



"""simple docstring"""


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 			_a (					lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""



			@register_to_config
			def __init__(		self     :    Any				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    float				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    bool = False				,   )->Union[str, Any]:
							super().__init__()

							_UpperCAmelCase					=    nn.Embedding(__UpperCamelCase				,   __UpperCamelCase   )

							_UpperCAmelCase					=    nn.Embedding(__UpperCamelCase				,   __UpperCamelCase   )
							_UpperCAmelCase					=    False

							_UpperCAmelCase					=    nn.Dropout(p=__UpperCamelCase   )

							_UpperCAmelCase					=    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				,   )

							_UpperCAmelCase					=    nn.ModuleList()
							for lyr_num in range(__UpperCamelCase   ):
											_UpperCAmelCase					=    TaBlock(__UpperCamelCase   )
											self.encoders.append(__UpperCamelCase   )

							_UpperCAmelCase					=    TaLayerNorm(__UpperCamelCase   )
							_UpperCAmelCase					=    nn.Dropout(p=__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    self.token_embedder(__UpperCamelCase   )

							_UpperCAmelCase					=    encoder_input_tokens.shape[1]
							_UpperCAmelCase					=    torch.arange(__UpperCamelCase				,   device=encoder_input_tokens.device   )
							x += self.position_encoding(__UpperCamelCase   )

							_UpperCAmelCase					=    self.dropout_pre(__UpperCamelCase   )

							# inverted the attention mask
							_UpperCAmelCase					=    encoder_input_tokens.size()
							_UpperCAmelCase					=    self.get_extended_attention_mask(__UpperCamelCase				,   __UpperCamelCase   )

							for lyr in self.encoders:
											_UpperCAmelCase					=    lyr(__UpperCamelCase				,   __UpperCamelCase   )[0]
							_UpperCAmelCase					=    self.layer_norm(__UpperCamelCase   )

							return self.dropout_post(__UpperCamelCase   ), encoder_inputs_mask








"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple   )->int:
							_UpperCAmelCase					=    inspect.getfile(accelerate.test_utils   )
							_UpperCAmelCase					=    os.path.sep.join(
							    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']   )

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							_UpperCAmelCase					=    test_metrics



			@require_cpu
			def  lowercase__						(		self     :    Any   )->int:
							debug_launcher(self.test_metrics.main				,   num_processes=1   )



			@require_cpu
			def  lowercase__						(		self     :    List[str]   )->List[str]:
							debug_launcher(self.test_metrics.main   )



			@require_single_gpu
			def  lowercase__						(		self     :    List[Any]   )->Dict:
							self.test_metrics.main()



			@require_multi_gpu
			def  lowercase__						(		self     :    str   )->int:
							print(F'Found {torch.cuda.device_count()} devices.'   )
							_UpperCAmelCase					=    ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
							with patch_environment(omp_num_threads=1   ):
											execute_subprocess_async(__UpperCamelCase				,   env=os.environ.copy()   )



