



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler



class       _lowercase				(  unittest.TestCase      ):





	"""simple docstring"""
	# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
	__A		      =				(ScoreSdeVeScheduler,)
	__A		      =				()




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  4
						a   	=  3
						a   	=  8
						a   	=  8

						a   	=  torch.rand((batch_size, num_channels, height, width)						)

						return sample




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  4
						a   	=  3
						a   	=  8
						a   	=  8

						a   	=  batch_size * num_channels * height * width
						a   	=  torch.arange(__UpperCAmelCase						)
						a   	=  sample.reshape(__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						)
						a   	=  sample / num_elems
						a   	=  sample.permute(3						,			0						,			1						,			2						)

						return sample




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						def model(lowerCamelCase_						,			lowerCamelCase_						,			*lowerCamelCase_						):
											return sample * t / (t + 1)

						return model




	def 	UpperCamelCase_			(self						,			**lowerCamelCase_						):
						"""simple docstring"""

						a   	=  {
						    "num_train_timesteps": 2000,
						    "snr": 0.15,
						    "sigma_min": 0.01,
						    "sigma_max": 1348,
						    "sampling_eps": 1E-5,
						}

						config.update(**__UpperCAmelCase						)
						return config




	def 	UpperCamelCase_			(self						,			lowerCamelCase_=0						,			**lowerCamelCase_						):
						"""simple docstring"""

						a   	=  dict(self.forward_default_kwargs						)

						for scheduler_class in self.scheduler_classes:
											a   	=  self.dummy_sample
											a   	=  0.1 * sample

											a   	=  self.get_scheduler_config(**__UpperCAmelCase						)
											a   	=  scheduler_class(**__UpperCAmelCase						)

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__UpperCAmelCase						)
																a   	=  scheduler_class.from_pretrained(__UpperCAmelCase						)

											a   	=  scheduler.step_pred(
											    __UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample
											a   	=  new_scheduler.step_pred(
											    __UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample

											assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

											a   	=  scheduler.step_correct(__UpperCAmelCase						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample
											a   	=  new_scheduler.step_correct(
											    __UpperCAmelCase						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample

											assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler correction are not identical"




	def 	UpperCamelCase_			(self						,			lowerCamelCase_=0						,			**lowerCamelCase_						):
						"""simple docstring"""

						a   	=  dict(self.forward_default_kwargs						)
						kwargs.update(__UpperCAmelCase						)

						for scheduler_class in self.scheduler_classes:
											a   	=  self.dummy_sample
											a   	=  0.1 * sample

											a   	=  self.get_scheduler_config()
											a   	=  scheduler_class(**__UpperCAmelCase						)

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__UpperCAmelCase						)
																a   	=  scheduler_class.from_pretrained(__UpperCAmelCase						)

											a   	=  scheduler.step_pred(
											    __UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample
											a   	=  new_scheduler.step_pred(
											    __UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample

											assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

											a   	=  scheduler.step_correct(__UpperCAmelCase						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample
											a   	=  new_scheduler.step_correct(
											    __UpperCAmelCase						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample

											assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler correction are not identical"




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						for timesteps in [10, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__UpperCAmelCase						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]						,			[1, 100, 1000]						):
											self.check_over_configs(sigma_min=__UpperCAmelCase						,			sigma_max=__UpperCAmelCase						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						for t in [0.1, 0.5, 0.75]:
											self.check_over_forward(time_step=__UpperCAmelCase						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  dict(self.forward_default_kwargs						)

						a   	=  self.scheduler_classes[0]
						a   	=  self.get_scheduler_config()
						a   	=  scheduler_class(**__UpperCAmelCase						)

						a   	=  3

						a   	=  self.dummy_model()
						a   	=  self.dummy_sample_deter

						scheduler.set_sigmas(__UpperCAmelCase						)
						scheduler.set_timesteps(__UpperCAmelCase						)
						a   	=  torch.manual_seed(0						)

						for i, t in enumerate(scheduler.timesteps						):
											a   	=  scheduler.sigmas[i]

											for _ in range(scheduler.config.correct_steps						):
																with torch.no_grad():
																					a   	=  model(__UpperCAmelCase						,			__UpperCAmelCase						)
																a   	=  scheduler.step_correct(__UpperCAmelCase						,			__UpperCAmelCase						,			generator=__UpperCAmelCase						,			**__UpperCAmelCase						).prev_sample

											with torch.no_grad():
																a   	=  model(__UpperCAmelCase						,			__UpperCAmelCase						)

											a   	=  scheduler.step_pred(__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			generator=__UpperCAmelCase						,			**__UpperCAmelCase						)
											a	,							a   	=  output.prev_sample, output.prev_sample_mean

						a   	=  torch.sum(torch.abs(__UpperCAmelCase						)						)
						a   	=  torch.mean(torch.abs(__UpperCAmelCase						)						)

						assert np.isclose(result_sum.item()						,			14372758528.0						)
						assert np.isclose(result_mean.item()						,			18714530.0						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  dict(self.forward_default_kwargs						)

						a   	=  kwargs.pop("num_inference_steps"						,			__UpperCAmelCase						)

						for scheduler_class in self.scheduler_classes:
											a   	=  self.get_scheduler_config()
											a   	=  scheduler_class(**__UpperCAmelCase						)

											a   	=  self.dummy_sample
											a   	=  0.1 * sample

											if num_inference_steps is not None and hasattr(__UpperCAmelCase						,			"set_timesteps"						):
																scheduler.set_timesteps(__UpperCAmelCase						)
											elif num_inference_steps is not None and not hasattr(__UpperCAmelCase						,			"set_timesteps"						):
																a   	=  num_inference_steps

											a   	=  scheduler.step_pred(__UpperCAmelCase						,			0						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample
											a   	=  scheduler.step_pred(__UpperCAmelCase						,			1						,			__UpperCAmelCase						,			generator=torch.manual_seed(0						)						,			**__UpperCAmelCase						).prev_sample

											self.assertEqual(output_a.shape						,			sample.shape						)
											self.assertEqual(output_a.shape						,			output_a.shape						)


"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[str]          =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )



