



"""simple docstring"""





# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE					:  int        =						'''facebook/wmt19-en-de'''
SCREAMING_SNAKE_CASE					:  Dict        =						FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE					:  Optional[int]        =						FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

SCREAMING_SNAKE_CASE					:  Dict        =						FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE					:  int        =						tokenizer(['''Making tiny model'''], return_tensors='''pt''')
SCREAMING_SNAKE_CASE					:  Optional[Any]        =						tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE					:  Optional[Any]        =						'''tiny-wmt19-en-de'''
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de






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

import pytest
from parameterized import 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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]	)
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """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 					__snake_case      (		self   ,				a__=1):



             """simple docstring"""





             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}-single"""   ,				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}   ,				metric_definitions=self.env.metric_definitions   ,				py_version='''py36'''   ,				)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





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



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.create_estimator()

             # run training
             estimator.fit()

             # result dataframe
             _lowerCamelCase				:  Tuple						=		TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

             # extract kpis
             _lowerCamelCase				:  Tuple						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
             _lowerCamelCase				:  Dict						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
             # get train time from SageMaker job, this includes starting, preprocessing, stopping
             _lowerCamelCase				:  Optional[Any]						=		(
                 Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''   ,				99_9999)
             )

             # 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__)

