




"""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				(							__lowerCamelCase       ):

				'''simple docstring'''




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





				def 				snake_case_ (							self      :  str					,							snake_case      :  str					,							snake_case      :  str     ):






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





				def 				snake_case_ (							self      :  int					,							snake_case      :  Dict     ):
								UpperCAmelCase_					:str      			=      []
								UpperCAmelCase_					:Dict      			=      config_dict.copy()
								for key in config_dict.keys():
												if key.startswith('''_'''     ):
																temp_dict.pop(snake_case     )
																meta_keys.append(snake_case     )
								return (temp_dict, meta_keys)





				@torch.no_grad()
				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  List[Union[str, os.PathLike]]					,							**snake_case      :  List[str]     ):
								UpperCAmelCase_					:Optional[Any]      			=      kwargs.pop('''cache_dir'''					,							snake_case     )
								UpperCAmelCase_					:str      			=      kwargs.pop('''resume_download'''					,							snake_case     )
								UpperCAmelCase_					:Tuple      			=      kwargs.pop('''force_download'''					,							snake_case     )
								UpperCAmelCase_					:Tuple      			=      kwargs.pop('''proxies'''					,							snake_case     )
								UpperCAmelCase_					:Optional[int]      			=      kwargs.pop('''local_files_only'''					,							snake_case     )
								UpperCAmelCase_					:Tuple      			=      kwargs.pop('''use_auth_token'''					,							snake_case     )
								UpperCAmelCase_					:Tuple      			=      kwargs.pop('''revision'''					,							snake_case     )
								UpperCAmelCase_					:str      			=      kwargs.pop('''torch_dtype'''					,							snake_case     )
								UpperCAmelCase_					:List[Any]      			=      kwargs.pop('''device_map'''					,							snake_case     )

								UpperCAmelCase_					:List[str]      			=      kwargs.pop('''alpha'''					,							0.5     )
								UpperCAmelCase_					:Optional[int]      			=      kwargs.pop('''interp'''					,							snake_case     )

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

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

								# 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 '_'
								UpperCAmelCase_					:Tuple      			=      []
								for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
												UpperCAmelCase_					:Dict      			=      DiffusionPipeline.load_config(
												    snake_case					,							cache_dir=snake_case					,							resume_download=snake_case					,							force_download=snake_case					,							proxies=snake_case					,							local_files_only=snake_case					,							use_auth_token=snake_case					,							revision=snake_case					,							)
												config_dicts.append(snake_case     )

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

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

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

								UpperCAmelCase_					:Dict      			=      None
								if len(snake_case     ) > 2:
												UpperCAmelCase_					:Optional[int]      			=      os.path.join(cached_folders[2]     )

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

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

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

																for key in theta_a.keys():
																				if theta_a:
																								UpperCAmelCase_					:int      			=      theta_func(theta_a[key]					,							theta_a[key]					,							theta_a[key]					,							snake_case     )
																				else:
																								UpperCAmelCase_					:Optional[int]      			=      theta_func(theta_a[key]					,							theta_a[key]					,							snake_case					,							snake_case     )

																del theta_a
																del theta_a
																update_theta_a(snake_case     )

																del theta_a
								return final_pipe





				@staticmethod
				def 				snake_case_ (							snake_case      :  int					,							snake_case      :  str					,							snake_case      :  List[Any]					,							snake_case      :  str     ):
								return ((1 - alpha) * thetaa) + (alpha * thetaa)





				@staticmethod
				def 				snake_case_ (							snake_case      :  Optional[Any]					,							snake_case      :  List[Any]					,							snake_case      :  int					,							snake_case      :  Optional[Any]     ):
								UpperCAmelCase_					:Tuple      			=      alpha * alpha * (3 - (2 * alpha))
								return thetaa + ((thetaa - thetaa) * alpha)





				@staticmethod
				def 				snake_case_ (							snake_case      :  Dict					,							snake_case      :  List[Any]					,							snake_case      :  Optional[int]					,							snake_case      :  int     ):
								import math

								UpperCAmelCase_					:List[Any]      			=      0.5 - math.sin(math.asin(1.0 - 2.0 * alpha     ) / 3.0     )
								return thetaa + ((thetaa - thetaa) * alpha)





				@staticmethod
				def 				snake_case_ (							snake_case      :  Union[str, Any]					,							snake_case      :  Tuple					,							snake_case      :  Optional[int]					,							snake_case      :  Optional[int]     ):
								return thetaa + (thetaa - thetaa) * (1.0 - alpha)








"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase								=						{
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						["BartTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BartForCausalLM",
						    "BartForConditionalGeneration",
						    "BartForQuestionAnswering",
						    "BartForSequenceClassification",
						    "BartModel",
						    "BartPretrainedModel",
						    "PretrainedBartModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "TFBartForConditionalGeneration",
						    "TFBartForSequenceClassification",
						    "TFBartModel",
						    "TFBartPretrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "FlaxBartDecoderPreTrainedModel",
						    "FlaxBartForCausalLM",
						    "FlaxBartForConditionalGeneration",
						    "FlaxBartForQuestionAnswering",
						    "FlaxBartForSequenceClassification",
						    "FlaxBartModel",
						    "FlaxBartPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowerCamelCase								=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


