'''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     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			def __init__(       self):




					"""simple docstring"""


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



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  []
					lowerCAmelCase        =  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 							a_	(       self	,   __lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  kwargs.pop("""cache_dir"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  kwargs.pop("""resume_download"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  kwargs.pop("""force_download"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  kwargs.pop("""proxies"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  kwargs.pop("""local_files_only"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  kwargs.pop("""use_auth_token"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  kwargs.pop("""revision"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  kwargs.pop("""torch_dtype"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  kwargs.pop("""device_map"""	,   __lowerCAmelCase)

					lowerCAmelCase        =  kwargs.pop("""alpha"""	,   0.5)
					lowerCAmelCase        =  kwargs.pop("""interp"""	,   __lowerCAmelCase)

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

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

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

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

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

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

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

											lowerCAmelCase        =  getattr(__lowerCAmelCase	,   """load_state_dict""")
											lowerCAmelCase        =  (
											    safetensors.torch.load_file(__lowerCAmelCase)
											    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""))
											    else torch.load(__lowerCAmelCase	,   map_location="""cpu""")
											)
											lowerCAmelCase        =  None
											if checkpoint_path_a:
													lowerCAmelCase        =  (
													    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:
													lowerCAmelCase        =  theta_func(theta_a[key]	,   theta_a[key]	,   theta_a[key]	,   __lowerCAmelCase)
											else:
													lowerCAmelCase        =  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 							a_	(       __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


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



			@staticmethod
			def 							a_	(       __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  alpha * alpha * (3 - (2 * alpha))
					return thetaa + ((thetaa - thetaa) * alpha)



			@staticmethod
			def 							a_	(       __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					import math

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







			@staticmethod
			def 							a_	(       __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


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





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				   =  {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  ['''BartTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BartForCausalLM''',
						    '''BartForConditionalGeneration''',
						    '''BartForQuestionAnswering''',
						    '''BartForSequenceClassification''',
						    '''BartModel''',
						    '''BartPretrainedModel''',
						    '''PretrainedBartModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''TFBartForConditionalGeneration''',
						    '''TFBartForSequenceClassification''',
						    '''TFBartModel''',
						    '''TFBartPretrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''FlaxBartDecoderPreTrainedModel''',
						    '''FlaxBartForCausalLM''',
						    '''FlaxBartForConditionalGeneration''',
						    '''FlaxBartForQuestionAnswering''',
						    '''FlaxBartForSequenceClassification''',
						    '''FlaxBartModel''',
						    '''FlaxBartPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowercase				   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




