






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 					snake_case_							(       lowercase_      ,			lowercase_      ,			unittest.TestCase      ):







   """simple docstring"""



   __lowerCAmelCase      =						StableDiffusionParadigmsPipeline
   __lowerCAmelCase      =						TEXT_TO_IMAGE_PARAMS
   __lowerCAmelCase      =						TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase      =						TEXT_TO_IMAGE_IMAGE_PARAMS
   __lowerCAmelCase      =						TEXT_TO_IMAGE_IMAGE_PARAMS

   def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		int:


       """simple docstring"""



       torch.manual_seed(0	)
       _lowercase     =	UNetaDConditionModel(
           block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,       cross_attention_dim=32   ,       attention_head_dim=(2, 4)   ,       use_linear_projection=snake_case__   ,       )
       _lowercase     =	DDIMParallelScheduler(
           beta_start=0.0_00_85   ,       beta_end=0.0_12   ,       beta_schedule='scaled_linear'   ,       clip_sample=snake_case__   ,       set_alpha_to_one=snake_case__   ,       )
       torch.manual_seed(0	)
       _lowercase     =	AutoencoderKL(
           block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,       latent_channels=4   ,       sample_size=128   ,       )
       torch.manual_seed(0	)
       _lowercase     =	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=1_000   ,       hidden_act='gelu'   ,       projection_dim=512   ,       )
       _lowercase     =	CLIPTextModel(snake_case__	)
       _lowercase     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

       _lowercase     =	{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components

   def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Any=0	)  ->		str:


       """simple docstring"""



       if str(snake_case__	).startswith('mps'	):
           _lowercase     =	torch.manual_seed(snake_case__	)
       else:
           _lowercase     =	torch.Generator(device=snake_case__	).manual_seed(snake_case__	)
       _lowercase     =	{
           'prompt': 'a photograph of an astronaut riding a horse',
           'generator': generator,
           'num_inference_steps': 10,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'parallel': 3,
           'debug': True,
       }
       return inputs

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Dict:


       """simple docstring"""



       _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
       _lowercase     =	self.get_dummy_components()
       _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
       _lowercase     =	sd_pipe.to(snake_case__	)
       sd_pipe.set_progress_bar_config(disable=snake_case__	)

       _lowercase     =	self.get_dummy_inputs(snake_case__	)
       _lowercase     =	sd_pipe(**snake_case__	).images
       _lowercase     =	image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       _lowercase     =	np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]	)

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

   def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Union[str, Any]:


       """simple docstring"""



       _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
       _lowercase     =	self.get_dummy_components()
       torch.manual_seed(0	)
       _lowercase     =	DDPMParallelScheduler()
       torch.manual_seed(0	)
       _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
       _lowercase     =	sd_pipe.to(snake_case__	)
       sd_pipe.set_progress_bar_config(disable=snake_case__	)

       _lowercase     =	self.get_dummy_inputs(snake_case__	)
       _lowercase     =	sd_pipe(**snake_case__	).images
       _lowercase     =	image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       _lowercase     =	np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]	)

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

   def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Tuple:


       """simple docstring"""



       super().test_inference_batch_consistent(batch_sizes=[1, 2]	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		int:


       """simple docstring"""



       super().test_inference_batch_single_identical(batch_size=2   ,       expected_max_diff=3E-3	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		int:


       """simple docstring"""



       _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
       _lowercase     =	self.get_dummy_components()
       _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
       _lowercase     =	sd_pipe.to(snake_case__	)
       sd_pipe.set_progress_bar_config(disable=snake_case__	)

       _lowercase     =	self.get_dummy_inputs(snake_case__	)
       _lowercase     =	'french fries'
       _lowercase     =	sd_pipe(**snake_case__   ,       negative_prompt=snake_case__	)
       _lowercase     =	output.images
       _lowercase     =	image[0, -3:, -3:, -1]

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

       _lowercase     =	np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]	)

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






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







   """simple docstring"""



   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		List[Any]:


       """simple docstring"""



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	Tuple=0	)  ->		Dict:


       """simple docstring"""



       _lowercase     =	torch.Generator(device=snake_case__	).manual_seed(snake_case__	)
       _lowercase     =	{
           'prompt': 'a photograph of an astronaut riding a horse',
           'generator': generator,
           'num_inference_steps': 10,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
           'parallel': 3,
           'debug': True,
       }
       return inputs

   def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		List[str]:


       """simple docstring"""



       _lowercase     =	'stabilityai/stable-diffusion-2-base'
       _lowercase     =	DDIMParallelScheduler.from_pretrained(snake_case__   ,       subfolder='scheduler'	)
       _lowercase     =	StableDiffusionParadigmsPipeline.from_pretrained(snake_case__   ,       scheduler=snake_case__   ,       safety_checker=snake_case__	)
       pipe.to(snake_case__	)
       pipe.set_progress_bar_config(disable=snake_case__	)
       pipe.enable_attention_slicing()

       _lowercase     =	self.get_inputs()
       _lowercase     =	pipe(**snake_case__	).images
       _lowercase     =	image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)

       _lowercase     =	np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]	)

       assert np.abs(expected_slice - image_slice	).max() < 1E-2








from __future__ import annotations

import sys

class 					snake_case_							:







   """simple docstring"""



   def __init__(    self:	Any   ,       snake_case__:	str   ,       snake_case__:	int	)  ->		Optional[Any]:


       """simple docstring"""



       _lowercase     =	letter
       _lowercase     =	freq
       _lowercase     =	{}

   def __repr__(    self:	Optional[int]	)  ->		str:


       """simple docstring"""



       return f'''{self.letter}:{self.freq}'''






class 					snake_case_							:







   """simple docstring"""



   def __init__(    self:	Optional[int]   ,       snake_case__:	int   ,       snake_case__:	Letter | TreeNode   ,       snake_case__:	Letter | TreeNode	)  ->		List[Any]:


       """simple docstring"""



       _lowercase     =	freq
       _lowercase     =	left
       _lowercase     =	right






def        UpperCAmelCase      (   _lowercase      ):


    '''simple docstring'''



    _lowercase     =	{}
    with open(_lowercase      ) as f:
        while True:
            _lowercase     =	f.read(1      )
            if not c:
                break
            _lowercase     =	chars[c] + 1 if c in chars else 1
    return sorted((Letter(_lowercase,      _lowercase      ) for c, f in chars.items()),      key=lambda _lowercase      : x.freq      )
def        UpperCAmelCase      (   _lowercase      ):


    '''simple docstring'''



    _lowercase     =	letters  # type: ignore
    while len(_lowercase      ) > 1:
        _lowercase     =	response.pop(0      )
        _lowercase     =	response.pop(0      )
        _lowercase     =	left.freq + right.freq
        _lowercase     =	TreeNode(_lowercase,      _lowercase,      _lowercase      )
        response.append(_lowercase      )
        response.sort(key=lambda _lowercase      : x.freq      )
    return response[0]
def        UpperCAmelCase      (   _lowercase,      _lowercase      ):


    '''simple docstring'''



    if isinstance(_lowercase,      _lowercase      ):
        _lowercase     =	bitstring
        return [root]
    _lowercase     =	root  # type: ignore
    _lowercase     =	[]
    letters += traverse_tree(treenode.left,      bitstring + '0'      )
    letters += traverse_tree(treenode.right,      bitstring + '1'      )
    return letters
def        UpperCAmelCase      (   _lowercase      ):


    '''simple docstring'''



    _lowercase     =	parse_file(_lowercase      )
    _lowercase     =	build_tree(_lowercase      )
    _lowercase     =	{
        k: v for letter in traverse_tree(_lowercase,      ''      ) for k, v in letter.bitstring.items()
    }
    print(f'''Huffman Coding  of {file_path}: '''      )
    with open(_lowercase      ) as f:
        while True:
            _lowercase     =	f.read(1      )
            if not c:
                break
            print(letters[c],      end=' '      )
    print()


if __name__ == "__main__":
   # pass the file path to the huffman function
   huffman(sys.argv[1])
