

'''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 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):




    def __init__(					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          if dicta == dicta:
                return True
          else:
                SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       self._remove_meta_keys(lowercase__			)
                SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       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__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       []
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       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__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Any     =       kwargs.pop("cache_dir"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       kwargs.pop("resume_download"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			str     =       kwargs.pop("force_download"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       kwargs.pop("proxies"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       kwargs.pop("local_files_only"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       kwargs.pop("use_auth_token"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			int     =       kwargs.pop("revision"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       kwargs.pop("torch_dtype"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       kwargs.pop("device_map"				,     lowercase__			)

          SCREAMING_SNAKE_CASE_      :			str     =       kwargs.pop("alpha"				,     0.5			)
          SCREAMING_SNAKE_CASE_      :			Any     =       kwargs.pop("interp"				,     lowercase__			)

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

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       len(lowercase__			)
          # Ignore result from model_index_json comparision of the two checkpoints
          SCREAMING_SNAKE_CASE_      :			int     =       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 '_'
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       []
          for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                SCREAMING_SNAKE_CASE_      :			int     =       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__			)

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

                SCREAMING_SNAKE_CASE_      :			Dict     =       (
                    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
          SCREAMING_SNAKE_CASE_      :			Any     =       DiffusionPipeline.from_pretrained(
              cached_folders[0]				,     torch_dtype=lowercase__				,     device_map=lowercase__			)
          final_pipe.to(self.device			)

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       None
          if len(lowercase__			) > 2:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       os.path.join(cached_folders[2]			)

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

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

                            SCREAMING_SNAKE_CASE_      :			Dict     =       getattr(lowercase__				,     "load_state_dict"			)
                            SCREAMING_SNAKE_CASE_      :			Any     =       (
                                safetensors.torch.load_file(lowercase__			)
                                if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"			))
                                else torch.load(lowercase__				,     map_location="cpu"			)
                            )
                            SCREAMING_SNAKE_CASE_      :			Dict     =       None
                            if checkpoint_path_a:
                                  SCREAMING_SNAKE_CASE_      :			Tuple     =       (
                                      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:
                                  SCREAMING_SNAKE_CASE_      :			Tuple     =       theta_func(theta_a[key]				,     theta_a[key]				,     theta_a[key]				,     lowercase__			)
                            else:
                                  SCREAMING_SNAKE_CASE_      :			Tuple     =       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__			):






          """simple docstring"""






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




    @staticmethod
    def   __lowerCamelCase      (					lowercase__				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       alpha * alpha * (3 - (2 * alpha))
          return thetaa + ((thetaa - thetaa) * alpha)




    @staticmethod
    def   __lowerCamelCase      (					lowercase__				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          import math

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       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__			):






          """simple docstring"""






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



'''simple docstring'''

import io
import json
import subprocess


lowerCAmelCase_			:			Tuple			   =     [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

lowerCAmelCase_			:			str			   =     8







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase					:  Union[str, Any]         =    {}
     for src, tgt in pairs:
          _UpperCAmelCase					:  Union[str, Any]         =    f"{src}-{tgt}"
          _UpperCAmelCase					:  List[Any]         =    f"sacrebleu -t wmt19 -l {pair} --echo src".split()
          _UpperCAmelCase					:  List[str]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  str         =    f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
          _UpperCAmelCase					:  List[Any]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  int         =    {"""src""": src_lines[:n_objs], """tgt""": tgt_lines[:n_objs]}
     return text


lowerCAmelCase_			:			Optional[int]			   =     get_all_data(pairs, n_objs)
lowerCAmelCase_			:			str			   =     '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   lowerCAmelCase_			:			Tuple			   =     json.dump(text, f, indent=2, ensure_ascii=False)


