"""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 						UpperCamelCase     (       lowercase		):



				def __init__(self							: Dict)       ->      Tuple:
					self.register_to_config()
					super().__init__()



				def    _lowercase	(self							: int      ,    _A							: Any      ,    _A							: Optional[int])       ->      Optional[Any]:
					if dicta == dicta:
						return True
					else:
						__snake_case						,							__snake_case     :				int 						=					self._remove_meta_keys(_A)
						__snake_case						,							__snake_case     :				Tuple 						=					self._remove_meta_keys(_A)
						if configa == configa:
							print(f"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.")
							return True
					return False



				def    _lowercase	(self							: Optional[Any]      ,    _A							: Dict)       ->      Union[str, Any]:
					__snake_case     :				Optional[int] 						=					[]
					__snake_case     :				str 						=					config_dict.copy()
					for key in config_dict.keys():
						if key.startswith('_'):
							temp_dict.pop(_A)
							meta_keys.append(_A)
					return (temp_dict, meta_keys)



				@torch.no_grad()
				def    _lowercase	(self							: str      ,    _A							: List[Union[str, os.PathLike]]      ,    **_A							: Tuple)       ->      Optional[int]:
					__snake_case     :				Optional[Any] 						=					kwargs.pop('cache_dir'      ,    _A)
					__snake_case     :				Tuple 						=					kwargs.pop('resume_download'      ,    _A)
					__snake_case     :				List[str] 						=					kwargs.pop('force_download'      ,    _A)
					__snake_case     :				Dict 						=					kwargs.pop('proxies'      ,    _A)
					__snake_case     :				Dict 						=					kwargs.pop('local_files_only'      ,    _A)
					__snake_case     :				Optional[int] 						=					kwargs.pop('use_auth_token'      ,    _A)
					__snake_case     :				str 						=					kwargs.pop('revision'      ,    _A)
					__snake_case     :				List[str] 						=					kwargs.pop('torch_dtype'      ,    _A)
					__snake_case     :				List[Any] 						=					kwargs.pop('device_map'      ,    _A)

					__snake_case     :				str 						=					kwargs.pop('alpha'      ,    0.5)
					__snake_case     :				str 						=					kwargs.pop('interp'      ,    _A)

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

					__snake_case     :				Union[str, Any] 						=					len(_A)
					# Ignore result from model_index_json comparision of the two checkpoints
					__snake_case     :				Union[str, Any] 						=					kwargs.pop('force'      ,    _A)

					# 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     :				int 						=					[]
					for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
						__snake_case     :				List[str] 						=					DiffusionPipeline.load_config(
						    _A      ,    cache_dir=_A      ,    resume_download=_A      ,    force_download=_A      ,    proxies=_A      ,    local_files_only=_A      ,    use_auth_token=_A      ,    revision=_A      ,    )
						config_dicts.append(_A)

					__snake_case     :				int 						=					True
					for idx in range(1      ,    len(_A)):
						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(_A      ,    _A):
						__snake_case     :				int 						=					[k for k in config_dict.keys() if not k.startswith('_')]
						__snake_case     :				int 						=					[os.path.join(_A      ,    '*') for k in folder_names]
						allow_patterns += [
						    WEIGHTS_NAME,
						    SCHEDULER_CONFIG_NAME,
						    CONFIG_NAME,
						    ONNX_WEIGHTS_NAME,
						    DiffusionPipeline.config_name,
						]
						__snake_case     :				int 						=					config_dict.get('_class_name')
						__snake_case     :				str 						=					{'diffusers': __version__, 'pipeline_class': requested_pipeline_class}

						__snake_case     :				Tuple 						=					(
						    pretrained_model_name_or_path
						    if os.path.isdir(_A)
						    else snapshot_download(
						        _A      ,    cache_dir=_A      ,    resume_download=_A      ,    proxies=_A      ,    local_files_only=_A      ,    use_auth_token=_A      ,    revision=_A      ,    allow_patterns=_A      ,    user_agent=_A      ,    )
						)
						print('Cached Folder'      ,    _A)
						cached_folders.append(_A)

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

					__snake_case     :				Tuple 						=					None
					if len(_A) > 2:
						__snake_case     :				Any 						=					os.path.join(cached_folders[2])

					if interp == "sigmoid":
						__snake_case     :				str 						=					CheckpointMergerPipeline.sigmoid
					elif interp == "inv_sigmoid":
						__snake_case     :				int 						=					CheckpointMergerPipeline.inv_sigmoid
					elif interp == "add_diff":
						__snake_case     :				Optional[int] 						=					CheckpointMergerPipeline.add_difference
					else:
						__snake_case     :				Any 						=					CheckpointMergerPipeline.weighted_sum

					# Find each module's state dict.
					for attr in final_pipe.config.keys():
						if not attr.startswith('_'):
							__snake_case     :				int 						=					os.path.join(cached_folders[1]      ,    _A)
							if os.path.exists(_A):
								__snake_case     :				Tuple 						=					[
								    *glob.glob(os.path.join(_A      ,    '*.safetensors')),
								    *glob.glob(os.path.join(_A      ,    '*.bin')),
								]
								__snake_case     :				Optional[int] 						=					files[0] if len(_A) > 0 else None
							if len(_A) < 3:
								__snake_case     :				int 						=					None
							else:
								__snake_case     :				Optional[int] 						=					os.path.join(cached_folders[2]      ,    _A)
								if os.path.exists(_A):
									__snake_case     :				str 						=					[
									    *glob.glob(os.path.join(_A      ,    '*.safetensors')),
									    *glob.glob(os.path.join(_A      ,    '*.bin')),
									]
									__snake_case     :				Tuple 						=					files[0] if len(_A) > 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     :				List[Any] 						=					getattr(_A      ,    _A)
								if isinstance(_A      ,    _A):  # ignore requires_safety_checker boolean
									continue
								__snake_case     :				Any 						=					getattr(_A      ,    'state_dict')
								__snake_case     :				Optional[Any] 						=					theta_a()

								__snake_case     :				Dict 						=					getattr(_A      ,    'load_state_dict')
								__snake_case     :				Dict 						=					(
								    safetensors.torch.load_file(_A)
								    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'))
								    else torch.load(_A      ,    map_location='cpu')
								)
								__snake_case     :				Union[str, Any] 						=					None
								if checkpoint_path_a:
									__snake_case     :				Union[str, Any] 						=					(
									    safetensors.torch.load_file(_A)
									    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'))
									    else torch.load(_A      ,    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(_A)}")
								continue
							print(f"MERGING {attr}")

							for key in theta_a.keys():
								if theta_a:
									__snake_case     :				int 						=					theta_func(theta_a[key]      ,    theta_a[key]      ,    theta_a[key]      ,    _A)
								else:
									__snake_case     :				Tuple 						=					theta_func(theta_a[key]      ,    theta_a[key]      ,    _A      ,    _A)

							del theta_a
							del theta_a
							update_theta_a(_A)

							del theta_a
					return final_pipe



				@staticmethod
				def    _lowercase	(_A							: Any      ,    _A							: str      ,    _A							: Tuple      ,    _A							: Tuple)       ->      Union[str, Any]:
					return ((1 - alpha) * thetaa) + (alpha * thetaa)



				@staticmethod
				def    _lowercase	(_A							: List[str]      ,    _A							: Any      ,    _A							: Optional[Any]      ,    _A							: Any)       ->      Optional[Any]:
					__snake_case     :				List[str] 						=					alpha * alpha * (3 - (2 * alpha))
					return thetaa + ((thetaa - thetaa) * alpha)



				@staticmethod
				def    _lowercase	(_A							: List[Any]      ,    _A							: Any      ,    _A							: int      ,    _A							: Optional[Any])       ->      Dict:
					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    _lowercase	(_A							: str      ,    _A							: Tuple      ,    _A							: str      ,    _A							: Dict)       ->      Tuple:
					return thetaa + (thetaa - thetaa) * (1.0 - alpha)






"""simple docstring"""







import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


_a	:	List[Any]=					"none"
_a	:	List[str]=					"none"
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[int]     ,				UpperCAmelCase_       :							Tuple    )				->     Dict:

	'''simple docstring'''





	__snake_case     :				int 						=					value.split(UpperCAmelCase_    )
	return len(UpperCAmelCase_    ) > 1 and any((len(UpperCAmelCase_    ) == 3 for x in split)    )
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[Any]    )				->     Tuple:

	'''simple docstring'''





	if isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ):
		return value
	if isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ):
		return float(UpperCAmelCase_    )
	if not isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ):
		raise ValueError('Argument value is not a string. Can\'t parse it as float'    )
	__snake_case     :				Any 						=					value

	try:
		# Example: 1,000.7
		if "." in sanitized and "," in sanitized:
			return float(sanitized.replace(','     ,				''    )    )
		# 1,000
		if "," in sanitized and _split_thousands(','     ,				UpperCAmelCase_    ):
			return float(sanitized.replace(','     ,				''    )    )
		# 5,5556
		if "," in sanitized and sanitized.count(','    ) == 1 and not _split_thousands(','     ,				UpperCAmelCase_    ):
			return float(sanitized.replace(','     ,				'.'    )    )
		# 0.0.0.1
		if sanitized.count('.'    ) > 1:
			return float(sanitized.replace('.'     ,				''    )    )
		# 0,0,0,1
		if sanitized.count(','    ) > 1:
			return float(sanitized.replace(','     ,				''    )    )
		return float(UpperCAmelCase_    )
	except ValueError:
		# Avoid adding the sanitized value in the error message.
		raise ValueError('Unable to convert value to float'    )
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str    )				->     Any:

	'''simple docstring'''





	if answer is None:
		return None
	try:
		__snake_case     :				Dict 						=					convert_to_float(UpperCAmelCase_    )
		if isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ) and math.isnan(UpperCAmelCase_    ):
			return None
		return value
	except ValueError:
		return answer.lower()


_a	:	Union[str, Any]=					{
    "text": lambda x: x,
    "real": convert_to_float,
}

class 						UpperCamelCase     (       enum.Enum		):
				UpperCAmelCase							: int       =						0
				UpperCAmelCase							: Optional[Any]       =						1
				UpperCAmelCase							: List[Any]       =						2
				UpperCAmelCase							: Dict       =						3
				UpperCAmelCase							: Union[str, Any]       =						4
				UpperCAmelCase							: int       =						5







class 						UpperCamelCase     (       enum.Enum		):
				UpperCAmelCase							: List[str]       =						0
				UpperCAmelCase							: List[str]       =						1
				UpperCAmelCase							: int       =						2







@dataclasses.dataclass
class 						UpperCamelCase     :
				UpperCAmelCase							: Text
				UpperCAmelCase							: _Operator
				UpperCAmelCase							: Any







_a	:	Union[str, Any]=					re.compile(R"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[str]    )				->     Optional[Any]:

	'''simple docstring'''





	return list(_TOKENIZER.findall(x.lower()    )    )
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							List[Any]    )				->     List[str]:

	'''simple docstring'''





	if operator == _Operator.EQUALS:
		return src == tgt
	elif operator == _Operator.GREATER:
		return src > tgt
	elif operator == _Operator.LESSER:
		return src < tgt
	raise ValueError(F"Unknown operator: {operator}"    )
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]     ,				UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							Optional[Any]    )				->     Dict:

	'''simple docstring'''





	__snake_case     :				Optional[Any] 						=					table['types']
	return _TYPE_CONVERTER[types[column]](UpperCAmelCase_    )
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]    )				->     str:

	'''simple docstring'''





	return isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    )
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Dict     ,				UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	for cond in conditions:
		__snake_case     :				Any 						=					row[cond.column]

		__snake_case     :				Tuple 						=					_parse_value(UpperCAmelCase_     ,				cond.column     ,				cond.cmp_value    )

		if _is_string(UpperCAmelCase_    ) and _is_string(UpperCAmelCase_    ):
			__snake_case     :				str 						=					_normalize_for_match(UpperCAmelCase_    )
			__snake_case     :				Dict 						=					_normalize_for_match(UpperCAmelCase_    )

		if not isinstance(UpperCAmelCase_     ,				type(UpperCAmelCase_    )    ):
			raise ValueError('Type difference {} != {}'.format(type(UpperCAmelCase_    )     ,				type(UpperCAmelCase_    )    )    )

		if not _compare(cond.operator     ,				UpperCAmelCase_     ,				UpperCAmelCase_    ):
			return False
	return True
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							Optional[int]    )				->     Dict:

	'''simple docstring'''





	if not answer_coordinates:
		if aggregation_op == _Aggregation.COUNT:
			return 0.0
		else:
			return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
	if aggregation_op == _Aggregation.COUNT:
		return float(len(UpperCAmelCase_    )    )

	# If we have just one answer, if float returns it or try a conversion.
	__snake_case     :				Any 						=					[table['rows'][i][j] for (i, j) in answer_coordinates]
	if len(UpperCAmelCase_    ) == 1:
		try:
			return convert_to_float(values[0]    )
		except ValueError as e:
			if aggregation_op != _Aggregation.NONE:
				raise e

	if aggregation_op == _Aggregation.NONE:
		return None

	# Other aggregation only support numeric values. Bail out if we have strings.
	if not all((isinstance(UpperCAmelCase_     ,				(int, float)    ) for v in values)    ):
		return None

	if aggregation_op == _Aggregation.SUM:
		return float(sum(UpperCAmelCase_    )    )
	elif aggregation_op == _Aggregation.AVERAGE:
		return sum(UpperCAmelCase_    ) / len(UpperCAmelCase_    )
	else:
		raise ValueError(F"Unknown aggregation: {aggregation_op}"    )
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							int    )				->     str:

	'''simple docstring'''





	__snake_case     :				int 						=					sql_query['agg']
	if aggregation_op_index >= 3:
		__snake_case     :				Optional[Any] 						=					_Aggregation(UpperCAmelCase_    )
	else:
		__snake_case     :				Any 						=					_Aggregation.NONE

	__snake_case     :				Dict 						=					sql_query['sel']
	__snake_case     :				Dict 						=					[
	    _Condition(UpperCAmelCase_     ,				_Operator(UpperCAmelCase_    )     ,				UpperCAmelCase_    )
	    for column, operator, cmp_value in zip(
	        sql_query['conds']['column_index']     ,				sql_query['conds']['operator_index']     ,				sql_query['conds']['condition']    )
	]

	__snake_case     :				str 						=					[]
	for row in range(len(table['rows']    )    ):
		if _respect_conditions(UpperCAmelCase_     ,				table['rows'][row]     ,				UpperCAmelCase_    ):
			indices.append((row, target_column)    )

	if not indices:
		return [], aggregation_op

	if len(UpperCAmelCase_    ) == 1:
		return indices, aggregation_op

	# Parsing of MIN/MAX.
	if aggregation_op_index in (1, 2):
		__snake_case     :				Union[str, Any] 						=					{2: min, 1: max}
		__snake_case     :				Optional[Any] 						=					[(table['rows'][i][j], index) for index, (i, j) in enumerate(UpperCAmelCase_    )]
		__snake_case     :				Tuple 						=					functools.reduce(operators[sql_query['agg']]     ,				UpperCAmelCase_    )

		__snake_case     :				str 						=					[indices[reduced[1]]]
		return ret, _Aggregation.NONE

	return indices, aggregation_op
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Dict     ,				UpperCAmelCase_       :							List[Any]     ,				UpperCAmelCase_       :							Any    )				->     Optional[Any]:

	'''simple docstring'''





	if float_answer is not None:
		return [str(UpperCAmelCase_    )]
	return [str(table['real_rows'][r][c]    ) for r, c in answer_coordinates]

def     __UpperCAmelCase							(       UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							Tuple    )				->     List:

	'''simple docstring'''





	__snake_case						,							__snake_case     :				List[Any] 						=					_get_answer_coordinates(UpperCAmelCase_     ,				UpperCAmelCase_    )
	__snake_case     :				Optional[int] 						=					_get_float_answer(UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )
	__snake_case     :				Union[str, Any] 						=					_get_answer_text(UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )
	# keep the original data the same with TaPas
	if len(UpperCAmelCase_    ) == 0:
		__snake_case     :				List[str] 						=					[EMPTY_ANSWER]
	return answer_text





