




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      (	_lowerCamelCase				:    Any)      ->					Optional[int]:






							'''simple docstring'''





							return EnvironmentCommand()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					str:






							'''simple docstring'''





							return EnvironmentCommand(args.accelerate_config_file)




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	@staticmethod
	def      _lowerCamelCase       (      a	:ArgumentParser		)	->				Any:
								__UpperCamelCase			:     Dict         =      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	:int  ,     a	:List[str]  ,     *a	:Any		)	->				None:
								__UpperCamelCase			:     Dict         =      accelerate_config_file







	def      _lowerCamelCase       (      self	:Tuple		)	->				Optional[int]:
								__UpperCamelCase			:     Any         =      "not installed"
								if is_safetensors_available():
															import safetensors

															__UpperCamelCase			:     Union[str, Any]         =      safetensors.__version__
								elif importlib.util.find_spec("safetensors"		) is not None:
															import safetensors

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

								__UpperCamelCase			:     Optional[int]         =      "not installed"
								__UpperCamelCase			:     Tuple         =      "not found"
								if is_accelerate_available():
															import accelerate
															from accelerate.commands.config import default_config_file, load_config_from_file

															__UpperCamelCase			:     List[Any]         =      accelerate.__version__
															# Get the default from the config file.
															if self._accelerate_config_file is not None or os.path.isfile(a		):
																						__UpperCamelCase			:     Union[str, Any]         =      load_config_from_file(self._accelerate_config_file		).to_dict()

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

								__UpperCamelCase			:     Optional[int]         =      "not installed"
								__UpperCamelCase			:     Any         =      "NA"
								if is_torch_available():
															import torch

															__UpperCamelCase			:     List[Any]         =      torch.__version__
															__UpperCamelCase			:     List[Any]         =      torch.cuda.is_available()

								__UpperCamelCase			:     Any         =      "not installed"
								__UpperCamelCase			:     Union[str, Any]         =      "NA"
								if is_tf_available():
															import tensorflow as tf

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

								__UpperCamelCase			:     Any         =      "not installed"
								__UpperCamelCase			:     Optional[int]         =      "not installed"
								__UpperCamelCase			:     Union[str, Any]         =      "not installed"
								__UpperCamelCase			:     List[Any]         =      "NA"
								if is_flax_available():
															import flax
															import jax
															import jaxlib

															__UpperCamelCase			:     int         =      flax.__version__
															__UpperCamelCase			:     Any         =      jax.__version__
															__UpperCamelCase			:     int         =      jaxlib.__version__
															__UpperCamelCase			:     int         =      jax.lib.xla_bridge.get_backend().platform

								__UpperCamelCase			:     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(a		)		)

								return info







	@staticmethod
	def      _lowerCamelCase       (      a	:Optional[int]		)	->				List[Any]:
								return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)