





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_				(   _lowerCAmelCase			):


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



									"""simple docstring"""



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


							def    _snake_case     (							self			:str							, __A			:Union[str, Any]							, __A			:Tuple						)      ->      Dict:



									"""simple docstring"""



									if dicta == dicta:
											return True
									else:
											SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self._remove_meta_keys(A__						)
											SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self._remove_meta_keys(A__						)
											if configa == configa:
													print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''						)
													return True
									return False


							def    _snake_case     (							self			:Optional[Any]							, __A			:Dict						)      ->      Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					[]
									SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Union[str, Any]							, __A			:List[Union[str, os.PathLike]]							, **__A			:Union[str, Any]						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""cache_dir"""							, A__						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""resume_download"""							, A__						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""force_download"""							, A__						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""proxies"""							, A__						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""local_files_only"""							, A__						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""use_auth_token"""							, A__						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""revision"""							, A__						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""torch_dtype"""							, A__						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""device_map"""							, A__						)

									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""alpha"""							, 0.5						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""interp"""							, A__						)

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

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

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

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

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

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

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

															SCREAMING_SNAKE_CASE__								=					getattr(A__							, """load_state_dict"""						)
															SCREAMING_SNAKE_CASE__								=					(
															    safetensors.torch.load_file(A__						)
															    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""						))
															    else torch.load(A__							, map_location="""cpu"""						)
															)
															SCREAMING_SNAKE_CASE__								=					None
															if checkpoint_path_a:
																	SCREAMING_SNAKE_CASE__								=					(
																	    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:
																	SCREAMING_SNAKE_CASE__								=					theta_func(theta_a[key]							, theta_a[key]							, theta_a[key]							, A__						)
															else:
																	SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							__A			:Tuple							, __A			:List[Any]							, __A			:Tuple							, __A			:Union[str, Any]						)      ->      Union[str, Any]:



									"""simple docstring"""



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


							@staticmethod
							def    _snake_case     (							__A			:Any							, __A			:Optional[int]							, __A			:Any							, __A			:Dict						)      ->      Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					alpha * alpha * (3 - (2 * alpha))
									return thetaa + ((thetaa - thetaa) * alpha)


							@staticmethod
							def    _snake_case     (							__A			:Any							, __A			:Union[str, Any]							, __A			:Any							, __A			:Optional[Any]						)      ->      Any:



									"""simple docstring"""



									import math

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


							@staticmethod
							def    _snake_case     (							__A			:Optional[Any]							, __A			:Optional[int]							, __A			:List[str]							, __A			:List[Any]						)      ->      Dict:



									"""simple docstring"""



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





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]