



import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

UpperCamelCase_      			=  logging.get_logger(__name__)







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase = True      )			->			Union[str, Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	{}
						for name in model.attn_processors.keys():
												UpperCAmelCase_					=	name.endswith('''attn2.processor'''      ) and not (
												    name.startswith('''transformer_in'''      ) or '''temp_attentions''' in name.split('''.'''      )
												)
												UpperCAmelCase_					=	model.config.cross_attention_dim if has_cross_attention else None
												if name.startswith('''mid_block'''      ):
																		UpperCAmelCase_					=	model.config.block_out_channels[-1]
												elif name.startswith('''up_blocks'''      ):
																		UpperCAmelCase_					=	int(name[len('''up_blocks.'''      )]      )
																		UpperCAmelCase_					=	list(reversed(model.config.block_out_channels      )      )[block_id]
												elif name.startswith('''down_blocks'''      ):
																		UpperCAmelCase_					=	int(name[len('''down_blocks.'''      )]      )
																		UpperCAmelCase_					=	model.config.block_out_channels[block_id]
												elif name.startswith('''transformer_in'''      ):
																		# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
																		UpperCAmelCase_					=	8 * model.config.attention_head_dim

												UpperCAmelCase_					=	LoRAAttnProcessor(hidden_size=__UpperCAmelCase				,   cross_attention_dim=__UpperCAmelCase      )
												UpperCAmelCase_					=	lora_attn_procs[name].to(model.device      )

												if mock_weights:
																		# add 1 to weights to mock trained weights
																		with torch.no_grad():
																								lora_attn_procs[name].to_q_lora.up.weight += 1
																								lora_attn_procs[name].to_k_lora.up.weight += 1
																								lora_attn_procs[name].to_v_lora.up.weight += 1
																								lora_attn_procs[name].to_out_lora.up.weight += 1

						return lora_attn_procs







@skip_mps
class 				a_    (						_snake_case   ,					_snake_case   ,					unittest.TestCase							):
						UpperCamelCase__ :		str										=UNetaDConditionModel
						UpperCamelCase__ :		Optional[Any]										="sample"

						@property
						def       __a			(       self      :Tuple)						->   List[str]:
												UpperCAmelCase_					=	4
												UpperCAmelCase_					=	4
												UpperCAmelCase_					=	4
												UpperCAmelCase_					=	(32, 32)

												UpperCAmelCase_					=	floats_tensor((batch_size, num_channels, num_frames) + sizes).to(_lowercase)
												UpperCAmelCase_					=	torch.tensor([10]).to(_lowercase)
												UpperCAmelCase_					=	floats_tensor((batch_size, 4, 32)).to(_lowercase)

												return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

						@property
						def       __a			(       self      :List[Any])						->   Tuple:
												return (4, 4, 32, 32)

						@property
						def       __a			(       self      :str)						->   int:
												return (4, 4, 32, 32)

						def       __a			(       self      :int)						->   int:
												UpperCAmelCase_					=	{
												    '''block_out_channels''': (32, 64),
												    '''down_block_types''': (
												        '''CrossAttnDownBlock3D''',
												        '''DownBlock3D''',
												    ),
												    '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
												    '''cross_attention_dim''': 32,
												    '''attention_head_dim''': 8,
												    '''out_channels''': 4,
												    '''in_channels''': 4,
												    '''layers_per_block''': 1,
												    '''sample_size''': 32,
												}
												UpperCAmelCase_					=	self.dummy_input
												return init_dict, inputs_dict

						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()  ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''  ,		)
						def       __a			(       self      :Any)						->   List[Any]:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()
												UpperCAmelCase_					=	self.model_class(**_lowercase)

												model.enable_xformers_memory_efficient_attention()

												assert (
												    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
												    == "XFormersAttnProcessor"
												), "xformers is not enabled"

						def       __a			(       self      :int)						->   int:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	32

												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)
												model.eval()

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase)

																		if isinstance(_lowercase  ,		_lowercase):
																								UpperCAmelCase_					=	output.sample

												self.assertIsNotNone(_lowercase)
												UpperCAmelCase_					=	inputs_dict['''sample'''].shape
												self.assertEqual(output.shape  ,		_lowercase  ,		'''Input and output shapes do not match''')

						def       __a			(       self      :Union[str, Any])						->   Dict:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()
												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)
												model.eval()

												with torch.no_grad():
																		# Warmup pass when using mps (see #372)
																		if torch_device == "mps" and isinstance(_lowercase  ,		_lowercase):
																								model(**self.dummy_input)

																		UpperCAmelCase_					=	model(**_lowercase)
																		if isinstance(_lowercase  ,		_lowercase):
																								UpperCAmelCase_					=	first.sample

																		UpperCAmelCase_					=	model(**_lowercase)
																		if isinstance(_lowercase  ,		_lowercase):
																								UpperCAmelCase_					=	second.sample

												UpperCAmelCase_					=	first.cpu().numpy()
												UpperCAmelCase_					=	second.cpu().numpy()
												UpperCAmelCase_					=	out_a[~np.isnan(_lowercase)]
												UpperCAmelCase_					=	out_a[~np.isnan(_lowercase)]
												UpperCAmelCase_					=	np.amax(np.abs(out_a - out_a))
												self.assertLessEqual(_lowercase  ,		1E-5)

						def       __a			(       self      :List[str])						->   str:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	8

												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)
												model.eval()

												model.set_attention_slice('''auto''')
												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase)
												assert output is not None

												model.set_attention_slice('''max''')
												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase)
												assert output is not None

												model.set_attention_slice(2)
												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase)
												assert output is not None

						def       __a			(       self      :str)						->   Dict:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	8

												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase).sample

												UpperCAmelCase_					=	create_lora_layers(_lowercase)

												# make sure we can set a list of attention processors
												model.set_attn_processor(_lowercase)
												model.to(_lowercase)

												# test that attn processors can be set to itself
												model.set_attn_processor(model.attn_processors)

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase  ,		cross_attention_kwargs={'''scale''': 0.0}).sample
																		UpperCAmelCase_					=	model(**_lowercase  ,		cross_attention_kwargs={'''scale''': 0.5}).sample
																		UpperCAmelCase_					=	model(**_lowercase  ,		cross_attention_kwargs={'''scale''': 0.5}).sample

												assert (samplea - samplea).abs().max() < 3E-3
												assert (samplea - samplea).abs().max() < 3E-3

												# sample 2 and sample 3 should be different
												assert (samplea - samplea).abs().max() > 3E-3

						def       __a			(       self      :Dict)						->   Optional[Any]:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	8

												torch.manual_seed(0)
												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase).sample

												UpperCAmelCase_					=	create_lora_layers(_lowercase)
												model.set_attn_processor(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase  ,		cross_attention_kwargs={'''scale''': 0.5}).sample

												with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_attn_procs(_lowercase)
																		self.assertTrue(os.path.isfile(os.path.join(_lowercase  ,		'''pytorch_lora_weights.bin''')))
																		torch.manual_seed(0)
																		UpperCAmelCase_					=	self.model_class(**_lowercase)
																		new_model.to(_lowercase)
																		new_model.load_attn_procs(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	new_model(**_lowercase  ,		cross_attention_kwargs={'''scale''': 0.5}).sample

												assert (sample - new_sample).abs().max() < 1E-3

												# LoRA and no LoRA should NOT be the same
												assert (sample - old_sample).abs().max() > 1E-4

						def       __a			(       self      :Tuple)						->   Dict:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	8

												torch.manual_seed(0)
												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase).sample

												UpperCAmelCase_					=	create_lora_layers(_lowercase)
												model.set_attn_processor(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase  ,		cross_attention_kwargs={'''scale''': 0.5}).sample

												with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_attn_procs(_lowercase  ,		safe_serialization=_lowercase)
																		self.assertTrue(os.path.isfile(os.path.join(_lowercase  ,		'''pytorch_lora_weights.safetensors''')))
																		torch.manual_seed(0)
																		UpperCAmelCase_					=	self.model_class(**_lowercase)
																		new_model.to(_lowercase)
																		new_model.load_attn_procs(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	new_model(**_lowercase  ,		cross_attention_kwargs={'''scale''': 0.5}).sample

												assert (sample - new_sample).abs().max() < 3E-3

												# LoRA and no LoRA should NOT be the same
												assert (sample - old_sample).abs().max() > 1E-4

						def       __a			(       self      :Dict)						->   str:
												# enable deterministic behavior for gradient checkpointing
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	8

												torch.manual_seed(0)
												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)

												UpperCAmelCase_					=	create_lora_layers(_lowercase  ,		mock_weights=_lowercase)
												model.set_attn_processor(_lowercase)
												# Saving as torch, properly reloads with directly filename
												with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_attn_procs(_lowercase)
																		self.assertTrue(os.path.isfile(os.path.join(_lowercase  ,		'''pytorch_lora_weights.bin''')))
																		torch.manual_seed(0)
																		UpperCAmelCase_					=	self.model_class(**_lowercase)
																		new_model.to(_lowercase)
																		new_model.load_attn_procs(_lowercase  ,		weight_name='''pytorch_lora_weights.bin''')

						def       __a			(       self      :List[str])						->   Optional[int]:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	8

												torch.manual_seed(0)
												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)

												UpperCAmelCase_					=	create_lora_layers(_lowercase  ,		mock_weights=_lowercase)
												model.set_attn_processor(_lowercase)
												# Saving as torch, properly reloads with directly filename
												with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_attn_procs(_lowercase)
																		self.assertTrue(os.path.isfile(os.path.join(_lowercase  ,		'''pytorch_lora_weights.bin''')))
																		torch.manual_seed(0)
																		UpperCAmelCase_					=	self.model_class(**_lowercase)
																		new_model.to(_lowercase)
																		with self.assertRaises(_lowercase) as e:
																								new_model.load_attn_procs(_lowercase  ,		use_safetensors=_lowercase)
																		self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''  ,		str(e.exception))

						def       __a			(       self      :str)						->   str:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	8

												torch.manual_seed(0)
												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase).sample

												UpperCAmelCase_					=	create_lora_layers(_lowercase)
												model.set_attn_processor(_lowercase)

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase  ,		cross_attention_kwargs={'''scale''': 0.0}).sample

												model.set_attn_processor(AttnProcessor())

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase).sample

												assert (sample - new_sample).abs().max() < 1E-4
												assert (sample - old_sample).abs().max() < 3E-3

						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()  ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''  ,		)
						def       __a			(       self      :str)						->   Tuple:
												# enable deterministic behavior for gradient checkpointing
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()

												UpperCAmelCase_					=	4

												torch.manual_seed(0)
												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)
												UpperCAmelCase_					=	create_lora_layers(_lowercase)
												model.set_attn_processor(_lowercase)

												# default
												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase).sample

																		model.enable_xformers_memory_efficient_attention()
																		UpperCAmelCase_					=	model(**_lowercase).sample

																		model.disable_xformers_memory_efficient_attention()
																		UpperCAmelCase_					=	model(**_lowercase).sample

												assert (sample - on_sample).abs().max() < 1E-4
												assert (sample - off_sample).abs().max() < 1E-4




						def       __a			(       self      :Tuple)						->   str:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_init_args_and_inputs_for_common()
												UpperCAmelCase_					=	32

												UpperCAmelCase_					=	self.model_class(**_lowercase)
												model.to(_lowercase)
												model.eval()

												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase)[0]

												model.enable_forward_chunking()
												with torch.no_grad():
																		UpperCAmelCase_					=	model(**_lowercase)[0]

												self.assertEqual(output.shape  ,		output_a.shape  ,		'''Shape doesn\'t match''')
												assert np.abs(output.cpu() - output_a.cpu()).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.










from __future__ import annotations

import sys







class 				a_    :

						def __init__(       self      :Dict  ,		_lowercase      :str  ,		_lowercase      :int)						->   List[Any]:
												UpperCAmelCase_					=	letter
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	{}




						def __repr__(       self      :str)						->   str:
												return f"{self.letter}:{self.freq}"







class 				a_    :

						def __init__(       self      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :Letter | TreeNode  ,		_lowercase      :Letter | TreeNode)						->   Dict:
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	left
												UpperCAmelCase_					=	right







def 							A     (						__UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






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







def 							A     (						__UpperCAmelCase      )			->			Letter | TreeNode:



						'''simple docstring'''






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







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






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







def 							A     (						__UpperCAmelCase      )			->			None:



						'''simple docstring'''






						UpperCAmelCase_					=	parse_file(__UpperCAmelCase      )
						UpperCAmelCase_					=	build_tree(__UpperCAmelCase      )
						UpperCAmelCase_					=	{
						    k: v for letter in traverse_tree(__UpperCAmelCase				,   ''''''      ) for k, v in letter.bitstring.items()
						}
						print(f"Huffman Coding  of {file_path}: "      )
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	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])





