

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					(      UpperCamelCase__    ):

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

		def   lowerCAmelCase__       (   self  ,						_A  ,						_A):


						if dicta == dicta:
										return True
						else:
										SCREAMING_SNAKE_CASE_													=  self._remove_meta_keys(__lowerCamelCase)
										SCREAMING_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  ,						_A):
						SCREAMING_SNAKE_CASE_													=  []
						SCREAMING_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  ,						_A  ,						**_A):
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('cache_dir'  ,						__lowerCamelCase)
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('resume_download'  ,						__lowerCamelCase)
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('force_download'  ,						__lowerCamelCase)
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('proxies'  ,						__lowerCamelCase)
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('local_files_only'  ,						__lowerCamelCase)
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('use_auth_token'  ,						__lowerCamelCase)
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('revision'  ,						__lowerCamelCase)
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('torch_dtype'  ,						__lowerCamelCase)
						SCREAMING_SNAKE_CASE_													=  kwargs.pop('device_map'  ,						__lowerCamelCase)

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

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

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

						SCREAMING_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.
						SCREAMING_SNAKE_CASE_													=  []
						for pretrained_model_name_or_path, config_dict in zip(__lowerCamelCase  ,						__lowerCamelCase):
										SCREAMING_SNAKE_CASE_													=  [k for k in config_dict.keys() if not k.startswith('_')]
										SCREAMING_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,
										]
										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(__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
						SCREAMING_SNAKE_CASE_													=  DiffusionPipeline.from_pretrained(
						    cached_folders[0]  ,						torch_dtype=__lowerCamelCase  ,						device_map=__lowerCamelCase)
						final_pipe.to(self.device)

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

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__					: Optional[int]								=      logging.get_logger(__name__)

UpperCamelCase__					: int								=      {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					'pegasus_x'
  __lowerCAmelCase       :		Optional[int]  	=					['past_key_values']
  __lowerCAmelCase       :		List[str]  	=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(   self  ,						_A=96103  ,						_A=16384  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=0.0  ,						_A=True  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=0  ,						_A=True  ,						_A=0  ,						_A=1  ,						_A=1  ,						_A=32  ,						_A=512  ,						_A=True  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  d_model
      SCREAMING_SNAKE_CASE_													=  encoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  encoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  decoder_layers
      SCREAMING_SNAKE_CASE_													=  decoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  init_std
      SCREAMING_SNAKE_CASE_													=  encoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  decoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True

      SCREAMING_SNAKE_CASE_													=  num_global_tokens
      SCREAMING_SNAKE_CASE_													=  block_size
      SCREAMING_SNAKE_CASE_													=  stagger_local_blocks

      super().__init__(
          pad_token_id=_A  ,						eos_token_id=_A  ,						is_encoder_decoder=_A  ,						decoder_start_token_id=_A  ,						forced_eos_token_id=_A  ,						**_A  ,						)

  @property
  def   lowerCAmelCase__       (   self):
      return self.encoder_attention_heads


  @property
  def   lowerCAmelCase__       (   self):
      return self.d_model


