





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							):
        super().__init__()
        self.register_modules(transformer=_UpperCamelCase				,							vae=_UpperCamelCase				,							scheduler=_UpperCamelCase					)

        # create a imagenet -> id dictionary for easier use
        _UpperCAmelCase           =   {}
        if idalabel is not None:
           for key, value in idalabel.items():
              for label in value.split(''','''					):
                 _UpperCAmelCase           =   int(_UpperCamelCase					)
           _UpperCAmelCase           =   dict(sorted(self.labels.items()					)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):

        if not isinstance(_UpperCamelCase				,							_UpperCamelCase					):
           _UpperCAmelCase           =   list(_UpperCamelCase					)

        for l in label:
           if l not in self.labels:
              raise ValueError(
                  f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''					)

        return [self.labels[l] for l in label]





     @torch.no_grad()
     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = 4.0				,							_UpperCamelCase = None				,							_UpperCamelCase = 50				,							_UpperCamelCase = "pil"				,							_UpperCamelCase = True				,							):
        _UpperCAmelCase           =   len(_UpperCamelCase					)
        _UpperCAmelCase           =   self.transformer.config.sample_size
        _UpperCAmelCase           =   self.transformer.config.in_channels

        _UpperCAmelCase           =   randn_tensor(
            shape=(batch_size, latent_channels, latent_size, latent_size)				,							generator=_UpperCamelCase				,							device=self.device				,							dtype=self.transformer.dtype				,							)
        _UpperCAmelCase           =   torch.cat([latents] * 2					) if guidance_scale > 1 else latents

        _UpperCAmelCase           =   torch.tensor(_UpperCamelCase				,							device=self.device					).reshape(-1					)
        _UpperCAmelCase           =   torch.tensor([1000] * batch_size				,							device=self.device					)
        _UpperCAmelCase           =   torch.cat([class_labels, class_null]				,							0					) if guidance_scale > 1 else class_labels

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

        for t in self.progress_bar(self.scheduler.timesteps					):
           if guidance_scale > 1:
              _UpperCAmelCase           =   latent_model_input[: len(_UpperCamelCase					) // 2]
              _UpperCAmelCase           =   torch.cat([half, half]				,							dim=0					)
           _UpperCAmelCase           =   self.scheduler.scale_model_input(_UpperCamelCase				,							_UpperCamelCase					)

           _UpperCAmelCase           =   t
           if not torch.is_tensor(_UpperCamelCase					):
              # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
              # This would be a good case for the `match` statement (Python 3.10+)
              _UpperCAmelCase           =   latent_model_input.device.type == '''mps'''
              if isinstance(_UpperCamelCase				,							_UpperCamelCase					):
                 _UpperCAmelCase           =   torch.floataa if is_mps else torch.floataa
              else:
                 _UpperCAmelCase           =   torch.intaa if is_mps else torch.intaa
              _UpperCAmelCase           =   torch.tensor([timesteps]				,							dtype=_UpperCamelCase				,							device=latent_model_input.device					)
           elif len(timesteps.shape					) == 0:
              _UpperCAmelCase           =   timesteps[None].to(latent_model_input.device					)
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           _UpperCAmelCase           =   timesteps.expand(latent_model_input.shape[0]					)
           # predict noise model_output
           _UpperCAmelCase           =   self.transformer(
               _UpperCamelCase				,							timestep=_UpperCamelCase				,							class_labels=_UpperCamelCase					).sample

           # perform guidance
           if guidance_scale > 1:
              _UpperCAmelCase       ,      _UpperCAmelCase           =   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
              _UpperCAmelCase       ,      _UpperCAmelCase           =   torch.split(_UpperCamelCase				,							len(_UpperCamelCase					) // 2				,							dim=0					)

              _UpperCAmelCase           =   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
              _UpperCAmelCase           =   torch.cat([half_eps, half_eps]				,							dim=0					)

              _UpperCAmelCase           =   torch.cat([eps, rest]				,							dim=1					)

           # learned sigma
           if self.transformer.config.out_channels // 2 == latent_channels:
              _UpperCAmelCase       ,      _UpperCAmelCase           =   torch.split(_UpperCamelCase				,							_UpperCamelCase				,							dim=1					)
           else:
              _UpperCAmelCase           =   noise_pred

           # compute previous image: x_t -> x_t-1
           _UpperCAmelCase           =   self.scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					).prev_sample

        if guidance_scale > 1:
           _UpperCAmelCase       ,      _UpperCAmelCase           =   latent_model_input.chunk(2				,							dim=0					)
        else:
           _UpperCAmelCase           =   latent_model_input

        _UpperCAmelCase           =   1 / self.vae.config.scaling_factor * latents
        _UpperCAmelCase           =   self.vae.decode(_UpperCamelCase					).sample

        _UpperCAmelCase           =   (samples / 2 + 0.5).clamp(0				,							1					)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        _UpperCAmelCase           =   samples.cpu().permute(0				,							2				,							3				,							1					).float().numpy()

        if output_type == "pil":
           _UpperCAmelCase           =   self.numpy_to_pil(_UpperCamelCase					)

        if not return_dict:
           return (samples,)

        return ImagePipelineOutput(images=_UpperCamelCase					)

from collections import OrderedDict
from typing import Mapping

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


__a         =   logging.get_logger(__name__)

__a         =   {
    '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 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[Any]       	=      'xlm'
	a     :Dict       	=      {
	    'hidden_size': 'emb_dim',
	    'num_attention_heads': 'n_heads',
	    'num_hidden_layers': 'n_layers',
	    'n_words': 'vocab_size',  # For backward compatibility
	}
	def __init__(			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict=3_0_1_4_5			,				SCREAMING_SNAKE_CASE_    :     Dict=2_0_4_8			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_6			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     str=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Dict=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=2_0_4_8**-0.5			,				SCREAMING_SNAKE_CASE_    :     Dict=1e-12			,				SCREAMING_SNAKE_CASE_    :     Any=0.02			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2			,				SCREAMING_SNAKE_CASE_    :     int=3			,				SCREAMING_SNAKE_CASE_    :     Dict=5			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]="first"			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				SCREAMING_SNAKE_CASE_    :     str=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=5			,				SCREAMING_SNAKE_CASE_    :     List[str]=5			,				SCREAMING_SNAKE_CASE_    :     Any=0			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0			,				**SCREAMING_SNAKE_CASE_    :     Dict			,				)			->	List[Any]:
					lowercase_	    =   vocab_size
					lowercase_	    =   emb_dim
					lowercase_	    =   n_layers
					lowercase_	    =   n_heads
					lowercase_	    =   dropout
					lowercase_	    =   attention_dropout
					lowercase_	    =   gelu_activation
					lowercase_	    =   sinusoidal_embeddings
					lowercase_	    =   causal
					lowercase_	    =   asm
					lowercase_	    =   n_langs
					lowercase_	    =   use_lang_emb
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   bos_index
					lowercase_	    =   eos_index
					lowercase_	    =   pad_index
					lowercase_	    =   unk_index
					lowercase_	    =   mask_index
					lowercase_	    =   is_encoder
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   embed_init_std
					lowercase_	    =   init_std
					lowercase_	    =   summary_type
					lowercase_	    =   summary_use_proj
					lowercase_	    =   summary_activation
					lowercase_	    =   summary_proj_to_labels
					lowercase_	    =   summary_first_dropout
					lowercase_	    =   start_n_top
					lowercase_	    =   end_n_top
					lowercase_	    =   mask_token_id
					lowercase_	    =   lang_id

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

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

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




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




