
"""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_  (__a					):

					'''simple docstring'''





					return EnvironmentCommand()
def 				A_  (__a					):

					'''simple docstring'''





					return EnvironmentCommand(args.accelerate_config_file					)




class __lowerCAmelCase					(       snake_case_   ):




			"""simple docstring"""







			@staticmethod
			def   lowerCamelCase__  (					_snake_case    :       ArgumentParser						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    parser.add_parser("env"						)
								download_parser.set_defaults(func=__snake_case						)
								download_parser.add_argument(
								    "--accelerate-config_file"							, default=__snake_case							, help="The accelerate config file to use for the default values in the launching script."							, )
								download_parser.set_defaults(func=__snake_case						)




			def __init__(					self    :       str							, _snake_case    :       Dict							, *_snake_case    :       List[str]						)   ->			None:







								"""simple docstring"""
								A_								=    accelerate_config_file




			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Any:







								"""simple docstring"""
								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_								=    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(__snake_case						):
																		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(__snake_case							, __snake_case						)
													    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(__snake_case						)						)

								return info






			@staticmethod
			def   lowerCamelCase__  (					_snake_case    :       str						)   ->			str:







								"""simple docstring"""
								return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]						) + "\n"











'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A__							:  Tuple    =logging.get_logger(__name__)

A__							:  Any    ={
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  '''imagegpt'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     str			=  {
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Optional[Any]=5_12 + 1     ,     __snake_case     :					Union[str, Any]=32 * 32     ,     __snake_case     :					List[Any]=5_12     ,     __snake_case     :					Any=24     ,     __snake_case     :					Optional[Any]=8     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					str="quick_gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					str=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Tuple=1E-5     ,     __snake_case     :					Optional[int]=0.02     ,     __snake_case     :					str=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					Dict=False     ,     __snake_case     :					Union[str, Any]=False     ,     **__snake_case     :					Union[str, Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				n_positions
            _lowerCAmelCase					=				n_embd
            _lowerCAmelCase					=				n_layer
            _lowerCAmelCase					=				n_head
            _lowerCAmelCase					=				n_inner
            _lowerCAmelCase					=				activation_function
            _lowerCAmelCase					=				resid_pdrop
            _lowerCAmelCase					=				embd_pdrop
            _lowerCAmelCase					=				attn_pdrop
            _lowerCAmelCase					=				layer_norm_epsilon
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scale_attn_weights
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				scale_attn_by_inverse_layer_idx
            _lowerCAmelCase					=				reorder_and_upcast_attn
            _lowerCAmelCase					=				tie_word_embeddings

            super().__init__(tie_word_embeddings=__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ]    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"FeatureExtractionMixin"     ,     __snake_case     :					int = 1     ,     __snake_case     :					int = -1     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional["TensorType"] = None     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     )							-> Mapping[str, Any]:
            _lowerCAmelCase					=				self._generate_dummy_images(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				dict(preprocessor(images=__snake_case     ,     return_tensors=__snake_case    )    )

            return inputs






