
'''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  UpperCAmelCase_					(						_snake_case       ):





 def __init__(     self):
     self.register_to_config()
     super().__init__()





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_):
     if dicta == dicta:
         return True
     else:
         snake_case_      ,			snake_case_			:     Dict        =    self._remove_meta_keys(A_)
         snake_case_      ,			snake_case_			:     Union[str, Any]        =    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			,							lowercase_):
     snake_case_			:     int        =    []
     snake_case_			:     Union[str, Any]        =    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			,							lowercase_			,							**lowercase_):
     snake_case_			:     Union[str, Any]        =    kwargs.pop("cache_dir"			,							A_)
     snake_case_			:     str        =    kwargs.pop("resume_download"			,							A_)
     snake_case_			:     List[str]        =    kwargs.pop("force_download"			,							A_)
     snake_case_			:     Optional[int]        =    kwargs.pop("proxies"			,							A_)
     snake_case_			:     Dict        =    kwargs.pop("local_files_only"			,							A_)
     snake_case_			:     Dict        =    kwargs.pop("use_auth_token"			,							A_)
     snake_case_			:     str        =    kwargs.pop("revision"			,							A_)
     snake_case_			:     List[Any]        =    kwargs.pop("torch_dtype"			,							A_)
     snake_case_			:     str        =    kwargs.pop("device_map"			,							A_)

     snake_case_			:     int        =    kwargs.pop("alpha"			,							0.5)
     snake_case_			:     List[str]        =    kwargs.pop("interp"			,							A_)

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

     snake_case_			:     List[Any]        =    len(A_)
     # Ignore result from model_index_json comparision of the two checkpoints
     snake_case_			:     str        =    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 '_'
     snake_case_			:     Tuple        =    []
     for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
         snake_case_			:     Optional[Any]        =    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_)

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

         snake_case_			:     str        =    (
             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
     snake_case_			:     Union[str, Any]        =    DiffusionPipeline.from_pretrained(
         cached_folders[0]			,							torch_dtype=A_			,							device_map=A_)
     final_pipe.to(self.device)

     snake_case_			:     List[str]        =    None
     if len(A_) > 2:
         snake_case_			:     int        =    os.path.join(cached_folders[2])

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

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

                 snake_case_			:     List[str]        =    getattr(A_			,							"load_state_dict")
                 snake_case_			:     int        =    (
                     safetensors.torch.load_file(A_)
                     if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"))
                     else torch.load(A_			,							map_location="cpu")
                 )
                 snake_case_			:     List[Any]        =    None
                 if checkpoint_path_a:
                     snake_case_			:     List[str]        =    (
                         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:
                     snake_case_			:     Dict        =    theta_func(theta_a[key]			,							theta_a[key]			,							theta_a[key]			,							A_)
                 else:
                     snake_case_			:     Any        =    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__		(     lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     return ((1 - alpha) * thetaa) + (alpha * thetaa)





 @staticmethod
 def 							snake_case__		(     lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Optional[int]        =    alpha * alpha * (3 - (2 * alpha))
     return thetaa + ((thetaa - thetaa) * alpha)





 @staticmethod
 def 							snake_case__		(     lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     import math

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





 @staticmethod
 def 							snake_case__		(     lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     return thetaa + (thetaa - thetaa) * (1.0 - alpha)







from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


A__										=      [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

A__										=      get_logger(__name__)


A__										=      {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    task_template_dict.get("task"						)
    if task_name is None:
        logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(__A						)}"						)
        return None
    SCREAMING_SNAKE_CASE__         =    NAMEaTEMPLATE.get(__A						)
    return template.from_dict(__A						)


