






from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
 from .deepspeed import (
     DeepSpeedEngineWrapper,
     DeepSpeedOptimizerWrapper,
     DeepSpeedSchedulerWrapper,
     DummyOptim,
     DummyScheduler,
     HfDeepSpeedConfig,
 )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers











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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dataset  ,						snake_case_      :							Dict[str, str]     ):
	snake_case__				:   Tuple     =						args.log_outputs
	snake_case__				:   Union[str, Any]     =						"_".join(args.dataset.split("/"     ) + [args.config, args.split]     )

	# load metric
	snake_case__				:   List[str]     =						load_metric("wer"     )
	snake_case__				:   List[str]     =						load_metric("cer"     )

	# compute metrics
	snake_case__				:   List[Any]     =						wer.compute(references=result["target"]  ,						predictions=result["prediction"]     )
	snake_case__				:   List[str]     =						cer.compute(references=result["target"]  ,						predictions=result["prediction"]     )

	# print & log results
	snake_case__				:   Dict     =						F'''WER: {wer_result}\nCER: {cer_result}'''
	print(snake_case_     )

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

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		snake_case__				:   Union[str, Any]     =						F'''log_{dataset_id}_predictions.txt'''
		snake_case__				:   int     =						F'''log_{dataset_id}_targets.txt'''

		with open(snake_case_  ,						"w"     ) as p, open(snake_case_  ,						"w"     ) as t:
			# mapping function to write output
			def write_to_file(snake_case_      :							Union[str, Any]  ,						snake_case_      :							Any     ):
				p.write(F'''{i}''' + "\n"     )
				p.write(batch["prediction"] + "\n"     )
				t.write(F'''{i}''' + "\n"     )
				t.write(batch["target"] + "\n"     )

			result.map(snake_case_  ,						with_indices=snake_case_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   List[Any]     =						"[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	snake_case__				:   Optional[int]     =						re.sub(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!
	snake_case__				:   Optional[Any]     =						["\n\n", "\n", "   ", "  "]

	for t in token_sequences_to_ignore:
		snake_case__				:   Optional[int]     =						" ".join(text.split(snake_case_     )     )

	return text







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	# load dataset
	snake_case__				:   int     =						load_dataset(args.dataset  ,						args.config  ,						split=args.split  ,						use_auth_token=snake_case_     )

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

	# load processor
	snake_case__				:   List[str]     =						AutoFeatureExtractor.from_pretrained(args.model_id     )
	snake_case__				:   List[Any]     =						feature_extractor.sampling_rate

	# resample audio
	snake_case__				:   Dict     =						dataset.cast_column("audio"  ,						Audio(sampling_rate=snake_case_     )     )

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

	# map function to decode audio
	def map_to_pred(snake_case_      :							Any     ):
		snake_case__				:   Union[str, Any]     =						asr(
		    batch["audio"]["array"]  ,						chunk_length_s=args.chunk_length_s  ,						stride_length_s=args.stride_length_s     )

		snake_case__				:   Optional[int]     =						prediction["text"]
		snake_case__				:   Optional[Any]     =						normalize_text(batch["sentence"]     )
		return batch

	# run inference on all examples
	snake_case__				:   Any     =						dataset.map(snake_case_  ,						remove_columns=dataset.column_names     )

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


if __name__ == "__main__":
	__lowerCamelCase      :    Dict   		=   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.""",
	)
	__lowerCamelCase      :    str   		=   parser.parse_args()

	main(args)



