


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				)    ->				Union[str, Any]:
  return EnvironmentCommand()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[str]:
  return EnvironmentCommand(args.accelerate_config_file				)


class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:ArgumentParser   ):
       snake_case__						= 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:List[str]     ,		_a:Dict     ,		*_a:Dict   ):
       snake_case__						= accelerate_config_file

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= '''not installed'''
       if is_safetensors_available():
         import safetensors

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

         snake_case__						= F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

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

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

         snake_case__						= (
             '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]   )
             if isinstance(_a     ,		_a   )
             else F"""\t{accelerate_config}"""
         )

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

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

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

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

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

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

       snake_case__						= {
           '''`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 	SCREAMING_SNAKE_CASE__						(					_a:Tuple   ):
       return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]   ) + "\n"





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__							:						Tuple							     =   {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Tuple							     =   ["""RobertaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Optional[int]							     =   [
       """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RobertaForCausalLM""",
       """RobertaForMaskedLM""",
       """RobertaForMultipleChoice""",
       """RobertaForQuestionAnswering""",
       """RobertaForSequenceClassification""",
       """RobertaForTokenClassification""",
       """RobertaModel""",
       """RobertaPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						List[str]							     =   [
       """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFRobertaForCausalLM""",
       """TFRobertaForMaskedLM""",
       """TFRobertaForMultipleChoice""",
       """TFRobertaForQuestionAnswering""",
       """TFRobertaForSequenceClassification""",
       """TFRobertaForTokenClassification""",
       """TFRobertaMainLayer""",
       """TFRobertaModel""",
       """TFRobertaPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						str							     =   [
       """FlaxRobertaForCausalLM""",
       """FlaxRobertaForMaskedLM""",
       """FlaxRobertaForMultipleChoice""",
       """FlaxRobertaForQuestionAnswering""",
       """FlaxRobertaForSequenceClassification""",
       """FlaxRobertaForTokenClassification""",
       """FlaxRobertaModel""",
       """FlaxRobertaPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
   from .tokenization_roberta import RobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roberta_fast import RobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta import (
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaForCausalLM,
          RobertaForMaskedLM,
          RobertaForMultipleChoice,
          RobertaForQuestionAnswering,
          RobertaForSequenceClassification,
          RobertaForTokenClassification,
          RobertaModel,
          RobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta import (
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaForCausalLM,
          TFRobertaForMaskedLM,
          TFRobertaForMultipleChoice,
          TFRobertaForQuestionAnswering,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
          FlaxRobertaPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase__							:						List[Any]							     =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

