


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


UpperCAmelCase     =       [
    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)

UpperCAmelCase     =       logging.getLogger()






def 			__lowerCAmelCase       ()->    Dict:




      """simple docstring"""

      snake_case_			= argparse.ArgumentParser()
      parser.add_argument('''-f'''   )
      snake_case_			= parser.parse_args()
      return args.f






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    List[Any]:




      """simple docstring"""

      snake_case_			= {}
      snake_case_			= os.path.join(SCREAMING_SNAKE_CASE   ,   '''all_results.json'''   )
      if os.path.exists(SCREAMING_SNAKE_CASE   ):
            with open(SCREAMING_SNAKE_CASE   ,   '''r'''   ) as f:
                  snake_case_			= json.load(SCREAMING_SNAKE_CASE   )
      else:
            raise ValueError(f'''can\'t find {path}'''   )
      return results






def 			__lowerCAmelCase       ()->    Optional[int]:




      """simple docstring"""

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


UpperCAmelCase     =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 @skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''   )
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_text_classification.main()
             # Reset the mixed precision policy so we don't break other tests
             tf.keras.mixed_precision.set_global_policy('''float32'''   )
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertGreaterEqual(result['''eval_accuracy''']						, 0.75   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_clm.main()
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertLess(result['''eval_perplexity''']						, 1_00   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_mlm.main()
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertLess(result['''eval_perplexity''']						, 42   )



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

       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_ner.main()
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertGreaterEqual(result['''accuracy''']						, 0.75   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_squad.main()
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertGreaterEqual(result['''f1''']						, 30   )
             self.assertGreaterEqual(result['''exact''']						, 30   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_swag.main()
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertGreaterEqual(result['''val_accuracy''']						, 0.8   )



 @slow
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_summarization.main()
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertGreaterEqual(result['''rouge1''']						, 10   )
             self.assertGreaterEqual(result['''rouge2''']						, 2   )
             self.assertGreaterEqual(result['''rougeL''']						, 7   )
             self.assertGreaterEqual(result['''rougeLsum''']						, 7   )



 @slow
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_translation.main()
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertGreaterEqual(result['''bleu''']						, 30   )





 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_auto_remove_tmp_dir()
       snake_case_			= 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(_UpperCAmelCase						, '''argv'''						, _UpperCAmelCase   ):
             run_image_classification.main()
             snake_case_			= get_results(_UpperCAmelCase   )
             self.assertGreaterEqual(result['''accuracy''']						, 0.7   )



from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase     =       logging.get_logger(__name__)







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    ["input_ids", "attention_mask"]



 def __init__(      self						, _UpperCAmelCase="[PAD]"						, _UpperCAmelCase="[BOS]"						, _UpperCAmelCase="[EOS]"						, _UpperCAmelCase="[MASK]"						, _UpperCAmelCase="[CLS]"						, _UpperCAmelCase="[SEP]"						, _UpperCAmelCase=20_48						, **_UpperCAmelCase						, ):
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else pad_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else bos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else eos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else mask_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else cls_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else sep_token

       super().__init__(
           pad_token=_UpperCAmelCase						, bos_token=_UpperCAmelCase						, eos_token=_UpperCAmelCase						, mask_token=_UpperCAmelCase						, cls_token=_UpperCAmelCase						, sep_token=_UpperCAmelCase						, model_max_length=_UpperCAmelCase						, **_UpperCAmelCase						, )

       snake_case_			= 2**8  # utf is 8 bits

       # define special tokens dict
       snake_case_			= {
           self.pad_token: 0,
           self.bos_token: 1,
           self.eos_token: 2,
           self.mask_token: 3,
           self.cls_token: 4,
           self.sep_token: 5,
       }
       snake_case_			= len(self.special_tokens_encoder   )
       snake_case_			= {v: k for k, v in self.special_tokens_encoder.items()}



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder   )
       for i in range(self._utf_vocab_size   ):
             snake_case_			= chr(_UpperCAmelCase   )
             snake_case_			= i + len(self.special_tokens_encoder   )
       return vocab



 @property
 def 		UpperCamelCase__	(      self   ):
       return self._utf_vocab_size + self._num_special_tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None						, _UpperCAmelCase = False   ):
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCAmelCase						, token_ids_a=_UpperCAmelCase						, already_has_special_tokens=_UpperCAmelCase   )

       # normal case: some special tokens
       if token_ids_a is None:
             return [1] + [0] * len(_UpperCAmelCase   ) + [1]
       return [1] + ([0] * len(_UpperCAmelCase   )) + [1] + ([0] * len(_UpperCAmelCase   )) + [1]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       else:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= [chr(_UpperCAmelCase   ) for i in text.encode('''utf-8'''   )]
       return tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if token in self.special_tokens_encoder:
             snake_case_			= self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
             snake_case_			= self.added_tokens_encoder[token]
       elif len(_UpperCAmelCase   ) != 1:
             snake_case_			= self.unk_token_id
       else:
             snake_case_			= ord(_UpperCAmelCase   ) + self._num_special_tokens
       return token_id



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if index in self.special_tokens_decoder:
             snake_case_			= self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
             snake_case_			= self.added_tokens_decoder[index]
       else:
             snake_case_			= chr(index - self._num_special_tokens   )
       return token



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= b''''''
       for token in tokens:
             if token in self.special_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.added_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.special_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             elif token in self.added_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             else:
                   snake_case_			= bytes([ord(_UpperCAmelCase   )]   )
             bstring += tok_string
       snake_case_			= bstring.decode('''utf-8'''						, errors='''replace'''   )
       return string





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       return ()