




'''simple docstring'''






import os

import pytest
from attr import dataclass


A_			      =		'us-east-1'  # defaults region






@dataclass
class        lowercase_      :
     A_											=			4_2
     A_											=			"arn:aws:iam::558105141721:role/sagemaker_execution_role"
     A_											=			{
         "task_name": "mnli",
         "per_device_train_batch_size": 1_6,
         "per_device_eval_batch_size": 1_6,
         "do_train": True,
         "do_eval": True,
         "do_predict": True,
         "output_dir": "/opt/ml/model",
         "overwrite_output_dir": True,
         "max_steps": 5_0_0,
         "save_steps": 5_5_0_0,
     }
     A_											=			{**hyperparameters, "max_steps": 1_0_0_0}





     @property
     def 			_lowerCAmelCase      (     self     :					int			):






           if self.framework == "pytorch":
                 return [
                     {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                     {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                     {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
                 ]
           else:
                 return [
                     {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                     {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                     {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
                 ]





     @property
     def 			_lowerCAmelCase      (     self     :					Any			):
           return F"{self.framework}-transfromers-test"





     @property
     def 			_lowerCAmelCase      (     self     :					int			):
           return F"./tests/sagemaker/scripts/{self.framework}"





     @property
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):






           if self.framework == "pytorch":
                 return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
           else:
                 return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"






@pytest.fixture(scope='class'		)
def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   Tuple	      =    SageMakerTestEnvironment(framework=request.cls.framework		)


'''simple docstring'''
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					:    Tuple           = TaTokenizer

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

lowerCAmelCase					:    Optional[int]           = TaTokenizerFast

lowerCAmelCase					:    Any           = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

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

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

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[Any]           = ['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					:    Tuple           = _LazyModule(
						    __name__,
						    globals()['__file__'],
						    _import_structure,
						    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
						    module_spec=__spec__,
						)




