

'''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 lowerCamelCase   (    __lowerCAmelCase				):

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

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							List[str]:
					if dicta == dicta:
						return True
					else:
						snake_case			,			snake_case							=					self._remove_meta_keys(lowercase_ )
						snake_case			,			snake_case							=					self._remove_meta_keys(lowercase_ )
						if configa == configa:
							print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.''' )
							return True
					return False

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							str:
					snake_case							=					[]
					snake_case							=					config_dict.copy()
					for key in config_dict.keys():
						if key.startswith('_' ):
							temp_dict.pop(lowercase_ )
							meta_keys.append(lowercase_ )
					return (temp_dict, meta_keys)

				@torch.no_grad()
				def 					_lowerCamelCase    (       self,						lowercase_,						**lowercase_ )    ->							Any:
					snake_case							=					kwargs.pop('cache_dir',						lowercase_ )
					snake_case							=					kwargs.pop('resume_download',						lowercase_ )
					snake_case							=					kwargs.pop('force_download',						lowercase_ )
					snake_case							=					kwargs.pop('proxies',						lowercase_ )
					snake_case							=					kwargs.pop('local_files_only',						lowercase_ )
					snake_case							=					kwargs.pop('use_auth_token',						lowercase_ )
					snake_case							=					kwargs.pop('revision',						lowercase_ )
					snake_case							=					kwargs.pop('torch_dtype',						lowercase_ )
					snake_case							=					kwargs.pop('device_map',						lowercase_ )

					snake_case							=					kwargs.pop('alpha',						0.5 )
					snake_case							=					kwargs.pop('interp',						lowercase_ )

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

					snake_case							=					len(lowercase_ )
					# Ignore result from model_index_json comparision of the two checkpoints
					snake_case							=					kwargs.pop('force',						lowercase_ )

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

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

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

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

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

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

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

								snake_case							=					getattr(lowercase_,						'load_state_dict' )
								snake_case							=					(
								    safetensors.torch.load_file(lowercase_ )
								    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors' ))
								    else torch.load(lowercase_,						map_location='cpu' )
								)
								snake_case							=					None
								if checkpoint_path_a:
									snake_case							=					(
									    safetensors.torch.load_file(lowercase_ )
									    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors' ))
									    else torch.load(lowercase_,						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(lowercase_ )}''' )
								continue
							print(F'''MERGING {attr}''' )

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

							del theta_a
							del theta_a
							update_theta_a(lowercase_ )

							del theta_a
					return final_pipe

				@staticmethod
				def 					_lowerCamelCase    (       lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Dict:
					return ((1 - alpha) * thetaa) + (alpha * thetaa)

				@staticmethod
				def 					_lowerCamelCase    (       lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							int:
					snake_case							=					alpha * alpha * (3 - (2 * alpha))
					return thetaa + ((thetaa - thetaa) * alpha)

				@staticmethod
				def 					_lowerCamelCase    (       lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Tuple:
					import math

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






				@staticmethod
				def 					_lowerCamelCase    (       lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Optional[int]:
					return thetaa + (thetaa - thetaa) * (1.0 - alpha)








'''simple docstring'''

from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     =      logging.get_logger(__name__)

lowerCAmelCase_     =      {
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''led'''
				snake_case_							     = {
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''hidden_size''': '''d_model''',
				    '''attention_probs_dropout_prob''': '''attention_dropout''',
				    '''initializer_range''': '''init_std''',
				}

				def __init__(       self,						lowercase_=50265,						lowercase_=16384,						lowercase_=1024,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=True,						lowercase_=True,						lowercase_="gelu",						lowercase_=1024,						lowercase_=0.1,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=0.02,						lowercase_=2,						lowercase_=0.0,						lowercase_=1,						lowercase_=0,						lowercase_=2,						lowercase_ = 512,						**lowercase_,						)    ->							Union[str, Any]:
					snake_case							=					vocab_size
					snake_case							=					max_encoder_position_embeddings
					snake_case							=					max_decoder_position_embeddings
					snake_case							=					d_model
					snake_case							=					encoder_ffn_dim
					snake_case							=					encoder_layers
					snake_case							=					encoder_attention_heads
					snake_case							=					decoder_ffn_dim
					snake_case							=					decoder_layers
					snake_case							=					decoder_attention_heads
					snake_case							=					dropout
					snake_case							=					attention_dropout
					snake_case							=					activation_dropout
					snake_case							=					activation_function
					snake_case							=					init_std
					snake_case							=					encoder_layerdrop
					snake_case							=					decoder_layerdrop
					snake_case							=					classifier_dropout
					snake_case							=					use_cache
					snake_case							=					encoder_layers
					snake_case							=					attention_window

					super().__init__(
					    pad_token_id=lowercase_,						bos_token_id=lowercase_,						eos_token_id=lowercase_,						is_encoder_decoder=lowercase_,						decoder_start_token_id=lowercase_,						**lowercase_,						)





