






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Namespace    )						->      str:





    """simple docstring"""

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


__a      :       List[str]				  =   "\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       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     @staticmethod
     def 		lowerCAmelCase_     (				UpperCamelCase_       :			ArgumentParser				):


         """simple docstring"""



         __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=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       :			Optional[int] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			str ,	*UpperCamelCase_       :			Optional[int] ,	):


         """simple docstring"""



         __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       :			Optional[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():
                 __A         =	self._tf_checkpoint
                 __A         =	""""""
             else:
                 __A         =	self._tf_checkpoint
                 __A         =	""""""
             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]"""				)












from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				42


class       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''




     @register_to_config
     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			Tuple[str] = ("DownEncoderBlock2D",) ,	UpperCamelCase_       :			Tuple[str] = ("UpDecoderBlock2D",) ,	UpperCamelCase_       :			Tuple[int] = (64,) ,	UpperCamelCase_       :			int = 1 ,	UpperCamelCase_       :			str = "silu" ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			int = 256 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			float = 0.18215 ,	UpperCamelCase_       :			str = "group" ,	):


         """simple docstring"""



         super().__init__()

         # pass init params to Encoder
         __A         =	Encoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	down_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	double_z=UpperCamelCase_ ,	)

         __A         =	vq_embed_dim if vq_embed_dim is not None else latent_channels

         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)
         __A         =	VectorQuantizer(UpperCamelCase_ ,	UpperCamelCase_ ,	beta=0.25 ,	remap=UpperCamelCase_ ,	sane_index_shape=UpperCamelCase_				)
         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)

         # pass init params to Decoder
         __A         =	Decoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	up_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	norm_type=UpperCamelCase_ ,	)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	self.encoder(UpperCamelCase_				)
         __A         =	self.quant_conv(UpperCamelCase_				)

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=UpperCamelCase_				)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         if not force_not_quantize:
             __A    ,	__A    ,	__A         =	self.quantize(UpperCamelCase_				)
         else:
             __A         =	h
         __A         =	self.post_quant_conv(UpperCamelCase_				)
         __A         =	self.decoder(UpperCamelCase_ ,	quant if self.config.norm_type == """spatial""" else None				)

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	sample
         __A         =	self.encode(UpperCamelCase_				).latents
         __A         =	self.decode(UpperCamelCase_				).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)




