





'''simple docstring'''
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


a__							:							str			=[
    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)

a__							:							Union[str, Any]			=logging.getLogger()
def     lowercase__       (       )			->				List[str]:



       """simple docstring"""






       __UpperCamelCase	=  argparse.ArgumentParser()
       parser.add_argument('-f'					)
       __UpperCamelCase	=  parser.parse_args()
       return args.f
def     lowercase__       (       __lowercase						:  Union[str, Any]					)			->				Optional[int]:



       """simple docstring"""






       __UpperCamelCase	=  {}
       __UpperCamelCase	=  os.path.join(__lowercase    ,  'all_results.json'					)
       if os.path.exists(__lowercase					):
              with open(__lowercase    ,  'r'					) as f:
                     __UpperCamelCase	=  json.load(__lowercase					)
       else:
              raise ValueError(F'''can\'t find {path}'''					)
       return results
def     lowercase__       (       )			->				Union[str, Any]:



       """simple docstring"""






       return bool(tf.config.list_physical_devices('GPU'					)					)


a__							:							int			=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



 @skip('Skipping until shape inference for to_tf_dataset PR is merged.'     )
 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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'     )
               __UpperCamelCase	=  get_results(__A     )
               self.assertGreaterEqual(result['eval_accuracy']							,     0.75     )






 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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()
               __UpperCamelCase	=  get_results(__A     )
               self.assertLess(result['eval_perplexity']							,     1_0_0     )






 def 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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()
               __UpperCamelCase	=  get_results(__A     )
               self.assertLess(result['eval_perplexity']							,     4_2     )






 def 	_lowerCamelCase (	self	:							str     ):
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        __UpperCamelCase	=  7 if get_gpu_count() > 1 else 2

        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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()
               __UpperCamelCase	=  get_results(__A     )
               self.assertGreaterEqual(result['accuracy']							,     0.75     )






 def 	_lowerCamelCase (	self	:							List[Any]     ):
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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()
               __UpperCamelCase	=  get_results(__A     )
               self.assertGreaterEqual(result['f1']							,     3_0     )
               self.assertGreaterEqual(result['exact']							,     3_0     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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()
               __UpperCamelCase	=  get_results(__A     )
               self.assertGreaterEqual(result['val_accuracy']							,     0.8     )






 @slow
 def 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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()
               __UpperCamelCase	=  get_results(__A     )
               self.assertGreaterEqual(result['rouge1']							,     1_0     )
               self.assertGreaterEqual(result['rouge2']							,     2     )
               self.assertGreaterEqual(result['rougeL']							,     7     )
               self.assertGreaterEqual(result['rougeLsum']							,     7     )






 @slow
 def 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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()
               __UpperCamelCase	=  get_results(__A     )
               self.assertGreaterEqual(result['bleu']							,     3_0     )






 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()
        __UpperCamelCase	=  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()
               __UpperCamelCase	=  get_results(__A     )
               self.assertGreaterEqual(result['accuracy']							,     0.7     )







'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


a__							:							Optional[Any]			=datasets.utils.logging.get_logger(__name__)

@dataclass
class 					snake_case					(   datasets.BuilderConfig	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[datasets.Features]      		=None






class 					snake_case					(   datasets.ArrowBasedBuilder	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : int      		=ArrowConfig






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return datasets.DatasetInfo(features=self.config.features     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							Dict     ):
        if not self.config.data_files:
               raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
        __UpperCamelCase	=  dl_manager.download_and_extract(self.config.data_files     )
        if isinstance(__A							,     (str, list, tuple)     ):
               __UpperCamelCase	=  data_files
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,     gen_kwargs={'files': files}     )]
        __UpperCamelCase	=  []
        for split_name, files in data_files.items():
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                      for file in itertools.chain.from_iterable(__A     ):
                             with open(__A							,     'rb'     ) as f:
                                    __UpperCamelCase	=  datasets.Features.from_arrow_schema(pa.ipc.open_stream(__A     ).schema     )
                             break
               splits.append(datasets.SplitGenerator(name=__A							,     gen_kwargs={'files': files}     )     )
        return splits






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							pa.Table     ):
        if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __UpperCamelCase	=  table_cast(__A							,     self.info.features.arrow_schema     )
        return pa_table






 def 	_lowerCamelCase (	self	:							int							,     __A	:							Optional[Any]     ):
        for file_idx, file in enumerate(itertools.chain.from_iterable(__A     )     ):
               with open(__A							,     'rb'     ) as f:
                      try:
                             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__A     )     ):
                                    __UpperCamelCase	=  pa.Table.from_batches([record_batch]     )
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield f'''{file_idx}_{batch_idx}''', self._cast_table(__A     )
                      except ValueError as e:
                             logger.error(f'''Failed to read file \'{file}\' with error {type(__A     )}: {e}'''     )
                             raise
