


"""simple docstring"""



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def   __lowerCAmelCase						(						lowercase  :  Namespace )	->						str:


    """simple docstring"""





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


__snake_case   			=    '''
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 _lowerCAmelCase				(      a__	):







   @staticmethod
   def  lowerCamelCase		(	UpperCamelCase__						)	->		int:

       '''simple docstring'''




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







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	*UpperCamelCase__						,	)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    logging.get_logger("transformers-cli/converting"						)

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







   def  lowerCamelCase		(	self						)	->		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(UpperCamelCase_						)

           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(UpperCamelCase_						)

           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(UpperCamelCase_						)

           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(UpperCamelCase_						)

           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(UpperCamelCase_						)

           if "ckpt" in self._tf_checkpoint.lower():
               snake_case    :      Union[str, Any]      					=    self._tf_checkpoint
               snake_case    :      List[str]      					=    ''''''
           else:
               snake_case    :      Dict      					=    self._tf_checkpoint
               snake_case    :      Any      					=    ''''''
           convert_transfo_xl_checkpoint_to_pytorch(
               UpperCamelCase_						,	self._config						,	self._pytorch_dump_output						,	UpperCamelCase_						)
       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(UpperCamelCase_						)

           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(UpperCamelCase_						)

           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 inspect
from typing import List, Optional, Tuple, Union

import torch

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



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           lowerCAmelCase    :		Any				=      '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

           lowerCAmelCase    :		Dict				=      (image / 2 + 0.5).clamp(0    , 1 )
           lowerCAmelCase    :		Dict				=      image.cpu().permute(0    , 2    , 3    , 1 ).numpy()
           if output_type == "pil":
                 lowerCAmelCase    :		List[str]				=      self.numpy_to_pil(UpperCamelCase_ )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


