
'''simple docstring'''
import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
						import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME






class      snake_case__    (	__SCREAMING_SNAKE_CASE ):







		"""simple docstring"""

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






				"""simple docstring"""







				self.register_to_config()
				super().__init__()



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       int	)		->       Any:






				"""simple docstring"""







				if dicta == dicta:
						return True
				else:
						snake_case       ,snake_case   :    List[Any]			       =		self._remove_meta_keys(UpperCamelCase__	)
						snake_case       ,snake_case   :    List[Any]			       =		self._remove_meta_keys(UpperCamelCase__	)
						if configa == configa:
								print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'	)
								return True
				return False



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     UpperCamelCase__							:       Dict	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    int			       =		[]
				snake_case   :    List[str]			       =		config_dict.copy()
				for key in config_dict.keys():
						if key.startswith('''_'''	):
								temp_dict.pop(UpperCamelCase__	)
								meta_keys.append(UpperCamelCase__	)
				return (temp_dict, meta_keys)



		@torch.no_grad()
		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       List[Union[str, os.PathLike]]   ,     **UpperCamelCase__							:       Tuple	)		->       str:






				"""simple docstring"""







				snake_case   :    Tuple			       =		kwargs.pop('''cache_dir'''   ,     UpperCamelCase__	)
				snake_case   :    Optional[Any]			       =		kwargs.pop('''resume_download'''   ,     UpperCamelCase__	)
				snake_case   :    List[str]			       =		kwargs.pop('''force_download'''   ,     UpperCamelCase__	)
				snake_case   :    str			       =		kwargs.pop('''proxies'''   ,     UpperCamelCase__	)
				snake_case   :    List[str]			       =		kwargs.pop('''local_files_only'''   ,     UpperCamelCase__	)
				snake_case   :    List[Any]			       =		kwargs.pop('''use_auth_token'''   ,     UpperCamelCase__	)
				snake_case   :    Tuple			       =		kwargs.pop('''revision'''   ,     UpperCamelCase__	)
				snake_case   :    Dict			       =		kwargs.pop('''torch_dtype'''   ,     UpperCamelCase__	)
				snake_case   :    str			       =		kwargs.pop('''device_map'''   ,     UpperCamelCase__	)

				snake_case   :    Tuple			       =		kwargs.pop('''alpha'''   ,     0.5	)
				snake_case   :    Any			       =		kwargs.pop('''interp'''   ,     UpperCamelCase__	)

				print('''Received list'''   ,     UpperCamelCase__	)
				print(f'Combining with alpha={alpha}, interpolation mode={interp}'	)

				snake_case   :    List[Any]			       =		len(UpperCamelCase__	)
				# Ignore result from model_index_json comparision of the two checkpoints
				snake_case   :    Optional[int]			       =		kwargs.pop('''force'''   ,     UpperCamelCase__	)

				# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
				if checkpoint_count > 3 or checkpoint_count < 2:
						raise ValueError(
						    '''Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being'''
						    ''' passed.'''	)

				print('''Received the right number of checkpoints'''	)
				# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
				# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

				# Validate that the checkpoints can be merged
				# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
				snake_case   :    Any			       =		[]
				for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
						snake_case   :    Tuple			       =		DiffusionPipeline.load_config(
						    UpperCamelCase__   ,     cache_dir=UpperCamelCase__   ,     resume_download=UpperCamelCase__   ,     force_download=UpperCamelCase__   ,     proxies=UpperCamelCase__   ,     local_files_only=UpperCamelCase__   ,     use_auth_token=UpperCamelCase__   ,     revision=UpperCamelCase__   ,     )
						config_dicts.append(UpperCamelCase__	)

				snake_case   :    Tuple			       =		True
				for idx in range(1   ,     len(UpperCamelCase__	)	):
						comparison_result &= self._compare_model_configs(config_dicts[idx - 1]   ,     config_dicts[idx]	)
						if not force and comparison_result is False:
								raise ValueError('''Incompatible checkpoints. Please check model_index.json for the models.'''	)
								print(config_dicts[0]   ,     config_dicts[1]	)
				print('''Compatible model_index.json files found'''	)
				# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
				snake_case   :    Tuple			       =		[]
				for pretrained_model_name_or_path, config_dict in zip(UpperCamelCase__   ,     UpperCamelCase__	):
						snake_case   :    int			       =		[k for k in config_dict.keys() if not k.startswith('''_'''	)]
						snake_case   :    str			       =		[os.path.join(UpperCamelCase__   ,     '''*'''	) for k in folder_names]
						allow_patterns += [
						    WEIGHTS_NAME,
						    SCHEDULER_CONFIG_NAME,
						    CONFIG_NAME,
						    ONNX_WEIGHTS_NAME,
						    DiffusionPipeline.config_name,
						]
						snake_case   :    Any			       =		config_dict.get('''_class_name'''	)
						snake_case   :    str			       =		{'''diffusers''': __version__, '''pipeline_class''': requested_pipeline_class}

						snake_case   :    List[Any]			       =		(
						    pretrained_model_name_or_path
						    if os.path.isdir(UpperCamelCase__	)
						    else snapshot_download(
						        UpperCamelCase__   ,     cache_dir=UpperCamelCase__   ,     resume_download=UpperCamelCase__   ,     proxies=UpperCamelCase__   ,     local_files_only=UpperCamelCase__   ,     use_auth_token=UpperCamelCase__   ,     revision=UpperCamelCase__   ,     allow_patterns=UpperCamelCase__   ,     user_agent=UpperCamelCase__   ,     )
						)
						print('''Cached Folder'''   ,     UpperCamelCase__	)
						cached_folders.append(UpperCamelCase__	)

				# Step 3:-
				# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
				snake_case   :    Optional[Any]			       =		DiffusionPipeline.from_pretrained(
				    cached_folders[0]   ,     torch_dtype=UpperCamelCase__   ,     device_map=UpperCamelCase__	)
				final_pipe.to(self.device	)

				snake_case   :    Dict			       =		None
				if len(UpperCamelCase__	) > 2:
						snake_case   :    Tuple			       =		os.path.join(cached_folders[2]	)

				if interp == "sigmoid":
						snake_case   :    Tuple			       =		CheckpointMergerPipeline.sigmoid
				elif interp == "inv_sigmoid":
						snake_case   :    Tuple			       =		CheckpointMergerPipeline.inv_sigmoid
				elif interp == "add_diff":
						snake_case   :    Optional[int]			       =		CheckpointMergerPipeline.add_difference
				else:
						snake_case   :    Tuple			       =		CheckpointMergerPipeline.weighted_sum

				# Find each module's state dict.
				for attr in final_pipe.config.keys():
						if not attr.startswith('''_'''	):
								snake_case   :    str			       =		os.path.join(cached_folders[1]   ,     UpperCamelCase__	)
								if os.path.exists(UpperCamelCase__	):
										snake_case   :    Tuple			       =		[
										    *glob.glob(os.path.join(UpperCamelCase__   ,     '''*.safetensors'''	)	),
										    *glob.glob(os.path.join(UpperCamelCase__   ,     '''*.bin'''	)	),
										]
										snake_case   :    int			       =		files[0] if len(UpperCamelCase__	) > 0 else None
								if len(UpperCamelCase__	) < 3:
										snake_case   :    int			       =		None
								else:
										snake_case   :    Optional[int]			       =		os.path.join(cached_folders[2]   ,     UpperCamelCase__	)
										if os.path.exists(UpperCamelCase__	):
												snake_case   :    Optional[Any]			       =		[
												    *glob.glob(os.path.join(UpperCamelCase__   ,     '''*.safetensors'''	)	),
												    *glob.glob(os.path.join(UpperCamelCase__   ,     '''*.bin'''	)	),
												]
												snake_case   :    Tuple			       =		files[0] if len(UpperCamelCase__	) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
								if checkpoint_path_a is None and checkpoint_path_a is None:
										print(f'Skipping {attr}: not present in 2nd or 3d model'	)
										continue
								try:
										snake_case   :    Tuple			       =		getattr(UpperCamelCase__   ,     UpperCamelCase__	)
										if isinstance(UpperCamelCase__   ,     UpperCamelCase__	):  # ignore requires_safety_checker boolean
												continue
										snake_case   :    Dict			       =		getattr(UpperCamelCase__   ,     '''state_dict'''	)
										snake_case   :    Dict			       =		theta_a()

										snake_case   :    Dict			       =		getattr(UpperCamelCase__   ,     '''load_state_dict'''	)
										snake_case   :    Any			       =		(
										    safetensors.torch.load_file(UpperCamelCase__	)
										    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''	))
										    else torch.load(UpperCamelCase__   ,     map_location='''cpu'''	)
										)
										snake_case   :    Tuple			       =		None
										if checkpoint_path_a:
												snake_case   :    Any			       =		(
												    safetensors.torch.load_file(UpperCamelCase__	)
												    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''	))
												    else torch.load(UpperCamelCase__   ,     map_location='''cpu'''	)
												)

										if not theta_a.keys() == theta_a.keys():
												print(f'Skipping {attr}: key mismatch'	)
												continue
										if theta_a and not theta_a.keys() == theta_a.keys():
												print(f'Skipping {attr}:y mismatch'	)
								except Exception as e:
										print(f'Skipping {attr} do to an unexpected error: {str(UpperCamelCase__	)}'	)
										continue
								print(f'MERGING {attr}'	)

								for key in theta_a.keys():
										if theta_a:
												snake_case   :    Union[str, Any]			       =		theta_func(theta_a[key]   ,     theta_a[key]   ,     theta_a[key]   ,     UpperCamelCase__	)
										else:
												snake_case   :    Tuple			       =		theta_func(theta_a[key]   ,     theta_a[key]   ,     UpperCamelCase__   ,     UpperCamelCase__	)

								del theta_a
								del theta_a
								update_theta_a(UpperCamelCase__	)

								del theta_a
				return final_pipe



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Optional[Any]   ,     UpperCamelCase__							:       Any	)		->       Union[str, Any]:






				"""simple docstring"""







				return ((1 - alpha) * thetaa) + (alpha * thetaa)



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       Optional[Any]   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       List[str]	)		->       int:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		alpha * alpha * (3 - (2 * alpha))
				return thetaa + ((thetaa - thetaa) * alpha)



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       Tuple   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       List[str]	)		->       Optional[Any]:






				"""simple docstring"""







				import math

				snake_case   :    int			       =		0.5 - math.sin(math.asin(1.0 - 2.0 * alpha	) / 3.0	)
				return thetaa + ((thetaa - thetaa) * alpha)



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       Tuple	)		->       Optional[Any]:






				"""simple docstring"""







				return thetaa + (thetaa - thetaa) * (1.0 - alpha)






import doctest

import projectq
from projectq.ops import H, Measure
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int							=						quantum_engine.allocate_qubit()
 H | qubit
 Measure | qubit
 return int(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       doctest.testmod()

       # initialises a new quantum backend
       lowercase_           =	projectq.MainEngine()

       # Generate a list of 10 random numbers
       lowercase_           =	[get_random_number(quantum_engine) for _ in range(1_0)]

       # Flushes the quantum engine from memory
       quantum_engine.flush()

       print('Random numbers', random_numbers_list)

