





'''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


snake_case_   :  str								=      logging.getLogger(__name__)

@dataclass
class     A_ :

   '''simple docstring'''





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




@dataclass
class     A_ :

   '''simple docstring'''





   _lowerCAmelCase						=    field(
       metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()  )}  )
   _lowerCAmelCase						=    field(
       metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}  )
   _lowerCAmelCase						=    field(
       default=128    ,			metadata={
           """help""": (
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated, sequences shorter will be padded."""
           )
       }    ,			)
   _lowerCAmelCase						=    field(
       default=SCREAMING_SNAKE_CASE__    ,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )




def  lowercase__(		_UpperCamelCase  : str				)->   Dict[str, torch.Tensor]:






     """simple docstring"""

     _UpperCamelCase											=   default_data_collator(_UpperCamelCase				)
     _UpperCamelCase											=   batch.pop("pairID" ,   _UpperCamelCase				)
     return batch



def  lowercase__(		)->   Union[str, Any]:






     """simple docstring"""

     _UpperCamelCase											=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   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											=   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											=   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											=   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											=   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											=   (
         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											=   (
         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											=   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											=   trainer.predict(_UpperCamelCase				)
          _UpperCamelCase											=   output.predictions
          _UpperCamelCase											=   np.argmax(_UpperCamelCase ,   axis=1				)

          _UpperCamelCase											=   [ex.pairID for ex in eval_dataset]
          _UpperCamelCase											=   os.path.join(training_args.output_dir ,   "hans_predictions.txt"				)
          _UpperCamelCase											=   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  lowercase__(		_UpperCamelCase  : List[Any]				)->   List[Any]:






     """simple docstring"""

     main()


if __name__ == "__main__":
   main()










'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class     A_ (  lowerCAmelCase_    ,			lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    StableDiffusionXLPipeline
			_lowerCAmelCase						=    TEXT_TO_IMAGE_PARAMS
			_lowerCAmelCase						=    TEXT_TO_IMAGE_BATCH_PARAMS
			_lowerCAmelCase						=    TEXT_TO_IMAGE_IMAGE_PARAMS
			_lowerCAmelCase						=    TEXT_TO_IMAGE_IMAGE_PARAMS




			def       a	(					self   ):
								torch.manual_seed(0   )
								_UpperCamelCase											=   UNetaDConditionModel(
								    block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,     attention_head_dim=(2, 4)			,     use_linear_projection=A_			,     addition_embed_type="text_time"			,     addition_time_embed_dim=8			,     transformer_layers_per_block=(1, 2)			,     projection_class_embeddings_input_dim=80			,     cross_attention_dim=64			,     )
								_UpperCamelCase											=   EulerDiscreteScheduler(
								    beta_start=0.0_0085			,     beta_end=0.012			,     steps_offset=1			,     beta_schedule="scaled_linear"			,     timestep_spacing="leading"			,     )
								torch.manual_seed(0   )
								_UpperCamelCase											=   AutoencoderKL(
								    block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,     latent_channels=4			,     sample_size=1_28			,     )
								torch.manual_seed(0   )
								_UpperCamelCase											=   CLIPTextConfig(
								    bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1e-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=10_00			,     hidden_act="gelu"			,     projection_dim=32			,     )
								_UpperCamelCase											=   CLIPTextModel(A_   )
								_UpperCamelCase											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			,     local_files_only=A_   )

								_UpperCamelCase											=   CLIPTextModelWithProjection(A_   )
								_UpperCamelCase											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			,     local_files_only=A_   )

								_UpperCamelCase											=   {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "text_encoder_2": text_encoder_a,
								    "tokenizer_2": tokenizer_a,
								    # "safety_checker": None,
								    # "feature_extractor": None,
								}
								return components




			def       a	(					self			,     A_			,     A_=0   ):
								if str(A_   ).startswith("mps"   ):
													_UpperCamelCase											=   torch.manual_seed(A_   )
								else:
													_UpperCamelCase											=   torch.Generator(device=A_   ).manual_seed(A_   )
								_UpperCamelCase											=   {
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "guidance_scale": 5.0,
								    "output_type": "numpy",
								}
								return inputs




			def       a	(					self   ):
								_UpperCamelCase											=   "cpu"  # ensure determinism for the device-dependent torch.Generator
								_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionXLPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase											=   self.get_dummy_inputs(A_   )
								_UpperCamelCase											=   sd_pipe(**A_   ).images
								_UpperCamelCase											=   image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								_UpperCamelCase											=   np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




			def       a	(					self   ):
								_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionXLPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								# forward without prompt embeds
								_UpperCamelCase											=   self.get_dummy_inputs(A_   )
								_UpperCamelCase											=   3 * ["this is a negative prompt"]
								_UpperCamelCase											=   negative_prompt
								_UpperCamelCase											=   3 * [inputs["prompt"]]

								_UpperCamelCase											=   sd_pipe(**A_   )
								_UpperCamelCase											=   output.images[0, -3:, -3:, -1]

								# forward with prompt embeds
								_UpperCamelCase											=   self.get_dummy_inputs(A_   )
								_UpperCamelCase											=   3 * ["this is a negative prompt"]
								_UpperCamelCase											=   3 * [inputs.pop("prompt"   )]

								(
								    (
								    _UpperCamelCase
								)				,	(
								    _UpperCamelCase
								)				,	(
								    _UpperCamelCase
								)				,	(
								    _UpperCamelCase
								)				,	
								)											=   sd_pipe.encode_prompt(A_			,     negative_prompt=A_   )

								_UpperCamelCase											=   sd_pipe(
								    **A_			,     prompt_embeds=A_			,     negative_prompt_embeds=A_			,     pooled_prompt_embeds=A_			,     negative_pooled_prompt_embeds=A_			,     )
								_UpperCamelCase											=   output.images[0, -3:, -3:, -1]

								# make sure that it's equal
								assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4




			def       a	(					self   ):
								super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )




			def       a	(					self   ):
								super().test_inference_batch_single_identical(expected_max_diff=3e-3   )




@slow
@require_torch_gpu
class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def       a	(					self   ):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




			def       a	(					self			,     A_			,     A_="cpu"			,     A_=torch.floataa			,     A_=0   ):
								_UpperCamelCase											=   torch.Generator(device=A_   ).manual_seed(A_   )
								_UpperCamelCase											=   np.random.RandomState(A_   ).standard_normal((1, 4, 64, 64)   )
								_UpperCamelCase											=   torch.from_numpy(A_   ).to(device=A_			,     dtype=A_   )
								_UpperCamelCase											=   {
								    "prompt": "a photograph of an astronaut riding a horse",
								    "latents": latents,
								    "generator": generator,
								    "num_inference_steps": 3,
								    "guidance_scale": 7.5,
								    "output_type": "numpy",
								}
								return inputs




			def       a	(					self   ):
								_UpperCamelCase											=   DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"   )
								pipe.to(A_   )
								pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase											=   self.get_inputs(A_   )
								_UpperCamelCase											=   pipe(**A_   ).images
								_UpperCamelCase											=   image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								_UpperCamelCase											=   np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]   )
								assert np.abs(image_slice - expected_slice   ).max() < 7e-3



