"""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       :						Tuple     =   logging.getLogger(__name__)


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





@dataclass
class 					UpperCAmelCase_     :
      lowerCamelCase__		:   str			  =  field(
          metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys())})
      lowerCamelCase__		:   str			  =  field(
          metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."})
      lowerCamelCase__		:   int			  =  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."
              )
          }			,							)
      lowerCamelCase__		:   bool			  =  field(
          default=_a			,							metadata={"help": "Overwrite the cached training and evaluation sets"})





def     a_					(				_lowerCAmelCase  :			List[InputFeatures]					):





       '''simple docstring'''

       lowercase__      :		List[Any]          =     default_data_collator(_lowerCAmelCase					)
       lowercase__      :		str          =     batch.pop('pairID'       ,			_lowerCAmelCase					)
       return batch





def     a_					(				):





       '''simple docstring'''

       lowercase__      :		List[str]          =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
       lowercase__   ,				lowercase__   ,				lowercase__      :		Tuple          =     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:
              lowercase__      :		List[str]          =     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.

       lowercase__      :		Union[str, Any]          =     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       ,			)
       lowercase__      :		Optional[Any]          =     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       ,			)
       lowercase__      :		List[Any]          =     AutoModelForSequenceClassification.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
       lowercase__      :		Union[str, Any]          =     (
           HansDataset(
               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       ,			)
           if training_args.do_train
           else None
       )
       lowercase__      :		Union[str, Any]          =     (
           HansDataset(
               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       ,			evaluate=_lowerCAmelCase       ,			)
           if training_args.do_eval
           else None
       )

       # Initialize our Trainer
       lowercase__      :		Union[str, Any]          =     Trainer(
           model=_lowerCAmelCase       ,			args=_lowerCAmelCase       ,			train_dataset=_lowerCAmelCase       ,			eval_dataset=_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
       if training_args.do_eval:
              logger.info('*** Evaluate ***'					)

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

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

              trainer._log(output.metrics					)





def     a_					(				_lowerCAmelCase  :			Dict					):





       '''simple docstring'''

       main()


if __name__ == "__main__":
       main()


"""simple docstring"""





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					UpperCAmelCase_     (   _a			,							unittest.TestCase):
      lowerCamelCase__		:   Tuple			  =  KandinskyPriorPipeline
      lowerCamelCase__		:   Tuple			  =  ["prompt"]
      lowerCamelCase__		:   List[str]			  =  ["prompt", "negative_prompt"]
      lowerCamelCase__		:   List[Any]			  =  [
          "num_images_per_prompt",
          "generator",
          "num_inference_steps",
          "latents",
          "negative_prompt",
          "guidance_scale",
          "output_type",
          "return_dict",
      ]
      lowerCamelCase__		:   Optional[Any]			  =  False


      @property
      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             return 3_2


      @property
      def 		_UpperCAmelCase	(       self							)	-> str:
             return 3_2


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             return self.time_input_dim


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             return self.time_input_dim * 4


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             return 1_0_0


      @property
      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Tuple          =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)
             return tokenizer


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     CLIPTextConfig(
                 bos_token_id=0		,      eos_token_id=2		,      hidden_size=self.text_embedder_hidden_size		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=3_7		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1_0_0_0		,      )
             return CLIPTextModelWithProjection(a							)


      @property
      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             torch.manual_seed(0							)

             lowercase__      :		int          =     {
                 'num_attention_heads': 2,
                 'attention_head_dim': 1_2,
                 'embedding_dim': self.text_embedder_hidden_size,
                 'num_layers': 1,
             }

             lowercase__      :		Tuple          =     PriorTransformer(**a							)
             # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
             lowercase__      :		int          =     nn.Parameter(torch.ones(model.clip_std.shape							)							)
             return model


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size		,      image_size=2_2_4		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=3_7		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1_4		,      )

             lowercase__      :		Optional[Any]          =     CLIPVisionModelWithProjection(a							)
             return model


      @property
      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		Dict          =     CLIPImageProcessor(
                 crop_size=2_2_4		,      do_center_crop=a		,      do_normalize=a		,      do_resize=a		,      image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,      image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,      resample=3		,      size=2_2_4		,      )

             return image_processor


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		List[str]          =     self.dummy_prior
             lowercase__      :		Tuple          =     self.dummy_image_encoder
             lowercase__      :		Optional[Any]          =     self.dummy_text_encoder
             lowercase__      :		List[str]          =     self.dummy_tokenizer
             lowercase__      :		Optional[int]          =     self.dummy_image_processor

             lowercase__      :		List[Any]          =     UnCLIPScheduler(
                 variance_type='fixed_small_log'		,      prediction_type='sample'		,      num_train_timesteps=1_0_0_0		,      clip_sample=a		,      clip_sample_range=10.0		,      )

             lowercase__      :		List[str]          =     {
                 'prior': prior,
                 'image_encoder': image_encoder,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'scheduler': scheduler,
                 'image_processor': image_processor,
             }

             return components


      def 		_UpperCAmelCase	(       self		,      a		,      a=0							)	-> Union[str, Any]:
             if str(a							).startswith('mps'							):
                    lowercase__      :		Optional[Any]          =     torch.manual_seed(a							)
             else:
                    lowercase__      :		Any          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		str          =     {
                 'prompt': 'horse',
                 'generator': generator,
                 'guidance_scale': 4.0,
                 'num_inference_steps': 2,
                 'output_type': 'np',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Optional[int]          =     'cpu'

             lowercase__      :		Optional[Any]          =     self.get_dummy_components()

             lowercase__      :		Optional[Any]          =     self.pipeline_class(**a							)
             lowercase__      :		Optional[int]          =     pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Optional[int]          =     pipe(**self.get_dummy_inputs(a							)							)
             lowercase__      :		Optional[Any]          =     output.image_embeds

             lowercase__      :		Dict          =     pipe(
                 **self.get_dummy_inputs(a							)		,      return_dict=a		,      )[0]

             lowercase__      :		List[str]          =     image[0, -1_0:]
             lowercase__      :		Optional[Any]          =     image_from_tuple[0, -1_0:]

             assert image.shape == (1, 3_2)

             lowercase__      :		Union[str, Any]          =     np.array(
                 [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]							)

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


      @skip_mps
      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Tuple          =     torch_device == 'cpu'
             lowercase__      :		Union[str, Any]          =     True
             lowercase__      :		int          =     False

             self._test_inference_batch_single_identical(
                 test_max_difference=a		,      relax_max_difference=a		,      test_mean_pixel_difference=a		,      )


      @skip_mps
      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Tuple          =     torch_device == 'cpu'
             lowercase__      :		Any          =     False

             self._test_attention_slicing_forward_pass(
                 test_max_difference=a		,      test_mean_pixel_difference=a		,      )

