

"""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


__UpperCAmelCase	=			logging.get_logger(__name__)  # pylint: disable=invalid-name
def 					lowerCAmelCase  (   self							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   image.to(device=SCREAMING_SNAKE_CASE_ )
 UpperCAmelCase__   :					Tuple   				=   image  # take image as image_embeddings
 UpperCAmelCase__   :					Union[str, Any]   				=   image_embeddings.unsqueeze(1 )

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

 if do_classifier_free_guidance:
  UpperCAmelCase__   :					Union[str, Any]   				=   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
  UpperCAmelCase__   :					List[Any]   				=   torch.cat([uncond_embeddings, image_embeddings] )

 return image_embeddings







class        __lowercase     (      __lowerCamelCase   ):

  def __init__(					self						:     Optional[int]   ,A						:     str   ,A						:     Optional[int]   ,A						:     Optional[int]   ,A						:     Tuple   ,A						:     Any = None   ,):






   '''simple docstring'''
   super().__init__()

   UpperCAmelCase__   :					Tuple   				=   {"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

   UpperCAmelCase__   :					Dict   				=   decoder_pipe_kwargs.get("""torch_dtype"""   ,A      ) or prior.dtype

   UpperCAmelCase__   :					Dict   				=   StableDiffusionImageVariationPipeline.from_pretrained(
       """lambdalabs/sd-image-variations-diffusers"""   ,**A      )

   # replace `_encode_image` method
   UpperCAmelCase__   :					str   				=   types.MethodType(_encode_image   ,self.decoder_pipe      )

   self.register_modules(
       prior=A   ,tokenizer=A   ,text_encoder=A   ,prior_scheduler=A   ,)

  def 				__lowercase    (					self						:     Dict   ,A						:     List[str]   ,A						:     List[Any]   ,A						:     List[str]   ,A						:     Optional[int]   ,A						:     Dict = None   ,A						:     str = None   ,):






   '''simple docstring'''






   if text_model_output is None:
    UpperCAmelCase__   :					List[Any]   				=   len(A      ) if isinstance(A   ,A      ) else 1
    # get prompt text embeddings
    UpperCAmelCase__   :					List[str]   				=   self.tokenizer(
        A   ,padding="""max_length"""   ,max_length=self.tokenizer.model_max_length   ,return_tensors="""pt"""   ,)
    UpperCAmelCase__   :					str   				=   text_inputs.input_ids
    UpperCAmelCase__   :					Union[str, Any]   				=   text_inputs.attention_mask.bool().to(A      )

    if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
     UpperCAmelCase__   :					str   				=   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}"      )
     UpperCAmelCase__   :					Any   				=   text_input_ids[:, : self.tokenizer.model_max_length]

    UpperCAmelCase__   :					Tuple   				=   self.text_encoder(text_input_ids.to(A      )      )

    UpperCAmelCase__   :					List[str]   				=   text_encoder_output.text_embeds
    UpperCAmelCase__   :					int   				=   text_encoder_output.last_hidden_state

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

   UpperCAmelCase__   :					Any   				=   text_embeddings.repeat_interleave(A   ,dim=0      )
   UpperCAmelCase__   :					List[str]   				=   text_encoder_hidden_states.repeat_interleave(A   ,dim=0      )
   UpperCAmelCase__   :					Optional[int]   				=   text_mask.repeat_interleave(A   ,dim=0      )

   if do_classifier_free_guidance:
    UpperCAmelCase__   :					Union[str, Any]   				=   [""""""] * batch_size

    UpperCAmelCase__   :					Optional[Any]   				=   self.tokenizer(
        A   ,padding="""max_length"""   ,max_length=self.tokenizer.model_max_length   ,truncation=A   ,return_tensors="""pt"""   ,)
    UpperCAmelCase__   :					int   				=   uncond_input.attention_mask.bool().to(A      )
    UpperCAmelCase__   :					Union[str, Any]   				=   self.text_encoder(uncond_input.input_ids.to(A      )      )

    UpperCAmelCase__   :					Any   				=   uncond_embeddings_text_encoder_output.text_embeds
    UpperCAmelCase__   :					List[str]   				=   uncond_embeddings_text_encoder_output.last_hidden_state

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

    UpperCAmelCase__   :					List[str]   				=   uncond_embeddings.shape[1]
    UpperCAmelCase__   :					Optional[int]   				=   uncond_embeddings.repeat(1   ,A      )
    UpperCAmelCase__   :					Any   				=   uncond_embeddings.view(batch_size * num_images_per_prompt   ,A      )

    UpperCAmelCase__   :					int   				=   uncond_text_encoder_hidden_states.shape[1]
    UpperCAmelCase__   :					Any   				=   uncond_text_encoder_hidden_states.repeat(1   ,A   ,1      )
    UpperCAmelCase__   :					Dict   				=   uncond_text_encoder_hidden_states.view(
        batch_size * num_images_per_prompt   ,A   ,-1      )
    UpperCAmelCase__   :					List[str]   				=   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
    UpperCAmelCase__   :					int   				=   torch.cat([uncond_embeddings, text_embeddings]      )
    UpperCAmelCase__   :					Tuple   				=   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]      )

    UpperCAmelCase__   :					List[Any]   				=   torch.cat([uncond_text_mask, text_mask]      )

   return text_embeddings, text_encoder_hidden_states, text_mask

  @property
  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''






   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						:     str   ,A						:     List[Any]   ,A						:     List[Any]   ,A						:     List[Any]   ,A						:     Optional[Any]   ,A						:     str   ,A						:     List[Any]      ):






   '''simple docstring'''






   if latents is None:
    UpperCAmelCase__   :					List[Any]   				=   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}"      )
    UpperCAmelCase__   :					List[str]   				=   latents.to(A      )

   UpperCAmelCase__   :					str   				=   latents * scheduler.init_noise_sigma
   return latents

  def 				__lowercase    (					self						:     Union[str, Any]   ,A						:     Optional[int] = None      ):






   '''simple docstring'''
   self.decoder_pipe.to(A      )
   super().to(A      )




  @torch.no_grad()
  def __call__(					self						:     int   ,A						:     Tuple = None   ,A						:     str = None   ,A						:     str = None   ,A						:     Union[str, Any] = 1   ,A						:     Optional[int] = 25   ,A						:     Any = None   ,A						:     Optional[int] = None   ,A						:     List[Any] = None   ,A						:     Union[str, Any] = None   ,A						:     Any = 4.0   ,A						:     Dict = 8.0   ,A						:     Any = 50   ,A						:     Optional[Any] = 1   ,A						:     Optional[int] = 0.0   ,A						:     List[Any] = "pil"   ,A						:     str = True   ,):






   '''simple docstring'''






   if prompt is not None:
    if isinstance(A   ,A      ):
     UpperCAmelCase__   :					Any   				=   1
    elif isinstance(A   ,A      ):
     UpperCAmelCase__   :					List[str]   				=   len(A      )
    else:
     raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(A      )}"      )
   else:
    UpperCAmelCase__   :					str   				=   text_model_output[0].shape[0]

   UpperCAmelCase__   :					Dict   				=   self._execution_device

   UpperCAmelCase__   :					List[str]   				=   batch_size * num_images_per_prompt

   UpperCAmelCase__   :					str   				=   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

   UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					str   				=   self._encode_prompt(
       A   ,A   ,A   ,A   ,A   ,A      )

   # prior

   self.prior_scheduler.set_timesteps(A   ,device=A      )
   UpperCAmelCase__   :					int   				=   self.prior_scheduler.timesteps

   UpperCAmelCase__   :					int   				=   self.prior.config.embedding_dim

   UpperCAmelCase__   :					Tuple   				=   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
    UpperCAmelCase__   :					List[Any]   				=   torch.cat([prior_latents] * 2      ) if do_classifier_free_guidance else prior_latents

    UpperCAmelCase__   :					List[str]   				=   self.prior(
        A   ,timestep=A   ,proj_embedding=A   ,encoder_hidden_states=A   ,attention_mask=A   ,).predicted_image_embedding

    if do_classifier_free_guidance:
     UpperCAmelCase__				, UpperCAmelCase__   :					Optional[Any]   				=   predicted_image_embedding.chunk(2      )
     UpperCAmelCase__   :					str   				=   predicted_image_embedding_uncond + prior_guidance_scale * (
         predicted_image_embedding_text - predicted_image_embedding_uncond
     )

    if i + 1 == prior_timesteps_tensor.shape[0]:
     UpperCAmelCase__   :					Any   				=   None
    else:
     UpperCAmelCase__   :					Dict   				=   prior_timesteps_tensor[i + 1]

    UpperCAmelCase__   :					str   				=   self.prior_scheduler.step(
        A   ,timestep=A   ,sample=A   ,generator=A   ,prev_timestep=A   ,).prev_sample

   UpperCAmelCase__   :					Union[str, Any]   				=   self.prior.post_process_latents(A      )

   UpperCAmelCase__   :					str   				=   prior_latents

   UpperCAmelCase__   :					str   				=   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 logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCAmelCase	=			logging.getLogger(__name__)







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,     )







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()   )}   )
  snake_case_			    =    field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
  snake_case_			    =    field(
      default=1_2_8		,     metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )







def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   default_data_collator(__UpperCamelCase )
 UpperCAmelCase__   :					List[str]   				=   batch.pop("""pairID"""							,      __UpperCamelCase )
 return batch
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
 UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					Union[str, Any]   				=   parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir )
     and os.listdir(training_args.output_dir )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
      """ --overwrite_output_dir to overcome.""" )

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,      datefmt="""%m/%d/%Y %H:%M:%S"""							,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,      )
 logger.warning(
     """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""							,      training_args.local_rank							,      training_args.device							,      training_args.n_gpu							,      bool(training_args.local_rank != -1 )							,      training_args.fpaa							,      )
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("""Training/evaluation parameters %s"""							,      __UpperCamelCase )

 # Set seed
 set_seed(training_args.seed )

 try:
  UpperCAmelCase__   :					Optional[int]   				=   hans_tasks_num_labels[data_args.task_name]
 except KeyError:
  raise ValueError("""Task not found: %s""" % (data_args.task_name) )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 UpperCAmelCase__   :					Tuple   				=   AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,      num_labels=__UpperCamelCase							,      finetuning_task=data_args.task_name							,      cache_dir=model_args.cache_dir							,      )
 UpperCAmelCase__   :					int   				=   AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,      cache_dir=model_args.cache_dir							,      )
 UpperCAmelCase__   :					List[Any]   				=   AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path							,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path )							,      config=__UpperCamelCase							,      cache_dir=model_args.cache_dir							,      )

 # Get datasets
 UpperCAmelCase__   :					Optional[int]   				=   (
     HansDataset(
         data_dir=data_args.data_dir							,      tokenizer=__UpperCamelCase							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      )
     if training_args.do_train
     else None
 )
 UpperCAmelCase__   :					str   				=   (
     HansDataset(
         data_dir=data_args.data_dir							,      tokenizer=__UpperCamelCase							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      evaluate=__UpperCamelCase							,      )
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 UpperCAmelCase__   :					List[Any]   				=   Trainer(
     model=__UpperCamelCase							,      args=__UpperCamelCase							,      train_dataset=__UpperCamelCase							,      eval_dataset=__UpperCamelCase							,      data_collator=__UpperCamelCase							,      )

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
 if training_args.do_eval:
  logger.info("""*** Evaluate ***""" )

  UpperCAmelCase__   :					Optional[Any]   				=   trainer.predict(__UpperCamelCase )
  UpperCAmelCase__   :					List[str]   				=   output.predictions
  UpperCAmelCase__   :					Dict   				=   np.argmax(__UpperCamelCase							,      axis=1 )

  UpperCAmelCase__   :					Optional[Any]   				=   [ex.pairID for ex in eval_dataset]
  UpperCAmelCase__   :					Dict   				=   os.path.join(training_args.output_dir							,      """hans_predictions.txt""" )
  UpperCAmelCase__   :					Dict   				=   eval_dataset.get_labels()
  if trainer.is_world_master():
   with open(__UpperCamelCase							,      """w""" ) as writer:
    writer.write("""pairID,gold_label\n""" )
    for pid, pred in zip(__UpperCamelCase							,      __UpperCamelCase ):
     writer.write("""ex""" + str(__UpperCamelCase ) + """,""" + label_list[int(__UpperCamelCase )] + """\n""" )

  trainer._log(output.metrics )
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 main()


if __name__ == "__main__":
      main()





