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 						a__							(   __snake_case		):



			def __init__(						self    ) ->    List[Any]:
								self.register_to_config()
								super().__init__()



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    List[Any]:
								if dicta == dicta:
													return True
								else:
													__a					,      __a							=   self._remove_meta_keys(UpperCAmelCase    )
													__a					,      __a							=   self._remove_meta_keys(UpperCAmelCase    )
													if configa == configa:
																		print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''    )
																		return True
								return False



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Dict:
								__a							=   []
								__a							=   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 __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						**UpperCAmelCase    ) ->    Optional[int]:
								__a							=   kwargs.pop('cache_dir'      ,						UpperCAmelCase    )
								__a							=   kwargs.pop('resume_download'      ,						UpperCAmelCase    )
								__a							=   kwargs.pop('force_download'      ,						UpperCAmelCase    )
								__a							=   kwargs.pop('proxies'      ,						UpperCAmelCase    )
								__a							=   kwargs.pop('local_files_only'      ,						UpperCAmelCase    )
								__a							=   kwargs.pop('use_auth_token'      ,						UpperCAmelCase    )
								__a							=   kwargs.pop('revision'      ,						UpperCAmelCase    )
								__a							=   kwargs.pop('torch_dtype'      ,						UpperCAmelCase    )
								__a							=   kwargs.pop('device_map'      ,						UpperCAmelCase    )

								__a							=   kwargs.pop('alpha'      ,						0.5    )
								__a							=   kwargs.pop('interp'      ,						UpperCAmelCase    )

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

								__a							=   len(UpperCAmelCase    )
								# Ignore result from model_index_json comparision of the two checkpoints
								__a							=   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 '_'
								__a							=   []
								for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
													__a							=   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    )

								__a							=   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.
								__a							=   []
								for pretrained_model_name_or_path, config_dict in zip(UpperCAmelCase      ,						UpperCAmelCase    ):
													__a							=   [k for k in config_dict.keys() if not k.startswith('_'    )]
													__a							=   [os.path.join(UpperCAmelCase      ,						'*'    ) for k in folder_names]
													allow_patterns += [
													    WEIGHTS_NAME,
													    SCHEDULER_CONFIG_NAME,
													    CONFIG_NAME,
													    ONNX_WEIGHTS_NAME,
													    DiffusionPipeline.config_name,
													]
													__a							=   config_dict.get('_class_name'    )
													__a							=   {'diffusers': __version__, 'pipeline_class': requested_pipeline_class}

													__a							=   (
													    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
								__a							=   DiffusionPipeline.from_pretrained(
								    cached_folders[0]      ,						torch_dtype=UpperCAmelCase      ,						device_map=UpperCAmelCase    )
								final_pipe.to(self.device    )

								__a							=   None
								if len(UpperCAmelCase    ) > 2:
													__a							=   os.path.join(cached_folders[2]    )

								if interp == "sigmoid":
													__a							=   CheckpointMergerPipeline.sigmoid
								elif interp == "inv_sigmoid":
													__a							=   CheckpointMergerPipeline.inv_sigmoid
								elif interp == "add_diff":
													__a							=   CheckpointMergerPipeline.add_difference
								else:
													__a							=   CheckpointMergerPipeline.weighted_sum

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

																							__a							=   getattr(UpperCAmelCase      ,						'load_state_dict'    )
																							__a							=   (
																							    safetensors.torch.load_file(UpperCAmelCase    )
																							    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'    ))
																							    else torch.load(UpperCAmelCase      ,						map_location='cpu'    )
																							)
																							__a							=   None
																							if checkpoint_path_a:
																												__a							=   (
																												    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:
																												__a							=   theta_func(theta_a[key]      ,						theta_a[key]      ,						theta_a[key]      ,						UpperCAmelCase    )
																							else:
																												__a							=   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 __SCREAMING_SNAKE_CASE							(						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Optional[int]:
								return ((1 - alpha) * thetaa) + (alpha * thetaa)



			@staticmethod
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Optional[Any]:
								__a							=   alpha * alpha * (3 - (2 * alpha))
								return thetaa + ((thetaa - thetaa) * alpha)



			@staticmethod
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Dict:
								import math

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







			@staticmethod
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    List[str]:
								return thetaa + (thetaa - thetaa) * (1.0 - alpha)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_				:		int       						=       {
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Dict       						=       ["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
	from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
	from .tokenization_fsmt import FSMTTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
	import sys

	lowerCamelCase_				:		Dict       						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




