




"""simple docstring"""

import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device






def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Tuple						=						{}
	for name in unet.attn_processors.keys():
		A_					:			List[str]						=						None if name.endswith('''attn1.processor'''		) else unet.config.cross_attention_dim
		if name.startswith('''mid_block'''		):
			A_					:			str						=						unet.config.block_out_channels[-1]
		elif name.startswith('''up_blocks'''		):
			A_					:			Union[str, Any]						=						int(name[len('''up_blocks.'''		)]		)
			A_					:			int						=						list(reversed(unet.config.block_out_channels		)		)[block_id]
		elif name.startswith('''down_blocks'''		):
			A_					:			Any						=						int(name[len('''down_blocks.'''		)]		)
			A_					:			Dict						=						unet.config.block_out_channels[block_id]
		A_					:			Union[str, Any]						=						(
		    LoRAAttnProcessor2_0 if hasattr(A_					, '''scaled_dot_product_attention'''		) else LoRAAttnProcessor
		)
		A_					:			Optional[Any]						=						lora_attn_processor_class(
		    hidden_size=A_					, cross_attention_dim=A_		)
	A_					:			List[str]						=						AttnProcsLayers(A_		)
	return lora_attn_procs, unet_lora_layers






def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Any						=						{}
	A_					:			Tuple						=						(
	    LoRAAttnProcessor2_0 if hasattr(A_					, '''scaled_dot_product_attention'''		) else LoRAAttnProcessor
	)
	for name, module in text_encoder.named_modules():
		if name.endswith(A_		):
			A_					:			Union[str, Any]						=						lora_attn_processor_class(
			    hidden_size=module.out_proj.out_features					, cross_attention_dim=A_		)
	return text_lora_attn_procs






def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			List[Any]						=						create_text_encoder_lora_attn_procs(A_		)
	A_					:			Tuple						=						AttnProcsLayers(A_		)
	return text_encoder_lora_layers






def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=False		):




	"""simple docstring"""






	for _, attn_proc in text_lora_attn_procs.items():
		# set up.weights
		for layer_name, layer_module in attn_proc.named_modules():
			if layer_name.endswith('''_lora'''		):
				A_					:			Dict						=						(
				    torch.randn_like(layer_module.up.weight		)
				    if randn_weight
				    else torch.zeros_like(layer_module.up.weight		)
				)
				A_					:			Any						=						torch.nn.Parameter(A_		)

class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					torch.manual_seed(0    )
					A_					:			Dict						=						UNetaDConditionModel(
					    block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=4 ,       out_channels=4 ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,       cross_attention_dim=32 ,       )
					A_					:			Optional[Any]						=						DDIMScheduler(
					    beta_start=0.00085 ,       beta_end=0.012 ,       beta_schedule='''scaled_linear''' ,       clip_sample=UpperCamelCase__ ,       set_alpha_to_one=UpperCamelCase__ ,       steps_offset=1 ,       )
					torch.manual_seed(0    )
					A_					:			Optional[int]						=						AutoencoderKL(
					    block_out_channels=[32, 64] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,       latent_channels=4 ,       )
					A_					:			Any						=						CLIPTextConfig(
					    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       intermediate_size=37 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=10_00 ,       )
					A_					:			Union[str, Any]						=						CLIPTextModel(UpperCamelCase__    )
					A_					:			List[Any]						=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

					A_				,	A_					:			Union[str, Any]						=						create_unet_lora_layers(UpperCamelCase__    )
					A_					:			Union[str, Any]						=						create_text_encoder_lora_layers(UpperCamelCase__    )

					A_					:			Optional[Any]						=						{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''safety_checker''': None,
					    '''feature_extractor''': None,
					}
					A_					:			Tuple						=						{
					    '''unet_lora_layers''': unet_lora_layers,
					    '''text_encoder_lora_layers''': text_encoder_lora_layers,
					    '''unet_lora_attn_procs''': unet_lora_attn_procs,
					}
					return pipeline_components, lora_components







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			List[Any]						=						1
					A_					:			Tuple						=						10
					A_					:			Optional[int]						=						4
					A_					:			Optional[int]						=						(32, 32)

					A_					:			Optional[int]						=						torch.manual_seed(0    )
					A_					:			Any						=						floats_tensor((batch_size, num_channels) + sizes    )
					A_					:			Dict						=						torch.randint(1 ,       UpperCamelCase__ ,       size=(batch_size, sequence_length) ,       generator=UpperCamelCase__    )

					A_					:			Tuple						=						{
					    '''prompt''': '''A painting of a squirrel eating a burger''',
					    '''generator''': generator,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 6.0,
					    '''output_type''': '''numpy''',
					}

					return noise, input_ids, pipeline_inputs

					# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						77

					A_					:			Dict						=						torch.randint(2 ,       56 ,       size=(1, max_seq_length) ,       generator=torch.manual_seed(0    )    )

					A_					:			Optional[Any]						=						{}
					A_					:			Optional[Any]						=						inputs
					return prepared_inputs







				def    a_					(							self				:   Optional[int] ,       _lowerCamelCase				:   Any    ):



					"""simple docstring"""

					A_				,	A_					:			Optional[int]						=						self.get_dummy_components()
					LoraLoaderMixin.save_lora_weights(
					    save_directory=UpperCamelCase__ ,       unet_lora_layers=lora_components['''unet_lora_layers'''] ,       text_encoder_lora_layers=lora_components['''text_encoder_lora_layers'''] ,       )
					self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__ ,       '''pytorch_lora_weights.bin'''    )    )    )







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_				,	A_					:			Dict						=						self.get_dummy_components()
					A_					:			int						=						StableDiffusionPipeline(**UpperCamelCase__    )
					A_					:			Any						=						sd_pipe.to(UpperCamelCase__    )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

					A_				,	A_				,	A_					:			int						=						self.get_dummy_inputs()

					A_					:			List[Any]						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			Tuple						=						original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
						LoraLoaderMixin.save_lora_weights(
						    save_directory=UpperCamelCase__ ,       unet_lora_layers=lora_components['''unet_lora_layers'''] ,       text_encoder_lora_layers=lora_components['''text_encoder_lora_layers'''] ,       )
						self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__ ,       '''pytorch_lora_weights.bin'''    )    )    )
						sd_pipe.load_lora_weights(UpperCamelCase__    )

					A_					:			List[str]						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			Any						=						lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__    ) ,       torch.from_numpy(UpperCamelCase__    )    )    )







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_				,	A_					:			List[Any]						=						self.get_dummy_components()
					A_					:			List[str]						=						StableDiffusionPipeline(**UpperCamelCase__    )
					A_					:			Dict						=						sd_pipe.to(UpperCamelCase__    )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

					A_				,	A_				,	A_					:			Dict						=						self.get_dummy_inputs()

					A_					:			Any						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			Any						=						original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
						LoraLoaderMixin.save_lora_weights(
						    save_directory=UpperCamelCase__ ,       unet_lora_layers=lora_components['''unet_lora_layers'''] ,       text_encoder_lora_layers=lora_components['''text_encoder_lora_layers'''] ,       safe_serialization=UpperCamelCase__ ,       )
						self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__ ,       '''pytorch_lora_weights.safetensors'''    )    )    )
						sd_pipe.load_lora_weights(UpperCamelCase__    )

					A_					:			List[str]						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			Dict						=						lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__    ) ,       torch.from_numpy(UpperCamelCase__    )    )    )







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_				,	A_					:			str						=						self.get_dummy_components()
					A_					:			List[Any]						=						lora_components['''unet_lora_attn_procs''']
					A_					:			List[str]						=						StableDiffusionPipeline(**UpperCamelCase__    )
					A_					:			List[str]						=						sd_pipe.to(UpperCamelCase__    )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

					A_				,	A_				,	A_					:			Any						=						self.get_dummy_inputs()

					A_					:			List[Any]						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			Union[str, Any]						=						original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
						A_					:			List[str]						=						sd_pipe.unet
						unet.set_attn_processor(UpperCamelCase__    )
						unet.save_attn_procs(UpperCamelCase__    )
						self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__ ,       '''pytorch_lora_weights.bin'''    )    )    )
						sd_pipe.load_lora_weights(UpperCamelCase__    )

					A_					:			Optional[int]						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			Optional[Any]						=						lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__    ) ,       torch.from_numpy(UpperCamelCase__    )    )    )







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_				,	A_					:			Union[str, Any]						=						self.get_dummy_components()
					A_					:			List[Any]						=						StableDiffusionPipeline(**UpperCamelCase__    )

					A_					:			str						=						self.get_dummy_tokens()

					# inference without lora
					A_					:			Dict						=						pipe.text_encoder(**UpperCamelCase__    )[0]
					assert outputs_without_lora.shape == (1, 77, 32)

					# create lora_attn_procs with zeroed out up.weights
					A_					:			int						=						create_text_encoder_lora_attn_procs(pipe.text_encoder    )
					set_lora_up_weights(UpperCamelCase__ ,       randn_weight=UpperCamelCase__    )

					# monkey patch
					pipe._modify_text_encoder(UpperCamelCase__    )

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					A_					:			List[str]						=						pipe.text_encoder(**UpperCamelCase__    )[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert torch.allclose(
					    UpperCamelCase__ ,       UpperCamelCase__    ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

					# create lora_attn_procs with randn up.weights
					A_					:			Tuple						=						create_text_encoder_lora_attn_procs(pipe.text_encoder    )
					set_lora_up_weights(UpperCamelCase__ ,       randn_weight=UpperCamelCase__    )

					# monkey patch
					pipe._modify_text_encoder(UpperCamelCase__    )

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					A_					:			Optional[Any]						=						pipe.text_encoder(**UpperCamelCase__    )[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert not torch.allclose(
					    UpperCamelCase__ ,       UpperCamelCase__    ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_				,	A_					:			Any						=						self.get_dummy_components()
					A_					:			Any						=						StableDiffusionPipeline(**UpperCamelCase__    )

					A_					:			Union[str, Any]						=						self.get_dummy_tokens()

					# inference without lora
					A_					:			Optional[int]						=						pipe.text_encoder(**UpperCamelCase__    )[0]
					assert outputs_without_lora.shape == (1, 77, 32)

					# create lora_attn_procs with randn up.weights
					A_					:			Optional[Any]						=						create_text_encoder_lora_attn_procs(pipe.text_encoder    )
					set_lora_up_weights(UpperCamelCase__ ,       randn_weight=UpperCamelCase__    )

					# monkey patch
					pipe._modify_text_encoder(UpperCamelCase__    )

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					A_					:			int						=						pipe.text_encoder(**UpperCamelCase__    )[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert not torch.allclose(
					    UpperCamelCase__ ,       UpperCamelCase__    ), "lora outputs should be different to without lora outputs"

					# remove monkey patch
					pipe._remove_text_encoder_monkey_patch()

					# inference with removed lora
					A_					:			Any						=						pipe.text_encoder(**UpperCamelCase__    )[0]
					assert outputs_without_lora_removed.shape == (1, 77, 32)

					assert torch.allclose(
					    UpperCamelCase__ ,       UpperCamelCase__    ), "remove lora monkey patch should restore the original outputs"







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_				,	A_					:			Optional[Any]						=						self.get_dummy_components()
					A_					:			List[str]						=						StableDiffusionPipeline(**UpperCamelCase__    )
					A_					:			Dict						=						sd_pipe.to(UpperCamelCase__    )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

					A_				,	A_				,	A_					:			str						=						self.get_dummy_inputs()

					with tempfile.TemporaryDirectory() as tmpdirname:
						LoraLoaderMixin.save_lora_weights(
						    save_directory=UpperCamelCase__ ,       unet_lora_layers=lora_components['''unet_lora_layers'''] ,       text_encoder_lora_layers=lora_components['''text_encoder_lora_layers'''] ,       )
						self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__ ,       '''pytorch_lora_weights.bin'''    )    )    )
						sd_pipe.load_lora_weights(UpperCamelCase__    )

					A_					:			Optional[int]						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			int						=						lora_images[0, -3:, -3:, -1]

					A_					:			Optional[int]						=						sd_pipe(**UpperCamelCase__ ,       cross_attention_kwargs={'''scale''': 0.5}    ).images
					A_					:			Optional[int]						=						lora_images_with_scale[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(
					    torch.allclose(torch.from_numpy(UpperCamelCase__    ) ,       torch.from_numpy(UpperCamelCase__    )    )    )







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					with tempfile.TemporaryDirectory() as tmpdirname:
						self.create_lora_weight_file(UpperCamelCase__    )

						A_				,	A_					:			Dict						=						self.get_dummy_components()
						A_					:			str						=						StableDiffusionPipeline(**UpperCamelCase__    )
						A_					:			Union[str, Any]						=						sd_pipe.to(UpperCamelCase__    )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

						# check if vanilla attention processors are used
						for _, module in sd_pipe.unet.named_modules():
							if isinstance(UpperCamelCase__ ,       UpperCamelCase__    ):
								self.assertIsInstance(module.processor ,       (AttnProcessor, AttnProcessor2_0)    )

								  # load LoRA weight file
						sd_pipe.load_lora_weights(UpperCamelCase__    )

						# check if lora attention processors are used
						for _, module in sd_pipe.unet.named_modules():
							if isinstance(UpperCamelCase__ ,       UpperCamelCase__    ):
								A_					:			List[str]						=						(
								    LoRAAttnProcessor2_0 if hasattr(UpperCamelCase__ ,       '''scaled_dot_product_attention'''    ) else LoRAAttnProcessor
								)
								self.assertIsInstance(module.processor ,       UpperCamelCase__    )







				@unittest.skipIf(torch_device != '''cuda''' ,       '''This test is supposed to run on GPU'''    )
				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					with tempfile.TemporaryDirectory() as tmpdirname:
						self.create_lora_weight_file(UpperCamelCase__    )

						A_				,	A_					:			List[str]						=						self.get_dummy_components()
						A_					:			Tuple						=						StableDiffusionPipeline(**UpperCamelCase__    )
						A_					:			List[Any]						=						sd_pipe.to(UpperCamelCase__    )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

						# enable XFormers
						sd_pipe.enable_xformers_memory_efficient_attention()

						# check if xFormers attention processors are used
						for _, module in sd_pipe.unet.named_modules():
							if isinstance(UpperCamelCase__ ,       UpperCamelCase__    ):
								self.assertIsInstance(module.processor ,       UpperCamelCase__    )

								  # load LoRA weight file
						sd_pipe.load_lora_weights(UpperCamelCase__    )

						# check if lora attention processors are used
						for _, module in sd_pipe.unet.named_modules():
							if isinstance(UpperCamelCase__ ,       UpperCamelCase__    ):
								self.assertIsInstance(module.processor ,       UpperCamelCase__    )







				@unittest.skipIf(torch_device != '''cuda''' ,       '''This test is supposed to run on GPU'''    )
				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_				,	A_					:			Dict						=						self.get_dummy_components()
					A_					:			Union[str, Any]						=						StableDiffusionPipeline(**UpperCamelCase__    )
					A_					:			Optional[Any]						=						sd_pipe.to(UpperCamelCase__    )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

					A_				,	A_				,	A_					:			str						=						self.get_dummy_inputs()

					# enable XFormers
					sd_pipe.enable_xformers_memory_efficient_attention()

					A_					:			Union[str, Any]						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			List[Any]						=						original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
						LoraLoaderMixin.save_lora_weights(
						    save_directory=UpperCamelCase__ ,       unet_lora_layers=lora_components['''unet_lora_layers'''] ,       text_encoder_lora_layers=lora_components['''text_encoder_lora_layers'''] ,       )
						self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__ ,       '''pytorch_lora_weights.bin'''    )    )    )
						sd_pipe.load_lora_weights(UpperCamelCase__    )

					A_					:			Any						=						sd_pipe(**UpperCamelCase__    ).images
					A_					:			List[Any]						=						lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__    ) ,       torch.from_numpy(UpperCamelCase__    )    )    )











"""simple docstring"""

import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						copy.deepcopy(_UpperCAmelCase		)
	A_					:			int						=						sorted(_UpperCAmelCase		)
	A_					:			Tuple						=						[bisect.bisect_right(_UpperCAmelCase					, _UpperCAmelCase		) for y in x]
	return quantized

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=0		):




	"""simple docstring"""






	A_					:			List[Any]						=						np.arange(start=3					, stop=_UpperCAmelCase					, step=4		).tolist() if k > 0 else [10]
	A_					:			List[Any]						=						_quantize(_UpperCAmelCase					, _UpperCAmelCase		)
	# count number of elements per group
	A_					:			Tuple						=						np.unique(_UpperCAmelCase					, return_counts=_UpperCAmelCase		)[1]
	A_					:			Optional[int]						=						[0] + bins + [np.inf]
	logger.info('''Using {} as bins for aspect lengths quantization'''.format(_UpperCAmelCase		)		)
	logger.info('''Count of instances per bin: {}'''.format(_UpperCAmelCase		)		)
	return groups

class     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Dict    ):



					"""simple docstring"""

					if not isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
						raise ValueError(
						    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_lowerCamelCase    )    )
					A_					:			List[str]						=						sampler
					A_					:			str						=						group_ids
					A_					:			Any						=						batch_size







				def __iter__(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			List[str]						=						defaultdict(_lowerCamelCase    )
					A_					:			Dict						=						defaultdict(_lowerCamelCase    )

					A_					:			List[str]						=						0
					for idx in self.sampler:
						A_					:			Union[str, Any]						=						self.group_ids[idx]
						buffer_per_group[group_id].append(_lowerCamelCase    )
						samples_per_group[group_id].append(_lowerCamelCase    )
						if len(buffer_per_group[group_id]    ) == self.batch_size:
							yield buffer_per_group[group_id]  # TODO
							num_batches += 1
							del buffer_per_group[group_id]
						assert len(buffer_per_group[group_id]    ) < self.batch_size

					# now we have run out of elements that satisfy
					# the group criteria, let's return the remaining
					# elements so that the size of the sampler is
					# deterministic
					A_					:			int						=						len(self    )
					A_					:			Dict						=						expected_num_batches - num_batches
					if num_remaining > 0:
						# for the remaining batches, group the batches by similar lengths
						A_					:			Optional[Any]						=						[]
						for group_id, idxs in sorted(buffer_per_group.items() ,       key=lambda _lowerCamelCase    : x[0]    ):
							batch_idx.extend(_lowerCamelCase    )
							if len(_lowerCamelCase    ) >= self.batch_size:
								yield batch_idx[: self.batch_size]
								A_					:			str						=						batch_idx[self.batch_size :]
								num_remaining -= 1
						if len(_lowerCamelCase    ) > 0:
							yield batch_idx
							num_remaining -= 1
					assert num_remaining == 0







				def __len__(							self				:   List[Any]    ):



					"""simple docstring"""

					return (len(self.sampler    ) + self.batch_size - 1) // self.batch_size





