


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]   ):
							return ConvertCommand(
							    args.model_type					,    args.tf_checkpoint					,    args.pytorch_dump_output					,    args.config					,    args.finetuning_task_name   )


snake_case							:       List[Any]     			=      "\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_   (a_      ):
					@staticmethod
					def  lowerCamelCase__(      __snake_case   :Union[str, Any]      )					->    List[Any]:
												a__             =     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=_A			,required=_A			,help='Model\'s type.'      )
												train_parser.add_argument(
												    '--tf_checkpoint'			,type=_A			,required=_A			,help='TensorFlow checkpoint path or folder.'      )
												train_parser.add_argument(
												    '--pytorch_dump_output'			,type=_A			,required=_A			,help='Path to the PyTorch saved model output.'      )
												train_parser.add_argument('--config'			,type=_A			,default=''			,help='Configuration file path or folder.'      )
												train_parser.add_argument(
												    '--finetuning_task_name'			,type=_A			,default=_A			,help='Optional fine-tuning task name if the TF model was a finetuned model.'			,)
												train_parser.set_defaults(func=_A      )
					def __init__(      self   :int			,__snake_case   :List[Any]			,__snake_case   :Tuple			,__snake_case   :Any			,__snake_case   :Optional[Any]			,__snake_case   :List[Any]			,*__snake_case   :Dict			,)					->    Dict:
												a__             =     logging.get_logger('transformers-cli/converting'      )

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



					def  lowerCamelCase__(      self   :Dict      )					->    Tuple:







												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(_A      )

																			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(_A      )

																			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(_A      )

																			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(_A      )

																			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(_A      )

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

																			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(_A      )

																			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]'      )











from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 	A_       (a_			):
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__()
				self.register_modules(unet=_A							,					scheduler=_A		)







	@torch.no_grad()
	def __call__(    self							,					_A = 1							,					_A = 5_0							,					_A = None							,					_A = "pil"							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.unet.config.sample_size
				UpperCAmelCase   		=				(batch_size, 3, img_size, img_size)

				UpperCAmelCase   		=				self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				UpperCAmelCase   		=				randn_tensor(_A							,					generator=_A							,					device=self.device		) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_A		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							UpperCAmelCase   		=				self.scheduler.schedule[t]
							UpperCAmelCase   		=				self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							UpperCAmelCase		,			UpperCAmelCase   		=				self.scheduler.add_noise_to_input(_A							,					_A							,					generator=_A		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							UpperCAmelCase   		=				(sigma_hat / 2) * model((sample_hat + 1) / 2							,					sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							UpperCAmelCase   		=				self.scheduler.step(_A							,					_A							,					_A							,					_A		)

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										UpperCAmelCase   		=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,					sigma_prev / 2		).sample
										UpperCAmelCase   		=				self.scheduler.step_correct(
										    _A							,					_A							,					_A							,					_A							,					step_output.prev_sample							,					step_output['''derivative''']							,					)
							UpperCAmelCase   		=				step_output.prev_sample

				UpperCAmelCase   		=				(sample / 2 + 0.5).clamp(0							,					1		)
				UpperCAmelCase   		=				sample.cpu().permute(0							,					2							,					3							,					1		).numpy()
				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)



