




'''simple docstring'''





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a		:	Any       =      logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

a		:	Optional[Any]       =      {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a		:	Optional[Any]       =      {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a		:	Dict       =      sorted(arg_to_scheduler.keys())
a		:	Tuple       =      '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''




class      UpperCamelCase       (	pl.LightningModule       ):



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase="base"		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(__lowerCAmelCase )
      _a				:							List[Any]							       =      0
      _a				:							Dict							       =      Path(self.hparams.output_dir )
      _a				:							Tuple							       =      self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
          _a				:							Optional[Any]							       =      AutoConfig.from_pretrained(
              self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,     **({"""num_labels""": num_labels} if num_labels is not None else {})		,     cache_dir=__lowerCAmelCase		,     **__lowerCAmelCase		,     )
      else:
          _a				:							PretrainedConfig							       =      config

      _a				:							Optional[Any]							       =      ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
      for p in extra_model_params:
          if getattr(self.hparams		,     __lowerCAmelCase		,     __lowerCAmelCase ):
              assert hasattr(self.config		,     __lowerCAmelCase ), f'''model config doesn\'t have a `{p}` attribute'''
              setattr(self.config		,     __lowerCAmelCase		,     getattr(self.hparams		,     __lowerCAmelCase ) )

      if tokenizer is None:
          _a				:							Union[str, Any]							       =      AutoTokenizer.from_pretrained(
              self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,     cache_dir=__lowerCAmelCase		,     )
      else:
          _a				:							PreTrainedTokenizer							       =      tokenizer
      _a				:							Union[str, Any]							       =      MODEL_MODES[mode]
      if model is None:
          _a				:							Optional[int]							       =      self.model_type.from_pretrained(
              self.hparams.model_name_or_path		,     from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path )		,     config=self.config		,     cache_dir=__lowerCAmelCase		,     )
      else:
          _a				:							str							       =      model



  def     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							int							       =      self.model_type.from_pretrained(*__lowerCAmelCase		,     **__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      arg_to_scheduler[self.hparams.lr_scheduler]
      _a				:							Union[str, Any]							       =      get_schedule_func(
          self.opt		,     num_warmup_steps=self.hparams.warmup_steps		,     num_training_steps=self.total_steps() )
      _a				:							List[Any]							       =      {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
      return scheduler



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.model
      _a				:							Tuple							       =      ["""bias""", """LayerNorm.weight"""]
      _a				:							Union[str, Any]							       =      [
          {
              """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
              """weight_decay""": self.hparams.weight_decay,
          },
          {
              """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
              """weight_decay""": 0.0,
          },
      ]
      if self.hparams.adafactor:
          _a				:							Union[str, Any]							       =      Adafactor(
              __lowerCAmelCase		,     lr=self.hparams.learning_rate		,     scale_parameter=__lowerCAmelCase		,     relative_step=__lowerCAmelCase )

      else:
          _a				:							List[str]							       =      AdamW(
              __lowerCAmelCase		,     lr=self.hparams.learning_rate		,     eps=self.hparams.adam_epsilon )
      _a				:							List[str]							       =      optimizer

      _a				:							Optional[Any]							       =      self.get_lr_scheduler()

      return [optimizer], [scheduler]



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      return self.validation_step(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      return self.validation_end(__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      max(1		,     self.hparams.gpus )  # TODO: consider num_tpu_cores
      _a				:							str							       =      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if mode == "test":
          _a				:							int							       =      len(self.test_dataloader().dataset )
      else:
          _a				:							int							       =      self.get_dataloader("""train"""		,     self.hparams.train_batch_size		,     shuffle=__lowerCAmelCase )
          _a				:							List[str]							       =      len(self.train_dataloader().dataset )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = False ):







      '''simple docstring'''


      raise NotImplementedError("""You must implement this for your task""" )



  def     a_							(			self ):







      '''simple docstring'''


      return self.train_loader



  def     a_							(			self ):







      '''simple docstring'''


      return self.get_dataloader("""dev"""		,     self.hparams.eval_batch_size		,     shuffle=__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      return self.get_dataloader("""test"""		,     self.hparams.eval_batch_size		,     shuffle=__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      return os.path.join(
          self.hparams.data_dir		,     """cached_{}_{}_{}""".format(
              __lowerCAmelCase		,     list(filter(__lowerCAmelCase		,     self.hparams.model_name_or_path.split("""/""" ) ) ).pop()		,     str(self.hparams.max_seq_length )		,     )		,     )



  @pl.utilities.rank_zero_only
  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.output_dir.joinpath("""best_tfmr""" )
      _a				:							Optional[int]							       =      self.step_count
      self.model.save_pretrained(__lowerCAmelCase )
      self.tokenizer.save_pretrained(__lowerCAmelCase )



  @staticmethod
  def     a_							(			__lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      parser.add_argument(
          """--model_name_or_path"""		,     default=__lowerCAmelCase		,     type=__lowerCAmelCase		,     required=__lowerCAmelCase		,     help="""Path to pretrained model or model identifier from huggingface.co/models"""		,     )
      parser.add_argument(
          """--config_name"""		,     default=""""""		,     type=__lowerCAmelCase		,     help="""Pretrained config name or path if not the same as model_name""" )
      parser.add_argument(
          """--tokenizer_name"""		,     default=__lowerCAmelCase		,     type=__lowerCAmelCase		,     help="""Pretrained tokenizer name or path if not the same as model_name"""		,     )
      parser.add_argument(
          """--cache_dir"""		,     default=""""""		,     type=__lowerCAmelCase		,     help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""		,     )
      parser.add_argument(
          """--encoder_layerdrop"""		,     type=__lowerCAmelCase		,     help="""Encoder layer dropout probability (Optional). Goes into model.config"""		,     )
      parser.add_argument(
          """--decoder_layerdrop"""		,     type=__lowerCAmelCase		,     help="""Decoder layer dropout probability (Optional). Goes into model.config"""		,     )
      parser.add_argument(
          """--dropout"""		,     type=__lowerCAmelCase		,     help="""Dropout probability (Optional). Goes into model.config"""		,     )
      parser.add_argument(
          """--attention_dropout"""		,     type=__lowerCAmelCase		,     help="""Attention dropout probability (Optional). Goes into model.config"""		,     )
      parser.add_argument("""--learning_rate"""		,     default=5e-5		,     type=__lowerCAmelCase		,     help="""The initial learning rate for Adam.""" )
      parser.add_argument(
          """--lr_scheduler"""		,     default="""linear"""		,     choices=__lowerCAmelCase		,     metavar=__lowerCAmelCase		,     type=__lowerCAmelCase		,     help="""Learning rate scheduler"""		,     )
      parser.add_argument("""--weight_decay"""		,     default=0.0		,     type=__lowerCAmelCase		,     help="""Weight decay if we apply some.""" )
      parser.add_argument("""--adam_epsilon"""		,     default=1e-8		,     type=__lowerCAmelCase		,     help="""Epsilon for Adam optimizer.""" )
      parser.add_argument("""--warmup_steps"""		,     default=0		,     type=__lowerCAmelCase		,     help="""Linear warmup over warmup_steps.""" )
      parser.add_argument("""--num_workers"""		,     default=4		,     type=__lowerCAmelCase		,     help="""kwarg passed to DataLoader""" )
      parser.add_argument("""--num_train_epochs"""		,     dest="""max_epochs"""		,     default=3		,     type=__lowerCAmelCase )
      parser.add_argument("""--train_batch_size"""		,     default=32		,     type=__lowerCAmelCase )
      parser.add_argument("""--eval_batch_size"""		,     default=32		,     type=__lowerCAmelCase )
      parser.add_argument("""--adafactor"""		,     action="""store_true""" )






class      UpperCamelCase       (	pl.Callback       ):



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      trainer.lr_schedulers[0]["""scheduler"""]
      _a				:							Union[str, Any]							       =      {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
      pl_module.logger.log_metrics(__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      rank_zero_info("""***** Validation results *****""" )
      _a				:							List[str]							       =      trainer.callback_metrics
      # Log results
      for key in sorted(__lowerCAmelCase ):
          if key not in ["log", "progress_bar"]:
              rank_zero_info("""{} = {}\n""".format(__lowerCAmelCase		,     str(metrics[key] ) ) )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      rank_zero_info("""***** Test results *****""" )
      _a				:							str							       =      trainer.callback_metrics
      # Log and save results to file
      _a				:							Optional[Any]							       =      os.path.join(pl_module.hparams.output_dir		,     """test_results.txt""" )
      with open(__lowerCAmelCase		,     """w""" ) as writer:
          for key in sorted(__lowerCAmelCase ):
              if key not in ["log", "progress_bar"]:
                  rank_zero_info("""{} = {}\n""".format(__lowerCAmelCase		,     str(metrics[key] ) ) )
                  writer.write("""{} = {}\n""".format(__lowerCAmelCase		,     str(metrics[key] ) ) )






def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        """--output_dir"""   ,	default=UpperCAmelCase_   ,	type=UpperCAmelCase_   ,	required=UpperCAmelCase_   ,	help="""The output directory where the model predictions and checkpoints will be written."""   ,	)
    parser.add_argument(
        """--fp16"""   ,	action="""store_true"""   ,	help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""   ,	)

    parser.add_argument(
        """--fp16_opt_level"""   ,	type=UpperCAmelCase_   ,	default="""O2"""   ,	help=(
            """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
            """See details at https://nvidia.github.io/apex/amp.html"""
        )   ,	)
    parser.add_argument("""--n_tpu_cores"""   ,	dest="""tpu_cores"""   ,	type=UpperCAmelCase_						)
    parser.add_argument("""--max_grad_norm"""   ,	dest="""gradient_clip_val"""   ,	default=1.0   ,	type=UpperCAmelCase_   ,	help="""Max gradient norm"""						)
    parser.add_argument("""--do_train"""   ,	action="""store_true"""   ,	help="""Whether to run training."""						)
    parser.add_argument("""--do_predict"""   ,	action="""store_true"""   ,	help="""Whether to run predictions on the test set."""						)
    parser.add_argument(
        """--gradient_accumulation_steps"""   ,	dest="""accumulate_grad_batches"""   ,	type=UpperCAmelCase_   ,	default=1   ,	help="""Number of updates steps to accumulate before performing a backward/update pass."""   ,	)
    parser.add_argument("""--seed"""   ,	type=UpperCAmelCase_   ,	default=42   ,	help="""random seed for initialization"""						)
    parser.add_argument(
        """--data_dir"""   ,	default=UpperCAmelCase_   ,	type=UpperCAmelCase_   ,	required=UpperCAmelCase_   ,	help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""   ,	)





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=True   ,	UpperCAmelCase_=[]   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=None   ,	**UpperCAmelCase_   ,	):
    pl.seed_everything(args.seed						)

    # init model
    _a				:							List[str]							       =      Path(model.hparams.output_dir						)
    odir.mkdir(exist_ok=UpperCAmelCase_						)

    # add custom checkpoints
    if checkpoint_callback is None:
        _a				:							Dict							       =      pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir   ,	prefix="""checkpoint"""   ,	monitor="""val_loss"""   ,	mode="""min"""   ,	save_top_k=1						)
    if early_stopping_callback:
        extra_callbacks.append(UpperCAmelCase_						)
    if logging_callback is None:
        _a				:							Dict							       =      LoggingCallback()

    _a				:							Optional[int]							       =      {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        _a				:							int							       =      16
        _a				:							Optional[Any]							       =      args.fpaa_opt_level

    if args.gpus > 1:
        _a				:							Dict							       =      """ddp"""

    _a				:							Tuple							       =      args.accumulate_grad_batches
    _a				:							Any							       =      extra_train_kwargs.get("""accelerator"""   ,	UpperCAmelCase_						)
    _a				:							Optional[Any]							       =      extra_train_kwargs.get("""profiler"""   ,	UpperCAmelCase_						)

    _a				:							Optional[Any]							       =      pl.Trainer.from_argparse_args(
        UpperCAmelCase_   ,	weights_summary=UpperCAmelCase_   ,	callbacks=[logging_callback] + extra_callbacks   ,	logger=UpperCAmelCase_   ,	checkpoint_callback=UpperCAmelCase_   ,	**UpperCAmelCase_   ,	)

    if args.do_train:
        trainer.fit(UpperCAmelCase_						)

    return trainer



"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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      UpperCamelCase__(      __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Tuple    				=		KandinskyPipeline
						lowerCAmelCase__	:    int    				=		[
						    'prompt',
						    'image_embeds',
						    'negative_image_embeds',
						]
						lowerCAmelCase__	:    str    				=		['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
						lowerCAmelCase__	:    Any    				=		[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						lowerCAmelCase__	:    Dict    				=		False





						@property
						def 					snake_case__						(							self  )       ->					List[Any]:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Any:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Dict:
													return self.time_input_dim





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													return self.time_input_dim * 4





						@property
						def 					snake_case__						(							self  )       ->					List[str]:
													return 1_00





						@property
						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
													return tokenizer





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													torch.manual_seed(0  )
													A__						  =				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=10_05		,)

													A__						  =				MultilingualCLIP(__UpperCAmelCase  )
													A__						  =				text_encoder.eval()

													return text_encoder





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													torch.manual_seed(0  )

													A__						  =				{
													    '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,
													}

													A__						  =				UNetaDConditionModel(**__UpperCAmelCase  )
													return model





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													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  )       ->					Any:
													torch.manual_seed(0  )
													A__						  =				VQModel(**self.dummy_movq_kwargs  )
													return model





						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				self.dummy_text_encoder
													A__						  =				self.dummy_tokenizer
													A__						  =				self.dummy_unet
													A__						  =				self.dummy_movq

													A__						  =				DDIMScheduler(
													    num_train_timesteps=10_00		,beta_schedule='linear'		,beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,clip_sample=__UpperCAmelCase		,set_alpha_to_one=__UpperCAmelCase		,steps_offset=1		,prediction_type='epsilon'		,thresholding=__UpperCAmelCase		,)

													A__						  =				{
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}
													return components





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase=0  )       ->					Optional[Any]:
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(__UpperCAmelCase  )  ).to(__UpperCAmelCase  )
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(seed + 1  )  ).to(__UpperCAmelCase  )
													if str(__UpperCAmelCase  ).startswith('mps'  ):
																				A__						  =				torch.manual_seed(__UpperCAmelCase  )
													else:
																				A__						  =				torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
													A__						  =				{
													    'prompt': 'horse',
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				'cpu'

													A__						  =				self.get_dummy_components()

													A__						  =				self.pipeline_class(**__UpperCAmelCase  )
													A__						  =				pipe.to(__UpperCAmelCase  )

													pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
													A__						  =				output.images

													A__						  =				pipe(
													    **self.get_dummy_inputs(__UpperCAmelCase  )		,return_dict=__UpperCAmelCase		,)[0]

													A__						  =				image[0, -3:, -3:, -1]
													A__						  =				image_from_tuple[0, -3:, -3:, -1]

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

													A__						  =				np.array(
													    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]  )

													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      UpperCamelCase__(      unittest.TestCase			):





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





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

													A__						  =				KandinskyPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-prior'		,torch_dtype=torch.floataa  )
													pipe_prior.to(__UpperCAmelCase  )

													A__						  =				KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'		,torch_dtype=torch.floataa  )
													A__						  =				pipeline.to(__UpperCAmelCase  )
													pipeline.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				'red cat, 4k photo'

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__  ,	A__						  =				pipe_prior(
													    __UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=5		,negative_prompt=''		,).to_tuple()

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__						  =				pipeline(
													    __UpperCAmelCase		,image_embeds=__UpperCAmelCase		,negative_image_embeds=__UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=1_00		,output_type='np'		,)

													A__						  =				output.images[0]

													assert image.shape == (5_12, 5_12, 3)

													assert_mean_pixel_difference(__UpperCAmelCase		,__UpperCAmelCase  )





