



'''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


_lowerCAmelCase										=     [
    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)

_lowerCAmelCase										=     logging.getLogger()
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= argparse.ArgumentParser()
    parser.add_argument("""-f"""      )
    lowerCAmelCase__			:  Union[str, Any]											= parser.parse_args()
    return args.f
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase="eval"      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= os.path.join(UpperCamelCase							,   f"""{split}_results.json"""      )
    if os.path.exists(UpperCamelCase      ):
        with open(UpperCamelCase							,   """r"""      ) as f:
            return json.load(UpperCamelCase      )
    raise ValueError(f"""can't find {path}"""      )


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






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  List[Any]											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  Optional[int]											= F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

      with patch.object(__UpperCAmelCase  ,"""argv"""  ,__UpperCAmelCase				):
          run_flax_glue.main()
          lowerCAmelCase__			:  List[Any]											= get_results(__UpperCAmelCase				)
          self.assertGreaterEqual(result["""eval_accuracy"""]  ,0.7_5				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  str											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  Optional[Any]											= F"""
            run_clm_flax.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 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

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




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Any											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  Union[str, Any]											= 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
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

      with patch.object(__UpperCAmelCase  ,"""argv"""  ,__UpperCAmelCase				):
          run_summarization_flax.main()
          lowerCAmelCase__			:  List[str]											= get_results(__UpperCAmelCase  ,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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  int											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  List[Any]											= F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

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




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Dict											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  List[str]											= F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(__UpperCAmelCase  ,"""argv"""  ,__UpperCAmelCase				):
          run_ta_mlm_flax.main()
          lowerCAmelCase__			:  Tuple											= get_results(__UpperCAmelCase				)
          self.assertGreaterEqual(result["""eval_accuracy"""]  ,0.4_2				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		int:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      lowerCAmelCase__			:  Optional[Any]											= 7 if get_gpu_count() > 1 else 2

      lowerCAmelCase__			:  Any											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  List[str]											= F"""
            run_flax_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
            --logging_steps 2 --eval_steps 2
            --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_flax_ner.main()
          lowerCAmelCase__			:  int											= get_results(__UpperCAmelCase				)
          self.assertGreaterEqual(result["""eval_accuracy"""]  ,0.7_5				)
          self.assertGreaterEqual(result["""eval_f1"""]  ,0.3				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  List[str]											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  Optional[int]											= 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
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

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







'''simple docstring'''





import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
 from transformers.models.rag.configuration_rag import RagConfig
 from transformers.models.rag.tokenization_rag import RagTokenizer






@require_faiss
@require_torch
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  str											= tempfile.mkdtemp()
      lowerCAmelCase__			:  List[Any]											= 8

      # DPR tok
      lowerCAmelCase__			:  int											= [
          """[UNK]""",
          """[CLS]""",
          """[SEP]""",
          """[PAD]""",
          """[MASK]""",
          """want""",
          """##want""",
          """##ed""",
          """wa""",
          """un""",
          """runn""",
          """##ing""",
          """,""",
          """low""",
          """lowest""",
      ]
      lowerCAmelCase__			:  List[Any]											= os.path.join(self.tmpdirname  ,"""dpr_tokenizer"""				)
      os.makedirs(__UpperCAmelCase  ,exist_ok=__UpperCAmelCase				)
      lowerCAmelCase__			:  Dict											= os.path.join(__UpperCAmelCase  ,DPR_VOCAB_FILES_NAMES["""vocab_file"""]				)
      with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""				) as vocab_writer:
          vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

      # BART tok
      lowerCAmelCase__			:  str											= [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      lowerCAmelCase__			:  List[Any]											= dict(zip(__UpperCAmelCase  ,range(len(__UpperCAmelCase				)				)				)				)
      lowerCAmelCase__			:  Optional[Any]											= ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      lowerCAmelCase__			:  Any											= {"""unk_token""": """<unk>"""}

      lowerCAmelCase__			:  str											= os.path.join(self.tmpdirname  ,"""bart_tokenizer"""				)
      os.makedirs(__UpperCAmelCase  ,exist_ok=__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= os.path.join(__UpperCAmelCase  ,BART_VOCAB_FILES_NAMES["""vocab_file"""]				)
      lowerCAmelCase__			:  Dict											= os.path.join(__UpperCAmelCase  ,BART_VOCAB_FILES_NAMES["""merges_file"""]				)
      with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""				) as fp:
          fp.write(json.dumps(__UpperCAmelCase				) + """\n"""				)
      with open(self.merges_file  ,"""w"""  ,encoding="""utf-8"""				) as fp:
          fp.write("""\n""".join(__UpperCAmelCase				)				)




  def 							UpperCAmelCase_     (   self				)   ->		DPRQuestionEncoderTokenizer:
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,"""dpr_tokenizer"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		BartTokenizer:
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,"""bart_tokenizer"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      shutil.rmtree(self.tmpdirname				)




  @require_tokenizers
  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Any											= os.path.join(self.tmpdirname  ,"""rag_tokenizer"""				)
      lowerCAmelCase__			:  Any											= RagConfig(question_encoder=DPRConfig().to_dict()  ,generator=BartConfig().to_dict()				)
      lowerCAmelCase__			:  str											= RagTokenizer(question_encoder=self.get_dpr_tokenizer()  ,generator=self.get_bart_tokenizer()				)
      rag_config.save_pretrained(__UpperCAmelCase				)
      rag_tokenizer.save_pretrained(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= RagTokenizer.from_pretrained(__UpperCAmelCase  ,config=__UpperCAmelCase				)
      self.assertIsInstance(new_rag_tokenizer.question_encoder  ,__UpperCAmelCase				)
      self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()  ,rag_tokenizer.question_encoder.get_vocab()				)
      self.assertIsInstance(new_rag_tokenizer.generator  ,__UpperCAmelCase				)
      self.assertEqual(new_rag_tokenizer.generator.get_vocab()  ,rag_tokenizer.generator.get_vocab()				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  List[str]											= RagTokenizer.from_pretrained("""facebook/rag-token-nq"""				)
      lowerCAmelCase__			:  Optional[Any]											= [
          """who got the first nobel prize in physics""",
          """when is the next deadpool movie being released""",
          """which mode is used for short wave broadcast service""",
          """who is the owner of reading football club""",
          """when is the next scandal episode coming out""",
          """when is the last time the philadelphia won the superbowl""",
          """what is the most current adobe flash player version""",
          """how many episodes are there in dragon ball z""",
          """what is the first step in the evolution of the eye""",
          """where is gall bladder situated in human body""",
          """what is the main mineral in lithium batteries""",
          """who is the president of usa right now""",
          """where do the greasers live in the outsiders""",
          """panda is a national animal of which country""",
          """what is the name of manchester united stadium""",
      ]
      lowerCAmelCase__			:  Dict											= tokenizer(__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  str											= RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""				)
      lowerCAmelCase__			:  str											= [
          """who got the first nobel prize in physics""",
          """when is the next deadpool movie being released""",
          """which mode is used for short wave broadcast service""",
          """who is the owner of reading football club""",
          """when is the next scandal episode coming out""",
          """when is the last time the philadelphia won the superbowl""",
          """what is the most current adobe flash player version""",
          """how many episodes are there in dragon ball z""",
          """what is the first step in the evolution of the eye""",
          """where is gall bladder situated in human body""",
          """what is the main mineral in lithium batteries""",
          """who is the president of usa right now""",
          """where do the greasers live in the outsiders""",
          """panda is a national animal of which country""",
          """what is the name of manchester united stadium""",
      ]
      lowerCAmelCase__			:  Tuple											= tokenizer(__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)


