import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 		snake_case__	(		lowercase					):
   return EnvironmentCommand()







class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	parser.add_parser("env" )
       download_parser.set_defaults(func=lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							int       =	huggingface_hub.__version__

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

          lowerCAmelCase_:							List[str]       =	torch.__version__
          lowerCAmelCase_:							int       =	torch.cuda.is_available()

       lowerCAmelCase_:							Tuple       =	"not installed"
       if is_transformers_available():
          import transformers

          lowerCAmelCase_:							Tuple       =	transformers.__version__

       lowerCAmelCase_:							Optional[int]       =	"not installed"
       if is_accelerate_available():
          import accelerate

          lowerCAmelCase_:							List[Any]       =	accelerate.__version__

       lowerCAmelCase_:							Dict       =	"not installed"
       if is_xformers_available():
          import xformers

          lowerCAmelCase_:							Dict       =	xformers.__version__

       lowerCAmelCase_:							Union[str, Any]       =	{
           "`diffusers` version": version,
           "Platform": platform.platform(),
           "Python version": platform.python_version(),
           "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
           "Huggingface_hub version": hub_version,
           "Transformers version": transformers_version,
           "Accelerate version": accelerate_version,
           "xFormers version": xformers_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.''' )