
import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCamelCase__	=							logging.get_logger(__name__)  # pylint: disable=invalid-name


def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				str:

		"""simple docstring"""






		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead"""						,  lowerCAmelCase						,  )
		if isinstance(lowerCAmelCase						,  torch.Tensor ):
				return image
		elif isinstance(lowerCAmelCase						,  PIL.Image.Image ):
				_UpperCamelCase						    =	[image]

		if isinstance(image[0]						,  PIL.Image.Image ):
				_UpperCamelCase    ,	_UpperCamelCase						    =	image[0].size
				_UpperCamelCase    ,	_UpperCamelCase						    =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				_UpperCamelCase						    =	[np.array(i.resize((w, h)						,  resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
				_UpperCamelCase						    =	np.concatenate(lowerCAmelCase						,  axis=0 )
				_UpperCamelCase						    =	np.array(lowerCAmelCase ).astype(np.floataa ) / 255.0
				_UpperCamelCase						    =	image.transpose(0						,  3						,  1						,  2 )
				_UpperCamelCase						    =	2.0 * image - 1.0
				_UpperCamelCase						    =	torch.from_numpy(lowerCAmelCase )
		elif isinstance(image[0]						,  torch.Tensor ):
				_UpperCamelCase						    =	torch.cat(lowerCAmelCase						,  dim=0 )
		return image


def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		if isinstance(lowerCAmelCase						,  torch.Tensor ):
				return mask
		elif isinstance(lowerCAmelCase						,  PIL.Image.Image ):
				_UpperCamelCase						    =	[mask]

		if isinstance(mask[0]						,  PIL.Image.Image ):
				_UpperCamelCase    ,	_UpperCamelCase						    =	mask[0].size
				_UpperCamelCase    ,	_UpperCamelCase						    =	(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
				_UpperCamelCase						    =	[np.array(m.convert("""L""" ).resize((w, h)						,  resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
				_UpperCamelCase						    =	np.concatenate(lowerCAmelCase						,  axis=0 )
				_UpperCamelCase						    =	mask.astype(np.floataa ) / 255.0
				_UpperCamelCase						    =	0
				_UpperCamelCase						    =	1
				_UpperCamelCase						    =	torch.from_numpy(lowerCAmelCase )
		elif isinstance(mask[0]						,  torch.Tensor ):
				_UpperCamelCase						    =	torch.cat(lowerCAmelCase						,  dim=0 )
		return mask







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: UNetaDModel
						UpperCamelCase_							: RePaintScheduler

						def __init__(			self       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

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







						@torch.no_grad()
						def __call__(			self       ,							a       ,							a       ,							a = 2_50       ,							a = 0.0       ,							a = 10       ,							a = 10       ,							a = None       ,							a = "pil"       ,							a = True       ,							)					->      Union[ImagePipelineOutput, Tuple]:


								'''simple docstring'''

								_UpperCamelCase						    =	image

								_UpperCamelCase						    =	_preprocess_image(a				)
								_UpperCamelCase						    =	original_image.to(device=self.device       ,							dtype=self.unet.dtype				)
								_UpperCamelCase						    =	_preprocess_mask(a				)
								_UpperCamelCase						    =	mask_image.to(device=self.device       ,							dtype=self.unet.dtype				)

								_UpperCamelCase						    =	original_image.shape[0]

								# sample gaussian noise to begin the loop
								if isinstance(a       ,							a				) and len(a				) != batch_size:
										raise ValueError(
										    F'You have passed a list of generators of length {len(a				)}, but requested an effective batch'
										    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

								_UpperCamelCase						    =	original_image.shape
								_UpperCamelCase						    =	randn_tensor(a       ,							generator=a       ,							device=self.device       ,							dtype=self.unet.dtype				)

								# set step values
								self.scheduler.set_timesteps(a       ,							a       ,							a       ,							self.device				)
								_UpperCamelCase						    =	eta

								_UpperCamelCase						    =	self.scheduler.timesteps[0] + 1
								_UpperCamelCase						    =	generator[0] if isinstance(a       ,							a				) else generator
								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
										if t < t_last:
												# predict the noise residual
												_UpperCamelCase						    =	self.unet(a       ,							a				).sample
												# compute previous image: x_t -> x_t-1
												_UpperCamelCase						    =	self.scheduler.step(a       ,							a       ,							a       ,							a       ,							a       ,							a				).prev_sample

										else:
												# compute the reverse: x_t-1 -> x_t
												_UpperCamelCase						    =	self.scheduler.undo_step(a       ,							a       ,							a				)
										_UpperCamelCase						    =	t

								_UpperCamelCase						    =	(image / 2 + 0.5).clamp(0       ,							1				)
								_UpperCamelCase						    =	image.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				)





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		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        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      Optional[int]:


								'''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=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       ,							a       ,							a       ,							a       ,							a       ,							a       ,							*a       ,							)					->      Optional[Any]:


								'''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  A_				(			self				)					->      Tuple:


								'''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(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():
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										else:
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										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]"""				)



