






'''simple docstring'''

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class        a_		(    lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   RobertaTokenizer
  lowercase  	=   RobertaTokenizerFast
  lowercase  	=   True
  lowercase  	=   {"""cls_token""": """<s>"""}

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCamelCase       						=      [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """<unk>""",
        ]
        UpperCamelCase       						=      dict(zip(_SCREAMING_SNAKE_CASE    ,	range(len(_SCREAMING_SNAKE_CASE   )   )   )   )
        UpperCamelCase       						=      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        UpperCamelCase       						=      {"""unk_token""": """<unk>"""}

        UpperCamelCase       						=      os.path.join(self.tmpdirname    ,	VOCAB_FILES_NAMES["""vocab_file"""]   )
        UpperCamelCase       						=      os.path.join(self.tmpdirname    ,	VOCAB_FILES_NAMES["""merges_file"""]   )
        with open(self.vocab_file    ,	"""w"""    ,	encoding="""utf-8"""   ) as fp:
              fp.write(json.dumps(_SCREAMING_SNAKE_CASE   ) + """\n"""   )
        with open(self.merges_file    ,	"""w"""    ,	encoding="""utf-8"""   ) as fp:
              fp.write("""\n""".join(_SCREAMING_SNAKE_CASE   )   )

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        kwargs.update(self.special_tokens_map   )
        return self.tokenizer_class.from_pretrained(self.tmpdirname    ,	**_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        kwargs.update(self.special_tokens_map   )
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,	**_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      """lower newer"""
        UpperCamelCase       						=      """lower newer"""
        return input_text, output_text

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.tokenizer_class(self.vocab_file    ,	self.merges_file    ,	**self.special_tokens_map   )
        UpperCamelCase       						=      """lower newer"""
        UpperCamelCase       						=      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
        UpperCamelCase       						=      tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )  # , add_prefix_space=True)
        self.assertListEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      tokens + [tokenizer.unk_token]
        UpperCamelCase       						=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_tokenizer()

        self.assertListEqual(tokenizer.encode("""Hello world!"""    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )    ,	[0, 31414, 232, 328, 2]   )
        self.assertListEqual(
            tokenizer.encode("""Hello world! cécé herlolip 418"""    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )    ,	[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]    ,	)

  @slow
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.tokenizer_class.from_pretrained("""roberta-base"""   )

        UpperCamelCase       						=      tokenizer.encode("""sequence builders"""    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.encode("""multi-sequence build"""    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      tokenizer.encode(
            """sequence builders"""    ,	add_special_tokens=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.encode(
            """sequence builders"""    ,	"""multi-sequence build"""    ,	add_special_tokens=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      """Encode this sequence."""
        UpperCamelCase       						=      tokenizer.byte_encoder[""" """.encode("""utf-8"""   )[0]]

        # Testing encoder arguments
        UpperCamelCase       						=      tokenizer.encode(_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
        self.assertNotEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      tokenizer.encode(_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
        self.assertEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        tokenizer.add_special_tokens({"""bos_token""": """<s>"""}   )
        UpperCamelCase       						=      tokenizer.encode(_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
        self.assertNotEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        # Testing spaces after special tokens
        UpperCamelCase       						=      """<mask>"""
        tokenizer.add_special_tokens(
            {"""mask_token""": AddedToken(_SCREAMING_SNAKE_CASE    ,	lstrip=_SCREAMING_SNAKE_CASE    ,	rstrip=_SCREAMING_SNAKE_CASE   )}   )  # mask token has a left space
        UpperCamelCase       						=      tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      """Encode <mask> sequence"""
        UpperCamelCase       						=      """Encode <mask>sequence"""

        UpperCamelCase       						=      tokenizer.encode(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      encoded.index(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
        self.assertEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      tokenizer.encode(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      encoded.index(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
        self.assertNotEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
                    UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      """A, <mask> AllenNLP sentence."""
                    UpperCamelCase       						=      tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE    ,	return_token_type_ids=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE    ,	return_token_type_ids=_SCREAMING_SNAKE_CASE   )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r["""token_type_ids"""]   )    ,	sum(tokens_p["""token_type_ids"""]   )   )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r["""attention_mask"""]   ) / len(tokens_r["""attention_mask"""]   )    ,	sum(tokens_p["""attention_mask"""]   ) / len(tokens_p["""attention_mask"""]   )    ,	)

                    UpperCamelCase       						=      tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]   )
                    UpperCamelCase       						=      tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]   )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p["""input_ids"""]    ,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )
                    self.assertSequenceEqual(tokens_r["""input_ids"""]    ,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )

                    self.assertSequenceEqual(
                        _SCREAMING_SNAKE_CASE    ,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )
                    self.assertSequenceEqual(
                        _SCREAMING_SNAKE_CASE    ,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        for trim_offsets, add_prefix_space in itertools.product([True, False]    ,	repeat=2   ):
              UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(
                  self.tmpdirname    ,	use_fast=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE    ,	trim_offsets=_SCREAMING_SNAKE_CASE   )

              UpperCamelCase       						=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
              UpperCamelCase       						=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()   )

              self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]    ,	_SCREAMING_SNAKE_CASE   )

              self.assertEqual(post_processor_state["""add_prefix_space"""]    ,	_SCREAMING_SNAKE_CASE   )
              self.assertEqual(post_processor_state["""trim_offsets"""]    ,	_SCREAMING_SNAKE_CASE   )


  def 						A__		(   self   )						->   str:



        """simple docstring"""






        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
                    UpperCamelCase       						=      """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                    UpperCamelCase       						=      F"{text_of_1_token} {text_of_1_token}"

                    UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE    ,	use_fast=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE    ,	trim_offsets=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      tokenizer_r(_SCREAMING_SNAKE_CASE    ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
                    self.assertEqual(encoding.offset_mapping[0]    ,	(0, len(_SCREAMING_SNAKE_CASE   ))   )
                    self.assertEqual(
                        encoding.offset_mapping[1]    ,	(len(_SCREAMING_SNAKE_CASE   ) + 1, len(_SCREAMING_SNAKE_CASE   ) + 1 + len(_SCREAMING_SNAKE_CASE   ))    ,	)

                    UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE    ,	use_fast=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE    ,	trim_offsets=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      tokenizer_r(_SCREAMING_SNAKE_CASE    ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
                    self.assertEqual(encoding.offset_mapping[0]    ,	(0, len(_SCREAMING_SNAKE_CASE   ))   )
                    self.assertEqual(
                        encoding.offset_mapping[1]    ,	(len(_SCREAMING_SNAKE_CASE   ) + 1, len(_SCREAMING_SNAKE_CASE   ) + 1 + len(_SCREAMING_SNAKE_CASE   ))    ,	)

                    UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE    ,	use_fast=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE    ,	trim_offsets=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      tokenizer_r(_SCREAMING_SNAKE_CASE    ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
                    self.assertEqual(encoding.offset_mapping[0]    ,	(0, len(_SCREAMING_SNAKE_CASE   ))   )
                    self.assertEqual(
                        encoding.offset_mapping[1]    ,	(len(_SCREAMING_SNAKE_CASE   ), len(_SCREAMING_SNAKE_CASE   ) + 1 + len(_SCREAMING_SNAKE_CASE   ))    ,	)

                    UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE    ,	use_fast=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE    ,	trim_offsets=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      tokenizer_r(_SCREAMING_SNAKE_CASE    ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
                    self.assertEqual(encoding.offset_mapping[0]    ,	(0, len(_SCREAMING_SNAKE_CASE   ))   )
                    self.assertEqual(
                        encoding.offset_mapping[1]    ,	(len(_SCREAMING_SNAKE_CASE   ), len(_SCREAMING_SNAKE_CASE   ) + 1 + len(_SCREAMING_SNAKE_CASE   ))    ,	)

                    UpperCamelCase       						=      F" {text}"

                    # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                    # )
                    # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                    # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                    # self.assertEqual(
                    #     encoding.offset_mapping[1],
                    #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                    # )

                    UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE    ,	use_fast=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE    ,	trim_offsets=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      tokenizer_r(_SCREAMING_SNAKE_CASE    ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
                    self.assertEqual(encoding.offset_mapping[0]    ,	(1, 1 + len(_SCREAMING_SNAKE_CASE   ))   )
                    self.assertEqual(
                        encoding.offset_mapping[1]    ,	(1 + len(_SCREAMING_SNAKE_CASE   ) + 1, 1 + len(_SCREAMING_SNAKE_CASE   ) + 1 + len(_SCREAMING_SNAKE_CASE   ))    ,	)

                    UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE    ,	use_fast=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE    ,	trim_offsets=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      tokenizer_r(_SCREAMING_SNAKE_CASE    ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
                    self.assertEqual(encoding.offset_mapping[0]    ,	(0, 1 + len(_SCREAMING_SNAKE_CASE   ))   )
                    self.assertEqual(
                        encoding.offset_mapping[1]    ,	(1 + len(_SCREAMING_SNAKE_CASE   ), 1 + len(_SCREAMING_SNAKE_CASE   ) + 1 + len(_SCREAMING_SNAKE_CASE   ))    ,	)

                    UpperCamelCase       						=      self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE    ,	use_fast=_SCREAMING_SNAKE_CASE    ,	add_prefix_space=_SCREAMING_SNAKE_CASE    ,	trim_offsets=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      tokenizer_r(_SCREAMING_SNAKE_CASE    ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
                    self.assertEqual(encoding.offset_mapping[0]    ,	(0, 1 + len(_SCREAMING_SNAKE_CASE   ))   )
                    self.assertEqual(
                        encoding.offset_mapping[1]    ,	(1 + len(_SCREAMING_SNAKE_CASE   ), 1 + len(_SCREAMING_SNAKE_CASE   ) + 1 + len(_SCREAMING_SNAKE_CASE   ))    ,	)








'''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 					lowercase__				(   __UpperCamelCase					)->				str:
      return EnvironmentCommand()







def 					lowercase__				(   __UpperCamelCase					)->				str:
      return EnvironmentCommand(args.accelerate_config_file					)






class        a_		(    lowerCamelCase       ):

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






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

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      accelerate_config_file

  def 						A__		(   self   )						->   List[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(_SCREAMING_SNAKE_CASE   ):
                    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(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
                  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(_SCREAMING_SNAKE_CASE   )   )

        return info


  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






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