
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       A       (					lowercase    ,       lowercase )   ->  Any:
		'''simple docstring'''

		UpperCamelCase		=     args.log_outputs
		UpperCamelCase		=     '_'.join(args.dataset.split('/' ) + [args.config, args.split] )

		# load metric
		UpperCamelCase		=     load_metric('wer' )
		UpperCamelCase		=     load_metric('cer' )

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

		# print & log results
		UpperCamelCase		=     f'''WER: {wer_result}\nCER: {cer_result}'''
		print(lowercase )

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

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

				with open(lowercase    ,       'w' ) as p, open(lowercase    ,       'w' ) as t:
						# mapping function to write output
						def write_to_file(lowercase    ,       lowercase ):
								p.write(f'''{i}''' + '\n' )
								p.write(batch['prediction'] + '\n' )
								t.write(f'''{i}''' + '\n' )
								t.write(batch['target'] + '\n' )

						result.map(lowercase    ,       with_indices=lowercase )


def       A       (					lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

		UpperCamelCase		=     re.sub(lowercase    ,       ''    ,       text.lower() )

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

		for t in token_sequences_to_ignore:
				UpperCamelCase		=     ' '.join(text.split(lowercase ) )

		return text


def       A       (					lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     load_dataset(args.dataset    ,       args.config    ,       split=args.split    ,       use_auth_token=lowercase )

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

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

		# resample audio
		UpperCamelCase		=     dataset.cast_column('audio'    ,       Audio(sampling_rate=lowercase ) )

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

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

				UpperCamelCase		=     prediction['text']
				UpperCamelCase		=     normalize_text(batch['sentence'] )
				return batch

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

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


if __name__ == "__main__":
					_UpperCAmelCase					: Optional[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.",
					)
					_UpperCAmelCase					: List[str]      =       parser.parse_args()

					main(args)


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def       A       (					lowercase )   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     [
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'encoder.embed_positions._float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowercase    ,       lowercase )


def       A       (					lowercase )   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     list(s_dict.keys() )
		for key in keys:
				if "transformer_layers" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )
				elif "subsample" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )


def       A       (					lowercase )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase	,  UpperCamelCase		=     emb.weight.shape
		UpperCamelCase		=     nn.Linear(lowercase    ,       lowercase    ,       bias=lowercase )
		UpperCamelCase		=     emb.weight.data
		return lin_layer


def       A       (					lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     torch.load(lowercase    ,       map_location='cpu' )
		UpperCamelCase		=     mam_aaa['args']
		UpperCamelCase		=     mam_aaa['model']
		UpperCamelCase		=     state_dict['decoder.output_projection.weight']

		remove_ignore_keys_(lowercase )
		rename_keys(lowercase )

		UpperCamelCase		=     state_dict['decoder.embed_tokens.weight'].shape[0]

		UpperCamelCase		=     args.share_decoder_input_output_embed

		UpperCamelCase		=     [int(lowercase ) for i in args.conv_kernel_sizes.split(',' )]
		UpperCamelCase		=     SpeechaTextConfig(
		    vocab_size=lowercase    ,       max_source_positions=args.max_source_positions    ,       max_target_positions=args.max_target_positions    ,       encoder_layers=args.encoder_layers    ,       decoder_layers=args.decoder_layers    ,       encoder_attention_heads=args.encoder_attention_heads    ,       decoder_attention_heads=args.decoder_attention_heads    ,       encoder_ffn_dim=args.encoder_ffn_embed_dim    ,       decoder_ffn_dim=args.decoder_ffn_embed_dim    ,       d_model=args.encoder_embed_dim    ,       dropout=args.dropout    ,       attention_dropout=args.attention_dropout    ,       activation_dropout=args.activation_dropout    ,       activation_function='relu'    ,       num_conv_layers=len(lowercase )    ,       conv_channels=args.conv_channels    ,       conv_kernel_sizes=lowercase    ,       input_feat_per_channel=args.input_feat_per_channel    ,       input_channels=args.input_channels    ,       tie_word_embeddings=lowercase    ,       num_beams=5    ,       max_length=200    ,       use_cache=lowercase    ,       decoder_start_token_id=2    ,       early_stopping=lowercase    ,       )

		UpperCamelCase		=     SpeechaTextForConditionalGeneration(lowercase )
		UpperCamelCase	,  UpperCamelCase		=     model.model.load_state_dict(lowercase    ,       strict=lowercase )
		if len(lowercase ) > 0 and not set(lowercase ) <= {
		    "encoder.embed_positions.weights",
		    "decoder.embed_positions.weights",
		}:
				raise ValueError(
				    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
				    f''' but all the following weights are missing {missing}''' )

		if tie_embeds:
				UpperCamelCase		=     make_linear_from_emb(model.model.decoder.embed_tokens )
		else:
				UpperCamelCase		=     lm_head_weights

		model.save_pretrained(lowercase )


if __name__ == "__main__":
					_UpperCAmelCase					: Optional[Any]      =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					_UpperCAmelCase					: Dict      =       parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
