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 	_lowercase		(      snake_case_ ):

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

  """simple docstring"""





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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							List[str]     ,   snake_case			:							str    )     ->		List[Any]:

  """simple docstring"""





  if dicta == dicta:
   return True
  else:
   UpperCamelCase_,				UpperCamelCase_      :		Any				=      self._remove_meta_keys(snake_case    )
   UpperCamelCase_,				UpperCamelCase_      :		List[str]				=      self._remove_meta_keys(snake_case    )
   if configa == configa:
    print(f"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."    )
    return True
  return False

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							Dict    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      []
  UpperCamelCase_      :		Dict				=      config_dict.copy()
  for key in config_dict.keys():
   if key.startswith('_'    ):
    temp_dict.pop(snake_case    )
    meta_keys.append(snake_case    )
  return (temp_dict, meta_keys)

 @torch.no_grad()
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							List[Union[str, os.PathLike]]     ,   **snake_case			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      kwargs.pop('cache_dir'     ,   snake_case    )
  UpperCamelCase_      :		Optional[Any]				=      kwargs.pop('resume_download'     ,   snake_case    )
  UpperCamelCase_      :		List[str]				=      kwargs.pop('force_download'     ,   snake_case    )
  UpperCamelCase_      :		Any				=      kwargs.pop('proxies'     ,   snake_case    )
  UpperCamelCase_      :		Dict				=      kwargs.pop('local_files_only'     ,   snake_case    )
  UpperCamelCase_      :		Optional[Any]				=      kwargs.pop('use_auth_token'     ,   snake_case    )
  UpperCamelCase_      :		Optional[Any]				=      kwargs.pop('revision'     ,   snake_case    )
  UpperCamelCase_      :		int				=      kwargs.pop('torch_dtype'     ,   snake_case    )
  UpperCamelCase_      :		List[str]				=      kwargs.pop('device_map'     ,   snake_case    )

  UpperCamelCase_      :		Optional[int]				=      kwargs.pop('alpha'     ,   0.5    )
  UpperCamelCase_      :		Any				=      kwargs.pop('interp'     ,   snake_case    )

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

  UpperCamelCase_      :		int				=      len(snake_case    )
  # Ignore result from model_index_json comparision of the two checkpoints
  UpperCamelCase_      :		Optional[int]				=      kwargs.pop('force'     ,   snake_case    )

  # 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 '_'
  UpperCamelCase_      :		Dict				=      []
  for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
   UpperCamelCase_      :		Union[str, Any]				=      DiffusionPipeline.load_config(
       snake_case     ,   cache_dir=snake_case     ,   resume_download=snake_case     ,   force_download=snake_case     ,   proxies=snake_case     ,   local_files_only=snake_case     ,   use_auth_token=snake_case     ,   revision=snake_case     ,   )
   config_dicts.append(snake_case    )

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

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

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

  UpperCamelCase_      :		Optional[Any]				=      None
  if len(snake_case    ) > 2:
   UpperCamelCase_      :		List[Any]				=      os.path.join(cached_folders[2]    )

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

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

     UpperCamelCase_      :		int				=      getattr(snake_case     ,   'load_state_dict'    )
     UpperCamelCase_      :		Optional[Any]				=      (
         safetensors.torch.load_file(snake_case    )
         if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'    ))
         else torch.load(snake_case     ,   map_location='cpu'    )
     )
     UpperCamelCase_      :		Any				=      None
     if checkpoint_path_a:
      UpperCamelCase_      :		Dict				=      (
          safetensors.torch.load_file(snake_case    )
          if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'    ))
          else torch.load(snake_case     ,   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(snake_case    )}"    )
     continue
    print(f"MERGING {attr}"    )

    for key in theta_a.keys():
     if theta_a:
      UpperCamelCase_      :		Optional[Any]				=      theta_func(theta_a[key]     ,   theta_a[key]     ,   theta_a[key]     ,   snake_case    )
     else:
      UpperCamelCase_      :		List[Any]				=      theta_func(theta_a[key]     ,   theta_a[key]     ,   snake_case     ,   snake_case    )

    del theta_a
    del theta_a
    update_theta_a(snake_case    )

    del theta_a
  return final_pipe

 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				snake_case			:							Any     ,   snake_case			:							Optional[int]     ,   snake_case			:							Optional[int]     ,   snake_case			:							Tuple    )     ->		List[str]:

  """simple docstring"""





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

 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				snake_case			:							Optional[Any]     ,   snake_case			:							int     ,   snake_case			:							List[Any]     ,   snake_case			:							List[Any]    )     ->		Optional[Any]:

  """simple docstring"""





  UpperCamelCase_      :		int				=      alpha * alpha * (3 - (2 * alpha))
  return thetaa + ((thetaa - thetaa) * alpha)

 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				snake_case			:							Any     ,   snake_case			:							str     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							int    )     ->		Any:

  """simple docstring"""





  import math

  UpperCamelCase_      :		Optional[Any]				=      0.5 - math.sin(math.asin(1.0 - 2.0 * alpha    ) / 3.0    )
  return thetaa + ((thetaa - thetaa) * alpha)


 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				snake_case			:							Any     ,   snake_case			:							List[Any]     ,   snake_case			:							str     ,   snake_case			:							List[str]    )     ->		Any:

  """simple docstring"""





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




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


a_					      = logging.get_logger(__name__)

a_					      = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a_					      = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

a_					      = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

a_					      = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}





class 	_lowercase		(      snake_case_ ):
 lowercase         =     VOCAB_FILES_NAMES
 lowercase         =     PRETRAINED_VOCAB_FILES_MAP
 lowercase         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase         =     ['input_ids', 'attention_mask', 'token_type_ids']
 lowercase         =     DebertaTokenizer

 def __init__(				self			:							Optional[int]     ,   snake_case			:							Any=None     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Optional[Any]=None     ,   snake_case			:							List[str]="replace"     ,   snake_case			:							int="[CLS]"     ,   snake_case			:							Any="[SEP]"     ,   snake_case			:							int="[SEP]"     ,   snake_case			:							Union[str, Any]="[CLS]"     ,   snake_case			:							List[Any]="[UNK]"     ,   snake_case			:							List[str]="[PAD]"     ,   snake_case			:							Tuple="[MASK]"     ,   snake_case			:							int=False     ,   **snake_case			:							List[str]     ,   )     ->		Any:

  """simple docstring"""





  super().__init__(
      snake_case     ,   snake_case     ,   tokenizer_file=snake_case     ,   errors=snake_case     ,   bos_token=snake_case     ,   eos_token=snake_case     ,   unk_token=snake_case     ,   sep_token=snake_case     ,   cls_token=snake_case     ,   pad_token=snake_case     ,   mask_token=snake_case     ,   add_prefix_space=snake_case     ,   **snake_case     ,   )
  UpperCamelCase_      :		str				=      kwargs.pop('add_bos_token'     ,   snake_case    )

  UpperCamelCase_      :		Tuple				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'     ,   snake_case    ) != add_prefix_space:
   UpperCamelCase_      :		str				=      getattr(snake_case     ,   pre_tok_state.pop('type'    )    )
   UpperCamelCase_      :		List[Any]				=      add_prefix_space
   UpperCamelCase_      :		List[str]				=      pre_tok_class(**snake_case    )

  UpperCamelCase_      :		int				=      add_prefix_space

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		str:

  """simple docstring"""





  if self._mask_token is None:
   if self.verbose:
    logger.error('Using mask_token, but it is not set yet.'    )
   return None
  return str(self._mask_token    )

 @mask_token.setter
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      AddedToken(snake_case     ,   lstrip=snake_case     ,   rstrip=snake_case    ) if isinstance(snake_case     ,   snake_case    ) else value
  UpperCamelCase_      :		Optional[int]				=      value

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  UpperCamelCase_      :		Dict				=      [self.cls_token_id]
  UpperCamelCase_      :		Tuple				=      [self.sep_token_id]
  return cls + token_ids_a + sep + token_ids_a + sep

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      [self.sep_token_id]
  UpperCamelCase_      :		List[str]				=      [self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep    ) * [0]
  return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   *snake_case			:							Any     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      kwargs.get('is_split_into_words'     ,   snake_case    )
  assert self.add_prefix_space or not is_split_into_words, (
      f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
      "to use it with pretokenized inputs."
  )

  return super()._batch_encode_plus(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   *snake_case			:							List[str]     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      kwargs.get('is_split_into_words'     ,   snake_case    )

  assert self.add_prefix_space or not is_split_into_words, (
      f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
      "to use it with pretokenized inputs."
  )

  return super()._encode_plus(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							str     ,   snake_case			:							Optional[str] = None    )     ->		Tuple[str]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self._tokenizer.model.save(snake_case     ,   name=snake_case    )
  return tuple(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							"Conversation"    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(snake_case     ,   add_special_tokens=snake_case    ) + [self.eos_token_id]    )

  if len(snake_case    ) > self.model_max_length:
   UpperCamelCase_      :		Optional[int]				=      input_ids[-self.model_max_length :]
  return input_ids



