


'''simple docstring'''
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class   lowercase      (				_lowercase   ):

		"""simple docstring"""

		_a       					=							(PNDMScheduler,)
		_a       					=							(('''num_inference_steps''', 50),)




		def        lowerCAmelCase__							(						self       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					{
							    """num_train_timesteps""": 1000,
							    """beta_start""": 0.0001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							}

							config.update(**__A				)
							return config




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=0       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					dict(self.forward_default_kwargs				)
							UpperCamelCase__  :str				   =					kwargs.pop('''num_inference_steps'''       ,  __A				)
							UpperCamelCase__  :str				   =					self.dummy_sample
							UpperCamelCase__  :Any				   =					0.1 * sample
							UpperCamelCase__  :List[str]				   =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__  :Dict				   =					self.get_scheduler_config(**__A				)
												UpperCamelCase__  :Optional[Any]				   =					scheduler_class(**__A				)
												scheduler.set_timesteps(__A				)
												# copy over dummy past residuals
												UpperCamelCase__  :Union[str, Any]				   =					dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(__A				)
																	UpperCamelCase__  :str				   =					scheduler_class.from_pretrained(__A				)
																	new_scheduler.set_timesteps(__A				)
																	# copy over dummy past residuals
																	UpperCamelCase__  :Any				   =					dummy_past_residuals[:]

												UpperCamelCase__  :List[str]				   =					scheduler.step_prk(__A       ,  __A       ,  __A       ,  **__A				).prev_sample
												UpperCamelCase__  :int				   =					new_scheduler.step_prk(__A       ,  __A       ,  __A       ,  **__A				).prev_sample

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

												UpperCamelCase__  :int				   =					scheduler.step_plms(__A       ,  __A       ,  __A       ,  **__A				).prev_sample
												UpperCamelCase__  :List[str]				   =					new_scheduler.step_plms(__A       ,  __A       ,  __A       ,  **__A				).prev_sample

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=0       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					dict(self.forward_default_kwargs				)
							UpperCamelCase__  :Dict				   =					kwargs.pop('''num_inference_steps'''       ,  __A				)
							UpperCamelCase__  :int				   =					self.dummy_sample
							UpperCamelCase__  :Tuple				   =					0.1 * sample
							UpperCamelCase__  :Optional[int]				   =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__  :Any				   =					self.get_scheduler_config()
												UpperCamelCase__  :Union[str, Any]				   =					scheduler_class(**__A				)
												scheduler.set_timesteps(__A				)

												# copy over dummy past residuals (must be after setting timesteps)
												UpperCamelCase__  :Dict				   =					dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(__A				)
																	UpperCamelCase__  :List[str]				   =					scheduler_class.from_pretrained(__A				)
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(__A				)

																	# copy over dummy past residual (must be after setting timesteps)
																	UpperCamelCase__  :List[Any]				   =					dummy_past_residuals[:]

												UpperCamelCase__  :List[str]				   =					scheduler.step_prk(__A       ,  __A       ,  __A       ,  **__A				).prev_sample
												UpperCamelCase__  :Any				   =					new_scheduler.step_prk(__A       ,  __A       ,  __A       ,  **__A				).prev_sample

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

												UpperCamelCase__  :Tuple				   =					scheduler.step_plms(__A       ,  __A       ,  __A       ,  **__A				).prev_sample
												UpperCamelCase__  :Tuple				   =					new_scheduler.step_plms(__A       ,  __A       ,  __A       ,  **__A				).prev_sample

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




		def        lowerCAmelCase__							(						self       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.scheduler_classes[0]
							UpperCamelCase__  :List[Any]				   =					self.get_scheduler_config(**__A				)
							UpperCamelCase__  :List[Any]				   =					scheduler_class(**__A				)

							UpperCamelCase__  :Optional[int]				   =					10
							UpperCamelCase__  :Optional[Any]				   =					self.dummy_model()
							UpperCamelCase__  :Optional[Any]				   =					self.dummy_sample_deter
							scheduler.set_timesteps(__A				)

							for i, t in enumerate(scheduler.prk_timesteps				):
												UpperCamelCase__  :Tuple				   =					model(__A       ,  __A				)
												UpperCamelCase__  :List[str]				   =					scheduler.step_prk(__A       ,  __A       ,  __A				).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps				):
												UpperCamelCase__  :Optional[Any]				   =					model(__A       ,  __A				)
												UpperCamelCase__  :Tuple				   =					scheduler.step_plms(__A       ,  __A       ,  __A				).prev_sample

							return sample




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					dict(self.forward_default_kwargs				)

							UpperCamelCase__  :Optional[int]				   =					kwargs.pop('''num_inference_steps'''       ,  __A				)

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__  :str				   =					self.get_scheduler_config()
												UpperCamelCase__  :List[str]				   =					scheduler_class(**__A				)

												UpperCamelCase__  :Any				   =					self.dummy_sample
												UpperCamelCase__  :str				   =					0.1 * sample

												if num_inference_steps is not None and hasattr(__A       ,  '''set_timesteps'''				):
																	scheduler.set_timesteps(__A				)
												elif num_inference_steps is not None and not hasattr(__A       ,  '''set_timesteps'''				):
																	UpperCamelCase__  :Union[str, Any]				   =					num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												UpperCamelCase__  :Optional[int]				   =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												UpperCamelCase__  :Any				   =					dummy_past_residuals[:]

												UpperCamelCase__  :Any				   =					scheduler.step_prk(__A       ,  0       ,  __A       ,  **__A				).prev_sample
												UpperCamelCase__  :Tuple				   =					scheduler.step_prk(__A       ,  1       ,  __A       ,  **__A				).prev_sample

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

												UpperCamelCase__  :Dict				   =					scheduler.step_plms(__A       ,  0       ,  __A       ,  **__A				).prev_sample
												UpperCamelCase__  :List[Any]				   =					scheduler.step_plms(__A       ,  1       ,  __A       ,  **__A				).prev_sample

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=__A				)

							UpperCamelCase__  :int				   =					self.scheduler_classes[0]
							UpperCamelCase__  :Any				   =					self.get_scheduler_config(steps_offset=1				)
							UpperCamelCase__  :List[str]				   =					scheduler_class(**__A				)
							scheduler.set_timesteps(10				)
							assert torch.equal(
							    scheduler.timesteps       ,  torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)       ,  )




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001]       ,  [0.002, 0.02]				):
												self.check_over_configs(beta_start=__A       ,  beta_end=__A				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__A				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__A				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for t in [1, 5, 10]:
												self.check_over_forward(time_step=__A				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 5, 10]       ,  [10, 50, 100]				):
												self.check_over_forward(num_inference_steps=__A				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					27

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__  :Tuple				   =					self.dummy_sample
												UpperCamelCase__  :List[str]				   =					0.1 * sample

												UpperCamelCase__  :List[Any]				   =					self.get_scheduler_config()
												UpperCamelCase__  :int				   =					scheduler_class(**__A				)

												scheduler.set_timesteps(__A				)

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2]				):
																	UpperCamelCase__  :Any				   =					scheduler.step_prk(__A       ,  __A       ,  __A				).prev_sample




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							with self.assertRaises(__A				):
												UpperCamelCase__  :Optional[int]				   =					self.scheduler_classes[0]
												UpperCamelCase__  :int				   =					self.get_scheduler_config()
												UpperCamelCase__  :Tuple				   =					scheduler_class(**__A				)

												scheduler.step_plms(self.dummy_sample       ,  1       ,  self.dummy_sample				).prev_sample




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					self.full_loop()
							UpperCamelCase__  :int				   =					torch.sum(torch.abs(__A				)				)
							UpperCamelCase__  :str				   =					torch.mean(torch.abs(__A				)				)

							assert abs(result_sum.item() - 198.1318				) < 1e-2
							assert abs(result_mean.item() - 0.2580				) < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					self.full_loop(prediction_type='''v_prediction'''				)
							UpperCamelCase__  :Tuple				   =					torch.sum(torch.abs(__A				)				)
							UpperCamelCase__  :List[Any]				   =					torch.mean(torch.abs(__A				)				)

							assert abs(result_sum.item() - 67.3986				) < 1e-2
							assert abs(result_mean.item() - 0.0878				) < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					self.full_loop(set_alpha_to_one=__A       ,  beta_start=0.01				)
							UpperCamelCase__  :List[Any]				   =					torch.sum(torch.abs(__A				)				)
							UpperCamelCase__  :Optional[int]				   =					torch.mean(torch.abs(__A				)				)

							assert abs(result_sum.item() - 230.0399				) < 1e-2
							assert abs(result_mean.item() - 0.2995				) < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.full_loop(set_alpha_to_one=__A       ,  beta_start=0.01				)
							UpperCamelCase__  :Optional[int]				   =					torch.sum(torch.abs(__A				)				)
							UpperCamelCase__  :List[Any]				   =					torch.mean(torch.abs(__A				)				)

							assert abs(result_sum.item() - 186.9482				) < 1e-2
							assert abs(result_mean.item() - 0.2434				) < 1e-3


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





