






import json
import sys

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				with open(snake_case,  encoding='''utf-8''' ) as f:
								__magic_name__		:Tuple        =			json.load(snake_case )

				__magic_name__		:List[str]        =			['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

				for benchmark_name in sorted(snake_case ):
								__magic_name__		:Tuple        =			results[benchmark_name]

								__magic_name__		:Tuple        =			benchmark_name.split('''/''' )[-1]
								output_md.append(f'''### Benchmark: {benchmark_file_name}''' )

								__magic_name__		:Any        =			'''| metric |'''
								__magic_name__		:Optional[int]        =			'''|--------|'''
								__magic_name__		:Any        =			'''| new / old (diff) |'''
								for metric_name in sorted(snake_case ):
												__magic_name__		:Dict        =			benchmark_res[metric_name]
												__magic_name__		:List[Any]        =			metric_vals['''new''']
												__magic_name__		:str        =			metric_vals.get('''old''',  snake_case )
												__magic_name__		:Optional[Any]        =			metric_vals.get('''diff''',  snake_case )

												__magic_name__		:List[str]        =			f''' {new_val:f}''' if isinstance(snake_case,  (int, float) ) else '''None'''

												if old_val is not None:
																val_str += f''' / {old_val:f}''' if isinstance(snake_case,  (int, float) ) else "None"
												if dif_val is not None:
																val_str += f''' ({dif_val:f})''' if isinstance(snake_case,  (int, float) ) else "None"

												title += " " + metric_name + " |"
												lines += "---|"
												value += val_str + " |"

								output_md += [title, lines, value, " "]

				output_md.append('''</details>''' )

				with open(snake_case,  '''w''',  encoding='''utf-8''' ) as f:
								f.writelines('''\n'''.join(snake_case ) )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		sys.argv[1]
					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		sys.argv[2]

					format_json_to_md(input_json_file, output_md_file)














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


SCREAMING_SNAKE_CASE__      :			List[str]							=		[
    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)

SCREAMING_SNAKE_CASE__      :			int							=		logging.getLogger()

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:int        =      argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				__magic_name__		:Tuple        =      parser.parse_args()
				return args.f

def      __lowercase					( snake_case,  snake_case="eval" ):







				"""simple docstring"""






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


SCREAMING_SNAKE_CASE__      :			Tuple							=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 						lowerCamelCase_				(      lowerCamelCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_flax_glue.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_clm_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					1_0_0     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[str]        =      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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_summarization_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase       ,					split='''test'''     )
												self.assertGreaterEqual(result['''test_rouge1''']       ,					1_0     )
												self.assertGreaterEqual(result['''test_rouge2''']       ,					2     )
												self.assertGreaterEqual(result['''test_rougeL''']       ,					7     )
												self.assertGreaterEqual(result['''test_rougeLsum''']       ,					7     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_mlm_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					4_2     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_ta_mlm_flax.main()
												__magic_name__		:Dict        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.42     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								__magic_name__		:int        =      7 if get_gpu_count() > 1 else 2

								__magic_name__		:str        =      self.get_auto_remove_tmp_dir()
								__magic_name__		: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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_flax_ner.main()
												__magic_name__		:Any        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					0.3     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		: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(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_qa.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					3_0     )
												self.assertGreaterEqual(result['''eval_exact''']       ,					3_0     )






