


"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a_     :   Union[str, Any]       							=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_     :   Optional[Any]       							=    []
for i in range(6):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
		    )
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
		    )
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
		)

		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	List[str]:
				_A					  =			state_dict.pop(__lowercase   )
				_A					  =			val






def      a__ (				__lowercase   )	->	List[str]:
				_A					  =			OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_A					  =			key.replace("backbone.0.body"     ,			"backbone.conv_encoder.model"   )
												_A					  =			value
								else:
												_A					  =			value

				return new_state_dict






def      a__ (				__lowercase     ,			__lowercase=False   )	->	Any:
				_A					  =			""
				if is_panoptic:
								_A					  =			"conditional_detr."

				# first: transformer encoder
				for i in range(6   ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								_A					  =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
								_A					  =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
								# next, add query, keys and values (in that order) to the state dict
								_A					  =			in_proj_weight[:256, :]
								_A					  =			in_proj_bias[:256]
								_A					  =			in_proj_weight[256:512, :]
								_A					  =			in_proj_bias[256:512]
								_A					  =			in_proj_weight[-256:, :]
								_A					  =			in_proj_bias[-256:]






def      a__ (				)	->	int:
				_A					  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_A					  =			Image.open(requests.get(__lowercase     ,			stream=__lowercase   ).raw   )

				return im






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase   )	->	Any:
				_A					  =			ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								_A					  =			"resnet101"
				if "dc5" in model_name:
								_A					  =			True
				_A					  =			"panoptic" in model_name
				if is_panoptic:
								_A					  =			250
				else:
								_A					  =			91
								_A					  =			"huggingface/label-files"
								_A					  =			"coco-detection-id2label.json"
								_A					  =			json.load(open(hf_hub_download(__lowercase     ,			__lowercase     ,			repo_type="dataset"   )     ,			"r"   )   )
								_A					  =			{int(__lowercase   ): v for k, v in idalabel.items()}
								_A					  =			idalabel
								_A					  =			{v: k for k, v in idalabel.items()}

				# load image processor
				_A					  =			"coco_panoptic" if is_panoptic else "coco_detection"
				_A					  =			ConditionalDetrImageProcessor(format=__lowercase   )

				# prepare image
				_A					  =			prepare_img()
				_A					  =			image_processor(images=__lowercase     ,			return_tensors="pt"   )
				_A					  =			encoding["pixel_values"]

				logger.info(f"""Converting model {model_name}..."""   )

				# load original model from torch hub
				_A					  =			torch.hub.load("DeppMeng/ConditionalDETR"     ,			__lowercase     ,			pretrained=__lowercase   ).eval()
				_A					  =			conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												_A					  =			"conditional_detr." + src
								rename_key(__lowercase     ,			__lowercase     ,			__lowercase   )
				_A					  =			rename_backbone_keys(__lowercase   )
				# query, key and value matrices need special treatment
				read_in_q_k_v(__lowercase     ,			is_panoptic=__lowercase   )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				_A					  =			"conditional_detr.model." if is_panoptic else "model."
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith("conditional_detr"   )
												    and not key.startswith("class_labels_classifier"   )
												    and not key.startswith("bbox_predictor"   )
												):
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
												elif key.startswith("bbox_attention"   ) or key.startswith("mask_head"   ):
																continue
												else:
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
								else:
												if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
    # finally, create HuggingFace model and load state dict
				_A					  =			ConditionalDetrForSegmentation(__lowercase   ) if is_panoptic else ConditionalDetrForObjectDetection(__lowercase   )
				model.load_state_dict(__lowercase   )
				model.eval()
				model.push_to_hub(repo_id=__lowercase     ,			organization="DepuMeng"     ,			commit_message="Add model"   )
				# verify our conversion
				_A					  =			conditional_detr(__lowercase   )
				_A					  =			model(__lowercase   )
				assert torch.allclose(outputs.logits     ,			original_outputs["pred_logits"]     ,			atol=1E-4   )
				assert torch.allclose(outputs.pred_boxes     ,			original_outputs["pred_boxes"]     ,			atol=1E-4   )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks     ,			original_outputs["pred_masks"]     ,			atol=1E-4   )

				# Save model and image processor
				logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
				Path(__lowercase   ).mkdir(exist_ok=__lowercase   )
				model.save_pretrained(__lowercase   )
				image_processor.save_pretrained(__lowercase   )


if __name__ == "__main__":
		a_     :   int       							=    argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    default="conditional_detr_resnet50",
		    type=str,
		    help="Name of the CONDITIONAL_DETR model you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		a_     :   Tuple       							=    parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""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 snake_case      (  _UpperCamelCase):







		def __init__(					self      :       List[Any]    ,   a__      :       Any		)       ->						Any:





						'''simple docstring'''







						_A					  =			data







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





						'''simple docstring'''







						for element in self.data:
										yield element






def      a__ (				__lowercase=True   )	->	Tuple:
				_A					  =			Accelerator(even_batches=__lowercase   )
				assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
				return accelerator






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = False   )	->	Union[str, Any]:
				if iterable:
								_A					  =			DummyIterableDataset(torch.as_tensor(range(__lowercase   )   )   )
				else:
								_A					  =			TensorDataset(torch.as_tensor(range(__lowercase   )   )   )

				_A					  =			DataLoader(__lowercase     ,			batch_size=__lowercase   )
				_A					  =			accelerator.prepare(__lowercase   )

				return dl






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			)	->	Dict:
				_A					  =			create_dataloader(accelerator=__lowercase     ,			dataset_size=__lowercase     ,			batch_size=__lowercase   )

				_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      a__ (				)	->	List[str]:
				_A					  =			create_accelerator()

				# without padding, we would expect a different number of batches
				verify_dataloader_batch_sizes(
				    __lowercase     ,			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(
				    __lowercase     ,			dataset_size=7     ,			batch_size=2     ,			process_0_expected_batch_sizes=[2, 2]     ,			process_1_expected_batch_sizes=[2, 2]     ,			)






def      a__ (				)	->	List[Any]:
				_A					  =			create_accelerator(even_batches=__lowercase   )

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

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






def      a__ (				)	->	int:
				_A					  =			create_accelerator(even_batches=__lowercase   )

				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )

				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

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

				accelerator.wait_for_everyone()

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






def      a__ (				__lowercase   )	->	List[str]:
				with warnings.catch_warnings(record=__lowercase   ) as w:
								with accelerator.join_uneven_inputs([Mock()]   ):
												pass

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






def      a__ (				)	->	Tuple:
				_A					  =			True
				_A					  =			False
				_A					  =			create_accelerator(even_batches=__lowercase   )
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
								_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      a__ (				)	->	int:
				_A					  =			True
				_A					  =			False
				_A					  =			create_accelerator(even_batches=__lowercase   )
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1     ,			iterable=__lowercase   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				with warnings.catch_warnings():
								warnings.filterwarnings("ignore"   )
								try:
												with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
																_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      a__ (				)	->	Optional[Any]:
				_A					  =			create_accelerator()
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1     ,			iterable=__lowercase   )

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

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






def      a__ (				)	->	Optional[Any]:
				_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(__lowercase   )
				_A					  =			original_state


if __name__ == "__main__":
		main()