



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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     int:
   return EnvironmentCommand()

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any				)  ->     str:
   return EnvironmentCommand(args.accelerate_config_file				)


class 	lowerCamelCase__	(       __snake_case):



 """simple docstring"""

 @staticmethod
 def     snake_case_      (    __lowerCAmelCase					:					Optional[Any]      )   ->	int:
    _A      =       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					:					List[str]			,						__lowerCAmelCase					:					str			,						*__lowerCAmelCase					:					Dict      )   ->	None:
    _A      =       accelerate_config_file

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[Any]:
    _A      =       '''not installed'''
    if is_safetensors_available():
       import safetensors

       _A      =       safetensors.__version__
    elif importlib.util.find_spec('''safetensors'''      ) is not None:
       import safetensors

       _A      =       f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

    _A      =       '''not installed'''
    _A      =       '''not found'''
    if is_accelerate_available():
       import accelerate
       from accelerate.commands.config import default_config_file, load_config_from_file

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

       _A      =       (
           '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]      )
           if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      )
           else f'''\t{accelerate_config}'''
       )

    _A      =       '''not installed'''
    _A      =       '''NA'''
    if is_torch_available():
       import torch

       _A      =       torch.__version__
       _A      =       torch.cuda.is_available()

    _A      =       '''not installed'''
    _A      =       '''NA'''
    if is_tf_available():
       import tensorflow as tf

       _A      =       tf.__version__
       try:
          # deprecated in v2.1
          _A      =       tf.test.is_gpu_available()
       except AttributeError:
          # returns list of devices, convert to bool
          _A      =       bool(tf.config.list_physical_devices('''GPU'''      )      )

    _A      =       '''not installed'''
    _A      =       '''not installed'''
    _A      =       '''not installed'''
    _A      =       '''NA'''
    if is_flax_available():
       import flax
       import jax
       import jaxlib

       _A      =       flax.__version__
       _A      =       jax.__version__
       _A      =       jaxlib.__version__
       _A      =       jax.lib.xla_bridge.get_backend().platform

    _A      =       {
        '''`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     snake_case_      (    __lowerCAmelCase					:					Any      )   ->	Tuple:
    return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]      ) + "\n"





'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''timesformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=224		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3072		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="divided_space_time"		,     SCREAMING_SNAKE_CASE=0		,     **SCREAMING_SNAKE_CASE		,     )					->							Tuple:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  List[str]   =		num_channels
         A			:  str   =		num_frames

         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  Any   =		num_attention_heads
         A			:  Optional[Any]   =		intermediate_size
         A			:  Union[str, Any]   =		hidden_act
         A			:  Tuple   =		hidden_dropout_prob
         A			:  Optional[int]   =		attention_probs_dropout_prob
         A			:  Tuple   =		initializer_range
         A			:  List[Any]   =		layer_norm_eps
         A			:  Union[str, Any]   =		qkv_bias

         A			:  List[str]   =		attention_type
         A			:  Union[str, Any]   =		drop_path_rate




