




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       (IPNDMScheduler,)
						UpperCamelCase      =       (("""num_inference_steps""", 50),)

						def      A__    (		self :int     ,			**__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										={"""num_train_timesteps""": 10_00}
										config.update(**__snake_case			)
										return config

						def      A__    (		self :int     ,			__snake_case :Any=0     ,			**__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=dict(self.forward_default_kwargs			)
										__magic_name__						:						List[str]										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						str										=self.dummy_sample
										__magic_name__						:						Dict										=0.1 * sample
										__magic_name__						:						Any										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						int										=self.get_scheduler_config(**__snake_case			)
														__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)
														# copy over dummy past residuals
														__magic_name__						:						List[str]										=dummy_past_residuals[:]

														if time_step is None:
																		__magic_name__						:						Dict										=scheduler.timesteps[len(scheduler.timesteps			) // 2]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case			)
																		__magic_name__						:						int										=scheduler_class.from_pretrained(__snake_case			)
																		new_scheduler.set_timesteps(__snake_case			)
																		# copy over dummy past residuals
																		__magic_name__						:						Tuple										=dummy_past_residuals[:]

														__magic_name__						:						Optional[int]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						List[str]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						List[str]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										pass

						def      A__    (		self :Optional[int]     ,			__snake_case :Optional[Any]=0     ,			**__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						int										=dict(self.forward_default_kwargs			)
										__magic_name__						:						Dict										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						str										=self.dummy_sample
										__magic_name__						:						Optional[Any]										=0.1 * sample
										__magic_name__						:						Union[str, Any]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Dict										=self.get_scheduler_config()
														__magic_name__						:						Optional[Any]										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)

														# copy over dummy past residuals (must be after setting timesteps)
														__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

														if time_step is None:
																		__magic_name__						:						Optional[Any]										=scheduler.timesteps[len(scheduler.timesteps			) // 2]

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

																		# copy over dummy past residual (must be after setting timesteps)
																		__magic_name__						:						Tuple										=dummy_past_residuals[:]

														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Union[str, Any]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Tuple										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Optional[Any]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Optional[Any]     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.scheduler_classes[0]
										__magic_name__						:						List[str]										=self.get_scheduler_config(**__snake_case			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)

										__magic_name__						:						List[str]										=10
										__magic_name__						:						Optional[Any]										=self.dummy_model()
										__magic_name__						:						Any										=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case			)

										for i, t in enumerate(scheduler.timesteps			):
														__magic_name__						:						Union[str, Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										for i, t in enumerate(scheduler.timesteps			):
														__magic_name__						:						Optional[Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Optional[Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										return sample

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=dict(self.forward_default_kwargs			)

										__magic_name__						:						str										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Union[str, Any]										=self.get_scheduler_config()
														__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)

														__magic_name__						:						Tuple										=self.dummy_sample
														__magic_name__						:						Tuple										=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case     ,			"""set_timesteps"""			):
																		scheduler.set_timesteps(__snake_case			)
														elif num_inference_steps is not None and not hasattr(__snake_case     ,			"""set_timesteps"""			):
																		__magic_name__						:						Dict										=num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														__magic_name__						:						List[str]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														__magic_name__						:						str										=dummy_past_residuals[:]

														__magic_name__						:						Optional[Any]										=scheduler.timesteps[5]
														__magic_name__						:						List[str]										=scheduler.timesteps[6]

														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Tuple										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Optional[Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Tuple			):
										'''simple docstring'''

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

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]     ,			[10, 50, 1_00]			):
														self.check_over_forward(num_inference_steps=__snake_case     ,			time_step=__snake_case			)


						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.full_loop()
										__magic_name__						:						str										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_mean.item() - 2_54_05_29			) < 10








'''simple docstring'''







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def       lowercase					(_A       ):







       """simple docstring"""







       return ConvertCommand(
           args.model_type ,    args.tf_checkpoint ,    args.pytorch_dump_output ,    args.config ,    args.finetuning_task_name       )


lowerCAmelCase     :  Optional[int]											=    """
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__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @staticmethod
     def a						(			snake_case__ ):

            '''simple docstring'''

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







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

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							logging.get_logger('transformers-cli/converting' )

            self._logger.info(F'Loading model {model_type}' )
            _lowerCAmelCase     :				Any       =							model_type
            _lowerCAmelCase     :				Any       =							tf_checkpoint
            _lowerCAmelCase     :				List[str]       =							pytorch_dump_output
            _lowerCAmelCase     :				int       =							config
            _lowerCAmelCase     :				str       =							finetuning_task_name







     def a						(			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(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(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(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(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(snake_case__ )

                   if "ckpt" in self._tf_checkpoint.lower():
                          _lowerCAmelCase     :				List[Any]       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Optional[int]       =							''
                   else:
                          _lowerCAmelCase     :				int       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Dict       =							''
                   convert_transfo_xl_checkpoint_to_pytorch(
                       snake_case__   ,						self._config   ,						self._pytorch_dump_output   ,						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(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(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]' )

