




from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCamelCase       :	List[str]									=     TypeVar("KEY")
lowerCamelCase       :	str									=     TypeVar("VAL")







@dataclass(frozen=UpperCamelCase							,   slots=UpperCamelCase     )
class 						A(		Generic[KEY, VAL]     ):



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		42







class 						A(		_Item     ):



 '''simple docstring'''


 def __init__(       self      :						Tuple						)     ->			None:

       """simple docstring"""






       super().__init__(A_		,							A_						)





 def __bool__(       self      :						List[Any]						)     ->			bool:

       """simple docstring"""






       return False

lowerCamelCase       :	Optional[Any]									=     _DeletedItem()







class 						A(		MutableMapping[KEY, VAL]     ):



 '''simple docstring'''


 def __init__(       self      :						str		,							A_      :						int = 8		,							A_      :						float = 0.75						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					initial_block_size
       lowerCamelCase_	  =					[None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       lowerCamelCase_	  =					capacity_factor
       lowerCamelCase_	  =					0

 def        a__				(       self      :						Dict		,							A_      :						KEY						)     ->			int:

       """simple docstring"""






       return hash(A_						) % len(self._buckets						)

 def        a__				(       self      :						List[Any]		,							A_      :						int						)     ->			int:

       """simple docstring"""






       return (ind + 1) % len(self._buckets						)

 def        a__				(       self      :						Tuple		,							A_      :						int		,							A_      :						KEY		,							A_      :						VAL						)     ->			bool:

       """simple docstring"""






       lowerCamelCase_	  =					self._buckets[ind]
       if not stored:
             lowerCamelCase_	  =					_Item(A_		,							A_						)
             self._len += 1
             return True
       elif stored.key == key:
             lowerCamelCase_	  =					_Item(A_		,							A_						)
             return True
       else:
             return False

 def        a__				(       self      :						List[str]						)     ->			bool:

       """simple docstring"""






       lowerCamelCase_	  =					len(self._buckets						) * self._capacity_factor
       return len(self						) >= int(A_						)

 def        a__				(       self      :						Dict						)     ->			bool:

       """simple docstring"""






       if len(self._buckets						) <= self._initial_block_size:
             return False
       lowerCamelCase_	  =					len(self._buckets						) * self._capacity_factor / 2
       return len(self						) < limit

 def        a__				(       self      :						Any		,							A_      :						int						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					self._buckets
       lowerCamelCase_	  =					[None] * new_size
       lowerCamelCase_	  =					0
       for item in old_buckets:
             if item:
                   self._add_item(item.key		,							item.val						)

 def        a__				(       self      :						Tuple						)     ->			None:

       """simple docstring"""






       self._resize(len(self._buckets						) * 2						)

 def        a__				(       self      :						Any						)     ->			None:

       """simple docstring"""






       self._resize(len(self._buckets						) // 2						)

 def        a__				(       self      :						Tuple		,							A_      :						KEY						)     ->			Iterator[int]:

       """simple docstring"""






       lowerCamelCase_	  =					self._get_bucket_index(A_						)
       for _ in range(len(self._buckets						)						):
             yield ind
             lowerCamelCase_	  =					self._get_next_ind(A_						)

 def        a__				(       self      :						int		,							A_      :						KEY		,							A_      :						VAL						)     ->			None:

       """simple docstring"""






       for ind in self._iterate_buckets(A_						):
             if self._try_set(A_		,							A_		,							A_						):
                   break

 def __setitem__(       self      :						List[str]		,							A_      :						KEY		,							A_      :						VAL						)     ->			None:

       """simple docstring"""






       if self._is_full():
             self._size_up()

       self._add_item(A_		,							A_						)

 def __delitem__(       self      :						int		,							A_      :						KEY						)     ->			None:

       """simple docstring"""






       for ind in self._iterate_buckets(A_						):
             lowerCamelCase_	  =					self._buckets[ind]
             if item is None:
                   raise KeyError(A_						)
             if item is _deleted:
                   continue
             if item.key == key:
                   lowerCamelCase_	  =					_deleted
                   self._len -= 1
                   break
       if self._is_sparse():
             self._size_down()

 def __getitem__(       self      :						Optional[int]		,							A_      :						KEY						)     ->			VAL:

       """simple docstring"""






       for ind in self._iterate_buckets(A_						):
             lowerCamelCase_	  =					self._buckets[ind]
             if item is None:
                   break
             if item is _deleted:
                   continue
             if item.key == key:
                   return item.val
       raise KeyError(A_						)

 def __len__(       self      :						int						)     ->			int:

       """simple docstring"""






       return self._len

 def __iter__(       self      :						List[Any]						)     ->			Iterator[KEY]:

       """simple docstring"""






       yield from (item.key for item in self._buckets if item)





 def __repr__(       self      :						Dict						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					' ,'.join(
           f"""{item.key}: {item.val}""" for item in self._buckets if item						)
       return f"""HashMap({val_string})"""









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       :	List[Any]									=     [
    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       :	Tuple									=     logging.getLogger()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Dict="eval"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					os.path.join(lowercase			,							f"""{split}_results.json"""  )
      if os.path.exists(lowercase  ):
            with open(lowercase			,							'r'  ) as f:
                  return json.load(lowercase  )
      raise ValueError(f"""can't find {path}"""  )


lowerCamelCase       :	str									=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					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(A_		,							'argv'		,							A_						):
             run_flax_glue.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.75						)

 @slow
 def        a__				(       self      :						List[str]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					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(A_		,							'argv'		,							A_						):
             run_clm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertLess(result['eval_perplexity']		,							100						)

 @slow
 def        a__				(       self      :						str						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					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(A_		,							'argv'		,							A_						):
             run_summarization_flax.main()
             lowerCamelCase_	  =					get_results(A_		,							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[int]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					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(A_		,							'argv'		,							A_						):
             run_mlm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertLess(result['eval_perplexity']		,							42						)

 @slow
 def        a__				(       self      :						Optional[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					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(A_		,							'argv'		,							A_						):
             run_ta_mlm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.42						)

 @slow
 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					7 if get_gpu_count() > 1 else 2

       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					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(A_		,							'argv'		,							A_						):
             run_flax_ner.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.75						)
             self.assertGreaterEqual(result['eval_f1']		,							0.3						)





 @slow
 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					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(A_		,							'argv'		,							A_						):
             run_qa.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_f1']		,							30						)
             self.assertGreaterEqual(result['eval_exact']		,							30						)



