

'''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   )						->       Optional[Any]:
     self.register_to_config()
     super().__init__()

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

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

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

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

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

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

     # 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(
          __lowerCamelCase,	cache_dir=__lowerCamelCase,	resume_download=__lowerCamelCase,	force_download=__lowerCamelCase,	proxies=__lowerCamelCase,	local_files_only=__lowerCamelCase,	use_auth_token=__lowerCamelCase,	revision=__lowerCamelCase,	)
      config_dicts.append(__lowerCamelCase   )

     snake_case							 =				True
     for idx in range(1,	len(__lowerCamelCase   )   ):
      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(__lowerCamelCase,	__lowerCamelCase   ):
      snake_case							 =				[k for k in config_dict.keys() if not k.startswith('_'   )]
      snake_case							 =				[os.path.join(__lowerCamelCase,	'*'   ) 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(__lowerCamelCase   )
          else snapshot_download(
              __lowerCamelCase,	cache_dir=__lowerCamelCase,	resume_download=__lowerCamelCase,	proxies=__lowerCamelCase,	local_files_only=__lowerCamelCase,	use_auth_token=__lowerCamelCase,	revision=__lowerCamelCase,	allow_patterns=__lowerCamelCase,	user_agent=__lowerCamelCase,	)
      )
      print('Cached Folder',	__lowerCamelCase   )
      cached_folders.append(__lowerCamelCase   )

     # 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=__lowerCamelCase,	device_map=__lowerCamelCase   )
     final_pipe.to(self.device   )

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

        snake_case							 =				getattr(__lowerCamelCase,	'load_state_dict'   )
        snake_case							 =				(
            safetensors.torch.load_file(__lowerCamelCase   )
            if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'   ))
            else torch.load(__lowerCamelCase,	map_location='cpu'   )
        )
        snake_case							 =				None
        if checkpoint_path_a:
         snake_case							 =				(
             safetensors.torch.load_file(__lowerCamelCase   )
             if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'   ))
             else torch.load(__lowerCamelCase,	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(__lowerCamelCase   )}'''   )
        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],	__lowerCamelCase   )
        else:
         snake_case							 =				theta_func(theta_a[key],	theta_a[key],	__lowerCamelCase,	__lowerCamelCase   )

       del theta_a
       del theta_a
       update_theta_a(__lowerCamelCase   )

       del theta_a
     return final_pipe

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

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

    @staticmethod
    def 				_lowerCamelCase						(					lowercase_,	lowercase_,	lowercase_,	lowercase_   )						->       Union[str, Any]:
     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[Any]:
     return thetaa + (thetaa - thetaa) * (1.0 - alpha)








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			{
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''mega'''

				def __init__(					self,	lowercase_=30522,	lowercase_=128,	lowercase_=4,	lowercase_=256,	lowercase_=16,	lowercase_=True,	lowercase_=64,	lowercase_=False,	lowercase_=-1,	lowercase_=None,	lowercase_=True,	lowercase_="scalenorm",	lowercase_=True,	lowercase_="silu",	lowercase_="softmax",	lowercase_=0.1,	lowercase_=0.1,	lowercase_=0.1,	lowercase_=False,	lowercase_=True,	lowercase_=256,	lowercase_=True,	lowercase_=0.1,	lowercase_=2048,	lowercase_=False,	lowercase_=2,	lowercase_=0.02,	lowercase_=0.2,	lowercase_=0.02,	lowercase_=1.0,	lowercase_=1,	lowercase_=0,	lowercase_=2,	lowercase_="rotary",	lowercase_=None,	lowercase_=True,	lowercase_=True,	**lowercase_,	)						->       List[Any]:
					super().__init__(pad_token_id=lowercase_,	bos_token_id=lowercase_,	eos_token_id=lowercase_,	**lowercase_   )

					snake_case							 =				vocab_size
					snake_case							 =				hidden_size
					snake_case							 =				num_hidden_layers
					snake_case							 =				activation
					snake_case							 =				attention_activation
					snake_case							 =				intermediate_size
					snake_case							 =				ema_projection_size
					snake_case							 =				bidirectional
					snake_case							 =				shared_representation_size
					snake_case							 =				use_chunking
					snake_case							 =				chunk_size
					snake_case							 =				truncation
					snake_case							 =				normalize_before_mega
					snake_case							 =				normalization_type
					snake_case							 =				norm_affine
					snake_case							 =				dropout_prob
					snake_case							 =				hidden_dropout_prob
					snake_case							 =				attention_probs_dropout_prob
					snake_case							 =				use_feature_dropout
					snake_case							 =				use_normalized_ffn
					snake_case							 =				nffn_hidden_size
					snake_case							 =				normalize_before_ffn
					snake_case							 =				nffn_activation_dropout_prob
					snake_case							 =				max_positions
					snake_case							 =				add_token_type_embeddings
					snake_case							 =				type_vocab_size
					snake_case							 =				initializer_range
					snake_case							 =				ema_delta_alpha_range
					snake_case							 =				ema_beta_range
					snake_case							 =				ema_gamma_omega_range
					snake_case							 =				relative_positional_bias
					snake_case							 =				use_cache
					snake_case							 =				classifier_dropout
					snake_case							 =				add_lm_hidden_dense_layer
					snake_case							 =				1  # not used but required by Hugging Face







class lowerCamelCase      (						__lowerCAmelCase							):

				@property
				def 				_lowerCamelCase						(					self   )						->       Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						snake_case							 =				{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						snake_case							 =				{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ]   )





