



from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
							import tensorflow as tf

							from ..tf_utils import stable_softmax


if is_torch_available():
							import torch


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


@add_end_docstrings(
    UpperCAmelCase_							,     R"\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    "							,     )
class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Union[str, Any]				,					_snake_case					:       GenericTensor					):






								"""simple docstring"""


								if self.framework == "tf":
											A__       =    tf.where(input_ids == self.tokenizer.mask_token_id					).numpy()
								elif self.framework == "pt":
											A__       =    torch.nonzero(input_ids == self.tokenizer.mask_token_id				,					as_tuple=_snake_case					)
								else:
											raise ValueError('Unsupported framework'					)
								return masked_index



					def     _a							(     self					:       List[Any]				,					_snake_case					:       GenericTensor					):






								"""simple docstring"""


								A__       =    self.get_masked_index(_snake_case					)
								A__       =    np.prod(masked_index.shape					)
								if numel < 1:
											raise PipelineException(
											    'fill-mask'				,					self.model.base_model_prefix				,					F'''No mask_token ({self.tokenizer.mask_token}) found on the input'''				,					)



					def     _a							(     self					:       Any				,					_snake_case					:       GenericTensor					):






								"""simple docstring"""


								if isinstance(_snake_case				,					_snake_case					):
											for model_input in model_inputs:
														self._ensure_exactly_one_mask_token(model_input['input_ids'][0]					)
								else:
											for input_ids in model_inputs["input_ids"]:
														self._ensure_exactly_one_mask_token(_snake_case					)



					def     _a							(     self					:       str				,					_snake_case					:       Tuple				,					_snake_case					:       Union[str, Any]=None				,					**_snake_case					:       Dict					):






								"""simple docstring"""


								if return_tensors is None:
											A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case					)
								self.ensure_exactly_one_mask_token(_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								A__       =    model_inputs['input_ids']
								return model_outputs



					def     _a							(     self					:       int				,					_snake_case					:       Any				,					_snake_case					:       Dict=5				,					_snake_case					:       List[str]=None					):






								"""simple docstring"""


								if target_ids is not None and target_ids.shape[0] < top_k:
											A__       =    target_ids.shape[0]
								A__       =    model_outputs['input_ids'][0]
								A__       =    model_outputs['logits']

								if self.framework == "tf":
											A__       =    tf.where(input_ids == self.tokenizer.mask_token_id					).numpy()[:, 0]

											A__       =    outputs.numpy()

											A__       =    outputs[0, masked_index, :]
											A__       =    stable_softmax(_snake_case				,					axis=-1					)
											if target_ids is not None:
														A__       =    tf.gather_nd(tf.squeeze(_snake_case				,					0					)				,					target_ids.reshape(-1				,					1					)					)
														A__       =    tf.expand_dims(_snake_case				,					0					)

											A__       =    tf.math.top_k(_snake_case				,					k=_snake_case					)
											A__     , A__       =    topk.values.numpy(), topk.indices.numpy()
								else:
											A__       =    torch.nonzero(input_ids == self.tokenizer.mask_token_id				,					as_tuple=_snake_case					).squeeze(-1					)
											# Fill mask pipeline supports only one ${mask_token} per sample

											A__       =    outputs[0, masked_index, :]
											A__       =    logits.softmax(dim=-1					)
											if target_ids is not None:
														A__       =    probs[..., target_ids]

											A__     , A__       =    probs.topk(_snake_case					)

								A__       =    []
								A__       =    values.shape[0] == 1
								for i, (_values, _predictions) in enumerate(zip(values.tolist()				,					predictions.tolist()					)					):
											A__       =    []
											for v, p in zip(_values				,					_predictions					):
														# Copy is important since we're going to modify this array in place
														A__       =    input_ids.numpy().copy()
														if target_ids is not None:
																	A__       =    target_ids[p].tolist()

														A__       =    p
														# Filter padding out:
														A__       =    tokens[np.where(tokens != self.tokenizer.pad_token_id					)]
														# Originally we skip special tokens to give readable output.
														# For multi masks though, the other [MASK] would be removed otherwise
														# making the output look odd, so we add them back
														A__       =    self.tokenizer.decode(_snake_case				,					skip_special_tokens=_snake_case					)
														A__       =    {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]					), 'sequence': sequence}
														row.append(_snake_case					)
											result.append(_snake_case					)
								if single_mask:
											return result[0]
								return result



					def     _a							(     self					:       List[str]				,					_snake_case					:       List[str]				,					_snake_case					:       List[Any]=None					):






								"""simple docstring"""


								if isinstance(_snake_case				,					_snake_case					):
											A__       =    [targets]
								try:
											A__       =    self.tokenizer.get_vocab()
								except Exception:
											A__       =    {}
								A__       =    []
								for target in targets:
											A__       =    vocab.get(_snake_case				,					_snake_case					)
											if id_ is None:
														A__       =    self.tokenizer(
														    _snake_case				,					add_special_tokens=_snake_case				,					return_attention_mask=_snake_case				,					return_token_type_ids=_snake_case				,					max_length=1				,					truncation=_snake_case				,					)['input_ids']
														if len(_snake_case					) == 0:
																	logger.warning(
																	    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
																	    'We cannot replace it with anything meaningful, ignoring it'					)
																	continue
														A__       =    input_ids[0]
														# XXX: If users encounter this pass
														# it becomes pretty slow, so let's make sure
														# The warning enables them to fix the input to
														# get faster performance.
														logger.warning(
														    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
														    F'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_					)}`.'''					)
											target_ids.append(id_					)
								A__       =    list(set(_snake_case					)					)
								if len(_snake_case					) == 0:
											raise ValueError('At least one target must be provided when passed.'					)
								A__       =    np.array(_snake_case					)
								return target_ids



					def     _a							(     self					:       List[Any]				,					_snake_case					:       List[Any]=None				,					_snake_case					:       Any=None					):






								"""simple docstring"""


								A__       =    {}

								if targets is not None:
											A__       =    self.get_target_ids(_snake_case				,					_snake_case					)
											A__       =    target_ids

								if top_k is not None:
											A__       =    top_k

								if self.tokenizer.mask_token_id is None:
											raise PipelineException(
											    'fill-mask'				,					self.model.base_model_prefix				,					'The tokenizer does not define a `mask_token`.'					)
								return {}, {}, postprocess_params



					def __call__(     self					:       Any				,					_snake_case					:       Optional[int]				,					*_snake_case					:       List[Any]				,					**_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    super().__call__(_snake_case				,					**_snake_case					)
								if isinstance(_snake_case				,					_snake_case					) and len(_snake_case					) == 1:
											return outputs[0]
								return outputs




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						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			A__       =    args.log_outputs
			A__       =    '_'.join(args.dataset.split('/'					) + [args.config, args.split]					)

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

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

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

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

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

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

									result.map(__UpperCamelCase ,	with_indices=__UpperCamelCase					)


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

			A__       =    re.sub(__UpperCamelCase ,	'' ,	text.lower()					)

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

			for t in token_sequences_to_ignore:
						A__       =    ' '.join(text.split(__UpperCamelCase					)					)

			return text


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			# load dataset
			A__       =    load_dataset(args.dataset ,	args.config ,	split=args.split ,	use_auth_token=__UpperCamelCase					)

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

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

			# resample audio
			A__       =    dataset.cast_column('audio' ,	Audio(sampling_rate=__UpperCamelCase					)					)

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

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

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

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      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.''',
							)
							SCREAMING_SNAKE_CASE__       =      parser.parse_args()

							main(args)




