'''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


__snake_case  : Optional[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)

__snake_case  : int		       =    logging.getLogger()





def 	_UpperCAmelCase       (				)      -> Optional[int]:
    A_      =  argparse.ArgumentParser()
    parser.add_argument('''-f'''  )
    A_      =  parser.parse_args()
    return args.f





def 	_UpperCAmelCase       (				_UpperCamelCase       :      List[str]  )      -> Optional[int]:
    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 	_UpperCAmelCase       (				)      -> str:
    return bool(tf.config.list_physical_devices('''GPU'''  )  )


__snake_case  : List[Any]		       =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       @skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''					)
       def    __A			( self					)						->	List[str]:
           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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               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(_SCREAMING_SNAKE_CASE					)
               self.assertGreaterEqual(result['''eval_accuracy''']       ,	0.75					)

       def    __A			( self					)						->	Optional[Any]:
           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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               run_clm.main()
               A_      =  get_results(_SCREAMING_SNAKE_CASE					)
               self.assertLess(result['''eval_perplexity''']       ,	100					)

       def    __A			( self					)						->	List[Any]:
           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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               run_mlm.main()
               A_      =  get_results(_SCREAMING_SNAKE_CASE					)
               self.assertLess(result['''eval_perplexity''']       ,	42					)

       def    __A			( self					)						->	List[str]:
           # 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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               run_ner.main()
               A_      =  get_results(_SCREAMING_SNAKE_CASE					)
               self.assertGreaterEqual(result['''accuracy''']       ,	0.75					)

       def    __A			( self					)						->	Optional[Any]:
           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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               run_squad.main()
               A_      =  get_results(_SCREAMING_SNAKE_CASE					)
               self.assertGreaterEqual(result['''f1''']       ,	30					)
               self.assertGreaterEqual(result['''exact''']       ,	30					)

       def    __A			( self					)						->	List[Any]:
           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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               run_swag.main()
               A_      =  get_results(_SCREAMING_SNAKE_CASE					)
               self.assertGreaterEqual(result['''val_accuracy''']       ,	0.8					)

       @slow
       def    __A			( self					)						->	List[Any]:
           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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               run_summarization.main()
               A_      =  get_results(_SCREAMING_SNAKE_CASE					)
               self.assertGreaterEqual(result['''rouge1''']       ,	10					)
               self.assertGreaterEqual(result['''rouge2''']       ,	2					)
               self.assertGreaterEqual(result['''rougeL''']       ,	7					)
               self.assertGreaterEqual(result['''rougeLsum''']       ,	7					)

       @slow
       def    __A			( self					)						->	Optional[int]:
           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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               run_translation.main()
               A_      =  get_results(_SCREAMING_SNAKE_CASE					)
               self.assertGreaterEqual(result['''bleu''']       ,	30					)

       def    __A			( self					)						->	Dict:
           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(_SCREAMING_SNAKE_CASE       ,	'''argv'''       ,	_SCREAMING_SNAKE_CASE					):
               run_image_classification.main()
               A_      =  get_results(_SCREAMING_SNAKE_CASE					)
               self.assertGreaterEqual(result['''accuracy''']       ,	0.7					)







'''simple docstring'''




from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__snake_case  : Union[str, Any]		       =    logging.get_logger(__name__)

__snake_case  : Union[str, Any]		       =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case  : List[str]		       =    {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__snake_case  : Optional[int]		       =    {
    'openai-gpt': 512,
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   int  					=							VOCAB_FILES_NAMES
       __lowercase   :   int  					=							PRETRAINED_VOCAB_FILES_MAP
       __lowercase   :   str  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase   :   Optional[Any]  					=							['input_ids', 'attention_mask']
       __lowercase   :   List[Any]  					=							OpenAIGPTTokenizer

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE="<unk>"       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @property
       def    __A			( self					)						->	Optional[Any]:
           return True

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	Tuple[str]:
           A_      =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE       ,	name=_SCREAMING_SNAKE_CASE					)
           return tuple(_SCREAMING_SNAKE_CASE					)






