



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


__snake_case						:Union[str, Any]      		=     logging.get_logger(__name__)  # pylint: disable=invalid-name

class     _A       (	__UpperCAmelCase      ,__UpperCAmelCase    ):



       @register_to_config
       def __init__(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     bool     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None):


           '''simple docstring'''

           super().__init__()

           __a						       =				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"

               __a						       =				torch.zeros(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				None

           __a						       =				torch.nn.Parameter(__SCREAMING_SNAKE_CASE)


class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    VQModel
       UpperCamelCase__  :    CLIPTextModel
       UpperCamelCase__  :    CLIPTokenizer
       UpperCamelCase__  :    TransformeraDModel
       UpperCamelCase__  :    LearnedClassifierFreeSamplingEmbeddings
       UpperCamelCase__  :    VQDiffusionScheduler



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     VQModel     ,		__SCREAMING_SNAKE_CASE							:     CLIPTextModel     ,		__SCREAMING_SNAKE_CASE							:     CLIPTokenizer     ,		__SCREAMING_SNAKE_CASE							:     TransformeraDModel     ,		__SCREAMING_SNAKE_CASE							:     VQDiffusionScheduler     ,		__SCREAMING_SNAKE_CASE							:     LearnedClassifierFreeSamplingEmbeddings     ,		):


           '''simple docstring'''

           super().__init__()

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



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				len(__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else 1

           # get prompt text embeddings
           __a						       =				self.tokenizer(
               __SCREAMING_SNAKE_CASE     ,		padding='''max_length'''     ,		max_length=self.tokenizer.model_max_length     ,		return_tensors='''pt'''     ,		)
           __a						       =				text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
               __a						       =				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}')
               __a						       =				text_input_ids[:, : self.tokenizer.model_max_length]
           __a						       =				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
           __a						       =				prompt_embeds / prompt_embeds.norm(dim=-1     ,		keepdim=__SCREAMING_SNAKE_CASE)

           # duplicate text embeddings for each generation per prompt
           __a						       =				prompt_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE     ,		dim=0)

           if do_classifier_free_guidance:
               if self.learned_classifier_free_sampling_embeddings.learnable:
                   __a						       =				self.learned_classifier_free_sampling_embeddings.embeddings
                   __a						       =				negative_prompt_embeds.unsqueeze(0).repeat(__SCREAMING_SNAKE_CASE     ,		1     ,		1)
               else:
                   __a						       =				[''''''] * batch_size

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

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
               __a						       =				negative_prompt_embeds.shape[1]
               __a						       =				negative_prompt_embeds.repeat(1     ,		__SCREAMING_SNAKE_CASE     ,		1)
               __a						       =				negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,		__SCREAMING_SNAKE_CASE     ,		-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
               __a						       =				torch.cat([negative_prompt_embeds, prompt_embeds])

           return prompt_embeds



       @torch.no_grad()
       def __call__(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, List[str]]     ,		__SCREAMING_SNAKE_CASE							:     int = 100     ,		__SCREAMING_SNAKE_CASE							:     float = 5.0     ,		__SCREAMING_SNAKE_CASE							:     float = 1.0     ,		__SCREAMING_SNAKE_CASE							:     int = 1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[torch.FloatTensor] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[str] = "pil"     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,		__SCREAMING_SNAKE_CASE							:     int = 1     ,		):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				1
           elif isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				len(__SCREAMING_SNAKE_CASE)
           else:
               raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE)}')

           __a						       =				batch_size * num_images_per_prompt

           __a						       =				guidance_scale > 1.0

           __a						       =				self._encode_prompt(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

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

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

           __a						       =				(batch_size, self.transformer.num_latent_pixels)
           if latents is None:
               __a						       =				self.transformer.num_vector_embeds - 1
               __a						       =				torch.full(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE).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).')
               __a						       =				latents.to(self.device)

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

           __a						       =				self.scheduler.timesteps.to(self.device)

           __a						       =				latents

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

               # predict the un-noised image
               # model_output == `log_p_x_0`
               __a						       =				self.transformer(__SCREAMING_SNAKE_CASE     ,		encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,		timestep=__SCREAMING_SNAKE_CASE).sample

               if do_classifier_free_guidance:
                   __a       ,   __a						       =				model_output.chunk(2)
                   __a						       =				model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                   model_output -= torch.logsumexp(__SCREAMING_SNAKE_CASE     ,		dim=1     ,		keepdim=__SCREAMING_SNAKE_CASE)

               __a						       =				self.truncate(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # remove `log(0)`'s (`-inf`s)
               __a						       =				model_output.clamp(-70)

               # compute the previous noisy sample x_t -> x_t-1
               __a						       =				self.scheduler.step(__SCREAMING_SNAKE_CASE     ,		timestep=__SCREAMING_SNAKE_CASE     ,		sample=__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

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

           __a						       =				self.vqvae.config.vq_embed_dim
           __a						       =				(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
           __a						       =				self.vqvae.quantize.get_codebook_entry(__SCREAMING_SNAKE_CASE     ,		shape=__SCREAMING_SNAKE_CASE)
           __a						       =				self.vqvae.decode(__SCREAMING_SNAKE_CASE     ,		force_not_quantize=__SCREAMING_SNAKE_CASE).sample

           __a						       =				(image / 2 + 0.5).clamp(0     ,		1)
           __a						       =				image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()

           if output_type == "pil":
               __a						       =				self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     torch.FloatTensor     ,		__SCREAMING_SNAKE_CASE							:     float):


           '''simple docstring'''

           __a       ,   __a						       =				torch.sort(__SCREAMING_SNAKE_CASE     ,		1     ,		descending=__SCREAMING_SNAKE_CASE)
           __a						       =				torch.exp(__SCREAMING_SNAKE_CASE)
           __a						       =				sorted_p_x_0.cumsum(dim=1) < truncation_rate

           # Ensure that at least the largest probability is not zeroed out
           __a						       =				torch.full_like(keep_mask[:, 0:1, :]     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				torch.cat((all_true, keep_mask)     ,		dim=1)
           __a						       =				keep_mask[:, :-1, :]

           __a						       =				keep_mask.gather(1     ,		indices.argsort(1))

           __a						       =				log_p_x_0.clone()

           __a						       =				-torch.inf  # -inf = log(0)

           return rv










"""simple docstring"""







import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

		lowercase__   :    List[Any]				=			ArgumentParser()
		parser.add_argument('''--streaming'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--local_rank'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--num_workers'''						,     type=__lowerCamelCase						,     default=0	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Optional[Any]				=			args.streaming
		lowercase__   :    List[Any]				=			args.num_workers

		lowercase__   :    Optional[Any]				=			{'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase	)]}
		lowercase__   :    Dict				=			IterableDataset.from_generator(__lowerCamelCase						,     gen_kwargs=__lowerCamelCase	)
		if not streaming:
				lowercase__   :    int				=			Dataset.from_list(list(__lowerCamelCase	)	)

		lowercase__   :    int				=			split_dataset_by_node(__lowerCamelCase						,     rank=__lowerCamelCase						,     world_size=__lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			torch.utils.data.DataLoader(__lowerCamelCase						,     num_workers=__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			NUM_SHARDS * NUM_ITEMS_PER_SHARD
		lowercase__   :    str				=			full_size // world_size
		expected_local_size += int(rank < (full_size % world_size)	)

		lowercase__   :    str				=			sum(1 for _ in dataloader	)
		if local_size != expected_local_size:
				raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()
