

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 			__lowerCAmelCase					( _lowerCamelCase      ,      unittest.TestCase							):
  _a						      =  KandinskyPriorPipeline
  _a						      =  ["""prompt"""]
  _a						      =  ["""prompt""", """negative_prompt"""]
  _a						      =  [
      """num_images_per_prompt""",
      """generator""",
      """num_inference_steps""",
      """latents""",
      """negative_prompt""",
      """guidance_scale""",
      """output_type""",
      """return_dict""",
  ]
  _a						      =  False




  @property
  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        return 32




  @property
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        return 32




  @property
  def   A__					(    self )  ->			str:
        '''simple docstring'''






        return self.time_input_dim




  @property
  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        return self.time_input_dim * 4




  @property
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        return 100




  @property
  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
        return tokenizer




  @property
  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        torch.manual_seed(0 )
        _lowercase     	=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=37							,       layer_norm_eps=1e-05							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=1_000							,       )
        return CLIPTextModelWithProjection(__UpperCamelCase )




  @property
  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        torch.manual_seed(0 )

        _lowercase     	={
            'num_attention_heads': 2,
            'attention_head_dim': 12,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        _lowercase     	=PriorTransformer(**__UpperCamelCase )
        # 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     	=nn.Parameter(torch.ones(model.clip_std.shape ) )
        return model




  @property
  def   A__					(    self )  ->			int:
        '''simple docstring'''






        torch.manual_seed(0 )
        _lowercase     	=CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size							,       image_size=224							,       projection_dim=self.text_embedder_hidden_size							,       intermediate_size=37							,       num_attention_heads=4							,       num_channels=3							,       num_hidden_layers=5							,       patch_size=14							,       )

        _lowercase     	=CLIPVisionModelWithProjection(__UpperCamelCase )
        return model




  @property
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=CLIPImageProcessor(
            crop_size=224							,       do_center_crop=__UpperCamelCase							,       do_normalize=__UpperCamelCase							,       do_resize=__UpperCamelCase							,       image_mean=[0.48145466, 0.4578275, 0.40821073]							,       image_std=[0.26862954, 0.26130258, 0.27577711]							,       resample=3							,       size=224							,       )

        return image_processor




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.dummy_prior
        _lowercase     	=self.dummy_image_encoder
        _lowercase     	=self.dummy_text_encoder
        _lowercase     	=self.dummy_tokenizer
        _lowercase     	=self.dummy_image_processor

        _lowercase     	=UnCLIPScheduler(
            variance_type='fixed_small_log'							,       prediction_type='sample'							,       num_train_timesteps=1_000							,       clip_sample=__UpperCamelCase							,       clip_sample_range=10.0							,       )

        _lowercase     	={
            'prior': prior,
            'image_encoder': image_encoder,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'scheduler': scheduler,
            'image_processor': image_processor,
        }

        return components




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=0 )  ->			Any:
        '''simple docstring'''






        if str(__UpperCamelCase ).startswith('mps' ):
              _lowercase     	=torch.manual_seed(__UpperCamelCase )
        else:
              _lowercase     	=torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
        _lowercase     	={
            'prompt': 'horse',
            'generator': generator,
            'guidance_scale': 4.0,
            'num_inference_steps': 2,
            'output_type': 'np',
        }
        return inputs




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	='cpu'

        _lowercase     	=self.get_dummy_components()

        _lowercase     	=self.pipeline_class(**__UpperCamelCase )
        _lowercase     	=pipe.to(__UpperCamelCase )

        pipe.set_progress_bar_config(disable=__UpperCamelCase )

        _lowercase     	=pipe(**self.get_dummy_inputs(__UpperCamelCase ) )
        _lowercase     	=output.image_embeds

        _lowercase     	=pipe(
            **self.get_dummy_inputs(__UpperCamelCase )							,       return_dict=__UpperCamelCase							,       )[0]

        _lowercase     	=image[0, -10:]
        _lowercase     	=image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        _lowercase     	=np.array(
            [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )

        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   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=torch_device == 'cpu'
        _lowercase     	=True
        _lowercase     	=False

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




  @skip_mps
  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=torch_device == 'cpu'
        _lowercase     	=False

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





#
# Sort large text files in a minimum amount of memory
#
import argparse
import os





class 			__lowerCAmelCase					:
  _a						      =  """block_{0}.dat"""




  def __init__(    self							,       lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=filename
        _lowercase     	=[]




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.BLOCK_FILENAME_FORMAT.format(lowerCAmelCase )
        with open(lowerCAmelCase							,       'w' ) as file:
              file.write(lowerCAmelCase )
        self.block_filenames.append(lowerCAmelCase )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        return self.block_filenames




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=None )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=0
        with open(self.filename ) as file:
              while True:
                    _lowercase     	=file.readlines(lowerCAmelCase )

                    if lines == []:
                          break

                    if sort_key is None:
                          lines.sort()
                    else:
                          lines.sort(key=lowerCAmelCase )

                    self.write_block(''.join(lowerCAmelCase )							,       lowerCAmelCase )
                    i += 1




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        map(os.remove							,       self.block_filenames )





class 			__lowerCAmelCase					:




  def   A__					(    self							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=-1
        _lowercase     	=None

        for i in range(len(lowerCAmelCase ) ):
              if min_str is None or choices[i] < min_str:
                    _lowercase     	=i

        return min_index





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase )  ->			int:
        '''simple docstring'''






        _lowercase     	=files
        _lowercase     	=set()
        _lowercase     	=len(lowerCAmelCase )
        _lowercase     	={i: None for i in range(self.num_buffers )}




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        return {
            i: self.buffers[i] for i in range(self.num_buffers ) if i not in self.empty
        }




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        for i in range(self.num_buffers ):
              if self.buffers[i] is None and i not in self.empty:
                    _lowercase     	=self.files[i].readline()

                    if self.buffers[i] == "":
                          self.empty.add(lowerCAmelCase )
                          self.files[i].close()

        if len(self.empty ) == self.num_buffers:
              return False

        return True




  def   A__					(    self							,       lowerCAmelCase )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.buffers[index]
        _lowercase     	=None

        return value





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase )  ->			Any:
        '''simple docstring'''






        _lowercase     	=merge_strategy




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=FilesArray(self.get_file_handles(lowerCAmelCase							,       lowerCAmelCase ) )
        with open(lowerCAmelCase							,       'w'							,       lowerCAmelCase ) as outfile:
              while buffers.refresh():
                    _lowercase     	=self.merge_strategy.select(buffers.get_dict() )
                    outfile.write(buffers.unshift(lowerCAmelCase ) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	={}

        for i in range(len(lowerCAmelCase ) ):
              _lowercase     	=open(filenames[i]							,       'r'							,       lowerCAmelCase )  # noqa: UP015

        return files





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=block_size




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=None )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.get_number_blocks(lowerCAmelCase							,       self.block_size )
        _lowercase     	=FileSplitter(lowerCAmelCase )
        splitter.split(self.block_size							,       lowerCAmelCase )

        _lowercase     	=FileMerger(NWayMerge() )
        _lowercase     	=self.block_size / (num_blocks + 1)
        merger.merge(splitter.get_block_filenames()							,       filename + '.out'							,       lowerCAmelCase )

        splitter.cleanup()




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Tuple:
        '''simple docstring'''






        return (os.stat(lowerCAmelCase ).st_size / block_size) + 1
def 					a				(      A__     :       Tuple					)   ->				Optional[Any]:





      """simple docstring"""



      if string[-1].lower() == "k":
            return int(string[:-1]					) * 1024
      elif string[-1].lower() == "m":
            return int(string[:-1]					) * 1024 * 1024
      elif string[-1].lower() == "g":
            return int(string[:-1]					) * 1024 * 1024 * 1024
      else:
            return int(A__					)
def 					a				(      )   ->				str:





      """simple docstring"""



      _lowercase     	=argparse.ArgumentParser()
      parser.add_argument(
          '-m'				,       '--mem'				,       help='amount of memory to use for sorting'				,       default='100M'					)
      parser.add_argument(
          'filename'				,       metavar='<filename>'				,       nargs=1				,       help='name of file to sort'					)
      _lowercase     	=parser.parse_args()

      _lowercase     	=ExternalSort(parse_memory(args.mem					)					)
      sorter.sort(args.filename[0]					)


if __name__ == "__main__":
  main()


