from collections import OrderedDict
from typing import Mapping

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


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

SCREAMING_SNAKE_CASE		:     str         =	{
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:Tuple      =			'''xlm'''
						a__		:Optional[int]      =			{
						    '''hidden_size''': '''emb_dim''',
						    '''num_attention_heads''': '''n_heads''',
						    '''num_hidden_layers''': '''n_layers''',
						    '''n_words''': '''vocab_size''',  # For backward compatibility
						}





						def __init__(self			,     __UpperCamelCase=30_145			,     __UpperCamelCase=2_048			,     __UpperCamelCase=12			,     __UpperCamelCase=16			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=True			,     __UpperCamelCase=False			,     __UpperCamelCase=False			,     __UpperCamelCase=False			,     __UpperCamelCase=1			,     __UpperCamelCase=True			,     __UpperCamelCase=512			,     __UpperCamelCase=2_048**-0.5			,     __UpperCamelCase=1E-1_2			,     __UpperCamelCase=0.02			,     __UpperCamelCase=0			,     __UpperCamelCase=1			,     __UpperCamelCase=2			,     __UpperCamelCase=3			,     __UpperCamelCase=5			,     __UpperCamelCase=True			,     __UpperCamelCase="first"			,     __UpperCamelCase=True			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     __UpperCamelCase=0.1			,     __UpperCamelCase=5			,     __UpperCamelCase=5			,     __UpperCamelCase=0			,     __UpperCamelCase=0			,     __UpperCamelCase=2			,     __UpperCamelCase=0			,     **__UpperCamelCase			,     )				->				List[str]:
												UpperCamelCase_		:							str  =		vocab_size
												UpperCamelCase_		:							str  =		emb_dim
												UpperCamelCase_		:							Dict  =		n_layers
												UpperCamelCase_		:							List[str]  =		n_heads
												UpperCamelCase_		:							int  =		dropout
												UpperCamelCase_		:							Optional[int]  =		attention_dropout
												UpperCamelCase_		:							str  =		gelu_activation
												UpperCamelCase_		:							Optional[Any]  =		sinusoidal_embeddings
												UpperCamelCase_		:							List[Any]  =		causal
												UpperCamelCase_		:							Dict  =		asm
												UpperCamelCase_		:							int  =		n_langs
												UpperCamelCase_		:							List[str]  =		use_lang_emb
												UpperCamelCase_		:							int  =		layer_norm_eps
												UpperCamelCase_		:							Dict  =		bos_index
												UpperCamelCase_		:							List[str]  =		eos_index
												UpperCamelCase_		:							Optional[Any]  =		pad_index
												UpperCamelCase_		:							Any  =		unk_index
												UpperCamelCase_		:							List[str]  =		mask_index
												UpperCamelCase_		:							Any  =		is_encoder
												UpperCamelCase_		:							Dict  =		max_position_embeddings
												UpperCamelCase_		:							Tuple  =		embed_init_std
												UpperCamelCase_		:							Optional[int]  =		init_std
												UpperCamelCase_		:							Any  =		summary_type
												UpperCamelCase_		:							Tuple  =		summary_use_proj
												UpperCamelCase_		:							Dict  =		summary_activation
												UpperCamelCase_		:							Union[str, Any]  =		summary_proj_to_labels
												UpperCamelCase_		:							Optional[int]  =		summary_first_dropout
												UpperCamelCase_		:							Optional[Any]  =		start_n_top
												UpperCamelCase_		:							Any  =		end_n_top
												UpperCamelCase_		:							Optional[Any]  =		mask_token_id
												UpperCamelCase_		:							int  =		lang_id

												if "n_words" in kwargs:
																		UpperCamelCase_		:							Any  =		kwargs["""n_words"""]

												super().__init__(pad_token_id=__UpperCamelCase			,     bos_token_id=__UpperCamelCase			,     **__UpperCamelCase						)


class        UpperCamelCase						(				__a				):





						@property
						def        A_							(self						)				->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																		UpperCamelCase_		:							Union[str, Any]  =		{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																		UpperCamelCase_		:							Optional[Any]  =		{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												        ("""token_type_ids""", dynamic_axis),
												    ]						)







import unittest

import torch

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


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

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

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

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

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

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






