


'''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__	(						UpperCamelCase__				):






						def __init__(			self      :Any     )	-> Dict:




													'''simple docstring'''
													self.register_to_config()
													super().__init__()






						def __UpperCAmelCase	(			self      :Optional[int]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> Any:




													'''simple docstring'''


													if dicta == dicta:
																				return True
													else:
																				_a   :			int					 =self._remove_meta_keys(__lowerCamelCase     )
																				_a   :			str					 =self._remove_meta_keys(__lowerCamelCase     )
																				if configa == configa:
																											print(f"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."     )
																											return True
													return False






						def __UpperCAmelCase	(			self      :Optional[int]					,						SCREAMING_SNAKE_CASE      :Dict     )	-> Union[str, Any]:




													'''simple docstring'''
													_a   :			int					 =[]
													_a   :			Optional[Any]					 =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 __UpperCAmelCase	(			self      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :List[Union[str, os.PathLike]]					,						**SCREAMING_SNAKE_CASE      :Dict     )	-> Tuple:




													'''simple docstring'''
													_a   :			Any					 =kwargs.pop("""cache_dir"""					,						__lowerCamelCase     )
													_a   :			Dict					 =kwargs.pop("""resume_download"""					,						__lowerCamelCase     )
													_a   :			str					 =kwargs.pop("""force_download"""					,						__lowerCamelCase     )
													_a   :			Tuple					 =kwargs.pop("""proxies"""					,						__lowerCamelCase     )
													_a   :			List[str]					 =kwargs.pop("""local_files_only"""					,						__lowerCamelCase     )
													_a   :			Tuple					 =kwargs.pop("""use_auth_token"""					,						__lowerCamelCase     )
													_a   :			Tuple					 =kwargs.pop("""revision"""					,						__lowerCamelCase     )
													_a   :			int					 =kwargs.pop("""torch_dtype"""					,						__lowerCamelCase     )
													_a   :			int					 =kwargs.pop("""device_map"""					,						__lowerCamelCase     )

													_a   :			Union[str, Any]					 =kwargs.pop("""alpha"""					,						0.5     )
													_a   :			Union[str, Any]					 =kwargs.pop("""interp"""					,						__lowerCamelCase     )

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

													_a   :			Union[str, Any]					 =len(__lowerCamelCase     )
													# Ignore result from model_index_json comparision of the two checkpoints
													_a   :			Optional[int]					 =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 '_'
													_a   :			Optional[int]					 =[]
													for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
																				_a   :			str					 =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     )

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

																				_a   :			Optional[Any]					 =(
																				    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
													_a   :			Optional[Any]					 =DiffusionPipeline.from_pretrained(
													    cached_folders[0]					,						torch_dtype=__lowerCamelCase					,						device_map=__lowerCamelCase     )
													final_pipe.to(self.device     )

													_a   :			str					 =None
													if len(__lowerCamelCase     ) > 2:
																				_a   :			Dict					 =os.path.join(cached_folders[2]     )

													if interp == "sigmoid":
																				_a   :			int					 =CheckpointMergerPipeline.sigmoid
													elif interp == "inv_sigmoid":
																				_a   :			Optional[Any]					 =CheckpointMergerPipeline.inv_sigmoid
													elif interp == "add_diff":
																				_a   :			Optional[Any]					 =CheckpointMergerPipeline.add_difference
													else:
																				_a   :			Dict					 =CheckpointMergerPipeline.weighted_sum

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

																																		_a   :			List[Any]					 =getattr(__lowerCamelCase					,						"""load_state_dict"""     )
																																		_a   :			Tuple					 =(
																																		    safetensors.torch.load_file(__lowerCamelCase     )
																																		    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""     ))
																																		    else torch.load(__lowerCamelCase					,						map_location="""cpu"""     )
																																		)
																																		_a   :			str					 =None
																																		if checkpoint_path_a:
																																									_a   :			str					 =(
																																									    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:
																																									_a   :			int					 =theta_func(theta_a[key]					,						theta_a[key]					,						theta_a[key]					,						__lowerCamelCase     )
																																		else:
																																									_a   :			Union[str, Any]					 =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 __UpperCAmelCase	(			SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :Optional[Any]     )	-> Optional[int]:




													'''simple docstring'''
													return ((1 - alpha) * thetaa) + (alpha * thetaa)






						@staticmethod
						def __UpperCAmelCase	(			SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :List[str]     )	-> Tuple:




													'''simple docstring'''
													_a   :			Tuple					 =alpha * alpha * (3 - (2 * alpha))
													return thetaa + ((thetaa - thetaa) * alpha)






						@staticmethod
						def __UpperCAmelCase	(			SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :int     )	-> Any:




													'''simple docstring'''
													import math

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






						@staticmethod
						def __UpperCAmelCase	(			SCREAMING_SNAKE_CASE      :Tuple					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :Optional[Any]     )	-> Optional[int]:




													'''simple docstring'''
													return thetaa + (thetaa - thetaa) * (1.0 - alpha)






'''simple docstring'''







import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
   A__:   Optional[Any]    =	argparse.ArgumentParser()

   # Hyperparameters sent by the client are passed as command-line arguments to the script.
   parser.add_argument('''--epochs''', type=int, default=1)
   parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
   parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
   parser.add_argument('''--model_name_or_path''', type=str)
   parser.add_argument('''--learning_rate''', type=str, default=5E-5)
   parser.add_argument('''--do_train''', type=bool, default=True)
   parser.add_argument('''--do_eval''', type=bool, default=True)
   parser.add_argument('''--output_dir''', type=str)

   A__   ,	A__:   List[Any]    =	parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A__:   Tuple    =	16
   A__:   List[str]    =	16

   # Set up logging
   A__:   str    =	logging.getLogger(__name__)

   logging.basicConfig(
       level=logging.getLevelName('''INFO'''),
       handlers=[logging.StreamHandler(sys.stdout)],
       format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
   )

   # Load model and tokenizer
   A__:   int    =	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A__:   Any    =	AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A__   ,	A__:   Optional[int]    =	load_dataset('''imdb''', split=['''train''', '''test'''])
   A__:   int    =	train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
   A__:   int    =	test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   A__:   Optional[int]    =	train_dataset.map(
       lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
   )
   train_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

   A__:   List[Any]    =	{
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['''input_ids''', '''attention_mask''']
   }
   A__:   str    =	tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
       args.per_device_train_batch_size
   )

   # Preprocess test dataset
   A__:   str    =	test_dataset.map(
       lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
   )
   test_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

   A__:   Tuple    =	{
       x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['''input_ids''', '''attention_mask''']
   }
   A__:   Any    =	tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
       args.per_device_eval_batch_size
   )

   # fine optimizer and loss
   A__:   Optional[int]    =	tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   A__:   Union[str, Any]    =	tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   A__:   List[Any]    =	[tf.keras.metrics.SparseCategoricalAccuracy()]
   model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

   A__:   str    =	time.time()
   A__:   Dict    =	model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A__:   Tuple    =	time.time() - start_train_time

   logger.info('''*** Train ***''')
   logger.info(F"train_runtime = {end_train_time}")
   for key, value in train_results.history.items():
      logger.info(F"  {key} = {value}")


