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    lowerCAmelCase_ (     __A							)     ->				Any:







      '''simple docstring'''


      return EnvironmentCommand()




def    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      return EnvironmentCommand(args.accelerate_config_file							)







class      A							(				UpperCAmelCase_     ):


   @staticmethod
   def 			lowercase_      (__UpperCAmelCase     :						ArgumentParser		)						->				List[Any]:
         """simple docstring"""



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


   def __init__(self     :						Optional[int]       ,  __UpperCAmelCase     :						str       ,  *__UpperCAmelCase     :						Tuple		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  accelerate_config_file


   def 			lowercase_      (self     :						List[Any]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  "not installed"
         if is_safetensors_available():
               import safetensors

               UpperCAmelCase__								=  safetensors.__version__
         elif importlib.util.find_spec("safetensors"		) is not None:
               import safetensors

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

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

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

               UpperCAmelCase__								=  (
                   "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]		)
                   if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		)
                   else f"""\t{accelerate_config}"""
               )

         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "NA"
         if is_torch_available():
               import torch

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

         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "NA"
         if is_tf_available():
               import tensorflow as tf

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

         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "NA"
         if is_flax_available():
               import flax
               import jax
               import jaxlib

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

         UpperCAmelCase__								=  {
             "`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(__UpperCAmelCase		)		)

         return info





   @staticmethod
   def 			lowercase_      (__UpperCAmelCase     :						Dict		)						->				Optional[int]:
         """simple docstring"""



         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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase							:  Any   	=						TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  int   	=						[
						    """MT5EncoderModel""",
						    """MT5ForConditionalGeneration""",
						    """MT5ForQuestionAnswering""",
						    """MT5Model""",
						    """MT5PreTrainedModel""",
						    """MT5Stack""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Tuple   	=						["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase							:  Optional[Any]   	=						_LazyModule(
						    __name__,
						    globals()["""__file__"""],
						    _import_structure,
						    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
						    module_spec=__spec__,
						)






