



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
							from sagemaker import Session, TrainingJobAnalytics
							from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''				,						'''False'''						)						) is not True				,						reason='''Skipping test because should only be run when releasing minor transformers version'''				,						)
@pytest.mark.usefixtures('''sm_env'''						)
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]						)
class 			__lowerCAmelCase    (			unittest.TestCase						):



			def     _lowerCamelCase       (						self   :  Dict)   ->    Any:



						"""simple docstring"""






						if self.framework == "pytorch":
									subprocess.run(
									    F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split() ,		encoding='utf-8' ,		check=_SCREAMING_SNAKE_CASE ,		)
						assert hasattr(self ,		'env')



			def     _lowerCamelCase       (						self   :  Any ,		A   :  List[str])   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   {
						    '''enabled''': True,
						    '''processes_per_host''': 8,
						}
						_UpperCAmelCase		       =   {
						    '''enabled''': True,
						    '''parameters''': {
						        '''microbatches''': 4,
						        '''placement_strategy''': '''spread''',
						        '''pipeline''': '''interleaved''',
						        '''optimize''': '''speed''',
						        '''partitions''': 4,
						        '''ddp''': True,
						    },
						}

						_UpperCAmelCase		       =   {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

						_UpperCAmelCase		       =   '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
						# creates estimator
						return HuggingFace(
						    entry_point=self.script ,		source_dir=self.env.test_path ,		role=self.env.role ,		image_uri=self.env.image_uri ,		base_job_name=F"{self.env.base_job_name}-{instance_count}-smp-{name_extension}" ,		instance_count=_SCREAMING_SNAKE_CASE ,		instance_type=self.instance_type ,		debugger_hook_config=_SCREAMING_SNAKE_CASE ,		hyperparameters={
						        **self.env.hyperparameters,
						        'model_name_or_path': self.model_name_or_path,
						        'max_steps': 5_00,
						    } ,		metric_definitions=self.env.metric_definitions ,		distribution=_SCREAMING_SNAKE_CASE ,		py_version='py36' ,		)



			def     _lowerCamelCase       (						self   :  int ,		A   :  Any)   ->    List[str]:



						"""simple docstring"""






						TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")



			@parameterized.expand([(1,)])
			def     _lowerCamelCase       (						self   :  Optional[Any] ,		A   :  Optional[int])   ->    Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.create_estimator(_SCREAMING_SNAKE_CASE)

						# run training
						estimator.fit()

						# result dataframe
						_UpperCAmelCase		       =   TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

						# extract kpis
						_UpperCAmelCase		       =   list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
						_UpperCAmelCase		       =   list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						_UpperCAmelCase		       =   (
						    Session().describe_training_job(estimator.latest_training_job.name).get('TrainingTimeInSeconds' ,		99_99_99)
						)

						# assert kpis
						assert train_runtime <= self.results["train_runtime"]
						assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy)
						assert all(t <= self.results['eval_loss'] for t in eval_loss)

						# dump tests result into json file to share in PR
						with open(F"{estimator.latest_training_job.name}.json" ,		'w') as outfile:
									json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} ,		_SCREAMING_SNAKE_CASE)




from typing import Mapping

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


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "t5"
       snake_case      =    ["past_key_values"]
       snake_case      =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3_2128				,       _SCREAMING_SNAKE_CASE=512				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=2048				,       _SCREAMING_SNAKE_CASE=6				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=8				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=128				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE="relu"				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=1				,       **_SCREAMING_SNAKE_CASE				,       )->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					vocab_size
         A_      :				int		     =					d_model
         A_      :				Optional[Any]		     =					d_kv
         A_      :				str		     =					d_ff
         A_      :				int		     =					num_layers
         A_      :				Any		     =					(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A_      :				Optional[Any]		     =					num_heads
         A_      :				Union[str, Any]		     =					relative_attention_num_buckets
         A_      :				Dict		     =					relative_attention_max_distance
         A_      :				List[str]		     =					dropout_rate
         A_      :				Dict		     =					layer_norm_epsilon
         A_      :				str		     =					initializer_factor
         A_      :				Dict		     =					feed_forward_proj
         A_      :				int		     =					use_cache

         A_      :				Optional[int]		     =					self.feed_forward_proj.split('''-'''		)
         A_      :				Optional[Any]		     =					act_info[-1]
         A_      :				Optional[Any]		     =					act_info[0] == '''gated'''

         if len(_SCREAMING_SNAKE_CASE		) > 1 and act_info[0] != "gated" or len(_SCREAMING_SNAKE_CASE		) > 2:
           raise ValueError(
               F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
               '''\'gated-gelu\' or \'relu\''''		)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
           A_      :				Tuple		     =					'''gelu_new'''

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE				,       is_encoder_decoder=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       @property
       def        _snake_case       (      self		)->Mapping[str, Mapping[int, str]]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					{
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
           A_      :				List[str]		     =					'''past_encoder_sequence + sequence'''
           A_      :				Optional[int]		     =					{0: '''batch'''}
           A_      :				str		     =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
           A_      :				Optional[int]		     =					{0: '''batch''', 1: '''decoder_sequence'''}
           A_      :				List[Any]		     =					{0: '''batch''', 1: '''decoder_sequence'''}

         if self.use_past:
           self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE				,       direction='''inputs'''		)

         return common_inputs







       @property
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         return 13




