
'''simple docstring'''




import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class  A				(							unittest.TestCase						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   str					=						UNetaDModel(
             block_out_channels=(32, 64)						,			layers_per_block=2						,			sample_size=32						,			in_channels=3						,			out_channels=3						,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')						,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')						,			)
         return model





     @property
     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   Any					=						VQModel(
             block_out_channels=[32, 64]						,			in_channels=3						,			out_channels=3						,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,			latent_channels=3						,			)
         return model





     @property
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   List[str]					=						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=1000						,			)
         return CLIPTextModel(SCREAMING_SNAKE_CASE	)





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






         """simple docstring"""






         A       :   Tuple					=						self.dummy_uncond_unet
         A       :   List[str]					=						DDIMScheduler()
         A       :   List[str]					=						self.dummy_vq_model

         A       :   str					=						LDMPipeline(unet=SCREAMING_SNAKE_CASE						,			vqvae=SCREAMING_SNAKE_CASE						,			scheduler=SCREAMING_SNAKE_CASE	)
         ldm.to(SCREAMING_SNAKE_CASE	)
         ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

         A       :   Any					=						torch.manual_seed(0	)
         A       :   int					=						ldm(generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''numpy'''	).images

         A       :   str					=						torch.manual_seed(0	)
         A       :   Optional[Any]					=						ldm(generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''numpy'''						,			return_dict=SCREAMING_SNAKE_CASE	)[0]

         A       :   List[str]					=						image[0, -3:, -3:, -1]
         A       :   Dict					=						image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A       :   Dict					=						np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]	)
         A       :   List[Any]					=						1e-2 if torch_device != '''mps''' else 3e-2

         assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance





@slow
@require_torch
class  A				(							unittest.TestCase						):





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






         """simple docstring"""






         A       :   List[Any]					=						LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''	)
         ldm.to(SCREAMING_SNAKE_CASE	)
         ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						torch.manual_seed(0	)
         A       :   str					=						ldm(generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=5						,			output_type='''numpy'''	).images

         A       :   str					=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         A       :   List[Any]					=						np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]	)
         A       :   Tuple					=						1e-2 if torch_device != '''mps''' else 3e-2

         assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							lowerCAmelCase_						=	length
							lowerCAmelCase_						=	rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase_						=	a * self.x + b + rng.normal(scale=0.1				,				size=(length,)     ).astype(np.floataa     )




				def __len__(					self     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      Any:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a + self.b






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			from datasets import load_dataset
			from transformers import AutoTokenizer

			lowerCAmelCase_						=	AutoTokenizer.from_pretrained("bert-base-cased"				)
			lowerCAmelCase_						=	{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			lowerCAmelCase_						=	load_dataset("csv"    ,							data_files=__lowerCAmelCase				)
			lowerCAmelCase_						=	datasets["train"].unique("label"				)

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

			def tokenize_function(__lowerCAmelCase  :  List[Any]				):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_						=	tokenizer(
						    examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=__lowerCAmelCase    ,							max_length=__lowerCAmelCase    ,							padding="max_length"				)
						if "label" in examples:
									lowerCAmelCase_						=	[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowerCAmelCase_						=	datasets.map(
			    __lowerCAmelCase    ,							batched=__lowerCAmelCase    ,							remove_columns=["sentence1", "sentence2", "label"]    ,							)

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(__lowerCAmelCase    ,							padding="max_length"    ,							max_length=128    ,							return_tensors="pt"				)
						return tokenizer.pad(__lowerCAmelCase    ,							padding="longest"    ,							return_tensors="pt"				)

			# Instantiate dataloaders.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



