

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  __snake_case					(      lowerCAmelCase__    ):

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

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A):
      if dicta == dicta:
          return True
      else:
          SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self._remove_meta_keys(_A)
          SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self._remove_meta_keys(_A)
          if configa == configa:
              print(f"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.""")
              return True
      return False

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  []
      SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__       (   self  ,						_A  ,						**_A):
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('cache_dir'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('resume_download'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('force_download'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('proxies'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('local_files_only'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('use_auth_token'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('revision'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('torch_dtype'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('device_map'  ,						_A)

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('alpha'  ,						0.5)
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('interp'  ,						_A)

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

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

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

          SCREAMING_SNAKE_CASE_													=  (
              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
      SCREAMING_SNAKE_CASE_													=  DiffusionPipeline.from_pretrained(
          cached_folders[0]  ,						torch_dtype=_A  ,						device_map=_A)
      final_pipe.to(self.device)

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

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

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

                  SCREAMING_SNAKE_CASE_													=  getattr(_A  ,						'load_state_dict')
                  SCREAMING_SNAKE_CASE_													=  (
                      safetensors.torch.load_file(_A)
                      if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'))
                      else torch.load(_A  ,						map_location='cpu')
                  )
                  SCREAMING_SNAKE_CASE_													=  None
                  if checkpoint_path_a:
                      SCREAMING_SNAKE_CASE_													=  (
                          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:
                      SCREAMING_SNAKE_CASE_													=  theta_func(theta_a[key]  ,						theta_a[key]  ,						theta_a[key]  ,						_A)
                  else:
                      SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__       (   _A  ,						_A  ,						_A  ,						_A):
      return ((1 - alpha) * thetaa) + (alpha * thetaa)

  @staticmethod
  def   lowerCAmelCase__       (   _A  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  alpha * alpha * (3 - (2 * alpha))
      return thetaa + ((thetaa - thetaa) * alpha)

  @staticmethod
  def   lowerCAmelCase__       (   _A  ,						_A  ,						_A  ,						_A):
      import math

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


  @staticmethod
  def   lowerCAmelCase__       (   _A  ,						_A  ,						_A  ,						_A):
      return thetaa + (thetaa - thetaa) * (1.0 - alpha)





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def    _UpperCAmelCase    (					):





    """simple docstring"""


    def filter_log(_SCREAMING_SNAKE_CASE   :						Dict    ):
        return False if "This IS expected if you are initializing" in record.msg else True

    SCREAMING_SNAKE_CASE_													=  datasets.utils.logging.get_logger('transformers.modeling_utils'    )
    logger.addFilter(_SCREAMING_SNAKE_CASE    )
    try:
        yield
    finally:
        logger.removeFilter(_SCREAMING_SNAKE_CASE    )


UpperCamelCase__					: List[str]								=      "\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

UpperCamelCase__					: Dict								=      "\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

UpperCamelCase__					: Dict								=      "\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION    )
class  __snake_case					(      datasets.Metric    ):

  def   lowerCAmelCase__       (   self):
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,						citation=_CITATION  ,						homepage='https://github.com/Tiiiger/bert_score'  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
              {
                  'predictions': datasets.Value('string'  ,						id='sequence'),
                  'references': datasets.Sequence(datasets.Value('string'  ,						id='sequence')  ,						id='references'),
              })  ,						codebase_urls=['https://github.com/Tiiiger/bert_score']  ,						reference_urls=[
              'https://github.com/Tiiiger/bert_score',
              'https://arxiv.org/abs/1904.09675',
          ]  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A=None  ,						_A=None  ,						_A=None  ,						_A=False  ,						_A=False  ,						_A=None  ,						_A=64  ,						_A=4  ,						_A=False  ,						_A=False  ,						_A=None  ,						_A=False  ,						):
      SCREAMING_SNAKE_CASE_													=  bert_score.utils.get_hash
      SCREAMING_SNAKE_CASE_													=  bert_score.BERTScorer

      if version.parse(bert_score.__version__) >= version.parse('0.3.10'):
          SCREAMING_SNAKE_CASE_													=  functools.partial(_A  ,						use_fast_tokenizer=_A)
          SCREAMING_SNAKE_CASE_													=  functools.partial(_A  ,						use_fast_tokenizer=_A)
      elif use_fast_tokenizer:
          raise ImportWarning(
              'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
              'You can install it with `pip install "bert-score>=0.3.10"`.')

      if model_type is None:
          assert lang is not None, "either lang or model_type should be specified"
          SCREAMING_SNAKE_CASE_													=  bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
          SCREAMING_SNAKE_CASE_													=  bert_score.utils.modelalayers[model_type]

      SCREAMING_SNAKE_CASE_													=  get_hash(
          model=_A  ,						num_layers=_A  ,						idf=_A  ,						rescale_with_baseline=_A  ,						use_custom_baseline=baseline_path is not None  ,						)

      with filter_logging_context():
          if not hasattr(self  ,						'cached_bertscorer') or self.cached_bertscorer.hash != hashcode:
              SCREAMING_SNAKE_CASE_													=  scorer(
                  model_type=_A  ,						num_layers=_A  ,						batch_size=_A  ,						nthreads=_A  ,						all_layers=_A  ,						idf=_A  ,						device=_A  ,						lang=_A  ,						rescale_with_baseline=_A  ,						baseline_path=_A  ,						)

      ((SCREAMING_SNAKE_CASE_)							,     (SCREAMING_SNAKE_CASE_)							,     (SCREAMING_SNAKE_CASE_))													=  self.cached_bertscorer.score(
          cands=_A  ,						refs=_A  ,						verbose=_A  ,						batch_size=_A  ,						)
      SCREAMING_SNAKE_CASE_													=  {
          'precision': P.tolist(),
          'recall': R.tolist(),
          'f1': F.tolist(),
          'hashcode': hashcode,
      }
      return output_dict

  def   lowerCAmelCase__       (   self  ,						_A=None  ,						_A=None  ,						**_A):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if references is not None:
          SCREAMING_SNAKE_CASE_													=  [[ref] if isinstance(_A  ,						_A) else ref for ref in references]
      super().add_batch(predictions=_A  ,						references=_A  ,						**_A)


  def   lowerCAmelCase__       (   self  ,						_A=None  ,						_A=None  ,						**_A):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if isinstance(_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  [reference]
      super().add(prediction=_A  ,						reference=_A  ,						**_A)


