



'''simple docstring'''





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 			a_	(				_lowerCAmelCase			) -> str:
  return EnvironmentCommand()

def 			a_	(				_lowerCAmelCase			) -> List[str]:
  return EnvironmentCommand(args.accelerate_config_file			)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  @staticmethod
  def 		_lowercase	(					_a :      ArgumentParser  )   ->   Tuple:
    __lowerCamelCase			:			List[Any]       			=					parser.add_parser('env'  )
    download_parser.set_defaults(func=_a  )
    download_parser.add_argument(
        '--accelerate-config_file'     ,      default=_a     ,      help='The accelerate config file to use for the default values in the launching script.'     ,      )
    download_parser.set_defaults(func=_a  )




  def __init__(					self :      str     ,      _a :      str     ,      *_a :      Tuple  )   ->   None:
    __lowerCamelCase			:			Union[str, Any]       			=					accelerate_config_file




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

      __lowerCamelCase			:			Optional[Any]       			=					safetensors.__version__
    elif importlib.util.find_spec('safetensors'  ) is not None:
      import safetensors

      __lowerCamelCase			:			List[str]       			=					f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

    __lowerCamelCase			:			str       			=					'not installed'
    __lowerCamelCase			:			Optional[int]       			=					'not found'
    if is_accelerate_available():
      import accelerate
      from accelerate.commands.config import default_config_file, load_config_from_file

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

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

    __lowerCamelCase			:			str       			=					'not installed'
    __lowerCamelCase			:			Any       			=					'NA'
    if is_torch_available():
      import torch

      __lowerCamelCase			:			int       			=					torch.__version__
      __lowerCamelCase			:			str       			=					torch.cuda.is_available()

    __lowerCamelCase			:			List[Any]       			=					'not installed'
    __lowerCamelCase			:			Tuple       			=					'NA'
    if is_tf_available():
      import tensorflow as tf

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

    __lowerCamelCase			:			str       			=					'not installed'
    __lowerCamelCase			:			int       			=					'not installed'
    __lowerCamelCase			:			Union[str, Any]       			=					'not installed'
    __lowerCamelCase			:			Dict       			=					'NA'
    if is_flax_available():
      import flax
      import jax
      import jaxlib

      __lowerCamelCase			:			str       			=					flax.__version__
      __lowerCamelCase			:			Optional[Any]       			=					jax.__version__
      __lowerCamelCase			:			int       			=					jaxlib.__version__
      __lowerCamelCase			:			List[str]       			=					jax.lib.xla_bridge.get_backend().platform

    __lowerCamelCase			:			Union[str, Any]       			=					{
        '`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(_a  )  )

    return info




  @staticmethod
  def 		_lowercase	(					_a :      Dict  )   ->   Any:
    return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]  ) + "\n"











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






