


"""simple docstring"""






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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


SCREAMING_SNAKE_CASE__				:  Dict						= logging.get_logger(__name__)  # pylint: disable=invalid-name




def        A_					(					self       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[Any]:
   a   :	List[Any]						=    image.to(device=_SCREAMING_SNAKE_CASE				)
   a   :	List[Any]						=    image  # take image as image_embeddings
   a   :	Any						=    image_embeddings.unsqueeze(1				)

   # duplicate image embeddings for each generation per prompt, using mps friendly method
   a	, a	, a   :	Tuple						=    image_embeddings.shape
   a   :	Any						=    image_embeddings.repeat(1       ,   _SCREAMING_SNAKE_CASE       ,   1				)
   a   :	Optional[Any]						=    image_embeddings.view(bs_embed * num_images_per_prompt       ,   _SCREAMING_SNAKE_CASE       ,   -1				)

   if do_classifier_free_guidance:
      a   :	Optional[int]						=    torch.zeros_like(_SCREAMING_SNAKE_CASE				)

      # 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   :	Any						=    torch.cat([uncond_embeddings, image_embeddings]				)

   return image_embeddings


class 	A_       (     _lowercase   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					)				->					Union[str, Any]:
     super().__init__()

     a   :	List[str]						=    {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

     a   :	int						=    decoder_pipe_kwargs.get('torch_dtype'  ,					__a						) or prior.dtype

     a   :	Optional[int]						=    StableDiffusionImageVariationPipeline.from_pretrained(
         'lambdalabs/sd-image-variations-diffusers'  ,					**__a						)

     # replace `_encode_image` method
     a   :	List[str]						=    types.MethodType(_encode_image  ,					self.decoder_pipe						)

     self.register_modules(
         prior=__a  ,					tokenizer=__a  ,					text_encoder=__a  ,					prior_scheduler=__a  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					)				->					Any:
     if text_model_output is None:
        a   :	Union[str, Any]						=    len(__a						) if isinstance(__a  ,					__a						) else 1
        # get prompt text embeddings
        a   :	Union[str, Any]						=    self.tokenizer(
            __a  ,					padding='max_length'  ,					max_length=self.tokenizer.model_max_length  ,					return_tensors='pt'  ,					)
        a   :	int						=    text_inputs.input_ids
        a   :	List[str]						=    text_inputs.attention_mask.bool().to(__a						)

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           a   :	int						=    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   :	Any						=    text_input_ids[:, : self.tokenizer.model_max_length]

        a   :	Dict						=    self.text_encoder(text_input_ids.to(__a						)						)

        a   :	Any						=    text_encoder_output.text_embeds
        a   :	Any						=    text_encoder_output.last_hidden_state

     else:
        a   :	Union[str, Any]						=    text_model_output[0].shape[0]
        a	, a   :	List[str]						=    text_model_output[0], text_model_output[1]
        a   :	Optional[Any]						=    text_attention_mask

     a   :	Optional[int]						=    text_embeddings.repeat_interleave(__a  ,					dim=0						)
     a   :	Optional[int]						=    text_encoder_hidden_states.repeat_interleave(__a  ,					dim=0						)
     a   :	Union[str, Any]						=    text_mask.repeat_interleave(__a  ,					dim=0						)

     if do_classifier_free_guidance:
        a   :	Optional[Any]						=    [''] * batch_size

        a   :	Dict						=    self.tokenizer(
            __a  ,					padding='max_length'  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__a  ,					return_tensors='pt'  ,					)
        a   :	Dict						=    uncond_input.attention_mask.bool().to(__a						)
        a   :	int						=    self.text_encoder(uncond_input.input_ids.to(__a						)						)

        a   :	List[Any]						=    uncond_embeddings_text_encoder_output.text_embeds
        a   :	Optional[Any]						=    uncond_embeddings_text_encoder_output.last_hidden_state

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

        a   :	Union[str, Any]						=    uncond_embeddings.shape[1]
        a   :	int						=    uncond_embeddings.repeat(1  ,					__a						)
        a   :	Optional[int]						=    uncond_embeddings.view(batch_size * num_images_per_prompt  ,					__a						)

        a   :	Optional[Any]						=    uncond_text_encoder_hidden_states.shape[1]
        a   :	str						=    uncond_text_encoder_hidden_states.repeat(1  ,					__a  ,					1						)
        a   :	str						=    uncond_text_encoder_hidden_states.view(
            batch_size * num_images_per_prompt  ,					__a  ,					-1						)
        a   :	int						=    uncond_text_mask.repeat_interleave(__a  ,					dim=0						)

        # done duplicates

        # 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   :	Union[str, Any]						=    torch.cat([uncond_embeddings, text_embeddings]						)
        a   :	Optional[Any]						=    torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

        a   :	Union[str, Any]						=    torch.cat([uncond_text_mask, text_mask]						)

     return text_embeddings, text_encoder_hidden_states, text_mask
  @property
  def 							lowercase_     (						self						)				->					List[str]:
     if self.device != torch.device('meta'						) or not hasattr(self.prior  ,					'_hf_hook'						):
        return self.device
     for module in self.prior.modules():
        if (
            hasattr(__a  ,					'_hf_hook'						)
            and hasattr(module._hf_hook  ,					'execution_device'						)
            and module._hf_hook.execution_device is not None
        ):
           return torch.device(module._hf_hook.execution_device						)
     return self.device
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					str:
     if latents is None:
        a   :	int						=    randn_tensor(__a  ,					generator=__a  ,					device=__a  ,					dtype=__a						)
     else:
        if latents.shape != shape:
           raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'						)
        a   :	Optional[Any]						=    latents.to(__a						)

     a   :	Dict						=    latents * scheduler.init_noise_sigma
     return latents
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					Optional[int]:
     self.decoder_pipe.to(__a						)
     super().to(__a						)






  @torch.no_grad()
  def __call__(						self  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = 1  ,					__UpperCAmelCase = 25  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = 4.0  ,					__UpperCAmelCase = 8.0  ,					__UpperCAmelCase = 50  ,					__UpperCAmelCase = 1  ,					__UpperCAmelCase = 0.0  ,					__UpperCAmelCase = "pil"  ,					__UpperCAmelCase = True  ,					)				->					str:
     if prompt is not None:
        if isinstance(__a  ,					__a						):
           a   :	int						=    1
        elif isinstance(__a  ,					__a						):
           a   :	Optional[Any]						=    len(__a						)
        else:
           raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__a						)}'						)
     else:
        a   :	List[Any]						=    text_model_output[0].shape[0]

     a   :	Optional[Any]						=    self._execution_device

     a   :	Dict						=    batch_size * num_images_per_prompt

     a   :	Tuple						=    prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

     a	, a	, a   :	Union[str, Any]						=    self._encode_prompt(
         __a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						)

     # prior

     self.prior_scheduler.set_timesteps(__a  ,					device=__a						)
     a   :	int						=    self.prior_scheduler.timesteps

     a   :	List[str]						=    self.prior.config.embedding_dim

     a   :	Any						=    self.prepare_latents(
         (batch_size, embedding_dim)  ,					text_embeddings.dtype  ,					__a  ,					__a  ,					__a  ,					self.prior_scheduler  ,					)

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

        a   :	Dict						=    self.prior(
            __a  ,					timestep=__a  ,					proj_embedding=__a  ,					encoder_hidden_states=__a  ,					attention_mask=__a  ,					).predicted_image_embedding

        if do_classifier_free_guidance:
           a	, a   :	str						=    predicted_image_embedding.chunk(2						)
           a   :	Dict						=    predicted_image_embedding_uncond + prior_guidance_scale * (
               predicted_image_embedding_text - predicted_image_embedding_uncond
           )

        if i + 1 == prior_timesteps_tensor.shape[0]:
           a   :	Dict						=    None
        else:
           a   :	str						=    prior_timesteps_tensor[i + 1]

        a   :	List[Any]						=    self.prior_scheduler.step(
            __a  ,					timestep=__a  ,					sample=__a  ,					generator=__a  ,					prev_timestep=__a  ,					).prev_sample

     a   :	Optional[int]						=    self.prior.post_process_latents(__a						)

     a   :	Union[str, Any]						=    prior_latents

     a   :	List[Any]						=    self.decoder_pipe(
         image=__a  ,					height=__a  ,					width=__a  ,					num_inference_steps=__a  ,					guidance_scale=__a  ,					generator=__a  ,					output_type=__a  ,					return_dict=__a  ,					num_images_per_prompt=__a  ,					eta=__a  ,					)
     return output












"""simple docstring"""





import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


lowerCAmelCase__        =      logging.getLogger(__name__)


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset."     )
		parser.add_argument(
		    "--dataset_name"	,						type=_SCREAMING_SNAKE_CASE	,						default="wikitext"	,						help="Name of the training. Explore datasets at: hf.co/datasets."	,						)
		parser.add_argument(
		    "--dataset_config"	,						type=_SCREAMING_SNAKE_CASE	,						default="wikitext-103-raw-v1"	,						help="Configuration name of the dataset."     )
		parser.add_argument(
		    "--batch_size"	,						type=_SCREAMING_SNAKE_CASE	,						default=1_000	,						help="Batch size during training."	,						)
		parser.add_argument(
		    "--vocab_size"	,						type=_SCREAMING_SNAKE_CASE	,						default=10_048	,						help="Size of the desired vocabulary."	,						)
		parser.add_argument(
		    "--limit"	,						default=_SCREAMING_SNAKE_CASE	,						type=_SCREAMING_SNAKE_CASE	,						help="Limit the number of shards (used for debugging)."	,						)
		parser.add_argument(
		    "--export_to_hub"	,						action="store_true"	,						)

		UpperCamelCase =						parser.parse_args()
		return args


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						datasets.load_dataset(args.dataset_name	,						args.dataset_config	,						split="train"     )

		if args.limit is not None:
				UpperCamelCase =						min(len(_SCREAMING_SNAKE_CASE     )	,						args.limit     )
				UpperCamelCase =						dataset.select(range(_SCREAMING_SNAKE_CASE     )     )
				logger.info(F"Limiting the dataset to {args.limit} entries."     )

		def batch_iterator():
				for i in range(0	,						len(_SCREAMING_SNAKE_CASE     )	,						args.batch_size     ):
						yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
		UpperCamelCase =						Tokenizer(Unigram()     )
		UpperCamelCase =						normalizers.Sequence([normalizers.Replace("``"	,						"\""     ), normalizers.Replace("''"	,						"\""     )]     )
		UpperCamelCase =						pre_tokenizers.Metaspace()

		# Prepare the trainer.
		UpperCamelCase =						UnigramTrainer(
		    unk_token="<unk>"	,						special_tokens=["[CLS]", "[SEP]", "<unk>", "<pad>", "[MASK]"]	,						vocab_size=args.vocab_size	,						)

		logger.info("Training the tokenizer."     )
		tokenizer.train_from_iterator(batch_iterator()	,						trainer=_SCREAMING_SNAKE_CASE     )
		logger.info("Tokenizer training complete!"     )

		UpperCamelCase =						tokenizer.token_to_id("[CLS]"     )
		UpperCamelCase =						tokenizer.token_to_id("[SEP]"     )
		UpperCamelCase =						processors.TemplateProcessing(
		    single="[CLS]:0 $A:0 [SEP]:0"	,						pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1"	,						special_tokens=[
		        ("[CLS]", cls_token_id),
		        ("[SEP]", sep_token_id),
		    ]	,						)
		UpperCamelCase =						decoders.Metaspace()

		if args.export_to_hub:
				logger.info("Exporting the trained tokenzier to Hub."     )
				UpperCamelCase =						AlbertTokenizerFast(tokenizer_object=_SCREAMING_SNAKE_CASE     )
				new_tokenizer.push_to_hub("unigram-tokenizer-dataset"     )


if __name__ == "__main__":
						lowerCAmelCase__        =      parse_args()
						main(args)






