
"""simple docstring"""



import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__          =	logging.getLogger(__name__)





@dataclass(frozen=lowercase      )
class 				a			:
      UpperCamelCase							:   str
      UpperCamelCase							:   str
      UpperCamelCase							:   Optional[str]           =     None
      UpperCamelCase							:   Optional[str]           =     None
      UpperCamelCase							:   Optional[str]           =     None





@dataclass(frozen=lowercase      )
class 				a			:
      UpperCamelCase							:   List[int]
      UpperCamelCase							:   Optional[List[int]]           =     None
      UpperCamelCase							:   Optional[List[int]]           =     None
      UpperCamelCase							:   Optional[Union[int, float]]           =     None
      UpperCamelCase							:   Optional[int]           =     None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset





    class 				a			(					lowercase      ):
          UpperCamelCase							:   List[InputFeatures]



          def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_=False     ,	UpperCamelCase_ = False     ,	):
              UpperCAmelCase__	:      Dict     							=					hans_processors[task]()

              UpperCAmelCase__	:      int     							=					os.path.join(
                  UpperCamelCase_     ,	'cached_{}_{}_{}_{}'.format(
                      'dev' if evaluate else 'train'     ,	tokenizer.__class__.__name__     ,	str(UpperCamelCase_      )     ,	UpperCamelCase_     ,	)     ,	)
              UpperCAmelCase__	:      Optional[Any]     							=					processor.get_labels()
              if tokenizer.__class__ in (
                  RobertaTokenizer,
                  RobertaTokenizerFast,
                  XLMRobertaTokenizer,
                  BartTokenizer,
                  BartTokenizerFast,
              ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					label_list[2], label_list[1]
              UpperCAmelCase__	:      Any     							=					label_list

              # Make sure only the first process in distributed training processes the dataset,
              # and the others will use the cache.
              UpperCAmelCase__	:      str     							=					cached_features_file + '.lock'
              with FileLock(UpperCamelCase_      ):
                  if os.path.exists(UpperCamelCase_      ) and not overwrite_cache:
                      logger.info(F'''Loading features from cached file {cached_features_file}'''      )
                      UpperCAmelCase__	:      Union[str, Any]     							=					torch.load(UpperCamelCase_      )
                  else:
                      logger.info(F'''Creating features from dataset file at {data_dir}'''      )

                      UpperCAmelCase__	:      Union[str, Any]     							=					(
                          processor.get_dev_examples(UpperCamelCase_      ) if evaluate else processor.get_train_examples(UpperCamelCase_      )
                      )

                      logger.info('Training examples: %s'     ,	len(UpperCamelCase_      )      )
                      UpperCAmelCase__	:      Any     							=					hans_convert_examples_to_features(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )
                      logger.info('Saving features into cached file %s'     ,	UpperCamelCase_      )
                      torch.save(self.features     ,	UpperCamelCase_      )



          def __len__(   self      ):
              return len(self.features      )



          def __getitem__(   self     ,	UpperCamelCase_      ):
              return self.features[i]



          def  __snake_case							(   self      ):
              return self.label_list


if is_tf_available():
    import tensorflow as tf





    class 				a			:
          UpperCamelCase							:   List[InputFeatures]



          def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = 128     ,	UpperCamelCase_=False     ,	UpperCamelCase_ = False     ,	):
              UpperCAmelCase__	:      int     							=					hans_processors[task]()
              UpperCAmelCase__	:      Optional[int]     							=					processor.get_labels()
              if tokenizer.__class__ in (
                  RobertaTokenizer,
                  RobertaTokenizerFast,
                  XLMRobertaTokenizer,
                  BartTokenizer,
                  BartTokenizerFast,
              ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  UpperCAmelCase__							,							UpperCAmelCase__	:      Tuple     							=					label_list[2], label_list[1]
              UpperCAmelCase__	:      Tuple     							=					label_list

              UpperCAmelCase__	:      str     							=					processor.get_dev_examples(UpperCamelCase_      ) if evaluate else processor.get_train_examples(UpperCamelCase_      )
              UpperCAmelCase__	:      str     							=					hans_convert_examples_to_features(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )

              def gen():
                  for ex_index, ex in tqdm.tqdm(enumerate(self.features      )     ,	desc='convert examples to features'      ):
                      if ex_index % 10_000 == 0:
                          logger.info('Writing example %d of %d' % (ex_index, len(UpperCamelCase_      ))      )

                      yield (
                          {
                              "example_id": 0,
                              "input_ids": ex.input_ids,
                              "attention_mask": ex.attention_mask,
                              "token_type_ids": ex.token_type_ids,
                          },
                          ex.label,
                      )

              UpperCAmelCase__	:      Any     							=					tf.data.Dataset.from_generator(
                  UpperCamelCase_     ,	(
                      {
                          'example_id': tf.intaa,
                          'input_ids': tf.intaa,
                          'attention_mask': tf.intaa,
                          'token_type_ids': tf.intaa,
                      },
                      tf.intaa,
                  )     ,	(
                      {
                          'example_id': tf.TensorShape([]      ),
                          'input_ids': tf.TensorShape([None, None]      ),
                          'attention_mask': tf.TensorShape([None, None]      ),
                          'token_type_ids': tf.TensorShape([None, None]      ),
                      },
                      tf.TensorShape([]      ),
                  )     ,	)



          def  __snake_case							(   self      ):
              return self.dataset



          def __len__(   self      ):
              return len(self.features      )



          def __getitem__(   self     ,	UpperCamelCase_      ):
              return self.features[i]



          def  __snake_case							(   self      ):
              return self.label_list





class 				a			(					lowercase      ):



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase_     ,	'heuristics_train_set.txt'      )      )     ,	'train'      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase_     ,	'heuristics_evaluation_set.txt'      )      )     ,	'dev'      )



      def  __snake_case							(   self      ):
          return ["contradiction", "entailment", "neutral"]



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[Any]     							=					[]
          for i, line in enumerate(UpperCamelCase_      ):
              if i == 0:
                  continue
              UpperCAmelCase__	:      List[Any]     							=					'%s-%s' % (set_type, line[0])
              UpperCAmelCase__	:      List[str]     							=					line[5]
              UpperCAmelCase__	:      Union[str, Any]     							=					line[6]
              UpperCAmelCase__	:      List[str]     							=					line[7][2:] if line[7].startswith('ex'      ) else line[7]
              UpperCAmelCase__	:      str     							=					line[0]
              examples.append(InputExample(guid=UpperCamelCase_     ,	text_a=UpperCamelCase_     ,	text_b=UpperCamelCase_     ,	label=UpperCamelCase_     ,	pairID=UpperCamelCase_      )      )
          return examples




def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,):
    UpperCAmelCase__	:      Union[str, Any]     							=					{label: i for i, label in enumerate(_snake_case )}

    UpperCAmelCase__	:      Dict     							=					[]
    for ex_index, example in tqdm.tqdm(enumerate(_snake_case )    ,desc='convert examples to features' ):
        if ex_index % 10000 == 0:
            logger.info('Writing example %d' % (ex_index) )

        UpperCAmelCase__	:      Optional[Any]     							=					tokenizer(
            example.text_a    ,example.text_b    ,add_special_tokens=_snake_case    ,max_length=_snake_case    ,padding='max_length'    ,truncation=_snake_case    ,return_overflowing_tokens=_snake_case    ,)

        UpperCAmelCase__	:      List[str]     							=					label_map[example.label] if example.label in label_map else 0

        UpperCAmelCase__	:      Optional[int]     							=					int(example.pairID )

        features.append(InputFeatures(**_snake_case    ,label=_snake_case    ,pairID=_snake_case ) )

    for i, example in enumerate(examples[:5] ):
        logger.info('*** Example ***' )
        logger.info(F'''guid: {example}''' )
        logger.info(F'''features: {features[i]}''' )

    return features


UpperCamelCase__          =	{
    'hans': 3,
}

UpperCamelCase__          =	{
    'hans': HansProcessor,
}



"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Any           =     KandinskyImgaImgPipeline
      UpperCamelCase							:   int           =     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
      UpperCamelCase							:   Optional[Any]           =     [
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
      ]
      UpperCamelCase							:   Union[str, Any]           =     [
          """generator""",
          """height""",
          """width""",
          """strength""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      UpperCamelCase							:   List[str]           =     False



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim * 4



      @property
      def  __snake_case							(   self      ):
          return 100



      @property
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'      )
          return tokenizer



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[int]     							=					MCLIPConfig(
              numDims=self.cross_attention_dim     ,	transformerDimensions=self.text_embedder_hidden_size     ,	hidden_size=self.text_embedder_hidden_size     ,	intermediate_size=37     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	vocab_size=1_005     ,	)

          UpperCAmelCase__	:      Tuple     							=					MultilingualCLIP(UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					text_encoder.eval()

          return text_encoder



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )

          UpperCAmelCase__	:      Optional[Any]     							=					{
              'in_channels': 4,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'text_image',
              'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
              'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
              'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
              'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
              'layers_per_block': 1,
              'encoder_hid_dim': self.text_embedder_hidden_size,
              'encoder_hid_dim_type': 'text_image_proj',
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': None,
          }

          UpperCAmelCase__	:      Optional[Any]     							=					UNetaDConditionModel(**UpperCamelCase_      )
          return model



      @property
      def  __snake_case							(   self      ):
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[Any]     							=					VQModel(**self.dummy_movq_kwargs      )
          return model



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.dummy_text_encoder
          UpperCAmelCase__	:      int     							=					self.dummy_tokenizer
          UpperCAmelCase__	:      int     							=					self.dummy_unet
          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_movq

          UpperCAmelCase__	:      Any     							=					{
              'num_train_timesteps': 1_000,
              'beta_schedule': 'linear',
              'beta_start': 0.00085,
              'beta_end': 0.012,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          UpperCAmelCase__	:      List[Any]     							=					DDIMScheduler(**UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      int     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(seed + 1      )      ).to(UpperCamelCase_      )
          # create init_image
          UpperCAmelCase__	:      Optional[int]     							=					floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
          UpperCAmelCase__	:      Union[str, Any]     							=					Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      ).resize((256, 256)      )

          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Dict     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[int]     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					{
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 10,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'cpu'

          UpperCAmelCase__	:      Any     							=					self.get_dummy_components()

          UpperCAmelCase__	:      Any     							=					self.pipeline_class(**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					pipe.to(UpperCamelCase_      )

          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					pipe(**self.get_dummy_inputs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      Optional[int]     							=					output.images

          UpperCAmelCase__	:      Dict     							=					pipe(
              **self.get_dummy_inputs(UpperCamelCase_      )     ,	return_dict=UpperCamelCase_     ,	)[0]

          UpperCAmelCase__	:      Dict     							=					image[0, -3:, -3:, -1]
          UpperCAmelCase__	:      Any     							=					image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCAmelCase__	:      Any     							=					np.array(
              [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
          assert (
              np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
          ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
          ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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



      def  __snake_case							(   self      ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy'      )

          UpperCAmelCase__	:      List[Any]     							=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
          UpperCAmelCase__	:      Any     							=					'A red cartoon frog, 4k'

          UpperCAmelCase__	:      Union[str, Any]     							=					KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'     ,	torch_dtype=torch.floataa      )
          pipe_prior.to(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'     ,	torch_dtype=torch.floataa      )
          UpperCAmelCase__	:      Optional[Any]     							=					pipeline.to(UpperCamelCase_      )

          pipeline.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					torch.Generator(device='cpu'      ).manual_seed(0      )
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					pipe_prior(
              UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=5     ,	negative_prompt=''     ,	).to_tuple()

          UpperCAmelCase__	:      Optional[Any]     							=					pipeline(
              UpperCamelCase_     ,	image=UpperCamelCase_     ,	image_embeds=UpperCamelCase_     ,	negative_image_embeds=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=100     ,	height=768     ,	width=768     ,	strength=0.2     ,	output_type='np'     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(UpperCamelCase_     ,	UpperCamelCase_      )

