



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


snake_case    :						List[str]  =						False




class 							lowerCAmelCase__  (	unittest.TestCase						):







					def  _lowercase			(       self      :     Any			,		_A      :     Union[str, Any]=32):
											set_seed(0)
											A__	:							Union[str, Any]						    = UNetaDModel(sample_size=_A			,		in_channels=3			,		out_channels=3)
											A__	:							str						    = torch.optim.SGD(model.parameters()			,		lr=0.00_01)
											return model, optimizer







					@slow
					def  _lowercase			(       self      :     List[Any]):
											A__	:							Tuple						    = "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
											A__	:							Union[str, Any]						    = DDPMScheduler(
											    num_train_timesteps=1000			,		beta_start=0.00_01			,		beta_end=0.02			,		beta_schedule="linear"			,		clip_sample=_A			,		)
											A__	:							Dict						    = DDIMScheduler(
											    num_train_timesteps=1000			,		beta_start=0.00_01			,		beta_end=0.02			,		beta_schedule="linear"			,		clip_sample=_A			,		)

											assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

											# shared batches for DDPM and DDIM
											set_seed(0)
											A__	:							Tuple						    = [torch.randn((4, 3, 32, 32)).clip(-1			,		1).to(_A) for _ in range(4)]
											A__	:							Any						    = [torch.randn((4, 3, 32, 32)).to(_A) for _ in range(4)]
											A__	:							int						    = [torch.randint(0			,		1000			,		(4,)).long().to(_A) for _ in range(4)]

											# train with a DDPM scheduler
											A__    ,       A__	:							Optional[int]						    = self.get_model_optimizer(resolution=32)
											model.train().to(_A)
											for i in range(4):
																	optimizer.zero_grad()
																	A__	:							Optional[int]						    = ddpm_scheduler.add_noise(clean_images[i]			,		noise[i]			,		timesteps[i])
																	A__	:							int						    = model(_A			,		timesteps[i]).sample
																	A__	:							Optional[int]						    = torch.nn.functional.mse_loss(_A			,		noise[i])
																	loss.backward()
																	optimizer.step()
											del model, optimizer

											# recreate the model and optimizer, and retry with DDIM
											A__    ,       A__	:							Dict						    = self.get_model_optimizer(resolution=32)
											model.train().to(_A)
											for i in range(4):
																	optimizer.zero_grad()
																	A__	:							str						    = ddim_scheduler.add_noise(clean_images[i]			,		noise[i]			,		timesteps[i])
																	A__	:							Union[str, Any]						    = model(_A			,		timesteps[i]).sample
																	A__	:							Union[str, Any]						    = torch.nn.functional.mse_loss(_A			,		noise[i])
																	loss.backward()
																	optimizer.step()
											del model, optimizer

											self.assertTrue(torch.allclose(_A			,		_A			,		atol=1e-5))
											self.assertTrue(torch.allclose(_A			,		_A			,		atol=1e-5))




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    :						Optional[int]  =						logging.get_logger(__name__)

snake_case    :						Union[str, Any]  =						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Optional[Any]           =						'data2vec-text'







					def __init__(       self      :     Optional[Any]			,		_A      :     List[str]=3_0522			,		_A      :     Optional[int]=768			,		_A      :     Union[str, Any]=12			,		_A      :     str=12			,		_A      :     Optional[int]=3072			,		_A      :     Dict="gelu"			,		_A      :     List[str]=0.1			,		_A      :     Any=0.1			,		_A      :     str=512			,		_A      :     Any=2			,		_A      :     str=0.02			,		_A      :     List[str]=1e-12			,		_A      :     Optional[int]=1			,		_A      :     Any=0			,		_A      :     Any=2			,		_A      :     List[Any]="absolute"			,		_A      :     Tuple=True			,		_A      :     str=None			,		**_A      :     List[Any]			,		):
											super().__init__(pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A			,		**_A)

											A__	:							Optional[int]						    = vocab_size
											A__	:							Tuple						    = hidden_size
											A__	:							str						    = num_hidden_layers
											A__	:							Union[str, Any]						    = num_attention_heads
											A__	:							Dict						    = hidden_act
											A__	:							List[Any]						    = intermediate_size
											A__	:							int						    = hidden_dropout_prob
											A__	:							Dict						    = attention_probs_dropout_prob
											A__	:							Union[str, Any]						    = max_position_embeddings
											A__	:							Tuple						    = type_vocab_size
											A__	:							Union[str, Any]						    = initializer_range
											A__	:							str						    = layer_norm_eps
											A__	:							int						    = position_embedding_type
											A__	:							str						    = use_cache
											A__	:							int						    = classifier_dropout




class 							lowerCAmelCase__  (	UpperCamelCase						):







					@property
					def  _lowercase			(       self      :     Tuple):
											if self.task == "multiple-choice":
																	A__	:							List[str]						    = {0: "batch", 1: "choice", 2: "sequence"}
											else:
																	A__	:							List[str]						    = {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											    ])