






"""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 							snake_case (  ):
			UpperCAmelCase_	:					int     	=   argparse.ArgumentParser()
			parser.add_argument("-f"		)
			UpperCAmelCase_	:					Optional[Any]     	=   parser.parse_args()
			return args.f

def 							snake_case (  A__      ,A__="eval"		):
			UpperCAmelCase_	:					Union[str, Any]     	=   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}"""		)


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







class      UpperCamelCase_			(__A	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[str]:
				UpperCAmelCase_	:					Dict     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					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()
							UpperCAmelCase_	:					List[str]     	=   get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]      ,      0.7_5		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Dict:
				UpperCAmelCase_	:					Dict     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					List[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(lowerCAmelCase_      ,      "argv"      ,      lowerCAmelCase_		):
							run_clm_flax.main()
							UpperCAmelCase_	:					Optional[Any]     	=   get_results(lowerCAmelCase_		)
							self.assertLess(result["eval_perplexity"]      ,      100		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					List[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(lowerCAmelCase_      ,      "argv"      ,      lowerCAmelCase_		):
							run_summarization_flax.main()
							UpperCAmelCase_	:					Dict     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					List[str]     	=   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()
							UpperCAmelCase_	:					Dict     	=   get_results(lowerCAmelCase_		)
							self.assertLess(result["eval_perplexity"]      ,      42		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					Dict     	=   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()
							UpperCAmelCase_	:					str     	=   get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]      ,      0.4_2		)





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

				UpperCAmelCase_	:					Optional[int]     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					Dict     	=   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()
							UpperCAmelCase_	:					Tuple     	=   get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]      ,      0.7_5		)
							self.assertGreaterEqual(result["eval_f1"]      ,      0.3		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					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()
							UpperCAmelCase_	:					List[str]     	=   get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_f1"]      ,      30		)
							self.assertGreaterEqual(result["eval_exact"]      ,      30		)











"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

def 							snake_case (  A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					str     	=   []
			UpperCAmelCase_	:					List[str]     	=   seq_shapes or {}
			for i in range(A__		):
						UpperCAmelCase_	:					int     	=   {}
						for col_id, (k, v) in enumerate(features.items()		):
									if isinstance(A__      ,_ArrayXD		):
												UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(*v.shape		).astype(v.dtype		)
									elif isinstance(A__      ,datasets.Value		):
												if v.dtype == "string":
															UpperCAmelCase_	:					List[str]     	=   "The small grey turtle was surprisingly fast when challenged."
												else:
															UpperCAmelCase_	:					List[str]     	=   np.random.randint(10      ,size=1		).astype(v.dtype		).item()
									elif isinstance(A__      ,datasets.Sequence		):
												while isinstance(A__      ,datasets.Sequence		):
															UpperCAmelCase_	:					List[str]     	=   v.feature
												UpperCAmelCase_	:					List[Any]     	=   seq_shapes[k]
												UpperCAmelCase_	:					Dict     	=   np.random.rand(*A__		).astype(v.dtype		)
									UpperCAmelCase_	:					Dict     	=   data

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

			with ArrowWriter(features=A__      ,path=A__		) as writer:
						for key, record in dummy_data:
									UpperCAmelCase_	:					Any     	=   features.encode_example(A__		)
									writer.write(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset



