




import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__A						:							str							=  logging.getLogger()

__A						:							List[Any]							=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	str       ,     a       :	List[str]			)			->					int:
										os.makedirs(a       ,     exist_ok=a			)
										SCREAMING_SNAKE_CASE           =					{"""source""": """What is love ?""", """target""": """life"""}
										SCREAMING_SNAKE_CASE           =					{"""train""": 12, """val""": 2, """test""": 2}
										for split in ["train", "test", "val"]:
													for field in ["source", "target"]:
																SCREAMING_SNAKE_CASE           =					"""\n""".join([contents[field]] * n_lines[split]			)
																with open(os.path.join(a       ,     f"""{split}.{field}"""			)       ,     """w"""			) as f:
																			f.write(a			)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	int       ,     a       :	str = "pytorch"			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE           =					os.path.join(a       ,     """output"""			)
										SCREAMING_SNAKE_CASE           =					os.path.join(a       ,     """data"""			)
										self._create_dummy_data(data_dir=a			)

										SCREAMING_SNAKE_CASE           =					f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

										if gpus > 0:
													testargs.append(f"""--gpus={gpus}"""			)
													if is_apex_available():
																testargs.append("""--fp16"""			)
										else:
													testargs.append("""--gpus=0"""			)
													testargs.append("""--distributed_backend=ddp_cpu"""			)
													testargs.append("""--num_processes=2"""			)

										SCREAMING_SNAKE_CASE           =					[sys.executable, str(Path(finetune_rag.__file__			).resolve()			)] + testargs
										execute_subprocess_async(a       ,     env=self.get_env()			)

										SCREAMING_SNAKE_CASE           =					os.path.join(a       ,     """metrics.json"""			)
										with open(a			) as f:
													SCREAMING_SNAKE_CASE           =					json.load(a			)
										return result





							@require_torch_gpu
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					self._run_finetune(gpus=1			)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,     0.2			)





							@require_torch_multi_gpu
							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self._run_finetune(gpus=2			)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,     0.2			)





							@require_torch_gpu
							@require_ray
							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					self._run_finetune(gpus=1       ,     distributed_retriever="""ray"""			)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,     0.2			)







							@require_torch_multi_gpu
							@require_ray
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					self._run_finetune(gpus=1       ,     distributed_retriever="""ray"""			)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,     0.2			)












import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					args.log_outputs
			SCREAMING_SNAKE_CASE           =					"""_""".join(args.dataset.split("""/"""   ) + [args.config, args.split]   )

			# load metric
			SCREAMING_SNAKE_CASE           =					load_metric("""wer"""   )
			SCREAMING_SNAKE_CASE           =					load_metric("""cer"""   )

			# compute metrics
			SCREAMING_SNAKE_CASE           =					wer.compute(references=result["""target"""]					,	predictions=result["""prediction"""]   )
			SCREAMING_SNAKE_CASE           =					cer.compute(references=result["""target"""]					,	predictions=result["""prediction"""]   )

			# print & log results
			SCREAMING_SNAKE_CASE           =					f"""WER: {wer_result}\nCER: {cer_result}"""
			print(SCREAMING_SNAKE_CASE   )

			with open(f"""{dataset_id}_eval_results.txt"""					,	"""w"""   ) as f:
						f.write(SCREAMING_SNAKE_CASE   )

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						SCREAMING_SNAKE_CASE           =					f"""log_{dataset_id}_predictions.txt"""
						SCREAMING_SNAKE_CASE           =					f"""log_{dataset_id}_targets.txt"""

						with open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as p, open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as t:
									# mapping function to write output
									def write_to_file(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
												p.write(f"""{i}""" + """\n"""   )
												p.write(batch["""prediction"""] + """\n"""   )
												t.write(f"""{i}""" + """\n"""   )
												t.write(batch["""target"""] + """\n"""   )

									result.map(SCREAMING_SNAKE_CASE					,	with_indices=SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			SCREAMING_SNAKE_CASE           =					re.sub(SCREAMING_SNAKE_CASE					,	""""""					,	text.lower()   )

			# In addition, we can normalize the target text, e.g. removing new lines characters etc...
			# note that order is important here!
			SCREAMING_SNAKE_CASE           =					["""\n\n""", """\n""", """   """, """  """]

			for t in token_sequences_to_ignore:
						SCREAMING_SNAKE_CASE           =					""" """.join(text.split(SCREAMING_SNAKE_CASE   )   )

			return text
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# load dataset
			SCREAMING_SNAKE_CASE           =					load_dataset(args.dataset					,	args.config					,	split=args.split					,	use_auth_token=SCREAMING_SNAKE_CASE   )

			# for testing: only process the first two examples as a test
			# dataset = dataset.select(range(10))

			# load processor
			SCREAMING_SNAKE_CASE           =					AutoFeatureExtractor.from_pretrained(args.model_id   )
			SCREAMING_SNAKE_CASE           =					feature_extractor.sampling_rate

			# resample audio
			SCREAMING_SNAKE_CASE           =					dataset.cast_column("""audio"""					,	Audio(sampling_rate=SCREAMING_SNAKE_CASE   )   )

			# load eval pipeline
			if args.device is None:
						SCREAMING_SNAKE_CASE           =					0 if torch.cuda.is_available() else -1
			SCREAMING_SNAKE_CASE           =					pipeline("""automatic-speech-recognition"""					,	model=args.model_id					,	device=args.device   )

			# map function to decode audio
			def map_to_pred(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					asr(
						    batch["""audio"""]["""array"""]					,	chunk_length_s=args.chunk_length_s					,	stride_length_s=args.stride_length_s   )

						SCREAMING_SNAKE_CASE           =					prediction["""text"""]
						SCREAMING_SNAKE_CASE           =					normalize_text(batch["""sentence"""]   )
						return batch

			# run inference on all examples
			SCREAMING_SNAKE_CASE           =					dataset.map(SCREAMING_SNAKE_CASE					,	remove_columns=dataset.column_names   )

			# compute and log_results
			# do not change function below
			log_results(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		__A						:							List[Any]							=  argparse.ArgumentParser()

		parser.add_argument(
		    """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
		)
		parser.add_argument(
		    """--dataset""",
		    type=str,
		    required=True,
		    help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
		)
		parser.add_argument(
		    """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
		)
		parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
		parser.add_argument(
		    """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
		)
		parser.add_argument(
		    """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
		)
		parser.add_argument(
		    """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
		)
		parser.add_argument(
		    """--device""",
		    type=int,
		    default=None,
		    help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
		)
		__A						:							Tuple							=  parser.parse_args()

		main(args)






