import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def 		snake_case__	(		lowercase					):
   return EnvironmentCommand()
def 		snake_case__	(		lowercase					):
   return EnvironmentCommand(args.accelerate_config_file					)







class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	parser.add_parser("env" )
       download_parser.set_defaults(func=lowerCamelCase__ )
       download_parser.add_argument(
           "--accelerate-config_file"       ,						default=lowerCamelCase__       ,						help="The accelerate config file to use for the default values in the launching script."       ,						)
       download_parser.set_defaults(func=lowerCamelCase__ )




    def __init__(    self       ,						lowerCamelCase__       ,						*lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	accelerate_config_file




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	"not installed"
       if is_safetensors_available():
          import safetensors

          lowerCAmelCase_:							List[Any]       =	safetensors.__version__
       elif importlib.util.find_spec("safetensors" ) is not None:
          import safetensors

          lowerCAmelCase_:							List[Any]       =	F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

       lowerCAmelCase_:							int       =	"not installed"
       lowerCAmelCase_:							Dict       =	"not found"
       if is_accelerate_available():
          import accelerate
          from accelerate.commands.config import default_config_file, load_config_from_file

          lowerCAmelCase_:							Tuple       =	accelerate.__version__
          # Get the default from the config file.
          if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase__ ):
             lowerCAmelCase_:							str       =	load_config_from_file(self._accelerate_config_file ).to_dict()

          lowerCAmelCase_:							Tuple       =	(
              "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
              if isinstance(lowerCamelCase__       ,						lowerCamelCase__ )
              else F'''\t{accelerate_config}'''
          )

       lowerCAmelCase_:							Optional[Any]       =	"not installed"
       lowerCAmelCase_:							List[str]       =	"NA"
       if is_torch_available():
          import torch

          lowerCAmelCase_:							int       =	torch.__version__
          lowerCAmelCase_:							Tuple       =	torch.cuda.is_available()

       lowerCAmelCase_:							Any       =	"not installed"
       lowerCAmelCase_:							Dict       =	"NA"
       if is_tf_available():
          import tensorflow as tf

          lowerCAmelCase_:							Dict       =	tf.__version__
          try:
             # deprecated in v2.1
             lowerCAmelCase_:							Optional[Any]       =	tf.test.is_gpu_available()
          except AttributeError:
             # returns list of devices, convert to bool
             lowerCAmelCase_:							Any       =	bool(tf.config.list_physical_devices("GPU" ) )

       lowerCAmelCase_:							Tuple       =	"not installed"
       lowerCAmelCase_:							Any       =	"not installed"
       lowerCAmelCase_:							List[str]       =	"not installed"
       lowerCAmelCase_:							Tuple       =	"NA"
       if is_flax_available():
          import flax
          import jax
          import jaxlib

          lowerCAmelCase_:							str       =	flax.__version__
          lowerCAmelCase_:							Dict       =	jax.__version__
          lowerCAmelCase_:							Union[str, Any]       =	jaxlib.__version__
          lowerCAmelCase_:							int       =	jax.lib.xla_bridge.get_backend().platform

       lowerCAmelCase_:							Dict       =	{
           "`transformers` version": version,
           "Platform": platform.platform(),
           "Python version": platform.python_version(),
           "Huggingface_hub version": huggingface_hub.__version__,
           "Safetensors version": F'''{safetensors_version}''',
           "Accelerate version": F'''{accelerate_version}''',
           "Accelerate config": F'''{accelerate_config_str}''',
           "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
           "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
           "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
           "Jax version": F'''{jax_version}''',
           "JaxLib version": F'''{jaxlib_version}''',
           "Using GPU in script?": "<fill in>",
           "Using distributed or parallel set-up in script?": "<fill in>",
       }

       print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
       print(self.format_dict(lowerCamelCase__ ) )

       return info




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :      Any            =    logging.get_logger(__name__)

a  :      List[str]            =    {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    'transfo-xl'
    SCREAMING_SNAKE_CASE:				List[Any]          =    ['mems']
    SCREAMING_SNAKE_CASE:				List[Any]          =    {
        'n_token': 'vocab_size',
        'hidden_size': 'd_model',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=267_735       ,						lowerCamelCase__=[20_000, 40_000, 200_000]       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=16       ,						lowerCamelCase__=64       ,						lowerCamelCase__=4_096       ,						lowerCamelCase__=4       ,						lowerCamelCase__=False       ,						lowerCamelCase__=18       ,						lowerCamelCase__=1_600       ,						lowerCamelCase__=1_000       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0       ,						lowerCamelCase__=-1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=True       ,						lowerCamelCase__="normal"       ,						lowerCamelCase__=0.0_1       ,						lowerCamelCase__=0.0_1       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Dict       =	vocab_size
       lowerCAmelCase_:							Any       =	[]
       self.cutoffs.extend(lowerCamelCase__ )
       if proj_share_all_but_first:
          lowerCAmelCase_:							int       =	[False] + [True] * len(self.cutoffs )
       else:
          lowerCAmelCase_:							Any       =	[False] + [False] * len(self.cutoffs )
       lowerCAmelCase_:							Dict       =	d_model
       lowerCAmelCase_:							Union[str, Any]       =	d_embed
       lowerCAmelCase_:							Dict       =	d_head
       lowerCAmelCase_:							Dict       =	d_inner
       lowerCAmelCase_:							List[str]       =	div_val
       lowerCAmelCase_:							List[str]       =	pre_lnorm
       lowerCAmelCase_:							List[str]       =	n_layer
       lowerCAmelCase_:							List[str]       =	n_head
       lowerCAmelCase_:							str       =	mem_len
       lowerCAmelCase_:							Any       =	same_length
       lowerCAmelCase_:							Optional[Any]       =	attn_type
       lowerCAmelCase_:							int       =	clamp_len
       lowerCAmelCase_:							Optional[int]       =	sample_softmax
       lowerCAmelCase_:							Optional[Any]       =	adaptive
       lowerCAmelCase_:							Optional[int]       =	dropout
       lowerCAmelCase_:							Union[str, Any]       =	dropatt
       lowerCAmelCase_:							Tuple       =	untie_r
       lowerCAmelCase_:							Union[str, Any]       =	init
       lowerCAmelCase_:							Optional[Any]       =	init_range
       lowerCAmelCase_:							Optional[Any]       =	proj_init_std
       lowerCAmelCase_:							Tuple       =	init_std
       lowerCAmelCase_:							Tuple       =	layer_norm_epsilon
       super().__init__(eos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )




    @property
    def      _a    (    self ):
       # Message copied from Transformer-XL documentation
       logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1




    @max_position_embeddings.setter
    def      _a    (    self       ,						lowerCamelCase__ ):
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )