


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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_UpperCAmelCase			:  Optional[Any]     =logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__							):







						'''simple docstring'''




						@register_to_config
						def __init__(				self   ,						__lowercase   ,						__lowercase = None   ,						__lowercase = None							)					->    Tuple:
										super().__init__()

										lowerCAmelCase_ :      str    				=				learnable

										if self.learnable:
														assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
														assert length is not None, "learnable=True requires `length` to be set"

														lowerCAmelCase_ :      Dict    				=				torch.zeros(__lowercase   ,						__lowercase							)
										else:
														lowerCAmelCase_ :      str    				=				None

										lowerCAmelCase_ :      Union[str, Any]    				=				torch.nn.Parameter(__lowercase							)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  VQModel
						SCREAMING_SNAKE_CASE__      :  CLIPTextModel
						SCREAMING_SNAKE_CASE__      :  CLIPTokenizer
						SCREAMING_SNAKE_CASE__      :  TransformeraDModel
						SCREAMING_SNAKE_CASE__      :  LearnedClassifierFreeSamplingEmbeddings
						SCREAMING_SNAKE_CASE__      :  VQDiffusionScheduler




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						)					->    str:
										super().__init__()

										self.register_modules(
										    vqvae=__lowercase   ,						transformer=__lowercase   ,						text_encoder=__lowercase   ,						tokenizer=__lowercase   ,						scheduler=__lowercase   ,						learned_classifier_free_sampling_embeddings=__lowercase   ,						)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    int:
										lowerCAmelCase_ :      Any    				=				len(__lowercase							) if isinstance(__lowercase   ,						__lowercase							) else 1

										# get prompt text embeddings
										lowerCAmelCase_ :      Optional[Any]    				=				self.tokenizer(
										    __lowercase   ,						padding='''max_length'''   ,						max_length=self.tokenizer.model_max_length   ,						return_tensors='''pt'''   ,						)
										lowerCAmelCase_ :      Optional[int]    				=				text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														lowerCAmelCase_ :      Any    				=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
														logger.warning(
														    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
														    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""							)
														lowerCAmelCase_ :      Dict    				=				text_input_ids[:, : self.tokenizer.model_max_length]
										lowerCAmelCase_ :      Optional[Any]    				=				self.text_encoder(text_input_ids.to(self.device							)							)[0]

										# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
										# While CLIP does normalize the pooled output of the text transformer when combining
										# the image and text embeddings, CLIP does not directly normalize the last hidden state.
										#
										# CLIP normalizing the pooled output.
										# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
										lowerCAmelCase_ :      Any    				=				prompt_embeds / prompt_embeds.norm(dim=-1   ,						keepdim=__lowercase							)

										# duplicate text embeddings for each generation per prompt
										lowerCAmelCase_ :      Optional[Any]    				=				prompt_embeds.repeat_interleave(__lowercase   ,						dim=0							)

										if do_classifier_free_guidance:
														if self.learned_classifier_free_sampling_embeddings.learnable:
																		lowerCAmelCase_ :      Optional[Any]    				=				self.learned_classifier_free_sampling_embeddings.embeddings
																		lowerCAmelCase_ :      Union[str, Any]    				=				negative_prompt_embeds.unsqueeze(0							).repeat(__lowercase   ,						1   ,						1							)
														else:
																		lowerCAmelCase_ :      Tuple    				=				[''''''] * batch_size

																		lowerCAmelCase_ :      Optional[Any]    				=				text_input_ids.shape[-1]
																		lowerCAmelCase_ :      Any    				=				self.tokenizer(
																		    __lowercase   ,						padding='''max_length'''   ,						max_length=__lowercase   ,						truncation=__lowercase   ,						return_tensors='''pt'''   ,						)
																		lowerCAmelCase_ :      Optional[int]    				=				self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
																		# See comment for normalizing text embeddings
																		lowerCAmelCase_ :      str    				=				negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1   ,						keepdim=__lowercase							)

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														lowerCAmelCase_ :      Any    				=				negative_prompt_embeds.shape[1]
														lowerCAmelCase_ :      List[str]    				=				negative_prompt_embeds.repeat(1   ,						__lowercase   ,						1							)
														lowerCAmelCase_ :      List[str]    				=				negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,						__lowercase   ,						-1							)

														# For classifier free guidance, we need to do two forward passes.
														# Here we concatenate the unconditional and text embeddings into a single batch
														# to avoid doing two forward passes
														lowerCAmelCase_ :      Dict    				=				torch.cat([negative_prompt_embeds, prompt_embeds]							)

										return prompt_embeds




						@torch.no_grad()
						def __call__(				self   ,						__lowercase   ,						__lowercase = 1_0_0   ,						__lowercase = 5.0   ,						__lowercase = 1.0   ,						__lowercase = 1   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = "pil"   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = 1   ,						)					->    Union[ImagePipelineOutput, Tuple]:
										if isinstance(__lowercase   ,						__lowercase							):
														lowerCAmelCase_ :      Optional[Any]    				=				1
										elif isinstance(__lowercase   ,						__lowercase							):
														lowerCAmelCase_ :      List[str]    				=				len(__lowercase							)
										else:
														raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__lowercase							)}"""							)

										lowerCAmelCase_ :      Tuple    				=				batch_size * num_images_per_prompt

										lowerCAmelCase_ :      List[Any]    				=				guidance_scale > 1.0

										lowerCAmelCase_ :      str    				=				self._encode_prompt(__lowercase   ,						__lowercase   ,						__lowercase							)

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(__lowercase   ,						__lowercase							) or callback_steps <= 0)
										):
														raise ValueError(
														    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
														    f""" {type(__lowercase							)}."""							)

										# get the initial completely masked latents unless the user supplied it

										lowerCAmelCase_ :      List[str]    				=				(batch_size, self.transformer.num_latent_pixels)
										if latents is None:
														lowerCAmelCase_ :      Any    				=				self.transformer.num_vector_embeds - 1
														lowerCAmelCase_ :      Any    				=				torch.full(__lowercase   ,						__lowercase							).to(self.device							)
										else:
														if latents.shape != latents_shape:
																		raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
														if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																		raise ValueError(
																		    '''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
																		    f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""							)
														lowerCAmelCase_ :      List[Any]    				=				latents.to(self.device							)

										# set timesteps
										self.scheduler.set_timesteps(__lowercase   ,						device=self.device							)

										lowerCAmelCase_ :      Dict    				=				self.scheduler.timesteps.to(self.device							)

										lowerCAmelCase_ :      Dict    				=				latents

										for i, t in enumerate(self.progress_bar(__lowercase							)							):
														# expand the sample if we are doing classifier free guidance
														lowerCAmelCase_ :      Tuple    				=				torch.cat([sample] * 2							) if do_classifier_free_guidance else sample

														# predict the un-noised image
														# model_output == `log_p_x_0`
														lowerCAmelCase_ :      int    				=				self.transformer(__lowercase   ,						encoder_hidden_states=__lowercase   ,						timestep=__lowercase							).sample

														if do_classifier_free_guidance:
																		lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[int]    				=				model_output.chunk(2							)
																		lowerCAmelCase_ :      Optional[Any]    				=				model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																		model_output -= torch.logsumexp(__lowercase   ,						dim=1   ,						keepdim=__lowercase							)

														lowerCAmelCase_ :      Optional[int]    				=				self.truncate(__lowercase   ,						__lowercase							)

														# remove `log(0)`'s (`-inf`s)
														lowerCAmelCase_ :      Any    				=				model_output.clamp(-7_0							)

														# compute the previous noisy sample x_t -> x_t-1
														lowerCAmelCase_ :      Dict    				=				self.scheduler.step(__lowercase   ,						timestep=__lowercase   ,						sample=__lowercase   ,						generator=__lowercase							).prev_sample

														# call the callback, if provided
														if callback is not None and i % callback_steps == 0:
																		callback(__lowercase   ,						__lowercase   ,						__lowercase							)

										lowerCAmelCase_ :      List[Any]    				=				self.vqvae.config.vq_embed_dim
										lowerCAmelCase_ :      Any    				=				(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
										lowerCAmelCase_ :      int    				=				self.vqvae.quantize.get_codebook_entry(__lowercase   ,						shape=__lowercase							)
										lowerCAmelCase_ :      Optional[Any]    				=				self.vqvae.decode(__lowercase   ,						force_not_quantize=__lowercase							).sample

										lowerCAmelCase_ :      Optional[int]    				=				(image / 2 + 0.5).clamp(0   ,						1							)
										lowerCAmelCase_ :      str    				=				image.cpu().permute(0   ,						2   ,						3   ,						1							).numpy()

										if output_type == "pil":
														lowerCAmelCase_ :      Union[str, Any]    				=				self.numpy_to_pil(__lowercase							)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase							)					->    torch.FloatTensor:
										lowerCAmelCase_				,  lowerCAmelCase_ :      int    				=				torch.sort(__lowercase   ,						1   ,						descending=__lowercase							)
										lowerCAmelCase_ :      Dict    				=				torch.exp(__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				sorted_p_x_0.cumsum(dim=1							) < truncation_rate

										# Ensure that at least the largest probability is not zeroed out
										lowerCAmelCase_ :      Tuple    				=				torch.full_like(keep_mask[:, 0:1, :]   ,						__lowercase							)
										lowerCAmelCase_ :      int    				=				torch.cat((all_true, keep_mask)   ,						dim=1							)
										lowerCAmelCase_ :      Optional[Any]    				=				keep_mask[:, :-1, :]

										lowerCAmelCase_ :      Dict    				=				keep_mask.gather(1   ,						indices.argsort(1							)							)

										lowerCAmelCase_ :      int    				=				log_p_x_0.clone()

										lowerCAmelCase_ :      List[str]    				=				-torch.inf  # -inf = log(0)

										return rv



_UpperCAmelCase			:  Dict     =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1_000}
				lowerCAmelCase_ :      Optional[int]    				=				0
				lowerCAmelCase_ :      List[str]    				=				0
				while place < len(lowerCAmelCase_ ):
								if (place + 1 < len(lowerCAmelCase_ )) and (vals[roman[place]] < vals[roman[place + 1]]):
												total += vals[roman[place + 1]] - vals[roman[place]]
												place += 2
								else:
												total += vals[roman[place]]
												place += 1
				return total



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      List[Any]    				=				[]
				for arabic, roman in ROMAN:
								((lowerCAmelCase_)				,  (lowerCAmelCase_)) :      Optional[int]    				=				divmod(lowerCAmelCase_ ,   lowerCAmelCase_ )
								result.append(roman * factor )
								if number == 0:
												break
				return "".join(lowerCAmelCase_ )


if __name__ == "__main__":
						import doctest

						doctest.testmod()