
"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 							_lowerCamelCase			(			__a     ):
		return ConvertCommand(
		    args.model_type,				args.tf_checkpoint,				args.pytorch_dump_output,				args.config,				args.finetuning_task_name     )


lowerCAmelCase__				=					'\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'





class 			snake_case  (				__lowercase ):

							@staticmethod
							def _lowercase       (SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			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=SCREAMING_SNAKE_CASE_							,							required=SCREAMING_SNAKE_CASE_							,							help='''Model\'s type.'''     )
									train_parser.add_argument(
									    '''--tf_checkpoint'''							,							type=SCREAMING_SNAKE_CASE_							,							required=SCREAMING_SNAKE_CASE_							,							help='''TensorFlow checkpoint path or folder.'''     )
									train_parser.add_argument(
									    '''--pytorch_dump_output'''							,							type=SCREAMING_SNAKE_CASE_							,							required=SCREAMING_SNAKE_CASE_							,							help='''Path to the PyTorch saved model output.'''     )
									train_parser.add_argument('''--config'''							,							type=SCREAMING_SNAKE_CASE_							,							default=''''''							,							help='''Configuration file path or folder.'''     )
									train_parser.add_argument(
									    '''--finetuning_task_name'''							,							type=SCREAMING_SNAKE_CASE_							,							default=SCREAMING_SNAKE_CASE_							,							help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,							)
									train_parser.set_defaults(func=SCREAMING_SNAKE_CASE_     )

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							*SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			logging.get_logger('''transformers-cli/converting'''     )

									self._logger.info(f'Loading model {model_type}'     )
									SCREAMING_SNAKE_CASE_    			=			model_type
									SCREAMING_SNAKE_CASE_    			=			tf_checkpoint
									SCREAMING_SNAKE_CASE_    			=			pytorch_dump_output
									SCREAMING_SNAKE_CASE_    			=			config
									SCREAMING_SNAKE_CASE_    			=			finetuning_task_name







							def _lowercase       (self     ):

									"""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(SCREAMING_SNAKE_CASE_     )

											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(SCREAMING_SNAKE_CASE_     )

											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(SCREAMING_SNAKE_CASE_     )

											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(SCREAMING_SNAKE_CASE_     )

											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(SCREAMING_SNAKE_CASE_     )

											if "ckpt" in self._tf_checkpoint.lower():
													SCREAMING_SNAKE_CASE_    			=			self._tf_checkpoint
													SCREAMING_SNAKE_CASE_    			=			''''''
											else:
													SCREAMING_SNAKE_CASE_    			=			self._tf_checkpoint
													SCREAMING_SNAKE_CASE_    			=			''''''
											convert_transfo_xl_checkpoint_to_pytorch(
											    SCREAMING_SNAKE_CASE_							,							self._config							,							self._pytorch_dump_output							,							SCREAMING_SNAKE_CASE_     )
									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(SCREAMING_SNAKE_CASE_     )

											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(SCREAMING_SNAKE_CASE_     )

											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]'''     )

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







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

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass