





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class   _UpperCamelCase  (       _a						):







					'''simple docstring'''

					lowerCamelCase     :     Dict			    =       (CMStochasticIterativeScheduler,)
					lowerCamelCase     :     List[str]			    =       10





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					**__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     {
						    """num_train_timesteps""": 2_01,
						    """sigma_min""": 0.002,
						    """sigma_max""": 80.0,
						}

						config.update(**_A		)
						return config





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     10

						UpperCAmelCase_								=     self.get_scheduler_config()
						UpperCAmelCase_								=     self.scheduler_classes[0](**_A		)

						scheduler.set_timesteps(_A		)

						UpperCAmelCase_								=     scheduler.timesteps[0]
						UpperCAmelCase_								=     scheduler.timesteps[1]

						UpperCAmelCase_								=     self.dummy_sample
						UpperCAmelCase_								=     0.1 * sample

						UpperCAmelCase_								=     scheduler.step(_A ,					_A ,					_A		).prev_sample
						UpperCAmelCase_								=     scheduler.step(_A ,					_A ,					_A		).prev_sample

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





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						for timesteps in [10, 50, 1_00, 10_00]:
							self.check_over_configs(num_train_timesteps=_A		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=_A		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.scheduler_classes[0]
						UpperCAmelCase_								=     self.get_scheduler_config()
						UpperCAmelCase_								=     scheduler_class(**_A		)

						UpperCAmelCase_								=     1
						scheduler.set_timesteps(_A		)
						UpperCAmelCase_								=     scheduler.timesteps

						UpperCAmelCase_								=     torch.manual_seed(0		)

						UpperCAmelCase_								=     self.dummy_model()
						UpperCAmelCase_								=     self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(_A		):
							# 1. scale model input
							UpperCAmelCase_								=     scheduler.scale_model_input(_A ,					_A		)

							# 2. predict noise residual
							UpperCAmelCase_								=     model(_A ,					_A		)

							# 3. predict previous sample x_t-1
							UpperCAmelCase_								=     scheduler.step(_A ,					_A ,					_A ,					generator=_A		).prev_sample

							UpperCAmelCase_								=     pred_prev_sample

						UpperCAmelCase_								=     torch.sum(torch.abs(_A		)		)
						UpperCAmelCase_								=     torch.mean(torch.abs(_A		)		)

						assert abs(result_sum.item() - 192.7614		) < 1e-2
						assert abs(result_mean.item() - 0.2510		) < 1e-3





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.scheduler_classes[0]
						UpperCAmelCase_								=     self.get_scheduler_config()
						UpperCAmelCase_								=     scheduler_class(**_A		)

						UpperCAmelCase_								=     [1_06, 0]
						scheduler.set_timesteps(timesteps=_A		)
						UpperCAmelCase_								=     scheduler.timesteps

						UpperCAmelCase_								=     torch.manual_seed(0		)

						UpperCAmelCase_								=     self.dummy_model()
						UpperCAmelCase_								=     self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							UpperCAmelCase_								=     scheduler.scale_model_input(_A ,					_A		)

							# 2. predict noise residual
							UpperCAmelCase_								=     model(_A ,					_A		)

							# 3. predict previous sample x_t-1
							UpperCAmelCase_								=     scheduler.step(_A ,					_A ,					_A ,					generator=_A		).prev_sample

							UpperCAmelCase_								=     pred_prev_sample

						UpperCAmelCase_								=     torch.sum(torch.abs(_A		)		)
						UpperCAmelCase_								=     torch.mean(torch.abs(_A		)		)

						assert abs(result_sum.item() - 347.6357		) < 1e-2
						assert abs(result_mean.item() - 0.4527		) < 1e-3





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.scheduler_classes[0]
						UpperCAmelCase_								=     self.get_scheduler_config()
						UpperCAmelCase_								=     scheduler_class(**_A		)

						UpperCAmelCase_								=     [39, 30, 12, 15, 0]

						with self.assertRaises(_A ,					msg="""`timesteps` must be in descending order."""		):
							scheduler.set_timesteps(timesteps=_A		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.scheduler_classes[0]
						UpperCAmelCase_								=     self.get_scheduler_config()
						UpperCAmelCase_								=     scheduler_class(**_A		)

						UpperCAmelCase_								=     [39, 30, 12, 1, 0]
						UpperCAmelCase_								=     len(_A		)

						with self.assertRaises(_A ,					msg="""Can only pass one of `num_inference_steps` or `timesteps`."""		):
							scheduler.set_timesteps(num_inference_steps=_A ,					timesteps=_A		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.scheduler_classes[0]
						UpperCAmelCase_								=     self.get_scheduler_config()
						UpperCAmelCase_								=     scheduler_class(**_A		)

						UpperCAmelCase_								=     [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    _A ,					msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" ,					):
							scheduler.set_timesteps(timesteps=_A		)









from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def    A_(			A   ):
	return ConvertCommand(
	    args.model_type				,      args.tf_checkpoint				,      args.pytorch_dump_output				,      args.config				,      args.finetuning_task_name   )


UpperCamelCase__   : Any					  = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					@staticmethod
					def        SCREAMING_SNAKE_CASE							(						__lowercase    :  ArgumentParser		):






						'''simple docstring'''







						UpperCAmelCase_								=     parser.add_parser(
						    """convert""" ,					help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.""" ,					)
						train_parser.add_argument("""--model_type""" ,					type=__lowercase ,					required=__lowercase ,					help="""Model's type."""		)
						train_parser.add_argument(
						    """--tf_checkpoint""" ,					type=__lowercase ,					required=__lowercase ,					help="""TensorFlow checkpoint path or folder."""		)
						train_parser.add_argument(
						    """--pytorch_dump_output""" ,					type=__lowercase ,					required=__lowercase ,					help="""Path to the PyTorch saved model output."""		)
						train_parser.add_argument("""--config""" ,					type=__lowercase ,					default="""""" ,					help="""Configuration file path or folder."""		)
						train_parser.add_argument(
						    """--finetuning_task_name""" ,					type=__lowercase ,					default=__lowercase ,					help="""Optional fine-tuning task name if the TF model was a finetuned model.""" ,					)
						train_parser.set_defaults(func=__lowercase		)





					def __init__(						self    :  Optional[int] ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					*__lowercase    :  Tuple ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     logging.get_logger("""transformers-cli/converting"""		)

						self._logger.info(F"""Loading model {model_type}"""		)
						UpperCAmelCase_								=     model_type
						UpperCAmelCase_								=     tf_checkpoint
						UpperCAmelCase_								=     pytorch_dump_output
						UpperCAmelCase_								=     config
						UpperCAmelCase_								=     finetuning_task_name





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						if self._model_type == "albert":
							try:
								from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "bert":
							try:
								from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "funnel":
							try:
								from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "t5":
							try:
								from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
							except ImportError:
								raise ImportError(__lowercase		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "gpt":
							from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
							    convert_openai_checkpoint_to_pytorch,
							)

							convert_openai_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "transfo_xl":
							try:
								from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
								    convert_transfo_xl_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							if "ckpt" in self._tf_checkpoint.lower():
								UpperCAmelCase_								=     self._tf_checkpoint
								UpperCAmelCase_								=     """"""
							else:
								UpperCAmelCase_								=     self._tf_checkpoint
								UpperCAmelCase_								=     """"""
							convert_transfo_xl_checkpoint_to_pytorch(
							    __lowercase ,					self._config ,					self._pytorch_dump_output ,					__lowercase		)
						elif self._model_type == "gpt2":
							try:
								from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
								    convert_gpta_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "xlnet":
							try:
								from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
								    convert_xlnet_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_xlnet_checkpoint_to_pytorch(
							    self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output ,					self._finetuning_task_name		)
						elif self._model_type == "xlm":
							from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
							    convert_xlm_checkpoint_to_pytorch,
							)

							convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint ,					self._pytorch_dump_output		)
						elif self._model_type == "lxmert":
							from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
							    convert_lxmert_checkpoint_to_pytorch,
							)

							convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint ,					self._pytorch_dump_output		)
						elif self._model_type == "rembert":
							from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
							    convert_rembert_tf_checkpoint_to_pytorch,
							)

							convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						else:
							raise ValueError(
							    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""		)


