





"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      __A   (      a_    :BertModel ,							a_    :str ,							a_    :str)		->		str:
				__a			:    List[str]      							=	('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				__a			:    Any      							=	(
				    ('''layer.''', '''layer_'''),
				    ('''word_embeddings.weight''', '''word_embeddings'''),
				    ('''position_embeddings.weight''', '''position_embeddings'''),
				    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
				    ('''.''', '''/'''),
				    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
				    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
				    ('''weight''', '''kernel'''),
				)

				if not os.path.isdir(a_):
								os.makedirs(a_)

				__a			:    List[Any]      							=	model.state_dict()

				def to_tf_var_name(a_    :str):
								for patt, repl in iter(a_):
												__a			:    int      							=	name.replace(a_ ,							a_)
								return F"""bert/{name}"""

				def create_tf_var(a_    :np.ndarray ,							a_    :str ,							a_    :tf.Session):
								__a			:    int      							=	tf.dtypes.as_dtype(tensor.dtype)
								__a			:    Optional[int]      							=	tf.get_variable(dtype=a_ ,							shape=tensor.shape ,							name=a_ ,							initializer=tf.zeros_initializer())
								session.run(tf.variables_initializer([tf_var]))
								session.run(a_)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												__a			:    Any      							=	to_tf_var_name(a_)
												__a			:    Tuple      							=	state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose):
																__a			:    List[Any]      							=	torch_tensor.T
												__a			:    Optional[Any]      							=	create_tf_var(tensor=a_ ,							name=a_ ,							session=a_)
												tf.keras.backend.set_value(a_ ,							a_)
												__a			:    int      							=	session.run(a_)
												print(F"""Successfully created {tf_name}: {np.allclose(a_ ,							a_)}""")

								__a			:    Tuple      							=	tf.train.Saver(tf.trainable_variables())
								saver.save(a_ ,							os.path.join(a_ ,							model_name.replace('''-''' ,							'''_''') + '''.ckpt'''))

def      __A   (      a_    :int=None)		->		str:
				__a			:    str      							=	argparse.ArgumentParser()
				parser.add_argument('''--model_name''' ,							type=a_ ,							required=a_ ,							help='''model name e.g. bert-base-uncased''')
				parser.add_argument(
				    '''--cache_dir''' ,							type=a_ ,							default=a_ ,							required=a_ ,							help='''Directory containing pytorch model''')
				parser.add_argument('''--pytorch_model_path''' ,							type=a_ ,							required=a_ ,							help='''/path/to/<pytorch-model-name>.bin''')
				parser.add_argument('''--tf_cache_dir''' ,							type=a_ ,							required=a_ ,							help='''Directory in which to save tensorflow model''')
				__a			:    Optional[Any]      							=	parser.parse_args(a_)

				__a			:    Optional[Any]      							=	BertModel.from_pretrained(
				    pretrained_model_name_or_path=args.model_name ,							state_dict=torch.load(args.pytorch_model_path) ,							cache_dir=args.cache_dir ,							)

				convert_pytorch_checkpoint_to_tf(model=a_ ,							ckpt_dir=args.tf_cache_dir ,							model_name=args.model_name)


if __name__ == "__main__":
		main()
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






