=UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Optional[int]										=       accelerator.prepare(UpperCAmelCase__ )
							create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1      ,      iterable=UpperCAmelCase__ )
							lowercase_					:			List[str]										=       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__ ):
																												lowercase_					:			Optional[Any]										=       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[Any]:
							lowercase_					:			Optional[Any]										=       create_accelerator()
							lowercase_					:			Optional[int]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			List[Any]										=       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	(      )			->      List[str]:
							lowercase_					:			List[Any]										=       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""" )
							lowercase_					:			List[Any]										=       accelerator.state.distributed_type
							lowercase_					:			Union[str, Any]										=       DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase__ )
							lowercase_					:			str										=       original_state


if __name__ == "__main__":
						main()





