'''simple docstring'''




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   a__				(       unittest.TestCase			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """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=a  ,    )
         assert hasattr(self  ,    '''env'''   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		int   ):





         """simple docstring"""



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

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

         __lowerCamelCase          =  '''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=a  ,    instance_type=self.instance_type  ,    debugger_hook_config=a  ,    hyperparameters={
                 **self.env.hyperparameters,
                 '''model_name_or_path''': self.model_name_or_path,
                 '''max_steps''': 5_00,
             }  ,    metric_definitions=self.env.metric_definitions  ,    distribution=a  ,    py_version='''py36'''  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		Dict   ):





         """simple docstring"""



         TrainingJobAnalytics(a   ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""   )






     @parameterized.expand([(1,)]   )
     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  self.create_estimator(a   )

         # run training
         estimator.fit()

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

         # extract kpis
         __lowerCamelCase          =  list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']   )
         __lowerCamelCase          =  list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']   )
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         __lowerCamelCase          =  (
             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}  ,    a   )

'''simple docstring'''




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={
    "microsoft/unispeech-sat-base-100h-libri-ft": (
        "https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					List[Any]							="unispeech-sat"






     def __init__(			self		:		Dict  ,    a		:		str=32  ,    a		:		Any=7_68  ,    a		:		Optional[Any]=12  ,    a		:		Optional[int]=12  ,    a		:		int=30_72  ,    a		:		int="gelu"  ,    a		:		Dict=0.1  ,    a		:		Dict=0.1  ,    a		:		List[Any]=0.1  ,    a		:		Tuple=0.0  ,    a		:		Optional[Any]=0.0  ,    a		:		Tuple=0.1  ,    a		:		List[Any]=0.1  ,    a		:		str=0.02  ,    a		:		List[Any]=1e-5  ,    a		:		int="group"  ,    a		:		Union[str, Any]="gelu"  ,    a		:		Optional[int]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)  ,    a		:		List[Any]=(5, 2, 2, 2, 2, 2, 2)  ,    a		:		int=(10, 3, 3, 3, 3, 2, 2)  ,    a		:		Optional[Any]=False  ,    a		:		Any=1_28  ,    a		:		Tuple=16  ,    a		:		str=False  ,    a		:		Optional[Any]=True  ,    a		:		Dict=0.05  ,    a		:		List[Any]=10  ,    a		:		Any=2  ,    a		:		Optional[Any]=0.0  ,    a		:		Optional[Any]=10  ,    a		:		Any=0  ,    a		:		Any=3_20  ,    a		:		str=2  ,    a		:		List[str]=0.1  ,    a		:		List[str]=1_00  ,    a		:		List[str]=2_56  ,    a		:		str=2_56  ,    a		:		Dict=0.1  ,    a		:		Optional[Any]="mean"  ,    a		:		str=False  ,    a		:		Tuple=False  ,    a		:		Optional[Any]=2_56  ,    a		:		int=(5_12, 5_12, 5_12, 5_12, 15_00)  ,    a		:		int=(5, 3, 3, 1, 1)  ,    a		:		Any=(1, 2, 3, 1, 1)  ,    a		:		Union[str, Any]=5_12  ,    a		:		Optional[int]=0  ,    a		:		Optional[int]=1  ,    a		:		Optional[int]=2  ,    a		:		int=5_04  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         super().__init__(**a  ,    pad_token_id=a  ,    bos_token_id=a  ,    eos_token_id=a   )
         __lowerCamelCase          =  hidden_size
         __lowerCamelCase          =  feat_extract_norm
         __lowerCamelCase          =  feat_extract_activation
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  conv_bias
         __lowerCamelCase          =  num_conv_pos_embeddings
         __lowerCamelCase          =  num_conv_pos_embedding_groups
         __lowerCamelCase          =  len(self.conv_dim   )
         __lowerCamelCase          =  num_hidden_layers
         __lowerCamelCase          =  intermediate_size
         __lowerCamelCase          =  hidden_act
         __lowerCamelCase          =  num_attention_heads
         __lowerCamelCase          =  hidden_dropout
         __lowerCamelCase          =  attention_dropout
         __lowerCamelCase          =  activation_dropout
         __lowerCamelCase          =  feat_proj_dropout
         __lowerCamelCase          =  final_dropout
         __lowerCamelCase          =  layerdrop
         __lowerCamelCase          =  layer_norm_eps
         __lowerCamelCase          =  initializer_range
         __lowerCamelCase          =  vocab_size
         __lowerCamelCase          =  num_clusters
         __lowerCamelCase          =  do_stable_layer_norm
         __lowerCamelCase          =  use_weighted_layer_sum

         if (
             (len(self.conv_stride   ) != self.num_feat_extract_layers)
             or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
             or (len(self.conv_dim   ) != self.num_feat_extract_layers)
         ):
             raise ValueError(
                 '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                 ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                 f""" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"""
                 f""" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __lowerCamelCase          =  apply_spec_augment
         __lowerCamelCase          =  mask_time_prob
         __lowerCamelCase          =  mask_time_length
         __lowerCamelCase          =  mask_time_min_masks
         __lowerCamelCase          =  mask_feature_prob
         __lowerCamelCase          =  mask_feature_length
         __lowerCamelCase          =  mask_feature_min_masks

         # parameters for pretraining with codevector quantized representations
         __lowerCamelCase          =  num_codevectors_per_group
         __lowerCamelCase          =  num_codevector_groups
         __lowerCamelCase          =  contrastive_logits_temperature
         __lowerCamelCase          =  feat_quantizer_dropout
         __lowerCamelCase          =  num_negatives
         __lowerCamelCase          =  codevector_dim
         __lowerCamelCase          =  proj_codevector_dim
         __lowerCamelCase          =  diversity_loss_weight

         # ctc loss
         __lowerCamelCase          =  ctc_loss_reduction
         __lowerCamelCase          =  ctc_zero_infinity

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         __lowerCamelCase          =  classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  xvector_output_dim






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return functools.reduce(operator.mul  ,    self.conv_stride  ,    1   )
