


'''simple docstring'''


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

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__magic_name__			=      [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
  import run_clm_flax
  import run_flax_glue
  import run_flax_ner
  import run_mlm_flax
  import run_qa
  import run_summarization_flax
  import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

__magic_name__			=      logging.getLogger()




def      lowerCamelCase					(      ):
      A_						:  Union[str, Any]          =				argparse.ArgumentParser()
      parser.add_argument("""-f""")
      A_						:  Any          =				parser.parse_args()
      return args.f




def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Optional[int]="eval"):
      A_						:  List[str]          =				os.path.join(__A							,						F'{split}_results.json')
      if os.path.exists(__A):
            with open(__A							,						"""r""") as f:
                  return json.load(__A)
      raise ValueError(F'can\'t find {path}')


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






class 	__lowerCAmelCase		(							_snake_case    ):


  '''simple docstring'''







  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.get_auto_remove_tmp_dir()
        A_						:  List[str]          =				f'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

        with patch.object(lowerCAmelCase__	,"""argv"""	,lowerCAmelCase__ ):
              run_flax_glue.main()
              A_						:  Dict          =				get_results(lowerCAmelCase__ )
              self.assertGreaterEqual(result["""eval_accuracy"""]	,0.75 )






  @slow
  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.get_auto_remove_tmp_dir()
        A_						:  List[Any]          =				f'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

        with patch.object(lowerCAmelCase__	,"""argv"""	,lowerCAmelCase__ ):
              run_clm_flax.main()
              A_						:  Union[str, Any]          =				get_results(lowerCAmelCase__ )
              self.assertLess(result["""eval_perplexity"""]	,100 )






  @slow
  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.get_auto_remove_tmp_dir()
        A_						:  Tuple          =				f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

        with patch.object(lowerCAmelCase__	,"""argv"""	,lowerCAmelCase__ ):
              run_summarization_flax.main()
              A_						:  Optional[int]          =				get_results(lowerCAmelCase__	,split="""test""" )
              self.assertGreaterEqual(result["""test_rouge1"""]	,10 )
              self.assertGreaterEqual(result["""test_rouge2"""]	,2 )
              self.assertGreaterEqual(result["""test_rougeL"""]	,7 )
              self.assertGreaterEqual(result["""test_rougeLsum"""]	,7 )






  @slow
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.get_auto_remove_tmp_dir()
        A_						:  Tuple          =				f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

        with patch.object(lowerCAmelCase__	,"""argv"""	,lowerCAmelCase__ ):
              run_mlm_flax.main()
              A_						:  Any          =				get_results(lowerCAmelCase__ )
              self.assertLess(result["""eval_perplexity"""]	,42 )






  @slow
  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.get_auto_remove_tmp_dir()
        A_						:  List[str]          =				f'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

        with patch.object(lowerCAmelCase__	,"""argv"""	,lowerCAmelCase__ ):
              run_ta_mlm_flax.main()
              A_						:  List[Any]          =				get_results(lowerCAmelCase__ )
              self.assertGreaterEqual(result["""eval_accuracy"""]	,0.42 )






  @slow
  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  str          =				7 if get_gpu_count() > 1 else 2

        A_						:  Union[str, Any]          =				self.get_auto_remove_tmp_dir()
        A_						:  Tuple          =				f'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

        with patch.object(lowerCAmelCase__	,"""argv"""	,lowerCAmelCase__ ):
              run_flax_ner.main()
              A_						:  Dict          =				get_results(lowerCAmelCase__ )
              self.assertGreaterEqual(result["""eval_accuracy"""]	,0.75 )
              self.assertGreaterEqual(result["""eval_f1"""]	,0.3 )






  @slow
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Any          =				self.get_auto_remove_tmp_dir()
        A_						:  Any          =				f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

        with patch.object(lowerCAmelCase__	,"""argv"""	,lowerCAmelCase__ ):
              run_qa.main()
              A_						:  Tuple          =				get_results(lowerCAmelCase__ )
              self.assertGreaterEqual(result["""eval_f1"""]	,30 )
              self.assertGreaterEqual(result["""eval_exact"""]	,30 )




'''simple docstring'''


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""image_processor""", """tokenizer"""]
  a_  					=							"""ViltImageProcessor"""
  a_  					=							("""BertTokenizer""", """BertTokenizerFast""")






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=None	,_a :       List[str]=None	,**_a :       Any ):

        '''simple docstring'''




        A_						:  Any          =				None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""	,_a	,)
              A_						:  List[str]          =				kwargs.pop("""feature_extractor""" )

        A_						:  List[Any]          =				image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("""You need to specify an `image_processor`.""" )
        if tokenizer is None:
              raise ValueError("""You need to specify a `tokenizer`.""" )

        super().__init__(_a	,_a )
        A_						:  Optional[Any]          =				self.image_processor






  def __call__(							self :       Any	,_a :       Tuple	,_a :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,_a :       bool = True	,_a :       Union[bool, str, PaddingStrategy] = False	,_a :       Union[bool, str, TruncationStrategy] = None	,_a :       Optional[int] = None	,_a :       int = 0	,_a :       Optional[int] = None	,_a :       Optional[bool] = None	,_a :       Optional[bool] = None	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = True	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Tuple	,):

        '''simple docstring'''




        A_						:  int          =				self.tokenizer(
            text=_a	,add_special_tokens=_a	,padding=_a	,truncation=_a	,max_length=_a	,stride=_a	,pad_to_multiple_of=_a	,return_token_type_ids=_a	,return_attention_mask=_a	,return_overflowing_tokens=_a	,return_special_tokens_mask=_a	,return_offsets_mapping=_a	,return_length=_a	,verbose=_a	,return_tensors=_a	,**_a	,)
        # add pixel_values + pixel_mask
        A_						:  Optional[int]          =				self.image_processor(_a	,return_tensors=_a )
        encoding.update(_a )

        return encoding






  def        _a							(							self :       List[Any]	,*_a :       Any	,**_a :       Any ):

        '''simple docstring'''




        return self.tokenizer.batch_decode(*_a	,**_a )






  def        _a							(							self :       int	,*_a :       int	,**_a :       Optional[int] ):

        '''simple docstring'''




        return self.tokenizer.decode(*_a	,**_a )






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.tokenizer.model_input_names
        A_						:  str          =				self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,_a	,)
        return self.image_processor_class






  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,_a	,)
        return self.image_processor
