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       ):



							"""simple docstring"""


							return EnvironmentCommand()


def        __lowerCAmelCase      (	_A       ):



							"""simple docstring"""


							return EnvironmentCommand(args.accelerate_config_file       )







class _lowercase       (							_UpperCAmelCase    ):





							"""simple docstring"""




							@staticmethod
							def      _UpperCAmelCase							(     UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						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  , UpperCAmelCase  , *UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						accelerate_config_file



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






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

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

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

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

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

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

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

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

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

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

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

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

														_lowercase					=						{
														    """`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      _UpperCAmelCase							(     UpperCAmelCase	):







														'''simple docstring'''






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









'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


a__					:			str          =     logging.get_logger(__name__)

a__					:			Any          =     {
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''xlm'''
	__lowerCAmelCase       						=  {
	    '''hidden_size''': '''emb_dim''',
	    '''num_attention_heads''': '''n_heads''',
	    '''num_hidden_layers''': '''n_layers''',
	    '''n_words''': '''vocab_size''',  # For backward compatibility
	}





	def __init__(		self      ,       UpperCamelCase_=3_0145      ,       UpperCamelCase_=2048      ,       UpperCamelCase_=12      ,       UpperCamelCase_=16      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=True      ,       UpperCamelCase_=False      ,       UpperCamelCase_=False      ,       UpperCamelCase_=False      ,       UpperCamelCase_=1      ,       UpperCamelCase_=True      ,       UpperCamelCase_=512      ,       UpperCamelCase_=2048**-0.5      ,       UpperCamelCase_=1E-1_2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=0      ,       UpperCamelCase_=1      ,       UpperCamelCase_=2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=5      ,       UpperCamelCase_=True      ,       UpperCamelCase_="first"      ,       UpperCamelCase_=True      ,       UpperCamelCase_=None      ,       UpperCamelCase_=True      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=5      ,       UpperCamelCase_=5      ,       UpperCamelCase_=0      ,       UpperCamelCase_=0      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0      ,       **UpperCamelCase_      ,       )      ->		List[str]:
		snake_case__	       =   vocab_size
		snake_case__	       =   emb_dim
		snake_case__	       =   n_layers
		snake_case__	       =   n_heads
		snake_case__	       =   dropout
		snake_case__	       =   attention_dropout
		snake_case__	       =   gelu_activation
		snake_case__	       =   sinusoidal_embeddings
		snake_case__	       =   causal
		snake_case__	       =   asm
		snake_case__	       =   n_langs
		snake_case__	       =   use_lang_emb
		snake_case__	       =   layer_norm_eps
		snake_case__	       =   bos_index
		snake_case__	       =   eos_index
		snake_case__	       =   pad_index
		snake_case__	       =   unk_index
		snake_case__	       =   mask_index
		snake_case__	       =   is_encoder
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   embed_init_std
		snake_case__	       =   init_std
		snake_case__	       =   summary_type
		snake_case__	       =   summary_use_proj
		snake_case__	       =   summary_activation
		snake_case__	       =   summary_proj_to_labels
		snake_case__	       =   summary_first_dropout
		snake_case__	       =   start_n_top
		snake_case__	       =   end_n_top
		snake_case__	       =   mask_token_id
		snake_case__	       =   lang_id

		if "n_words" in kwargs:
			snake_case__	       =   kwargs['n_words']

		super().__init__(pad_token_id=UpperCamelCase_      ,       bos_token_id=UpperCamelCase_      ,       **UpperCamelCase_		)






class 				__snake_case     (					__magic_name__							):





	@property
	def   _snake_case   (		self		)      ->		Mapping[str, Mapping[int, str]]:
		if self.task == "multiple-choice":
			snake_case__	       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
		else:
			snake_case__	       =   {0: 'batch', 1: 'sequence'}
		return OrderedDict(
		    [
		        ('input_ids', dynamic_axis),
		        ('attention_mask', dynamic_axis),
		        ('token_type_ids', dynamic_axis),
		    ]		)



