
'''simple docstring'''
import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 			__A					(			lowerCamelCase_     ,    unittest.TestCase      ):
 lowerCamelCase												=UnCLIPPipeline
 lowerCamelCase												=TEXT_TO_IMAGE_PARAMS - {
     '''negative_prompt''',
     '''height''',
     '''width''',
     '''negative_prompt_embeds''',
     '''guidance_scale''',
     '''prompt_embeds''',
     '''cross_attention_kwargs''',
 }
 lowerCamelCase												=TEXT_TO_IMAGE_BATCH_PARAMS
 lowerCamelCase												=[
     '''generator''',
     '''return_dict''',
     '''prior_num_inference_steps''',
     '''decoder_num_inference_steps''',
     '''super_res_num_inference_steps''',
 ]
 lowerCamelCase												=False
 @property
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   return 32
 @property
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   return 32
 @property
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   return self.time_input_dim
 @property
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   return self.time_input_dim * 4
 @property
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   return 1_00
 @property
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A		:  Optional[int]       				=		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
   return tokenizer
 @property
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   torch.manual_seed(0							)
   __A		:  Union[str, Any]       				=		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-0_5					, num_attention_heads=4					, num_hidden_layers=5					, pad_token_id=1					, vocab_size=10_00					, )
   return CLIPTextModelWithProjection(lowerCAmelCase_							)
 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   torch.manual_seed(0							)

   __A		:  Union[str, Any]       				=		{
       """num_attention_heads""": 2,
       """attention_head_dim""": 12,
       """embedding_dim""": self.text_embedder_hidden_size,
       """num_layers""": 1,
   }

   __A		:  Any       				=		PriorTransformer(**lowerCAmelCase_							)
   return model
 @property
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   torch.manual_seed(0							)

   __A		:  int       				=		{
       """clip_embeddings_dim""": self.text_embedder_hidden_size,
       """time_embed_dim""": self.time_embed_dim,
       """cross_attention_dim""": self.cross_attention_dim,
   }

   __A		:  List[str]       				=		UnCLIPTextProjModel(**lowerCAmelCase_							)
   return model
 @property
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   torch.manual_seed(0							)

   __A		:  List[str]       				=		{
       """sample_size""": 32,
       # RGB in channels
       """in_channels""": 3,
       # Out channels is double in channels because predicts mean and variance
       """out_channels""": 6,
       """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,
       """cross_attention_dim""": self.cross_attention_dim,
       """attention_head_dim""": 4,
       """resnet_time_scale_shift""": """scale_shift""",
       """class_embed_type""": """identity""",
   }

   __A		:  str       				=		UNetaDConditionModel(**lowerCAmelCase_							)
   return model
 @property
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   return {
       "sample_size": 64,
       "layers_per_block": 1,
       "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
       "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "in_channels": 6,
       "out_channels": 3,
   }
 @property
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   torch.manual_seed(0							)

   __A		:  List[str]       				=		UNetaDModel(**self.dummy_super_res_kwargs							)
   return model
 @property
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   torch.manual_seed(1							)

   __A		:  Union[str, Any]       				=		UNetaDModel(**self.dummy_super_res_kwargs							)
   return model
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A		:  Tuple       				=		self.dummy_prior
   __A		:  Optional[int]       				=		self.dummy_decoder
   __A		:  Optional[int]       				=		self.dummy_text_proj
   __A		:  int       				=		self.dummy_text_encoder
   __A		:  Dict       				=		self.dummy_tokenizer
   __A		:  int       				=		self.dummy_super_res_first
   __A		:  Dict       				=		self.dummy_super_res_last

   __A		:  List[Any]       				=		UnCLIPScheduler(
       variance_type="""fixed_small_log"""					, prediction_type="""sample"""					, num_train_timesteps=10_00					, clip_sample_range=5.0					, )

   __A		:  Dict       				=		UnCLIPScheduler(
       variance_type="""learned_range"""					, prediction_type="""epsilon"""					, num_train_timesteps=10_00					, )

   __A		:  str       				=		UnCLIPScheduler(
       variance_type="""fixed_small_log"""					, prediction_type="""epsilon"""					, num_train_timesteps=10_00					, )

   __A		:  List[str]       				=		{
       """prior""": prior,
       """decoder""": decoder,
       """text_proj""": text_proj,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """super_res_first""": super_res_first,
       """super_res_last""": super_res_last,
       """prior_scheduler""": prior_scheduler,
       """decoder_scheduler""": decoder_scheduler,
       """super_res_scheduler""": super_res_scheduler,
   }

   return components
 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     str					, lowerCamelCase      :     str=0							):



   """simple docstring"""





   if str(lowerCAmelCase_							).startswith("""mps"""							):
     __A		:  Dict       				=		torch.manual_seed(lowerCAmelCase_							)
   else:
     __A		:  Dict       				=		torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
   __A		:  Any       				=		{
       """prompt""": """horse""",
       """generator""": generator,
       """prior_num_inference_steps""": 2,
       """decoder_num_inference_steps""": 2,
       """super_res_num_inference_steps""": 2,
       """output_type""": """numpy""",
   }
   return inputs
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  List[Any]       				=		"""cpu"""

   __A		:  Optional[int]       				=		self.get_dummy_components()

   __A		:  List[Any]       				=		self.pipeline_class(**lowerCAmelCase_							)
   __A		:  str       				=		pipe.to(lowerCAmelCase_							)

   pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

   __A		:  Any       				=		pipe(**self.get_dummy_inputs(lowerCAmelCase_							)							)
   __A		:  Tuple       				=		output.images

   __A		:  Optional[int]       				=		pipe(
       **self.get_dummy_inputs(lowerCAmelCase_							)					, return_dict=lowerCAmelCase_					, )[0]

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

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

   __A		:  Optional[Any]       				=		np.array(
       [
           0.9_997,
           0.9_988,
           0.0_028,
           0.9_997,
           0.9_984,
           0.9_965,
           0.0_029,
           0.9_986,
           0.0_025,
       ]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  Dict       				=		torch.device("""cpu"""							)







   class 			__A					:
    lowerCamelCase												=1

   __A		:  int       				=		self.get_dummy_components()

   __A		:  Any       				=		self.pipeline_class(**lowerCAmelCase_							)
   __A		:  Tuple       				=		pipe.to(lowerCAmelCase_							)

   __A		:  Dict       				=		components["""prior"""]
   __A		:  Optional[int]       				=		components["""decoder"""]
   __A		:  List[str]       				=		components["""super_res_first"""]
   __A		:  int       				=		components["""tokenizer"""]
   __A		:  int       				=		components["""text_encoder"""]

   __A		:  List[Any]       				=		torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
   __A		:  Dict       				=		prior.dtype
   __A		:  Any       				=		1

   __A		:  Dict       				=		(batch_size, prior.config.embedding_dim)
   __A		:  Union[str, Any]       				=		pipe.prepare_latents(
       lowerCAmelCase_					, dtype=lowerCAmelCase_					, device=lowerCAmelCase_					, generator=lowerCAmelCase_					, latents=lowerCAmelCase_					, scheduler=DummyScheduler()							)
   __A		:  Optional[Any]       				=		(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
   __A		:  Union[str, Any]       				=		pipe.prepare_latents(
       lowerCAmelCase_					, dtype=lowerCAmelCase_					, device=lowerCAmelCase_					, generator=lowerCAmelCase_					, latents=lowerCAmelCase_					, scheduler=DummyScheduler()							)

   __A		:  Any       				=		(
       batch_size,
       super_res_first.config.in_channels // 2,
       super_res_first.config.sample_size,
       super_res_first.config.sample_size,
   )
   __A		:  Tuple       				=		pipe.prepare_latents(
       lowerCAmelCase_					, dtype=lowerCAmelCase_					, device=lowerCAmelCase_					, generator=lowerCAmelCase_					, latents=lowerCAmelCase_					, scheduler=DummyScheduler()							)

   pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

   __A		:  Dict       				=		"""this is a prompt example"""

   __A		:  Any       				=		torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
   __A		:  List[str]       				=		pipe(
       [prompt]					, generator=lowerCAmelCase_					, prior_num_inference_steps=2					, decoder_num_inference_steps=2					, super_res_num_inference_steps=2					, prior_latents=lowerCAmelCase_					, decoder_latents=lowerCAmelCase_					, super_res_latents=lowerCAmelCase_					, output_type="""np"""					, )
   __A		:  List[Any]       				=		output.images

   __A		:  Union[str, Any]       				=		tokenizer(
       lowerCAmelCase_					, padding="""max_length"""					, max_length=tokenizer.model_max_length					, return_tensors="""pt"""					, )
   __A		:  Optional[int]       				=		text_encoder(text_inputs.input_ids							)
   __A		:  List[Any]       				=		text_inputs.attention_mask

   __A		:  Tuple       				=		torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
   __A		:  Union[str, Any]       				=		pipe(
       generator=lowerCAmelCase_					, prior_num_inference_steps=2					, decoder_num_inference_steps=2					, super_res_num_inference_steps=2					, prior_latents=lowerCAmelCase_					, decoder_latents=lowerCAmelCase_					, super_res_latents=lowerCAmelCase_					, text_model_output=lowerCAmelCase_					, text_attention_mask=lowerCAmelCase_					, output_type="""np"""					, )[0]

   # make sure passing text embeddings manually is identical
   assert np.abs(image - image_from_text							).max() < 1e-4
 @skip_mps
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  int       				=		torch_device == """cpu"""

   self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_					, expected_max_diff=0.01							)
 @skip_mps
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  Any       				=		torch_device == """cpu"""
   __A		:  str       				=		True
   __A		:  Any       				=		[
       """prior_num_inference_steps""",
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]

   self._test_inference_batch_single_identical(
       test_max_difference=lowerCAmelCase_					, relax_max_difference=lowerCAmelCase_					, additional_params_copy_to_batched_inputs=lowerCAmelCase_					, )
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  Any       				=		[
       """prior_num_inference_steps""",
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]

   if torch_device == "mps":
     # TODO: MPS errors with larger batch sizes
     __A		:  int       				=		[2, 3]
     self._test_inference_batch_consistent(
         batch_sizes=lowerCAmelCase_					, additional_params_copy_to_batched_inputs=lowerCAmelCase_					, )
   else:
     self._test_inference_batch_consistent(
         additional_params_copy_to_batched_inputs=lowerCAmelCase_							)
 @skip_mps
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   return super().test_save_load_local()






 @skip_mps
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   return super().test_save_load_optional_components()







@nightly
class 			__A					(			unittest.TestCase      ):
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





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






 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  Optional[int]       				=		load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/unclip/karlo_v1_alpha_horse_cpu.npy"""							)

   __A		:  List[str]       				=		UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							)
   pipeline.set_progress_bar_config(disable=lowerCAmelCase_							)

   __A		:  List[str]       				=		torch.manual_seed(0							)
   __A		:  Tuple       				=		pipeline(
       """horse"""					, num_images_per_prompt=1					, generator=lowerCAmelCase_					, output_type="""np"""					, )

   __A		:  Tuple       				=		output.images[0]

   assert image.shape == (2_56, 2_56, 3)
   assert np.abs(expected_image - image							).max() < 1e-1







@slow
@require_torch_gpu
class 			__A					(			unittest.TestCase      ):
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  int       				=		load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/unclip/karlo_v1_alpha_horse_fp16.npy"""							)

   __A		:  Tuple       				=		UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""					, torch_dtype=torch.floataa							)
   __A		:  Tuple       				=		pipeline.to(lowerCAmelCase_							)
   pipeline.set_progress_bar_config(disable=lowerCAmelCase_							)

   __A		:  int       				=		torch.Generator(device="""cpu"""							).manual_seed(0							)
   __A		:  int       				=		pipeline(
       """horse"""					, generator=lowerCAmelCase_					, output_type="""np"""					, )

   __A		:  Optional[Any]       				=		output.images[0]

   assert image.shape == (2_56, 2_56, 3)

   assert_mean_pixel_difference(lowerCAmelCase_					, lowerCAmelCase_							)






 def 		lowercase_( self      :     str							):



   """simple docstring"""





   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   __A		:  List[str]       				=		UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""					, torch_dtype=torch.floataa							)
   __A		:  Union[str, Any]       				=		pipe.to(lowerCAmelCase_							)
   pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   __A		:  Union[str, Any]       				=		pipe(
       """horse"""					, num_images_per_prompt=1					, prior_num_inference_steps=2					, decoder_num_inference_steps=2					, super_res_num_inference_steps=2					, output_type="""np"""					, )

   __A		:  str       				=		torch.cuda.max_memory_allocated()
   # make sure that less than 7 GB is allocated
   assert mem_bytes < 7 * 10**9




from __future__ import annotations

import sys
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =letter
		_lowercase   =freq
		_lowercase   ={}


	def __repr__(   self       ):
		return F'''{self.letter}:{self.freq}'''

class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =freq
		_lowercase   =left
		_lowercase   =right

def 	__lowerCamelCase							(				__a    : str		)      ->       list[Letter]:
	_lowercase   ={}
	with open(__a		) 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(__a      ,							__a		) for c, f in chars.items())      ,							key=lambda __a		: x.freq		)

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

def 	__lowerCamelCase							(				__a    : Letter | TreeNode      ,							__a    : str		)      ->       list[Letter]:
	if isinstance(__a      ,							__a		):
		_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 	__lowerCamelCase							(				__a    : str		)      ->       None:
	_lowercase   =parse_file(__a		)
	_lowercase   =build_tree(__a		)
	_lowercase   ={
	    k: v for letter in traverse_tree(__a      ,							""		) for k, v in letter.bitstring.items()
	}
	print(f'''Huffman Coding  of {file_path}: '''		)
	with open(__a		) 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])




