





'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		snake_case__	       =   DDIMScheduler.from_config(scheduler.config		)

		self.register_modules(unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(UpperCamelCase_		)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

		# set step values
		self.scheduler.set_timesteps(UpperCamelCase_		)

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).prev_sample

		snake_case__	       =   (image / 2 + 0.5).clamp(0      ,       1		)
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		).numpy()
		if output_type == "pil":
			snake_case__	       =   self.numpy_to_pil(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)










'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


a__					:			str          =     logging.get_logger(__name__)

a__					:			Any          =     {
    '''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 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''xlm'''
	__lowerCAmelCase       						=  {
	    '''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_=3_0145      ,       UpperCamelCase_=2048      ,       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_=2048**-0.5      ,       UpperCamelCase_=1E-1_2      ,       UpperCamelCase_=0.0_2      ,       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]:
		snake_case__	       =   vocab_size
		snake_case__	       =   emb_dim
		snake_case__	       =   n_layers
		snake_case__	       =   n_heads
		snake_case__	       =   dropout
		snake_case__	       =   attention_dropout
		snake_case__	       =   gelu_activation
		snake_case__	       =   sinusoidal_embeddings
		snake_case__	       =   causal
		snake_case__	       =   asm
		snake_case__	       =   n_langs
		snake_case__	       =   use_lang_emb
		snake_case__	       =   layer_norm_eps
		snake_case__	       =   bos_index
		snake_case__	       =   eos_index
		snake_case__	       =   pad_index
		snake_case__	       =   unk_index
		snake_case__	       =   mask_index
		snake_case__	       =   is_encoder
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   embed_init_std
		snake_case__	       =   init_std
		snake_case__	       =   summary_type
		snake_case__	       =   summary_use_proj
		snake_case__	       =   summary_activation
		snake_case__	       =   summary_proj_to_labels
		snake_case__	       =   summary_first_dropout
		snake_case__	       =   start_n_top
		snake_case__	       =   end_n_top
		snake_case__	       =   mask_token_id
		snake_case__	       =   lang_id

		if "n_words" in kwargs:
			snake_case__	       =   kwargs['n_words']

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






class 				__snake_case     (					__magic_name__							):





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



