'''simple docstring'''





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
SCREAMING_SNAKE_CASE_:     List[str]  =data_utils.TransfoXLTokenizer
SCREAMING_SNAKE_CASE_:     str  =data_utils.TransfoXLCorpus
SCREAMING_SNAKE_CASE_:     List[str]  =data_utils
SCREAMING_SNAKE_CASE_:     Optional[Any]  =data_utils





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : List[Any]					,    snake_case_       : Union[str, Any]					,    snake_case_       : int			)    ->      Optional[Any]:


     '''simple docstring'''




     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(snake_case_					,    "rb"			) as fp:
               UpperCAmelCase_           =   pickle.load(snake_case_					,    encoding="latin1"			)
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          UpperCAmelCase_           =   pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
          print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""			)
          UpperCAmelCase_           =   corpus.vocab.__dict__
          torch.save(snake_case_					,    snake_case_			)

          UpperCAmelCase_           =   corpus.__dict__
          corpus_dict_no_vocab.pop("vocab"					,    snake_case_			)
          UpperCAmelCase_           =   pytorch_dump_folder_path + "/" + CORPUS_NAME
          print(f"""Save dataset to {pytorch_dataset_dump_path}"""			)
          torch.save(snake_case_					,    snake_case_			)

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          UpperCAmelCase_           =   os.path.abspath(snake_case_			)
          UpperCAmelCase_           =   os.path.abspath(snake_case_			)

          print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""			)
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               UpperCAmelCase_           =   TransfoXLConfig()
          else:
               UpperCAmelCase_           =   TransfoXLConfig.from_json_file(snake_case_			)
          print(f"""Building PyTorch model from configuration: {config}"""			)
          UpperCAmelCase_           =   TransfoXLLMHeadModel(snake_case_			)

          UpperCAmelCase_           =   load_tf_weights_in_transfo_xl(snake_case_					,    snake_case_					,    snake_case_			)
          # Save pytorch-model
          UpperCAmelCase_           =   os.path.join(snake_case_					,    snake_case_			)
          UpperCAmelCase_           =   os.path.join(snake_case_					,    snake_case_			)
          print(f"""Save PyTorch model to {os.path.abspath(snake_case_			)}"""			)
          torch.save(model.state_dict()					,    snake_case_			)
          print(f"""Save configuration file to {os.path.abspath(snake_case_			)}"""			)
          with open(snake_case_					,    "w"					,    encoding="utf-8"			) as f:
               f.write(config.to_json_string()			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     str  =argparse.ArgumentParser()
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        required=True,
        help='Path to the folder to store the PyTorch model or dataset/vocab.',
    )
    parser.add_argument(
        '--tf_checkpoint_path',
        default='',
        type=str,
        help='An optional path to a TensorFlow checkpoint path to be converted.',
    )
    parser.add_argument(
        '--transfo_xl_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained BERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--transfo_xl_dataset_file',
        default='',
        type=str,
        help='An optional dataset file to be converted in a vocabulary.',
    )
    SCREAMING_SNAKE_CASE_:     Dict  =parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )












import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase	:							Tuple				=logging.get_logger(__name__)

lowerCamelCase	:							Optional[Any]				={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Optional[Any]        = '''conditional_detr'''
 _lowerCAmelCase       :       List[Any]        = ['''past_key_values''']
 _lowerCAmelCase       :       List[str]        = {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }







 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Tuple=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=3							,   SCREAMING_SNAKE_CASE					:   Any=3_00							,   SCREAMING_SNAKE_CASE					:   Tuple=6							,   SCREAMING_SNAKE_CASE					:   int=20_48							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=8							,   SCREAMING_SNAKE_CASE					:   int=6							,   SCREAMING_SNAKE_CASE					:   Dict=20_48							,   SCREAMING_SNAKE_CASE					:   Optional[int]=8							,   SCREAMING_SNAKE_CASE					:   Dict=0.0							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   int="relu"							,   SCREAMING_SNAKE_CASE					:   Any=2_56							,   SCREAMING_SNAKE_CASE					:   int=0.1							,   SCREAMING_SNAKE_CASE					:   int=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=0.0							,   SCREAMING_SNAKE_CASE					:   List[str]=0.0_2							,   SCREAMING_SNAKE_CASE					:   Optional[int]=1.0							,   SCREAMING_SNAKE_CASE					:   List[str]=False							,   SCREAMING_SNAKE_CASE					:   int="sine"							,   SCREAMING_SNAKE_CASE					:   str="resnet50"							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   Tuple=False							,   SCREAMING_SNAKE_CASE					:   Tuple=2							,   SCREAMING_SNAKE_CASE					:   Tuple=5							,   SCREAMING_SNAKE_CASE					:   List[str]=2							,   SCREAMING_SNAKE_CASE					:   List[str]=1							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=1							,   SCREAMING_SNAKE_CASE					:   str=2							,   SCREAMING_SNAKE_CASE					:   Any=5							,   SCREAMING_SNAKE_CASE					:   int=2							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.2_5							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      if backbone_config is not None and use_timm_backbone:
           raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."						)

      if not use_timm_backbone:
           if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
           elif isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Union[str, Any]       				=  backbone_config.get("model_type"						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING[backbone_model_type]
                UpperCamelCase__			:   Any       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  use_timm_backbone
      UpperCamelCase__			:   List[Any]       				=  backbone_config
      UpperCamelCase__			:   Tuple       				=  num_channels
      UpperCamelCase__			:   List[Any]       				=  num_queries
      UpperCamelCase__			:   Dict       				=  d_model
      UpperCamelCase__			:   Any       				=  encoder_ffn_dim
      UpperCamelCase__			:   List[str]       				=  encoder_layers
      UpperCamelCase__			:   List[str]       				=  encoder_attention_heads
      UpperCamelCase__			:   Optional[int]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  decoder_attention_heads
      UpperCamelCase__			:   int       				=  dropout
      UpperCamelCase__			:   Optional[int]       				=  attention_dropout
      UpperCamelCase__			:   Any       				=  activation_dropout
      UpperCamelCase__			:   int       				=  activation_function
      UpperCamelCase__			:   int       				=  init_std
      UpperCamelCase__			:   List[Any]       				=  init_xavier_std
      UpperCamelCase__			:   List[str]       				=  encoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  decoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  encoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  auxiliary_loss
      UpperCamelCase__			:   List[str]       				=  position_embedding_type
      UpperCamelCase__			:   Optional[Any]       				=  backbone
      UpperCamelCase__			:   Optional[int]       				=  use_pretrained_backbone
      UpperCamelCase__			:   List[Any]       				=  dilation
      # Hungarian matcher
      UpperCamelCase__			:   List[str]       				=  class_cost
      UpperCamelCase__			:   Union[str, Any]       				=  bbox_cost
      UpperCamelCase__			:   int       				=  giou_cost
      # Loss coefficients
      UpperCamelCase__			:   str       				=  mask_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  dice_loss_coefficient
      UpperCamelCase__			:   int       				=  cls_loss_coefficient
      UpperCamelCase__			:   Tuple       				=  bbox_loss_coefficient
      UpperCamelCase__			:   Any       				=  giou_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  focal_alpha
      super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 @property
 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return self.encoder_attention_heads







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return self.d_model







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  copy.deepcopy(self.__dict__						)
      if self.backbone_config is not None:
           UpperCamelCase__			:   List[str]       				=  self.backbone_config.to_dict()
      UpperCamelCase__			:   int       				=  self.__class__.model_type
      return output







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = version.parse('''1.11'''       )







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ("pixel_mask", {0: "batch"}),
          ]						)







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return 1e-5







 @property
 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return 12