




import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE_						:					int			   =       [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-generation''',
        '''text-classification''',
        '''token-classification''',
        '''language-modeling''',
        '''multiple-choice''',
        '''question-answering''',
        '''summarization''',
        '''translation''',
        '''image-classification''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
 import run_clm
 import run_image_classification
 import run_mlm
 import run_ner
 import run_qa as run_squad
 import run_summarization
 import run_swag
 import run_text_classification
 import run_translation


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE_						:					List[Any]			   =       logging.getLogger()
def        __lowerCAmelCase ():
    a__      =       argparse.ArgumentParser()
    parser.add_argument("""-f"""		)
    a__      =       parser.parse_args()
    return args.f
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       {}
    a__      =       os.path.join(UpperCAmelCase_   ,    """all_results.json"""		)
    if os.path.exists(UpperCAmelCase_		):
        with open(UpperCAmelCase_   ,    """r"""		) as f:
            a__      =       json.load(UpperCAmelCase_		)
    else:
        raise ValueError(F"""can't find {path}"""		)
    return results
def        __lowerCAmelCase ():
    return bool(tf.config.list_physical_devices("""GPU"""		)		)


SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     @skip("""Skipping until shape inference for to_tf_dataset PR is merged."""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Optional[int]:






         """simple docstring"""
         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

         if is_cuda_available():
             testargs.append("""--fp16"""    )

         with patch.object(A						,				"""argv"""						,				A    ):
             run_text_classification.main()
             # Reset the mixed precision policy so we don't break other tests
             tf.keras.mixed_precision.set_global_policy("""float32"""    )
             a__      =       get_results(A    )
             self.assertGreaterEqual(result["""eval_accuracy"""]						,				0.7_5    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			List[str]:






         """simple docstring"""
         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

         if len(tf.config.list_physical_devices("""GPU"""    )    ) > 1:
             # Skipping because there are not enough batches to train the model + would need a drop_last to work.
             return

         with patch.object(A						,				"""argv"""						,				A    ):
             run_clm.main()
             a__      =       get_results(A    )
             self.assertLess(result["""eval_perplexity"""]						,				1_00    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			List[str]:






         """simple docstring"""
         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

         with patch.object(A						,				"""argv"""						,				A    ):
             run_mlm.main()
             a__      =       get_results(A    )
             self.assertLess(result["""eval_perplexity"""]						,				42    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			str:






         """simple docstring"""
         # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
         a__      =       7 if get_gpu_count() > 1 else 2

         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

         with patch.object(A						,				"""argv"""						,				A    ):
             run_ner.main()
             a__      =       get_results(A    )
             self.assertGreaterEqual(result["""accuracy"""]						,				0.7_5    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Tuple:






         """simple docstring"""
         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

         with patch.object(A						,				"""argv"""						,				A    ):
             run_squad.main()
             a__      =       get_results(A    )
             self.assertGreaterEqual(result["""f1"""]						,				30    )
             self.assertGreaterEqual(result["""exact"""]						,				30    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			int:






         """simple docstring"""
         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

         with patch.object(A						,				"""argv"""						,				A    ):
             run_swag.main()
             a__      =       get_results(A    )
             self.assertGreaterEqual(result["""val_accuracy"""]						,				0.8    )

     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			int:






         """simple docstring"""
         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

         with patch.object(A						,				"""argv"""						,				A    ):
             run_summarization.main()
             a__      =       get_results(A    )
             self.assertGreaterEqual(result["""rouge1"""]						,				10    )
             self.assertGreaterEqual(result["""rouge2"""]						,				2    )
             self.assertGreaterEqual(result["""rougeL"""]						,				7    )
             self.assertGreaterEqual(result["""rougeLsum"""]						,				7    )

     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Union[str, Any]:






         """simple docstring"""
         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

         with patch.object(A						,				"""argv"""						,				A    ):
             run_translation.main()
             a__      =       get_results(A    )
             self.assertGreaterEqual(result["""bleu"""]						,				30    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			Tuple:






         """simple docstring"""
         a__      =       self.get_auto_remove_tmp_dir()
         a__      =       f"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

         with patch.object(A						,				"""argv"""						,				A    ):
             run_image_classification.main()
             a__      =       get_results(A    )
             self.assertGreaterEqual(result["""accuracy"""]						,				0.7    )












import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    """tf"""
     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Zone of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of Cloud TPU-enabled project"""}       ,	)

     __UpperCAmelCase	: float						=    field(
         default=1.0       ,	metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}       ,	)

     __UpperCAmelCase	: bool						=    field(default=lowercase__       ,	metadata={"""help""": """Whether to activate the XLA compilation or not"""} )

     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple["tf.distribute.Strategy", int]:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         logger.info("""Tensorflow: setting up strategy"""    )

         a__      =       tf.config.list_physical_devices("""GPU"""    )

         # Set to float16 at first
         if self.fpaa:
             tf.keras.mixed_precision.set_global_policy("""mixed_float16"""    )

         if self.no_cuda:
             a__      =       tf.distribute.OneDeviceStrategy(device="""/cpu:0"""    )
         else:
             try:
                 if self.tpu_name:
                     a__      =       tf.distribute.cluster_resolver.TPUClusterResolver(
                         self.tpu_name						,				zone=self.tpu_zone						,				project=self.gcp_project    )
                 else:
                     a__      =       tf.distribute.cluster_resolver.TPUClusterResolver()
             except ValueError:
                 if self.tpu_name:
                     raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""    )
                 else:
                     a__      =       None

             if tpu:
                 # Set to bfloat16 in case of TPU
                 if self.fpaa:
                     tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""    )

                 tf.config.experimental_connect_to_cluster(A    )
                 tf.tpu.experimental.initialize_tpu_system(A    )

                 a__      =       tf.distribute.TPUStrategy(A    )

             elif len(A    ) == 0:
                 a__      =       tf.distribute.OneDeviceStrategy(device="""/cpu:0"""    )
             elif len(A    ) == 1:
                 a__      =       tf.distribute.OneDeviceStrategy(device="""/gpu:0"""    )
             elif len(A    ) > 1:
                 # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                 a__      =       tf.distribute.MirroredStrategy()
             else:
                 raise ValueError("""Cannot find the proper strategy, please check your environment properties."""    )

         return strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			"tf.distribute.Strategy":






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy.num_replicas_in_sync

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






         """simple docstring"""
         return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			int:






         """simple docstring"""
         if self.per_gpu_train_batch_size:
             logger.warning(
                 """Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future """
                 """version. Using `--per_device_train_batch_size` is preferred."""    )
         a__      =       self.per_gpu_train_batch_size or self.per_device_train_batch_size
         return per_device_batch_size * self.n_replicas

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			int:






         """simple docstring"""
         if self.per_gpu_eval_batch_size:
             logger.warning(
                 """Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future """
                 """version. Using `--per_device_eval_batch_size` is preferred."""    )
         a__      =       self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
         return per_device_batch_size * self.n_replicas






     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         warnings.warn(
             """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""						,				A						,				)
         return self._setup_strategy.num_replicas_in_sync






