'''simple docstring'''




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a   :	Any 							=   logging.getLogger(__name__)

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





       """simple docstring"""


       return (preds == labels).mean()






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






@dataclass
class 		__A       :
       snake_case       :str				    =					field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()   )}   )
       snake_case       :str				    =					field(metadata={"help": "Should contain the data files for the task."}   )
       snake_case       :int				    =					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."
               )
           } ,					)
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={"help": "Overwrite the cached training and evaluation sets"}   )





def _lowercase	(      )					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Any       				=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
       __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase  :				List[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"							,    lowerCamelCase__						)

       # Set seed
       set_seed(training_args.seed						)

       try:
              __UpperCAmelCase  :				Tuple       				=		processors[data_args.task_name]()
              __UpperCAmelCase  :				str       				=		processor.get_labels()
              __UpperCAmelCase  :				str       				=		len(lowerCamelCase__						)
       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=lowerCamelCase__							,    finetuning_task=data_args.task_name							,    cache_dir=model_args.cache_dir							,    )
       __UpperCAmelCase  :				Tuple       				=		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]       				=		AutoModelForMultipleChoice.from_pretrained(
           model_args.model_name_or_path							,    from_tf=bool(".ckpt" in model_args.model_name_or_path						)							,    config=lowerCamelCase__							,    cache_dir=model_args.cache_dir							,    )

       # Get datasets
       __UpperCAmelCase  :				int       				=		(
           MultipleChoiceDataset(
               data_dir=data_args.data_dir							,    tokenizer=lowerCamelCase__							,    task=data_args.task_name							,    max_seq_length=data_args.max_seq_length							,    overwrite_cache=data_args.overwrite_cache							,    mode=Split.train							,    )
           if training_args.do_train
           else None
       )
       __UpperCAmelCase  :				List[str]       				=		(
           MultipleChoiceDataset(
               data_dir=data_args.data_dir							,    tokenizer=lowerCamelCase__							,    task=data_args.task_name							,    max_seq_length=data_args.max_seq_length							,    overwrite_cache=data_args.overwrite_cache							,    mode=Split.dev							,    )
           if training_args.do_eval
           else None
       )

       def compute_metrics(lowerCamelCase__						) -> Dict:
              __UpperCAmelCase  :				int       				=		np.argmax(p.predictions							,    axis=1						)
              return {"acc": simple_accuracy(lowerCamelCase__							,    p.label_ids						)}

       # Data collator
       __UpperCAmelCase  :				Optional[Any]       				=		DataCollatorWithPadding(lowerCamelCase__							,    pad_to_multiple_of=8						) if training_args.fpaa else None

       # Initialize our Trainer
       __UpperCAmelCase  :				Dict       				=		Trainer(
           model=lowerCamelCase__							,    args=lowerCamelCase__							,    train_dataset=lowerCamelCase__							,    eval_dataset=lowerCamelCase__							,    compute_metrics=lowerCamelCase__							,    data_collator=lowerCamelCase__							,    )

       # 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
       __UpperCAmelCase  :				Optional[int]       				=		{}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"						)

              __UpperCAmelCase  :				int       				=		trainer.evaluate()

              __UpperCAmelCase  :				List[str]       				=		os.path.join(training_args.output_dir							,    "eval_results.txt"						)
              if trainer.is_world_master():
                     with open(lowerCamelCase__							,    "w"						) as writer:
                            logger.info("***** Eval results *****"						)
                            for key, value in result.items():
                                   logger.info("  %s = %s"							,    lowerCamelCase__							,    lowerCamelCase__						)
                                   writer.write("%s = %s\n" % (key, value)						)

                            results.update(lowerCamelCase__						)

       return results

def _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       main()


if __name__ == "__main__":
   main()







'''simple docstring'''




import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

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





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		[tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]						)






class 		__A       (__magic_name__ ,					__magic_name__ ,					__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Union[str, Any]				    =					StableDiffusionLatentUpscalePipeline
       snake_case       :Optional[int]				    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       snake_case       :List[str]				    =					PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       snake_case       :Optional[Any]				    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       snake_case       :Optional[Any]				    =					frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       snake_case       :Any				    =					frozenset([]   )

       snake_case       :Optional[int]				    =					True






       @property
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		1
              __UpperCAmelCase  :				Dict       				=		4
              __UpperCAmelCase  :				List[str]       				=		(16, 16)

              __UpperCAmelCase  :				Dict       				=		floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0      )      ).to(UpperCamelCase_      )
              return image






       def 			_snake_case						(      self      ):
              torch.manual_seed(0      )
              __UpperCAmelCase  :				List[str]       				=		UNetaDConditionModel(
                  act_fn="gelu"				,     attention_head_dim=8				,     norm_num_groups=UpperCamelCase_				,     block_out_channels=[32, 32, 64, 64]				,     time_cond_proj_dim=1_60				,     conv_in_kernel=1				,     conv_out_kernel=1				,     cross_attention_dim=32				,     down_block_types=(
                      "KDownBlock2D",
                      "KCrossAttnDownBlock2D",
                      "KCrossAttnDownBlock2D",
                      "KCrossAttnDownBlock2D",
                  )				,     in_channels=8				,     mid_block_type=UpperCamelCase_				,     only_cross_attention=UpperCamelCase_				,     out_channels=5				,     resnet_time_scale_shift="scale_shift"				,     time_embedding_type="fourier"				,     timestep_post_act="gelu"				,     up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")				,     )
              __UpperCAmelCase  :				int       				=		AutoencoderKL(
                  block_out_channels=[32, 32, 64, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=[
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                  ]				,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]				,     latent_channels=4				,     )
              __UpperCAmelCase  :				Optional[int]       				=		EulerDiscreteScheduler(prediction_type="sample"      )
              __UpperCAmelCase  :				int       				=		CLIPTextConfig(
                  bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=10_00				,     hidden_act="quick_gelu"				,     projection_dim=5_12				,     )
              __UpperCAmelCase  :				List[str]       				=		CLIPTextModel(UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

              __UpperCAmelCase  :				Union[str, Any]       				=		{
                  "unet": model.eval(),
                  "vae": vae.eval(),
                  "scheduler": scheduler,
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
              }

              return components






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=0      ):
              if str(UpperCamelCase_      ).startswith("mps"      ):
                     __UpperCAmelCase  :				str       				=		torch.manual_seed(UpperCamelCase_      )
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		{
                  "prompt": "A painting of a squirrel eating a burger",
                  "image": self.dummy_image.cpu(),
                  "generator": generator,
                  "num_inference_steps": 2,
                  "output_type": "numpy",
              }
              return inputs






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		"cpu"

              __UpperCAmelCase  :				List[str]       				=		self.get_dummy_components()
              __UpperCAmelCase  :				Tuple       				=		self.pipeline_class(**UpperCamelCase_      )
              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )

              __UpperCAmelCase  :				Any       				=		self.get_dummy_inputs(UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		pipe(**UpperCamelCase_      ).images
              __UpperCAmelCase  :				Any       				=		image[0, -3:, -3:, -1]

              self.assertEqual(image.shape				,     (1, 2_56, 2_56, 3)      )
              __UpperCAmelCase  :				Tuple       				=		np.array(
                  [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]      )
              __UpperCAmelCase  :				List[str]       				=		np.abs(image_slice.flatten() - expected_slice      ).max()
              self.assertLessEqual(UpperCamelCase_				,     1E-3      )






       def 			_snake_case						(      self      ):
              super().test_attention_slicing_forward_pass(expected_max_diff=7E-3      )






       def 			_snake_case						(      self      ):
              super().test_cpu_offload_forward_pass(expected_max_diff=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_inference_batch_single_identical(expected_max_diff=7E-3      )






       def 			_snake_case						(      self      ):
              super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_save_load_local(expected_max_difference=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_save_load_optional_components(expected_max_difference=3E-3      )







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		[
                  "DDIMScheduler",
                  "DDPMScheduler",
                  "PNDMScheduler",
                  "HeunDiscreteScheduler",
                  "EulerAncestralDiscreteScheduler",
                  "KDPM2DiscreteScheduler",
                  "KDPM2AncestralDiscreteScheduler",
                  "DPMSolverSDEScheduler",
              ]
              __UpperCAmelCase  :				Tuple       				=		self.get_dummy_components()
              __UpperCAmelCase  :				Union[str, Any]       				=		self.pipeline_class(**UpperCamelCase_      )

              # make sure that PNDM does not need warm-up
              pipe.scheduler.register_to_config(skip_prk_steps=UpperCamelCase_      )

              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		self.get_dummy_inputs(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		2

              __UpperCAmelCase  :				List[str]       				=		[]
              for scheduler_enum in KarrasDiffusionSchedulers:
                     if scheduler_enum.name in skip_schedulers:
                            # no sigma schedulers are not supported
                            # no schedulers
                            continue

                     __UpperCAmelCase  :				Optional[int]       				=		getattr(UpperCamelCase_				,     scheduler_enum.name      )
                     __UpperCAmelCase  :				List[str]       				=		scheduler_cls.from_config(pipe.scheduler.config      )
                     __UpperCAmelCase  :				Optional[int]       				=		pipe(**UpperCamelCase_      )[0]
                     outputs.append(UpperCamelCase_      )

              assert check_same_shape(UpperCamelCase_      )






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






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		torch.manual_seed(33      )

              __UpperCAmelCase  :				str       				=		StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"				,     torch_dtype=torch.floataa      )
              pipe.to("cuda"      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StableDiffusionLatentUpscalePipeline.from_pretrained(
                  "stabilityai/sd-x2-latent-upscaler"				,     torch_dtype=torch.floataa      )
              upscaler.to("cuda"      )

              __UpperCAmelCase  :				Optional[int]       				=		"a photo of an astronaut high resolution, unreal engine, ultra realistic"

              __UpperCAmelCase  :				Any       				=		pipe(UpperCamelCase_				,     generator=UpperCamelCase_				,     output_type="latent"      ).images

              __UpperCAmelCase  :				int       				=		upscaler(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     num_inference_steps=20				,     guidance_scale=0				,     generator=UpperCamelCase_				,     output_type="np"				,     ).images[0]

              __UpperCAmelCase  :				Optional[Any]       				=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"      )
              assert np.abs((expected_image - image).mean()      ) < 5E-2







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		torch.manual_seed(33      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StableDiffusionLatentUpscalePipeline.from_pretrained(
                  "stabilityai/sd-x2-latent-upscaler"				,     torch_dtype=torch.floataa      )
              upscaler.to("cuda"      )

              __UpperCAmelCase  :				Optional[Any]       				=		"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

              __UpperCAmelCase  :				str       				=		load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"      )

              __UpperCAmelCase  :				Dict       				=		upscaler(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     num_inference_steps=20				,     guidance_scale=0				,     generator=UpperCamelCase_				,     output_type="np"				,     ).images[0]

              __UpperCAmelCase  :				Tuple       				=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"      )
              assert np.abs((expected_image - image).max()      ) < 5E-2






