




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      __lowerCAmelCase			(					)     ->	Union[str, Any]:
  lowerCAmelCase__				:  Optional[int]		       =     argparse.ArgumentParser()
  parser.add_argument('''-f'''      )
  lowerCAmelCase__				:  str		       =     parser.parse_args()
  return args.f



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase="eval"      )     ->	Tuple:
  lowerCAmelCase__				:  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     (  _lowercase	):

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  List[str]		       =     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__				:  Dict		       =     get_results(__UpperCAmelCase		)
          self.assertGreaterEqual(result['''eval_accuracy''']      ,				0.75		)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  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__				:  Optional[int]		       =     get_results(__UpperCAmelCase		)
          self.assertLess(result['''eval_perplexity''']      ,				100		)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Tuple		       =     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__				:  Dict		       =     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 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Dict		       =     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__				:  Tuple		       =     get_results(__UpperCAmelCase		)
          self.assertLess(result['''eval_perplexity''']      ,				42		)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Union[str, Any]		       =     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__				:  Any		       =     get_results(__UpperCAmelCase		)
          self.assertGreaterEqual(result['''eval_accuracy''']      ,				0.42		)

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

        lowerCAmelCase__				:  Union[str, Any]		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Any		       =     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__				:  List[str]		       =     get_results(__UpperCAmelCase		)
          self.assertGreaterEqual(result['''eval_accuracy''']      ,				0.75		)
          self.assertGreaterEqual(result['''eval_f1''']      ,				0.3		)







      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     self.get_auto_remove_tmp_dir()
        lowerCAmelCase__				:  Dict		       =     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__				:  Any		       =     get_results(__UpperCAmelCase		)
          self.assertGreaterEqual(result['''eval_f1''']      ,				30		)
          self.assertGreaterEqual(result['''eval_exact''']      ,				30		)







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[Any]		       =     '''mps'''
  if device == "mps":
    print(
        '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
        ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
        ''' with generations.'''      )
  return device



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

