





'''simple docstring'''






from jiwer import compute_measures

import datasets


__UpperCamelCase					    =	"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

__UpperCamelCase					    =	"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

__UpperCamelCase					    =	"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION		)
class  _A (					datasets.Metric		):

		def 	lowercase__     (   self					:   int			)					->    Optional[int]:


					"""simple docstring"""

					return datasets.MetricInfo(
					    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
					        {
					            """predictions""": datasets.Value("""string"""  ,      id="""sequence"""			),
					            """references""": datasets.Value("""string"""  ,      id="""sequence"""			),
					        }			)  ,      codebase_urls=["""https://github.com/jitsi/jiwer/"""]  ,      reference_urls=[
					        """https://en.wikipedia.org/wiki/Word_error_rate""",
					    ]  ,      )

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   int=None  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   Union[str, Any]=False			)					->    str:


					"""simple docstring"""

					if concatenate_texts:
								return compute_measures(__magic_name__  ,      __magic_name__			)["wer"]
					else:
								__snake_case					:      Union[str, Any]							      =     0
								__snake_case					:      Tuple							      =     0
								for prediction, reference in zip(__magic_name__  ,      __magic_name__			):
											__snake_case					:      Dict							      =     compute_measures(__magic_name__  ,      __magic_name__			)
											incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
											total += measures["substitutions"] + measures["deletions"] + measures["hits"]
								return incorrect / total



'''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						:							int    							=						[
    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						:							List[Any]    							=						logging.getLogger()





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      argparse.ArgumentParser()
	parser.add_argument('''-f'''							)
	__UpperCAmelCase      					=      parser.parse_args()
	return args.f





def    lowerCAmelCase       (		UpperCamelCase__					: Union[str, Any]     ,       UpperCamelCase__					: Any="eval"							):


	"""simple docstring"""
	__UpperCAmelCase      					=      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						:							str    							=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class     A ( UpperCAmelCase ):





							def    snake_case__    (   self					:				List[Any]						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.7_5						)





							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						Optional[int]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertLess(result['''eval_perplexity''']  ,		1_0_0						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						List[str]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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()
									__UpperCAmelCase      					=      get_results(__a  ,		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    snake_case__    (   self					:				Any						)				->						List[Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertLess(result['''eval_perplexity''']  ,		4_2						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						str:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.4_2						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						Tuple:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								__UpperCAmelCase      					=      7 if get_gpu_count() > 1 else 2

								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.7_5						)
									self.assertGreaterEqual(result['''eval_f1''']  ,		0.3						)






							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						List[Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_f1''']  ,		3_0						)
									self.assertGreaterEqual(result['''eval_exact''']  ,		3_0						)
