






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_ (    __snake_case     :							List[str]      )     -> List[str]:





				'''simple docstring'''


				return EnvironmentCommand()





def 					lowercase_ (    __snake_case     :							Tuple      )     -> Optional[int]:





				'''simple docstring'''


				return EnvironmentCommand(args.accelerate_config_file      )


class 					_snake_case						(			__UpperCAmelCase ):






	@staticmethod
	def    lowerCAmelCase_							(		UpperCamelCase       )						->    Dict:
					snake_case__      :int         =   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       )						->    List[Any]:
					snake_case__      :Union[str, Any]         =   accelerate_config_file






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :List[str]         =   "not installed"
					if is_safetensors_available():
									import safetensors

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

									snake_case__      :Any         =   f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

					snake_case__      :Any         =   "not installed"
					snake_case__      :Optional[int]         =   "not found"
					if is_accelerate_available():
									import accelerate
									from accelerate.commands.config import default_config_file, load_config_from_file

									snake_case__      :str         =   accelerate.__version__
									# Get the default from the config file.
									if self._accelerate_config_file is not None or os.path.isfile(UpperCamelCase       ):
													snake_case__      :Optional[Any]         =   load_config_from_file(self._accelerate_config_file       ).to_dict()

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

					snake_case__      :int         =   "not installed"
					snake_case__      :Tuple         =   "NA"
					if is_torch_available():
									import torch

									snake_case__      :Union[str, Any]         =   torch.__version__
									snake_case__      :int         =   torch.cuda.is_available()

					snake_case__      :Any         =   "not installed"
					snake_case__      :List[Any]         =   "NA"
					if is_tf_available():
									import tensorflow as tf

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

					snake_case__      :List[str]         =   "not installed"
					snake_case__      :int         =   "not installed"
					snake_case__      :List[str]         =   "not installed"
					snake_case__      :List[Any]         =   "NA"
					if is_flax_available():
									import flax
									import jax
									import jaxlib

									snake_case__      :Optional[int]         =   flax.__version__
									snake_case__      :str         =   jax.__version__
									snake_case__      :List[Any]         =   jaxlib.__version__
									snake_case__      :Optional[Any]         =   jax.lib.xla_bridge.get_backend().platform

					snake_case__      :str         =   {
					    "`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    lowerCAmelCase_							(		UpperCamelCase       )						->    Any:
					return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]       ) + "\n"







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 					_snake_case						(			_A       ,    _A       ,    _A ):






	@register_to_config
	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = False					,)						->    int:
					super().__init__()

					snake_case__      :Union[str, Any]         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )

					snake_case__      :int         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )
					snake_case__      :Any         =   False

					snake_case__      :List[Any]         =   nn.Dropout(p=UpperCamelCase       )

					snake_case__      :Tuple         =   TaConfig(
					    vocab_size=UpperCamelCase					,d_model=UpperCamelCase					,num_heads=UpperCamelCase					,d_kv=UpperCamelCase					,d_ff=UpperCamelCase					,dropout_rate=UpperCamelCase					,feed_forward_proj=UpperCamelCase					,is_decoder=UpperCamelCase					,is_encoder_decoder=UpperCamelCase					,)

					snake_case__      :List[str]         =   nn.ModuleList()
					for lyr_num in range(UpperCamelCase       ):
									snake_case__      :List[Any]         =   TaBlock(UpperCamelCase       )
									self.encoders.append(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   TaLayerNorm(UpperCamelCase       )
					snake_case__      :Any         =   nn.Dropout(p=UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :str         =   self.token_embedder(UpperCamelCase       )

					snake_case__      :int         =   encoder_input_tokens.shape[1]
					snake_case__      :List[Any]         =   torch.arange(UpperCamelCase					,device=encoder_input_tokens.device       )
					x += self.position_encoding(UpperCamelCase       )

					snake_case__      :Optional[int]         =   self.dropout_pre(UpperCamelCase       )

					# inverted the attention mask
					snake_case__      :Optional[Any]         =   encoder_input_tokens.size()
					snake_case__      :Dict         =   self.get_extended_attention_mask(UpperCamelCase					,UpperCamelCase       )

					for lyr in self.encoders:
									snake_case__      :str         =   lyr(UpperCamelCase					,UpperCamelCase       )[0]
					snake_case__      :List[Any]         =   self.layer_norm(UpperCamelCase       )

					return self.dropout_post(UpperCamelCase       ), encoder_inputs_mask