


"""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					(     _UpperCamelCase):







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





						'''simple docstring'''







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







		def        a_       (    self					:				Dict			,      a__					:				List[str]			,      a__					:				Optional[Any]						)				->  Optional[int]:





						'''simple docstring'''







						if dicta == dicta:
										return True
						else:
										_A     ,						_A         =	self._remove_meta_keys(a__						)
										_A     ,						_A         =	self._remove_meta_keys(a__						)
										if configa == configa:
														print(F"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""						)
														return True
						return False







		def        a_       (    self					:				str			,      a__					:				Dict						)				->  Tuple:





						'''simple docstring'''







						_A         =	[]
						_A         =	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        a_       (    self					:				Optional[Any]			,      a__					:				List[Union[str, os.PathLike]]			,      **a__					:				List[Any]						)				->  Optional[Any]:





						'''simple docstring'''







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

						_A         =	kwargs.pop("alpha"			,      0.5						)
						_A         =	kwargs.pop("interp"			,      a__						)

						print("Received list"			,      a__						)
						print(F"""Combining with alpha={alpha}, interpolation mode={interp}"""						)

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

						_A         =	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.
						_A         =	[]
						for pretrained_model_name_or_path, config_dict in zip(a__			,      a__						):
										_A         =	[k for k in config_dict.keys() if not k.startswith("_"						)]
										_A         =	[os.path.join(a__			,      "*"						) 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(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
						_A         =	DiffusionPipeline.from_pretrained(
						    cached_folders[0]			,      torch_dtype=a__			,      device_map=a__						)
						final_pipe.to(self.device						)

						_A         =	None
						if len(a__						) > 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]			,      a__						)
														if os.path.exists(a__						):
																		_A         =	[
																		    *glob.glob(os.path.join(a__			,      "*.safetensors"						)						),
																		    *glob.glob(os.path.join(a__			,      "*.bin"						)						),
																		]
																		_A         =	files[0] if len(a__						) > 0 else None
														if len(a__						) < 3:
																		_A         =	None
														else:
																		_A         =	os.path.join(cached_folders[2]			,      a__						)
																		if os.path.exists(a__						):
																						_A         =	[
																						    *glob.glob(os.path.join(a__			,      "*.safetensors"						)						),
																						    *glob.glob(os.path.join(a__			,      "*.bin"						)						),
																						]
																						_A         =	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:
																		_A         =	getattr(a__			,      a__						)
																		if isinstance(a__			,      a__						):  # ignore requires_safety_checker boolean
																						continue
																		_A         =	getattr(a__			,      "state_dict"						)
																		_A         =	theta_a()

																		_A         =	getattr(a__			,      "load_state_dict"						)
																		_A         =	(
																		    safetensors.torch.load_file(a__						)
																		    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"						))
																		    else torch.load(a__			,      map_location="cpu"						)
																		)
																		_A         =	None
																		if checkpoint_path_a:
																						_A         =	(
																						    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:
																						_A         =	theta_func(theta_a[key]			,      theta_a[key]			,      theta_a[key]			,      a__						)
																		else:
																						_A         =	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        a_       (    a__					:				Union[str, Any]			,      a__					:				Optional[int]			,      a__					:				Dict			,      a__					:				Union[str, Any]						)				->  Dict:





						'''simple docstring'''







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







		@staticmethod
		def        a_       (    a__					:				str			,      a__					:				Any			,      a__					:				Tuple			,      a__					:				Union[str, Any]						)				->  Dict:





						'''simple docstring'''







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







		@staticmethod
		def        a_       (    a__					:				Any			,      a__					:				Tuple			,      a__					:				Tuple			,      a__					:				int						)				->  Union[str, Any]:





						'''simple docstring'''







						import math

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







		@staticmethod
		def        a_       (    a__					:				int			,      a__					:				Optional[Any]			,      a__					:				Optional[Any]			,      a__					:				int						)				->  List[str]:





						'''simple docstring'''







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



"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,      device=a__			,      dtype=torch.float			,      )
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]			,      a__			,      atol=1E-4						)						)