

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase      				=     logging.get_logger(__name__)

lowerCamelCase      				=     {
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""gptj"""
							__UpperCAmelCase       ={
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}




							def __init__( self					,						_A=5_0_4_0_0					,						_A=2_0_4_8					,						_A=4_0_9_6					,						_A=2_8					,						_A=1_6					,						_A=6_4					,						_A=None					,						_A="gelu_new"					,						_A=0.0					,						_A=0.0					,						_A=0.0					,						_A=1e-5					,						_A=0.02					,						_A=True					,						_A=5_0_2_5_6					,						_A=5_0_2_5_6					,						_A=False					,						**_A					,						):
												__lowerCAmelCase					=							vocab_size
												__lowerCAmelCase					=							n_positions
												__lowerCAmelCase					=							n_embd
												__lowerCAmelCase					=							n_layer
												__lowerCAmelCase					=							n_head
												__lowerCAmelCase					=							n_inner
												__lowerCAmelCase					=							rotary_dim
												__lowerCAmelCase					=							activation_function
												__lowerCAmelCase					=							resid_pdrop
												__lowerCAmelCase					=							embd_pdrop
												__lowerCAmelCase					=							attn_pdrop
												__lowerCAmelCase					=							layer_norm_epsilon
												__lowerCAmelCase					=							initializer_range
												__lowerCAmelCase					=							use_cache

												__lowerCAmelCase					=							bos_token_id
												__lowerCAmelCase					=							eos_token_id

												super().__init__(
												    bos_token_id=_A					,						eos_token_id=_A					,						tie_word_embeddings=_A					,						**_A							)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A = "default"					,						_A = None					,						_A = False					,						):
												super().__init__(_A					,						task=_A					,						patching_specs=_A					,						use_past=_A							)
												if not getattr(self._config					,						'pad_token_id'					,						_A							):
																	# TODO: how to do that better?
																	__lowerCAmelCase					=							0




							@property
							def        A__  ( self							):
												__lowerCAmelCase					=							OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}							)
												if self.use_past:
																	self.fill_with_past_key_values_(_A					,						direction='inputs'							)
																	__lowerCAmelCase					=							{0: 'batch', 1: 'past_sequence + sequence'}
												else:
																	__lowerCAmelCase					=							{0: 'batch', 1: 'sequence'}

												return common_inputs




							@property
							def        A__  ( self							):
												return self._config.n_layer




							@property
							def        A__  ( self							):
												return self._config.n_head




							def        A__  ( self					,						_A					,						_A = -1					,						_A = -1					,						_A = False					,						_A = None					,						):
												__lowerCAmelCase					=							super(_A					,						self							).generate_dummy_inputs(
												    _A					,						batch_size=_A					,						seq_length=_A					,						is_pair=_A					,						framework=_A							)

												# We need to order the input in the way they appears in the forward()
												__lowerCAmelCase					=							OrderedDict({'input_ids': common_inputs['input_ids']}							)

												# Need to add the past_keys
												if self.use_past:
																	if not is_torch_available():
																						raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'							)
																	else:
																						import torch

																						__lowerCAmelCase,	__lowerCAmelCase					=							common_inputs['input_ids'].shape
																						# Not using the same length for past_key_values
																						__lowerCAmelCase					=							seqlen + 2
																						__lowerCAmelCase					=							(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						__lowerCAmelCase					=							[
																						    (torch.zeros(_A							), torch.zeros(_A							)) for _ in range(self.num_layers							)
																						]

												__lowerCAmelCase					=							common_inputs['attention_mask']
												if self.use_past:
																	__lowerCAmelCase					=							ordered_inputs['attention_mask'].dtype
																	__lowerCAmelCase					=							torch.cat(
																	    [ordered_inputs['attention_mask'], torch.ones(_A					,						_A					,						dtype=_A							)]					,						dim=1							)

												return ordered_inputs




							@property
							def        A__  ( self							):
												return 1_3









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

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

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

					# print & log results
					__lowerCAmelCase					=							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:
										__lowerCAmelCase					=							F"""log_{dataset_id}_predictions.txt"""
										__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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

					__lowerCAmelCase					=							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!
					__lowerCAmelCase					=							['\n\n', '\n', '   ', '  ']

					for t in token_sequences_to_ignore:
										__lowerCAmelCase					=							' '.join(text.split(UpperCAmelCase__      )      )

					return text


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							AutoFeatureExtractor.from_pretrained(args.model_id      )
					__lowerCAmelCase					=							feature_extractor.sampling_rate

					# resample audio
					__lowerCAmelCase					=							dataset.cast_column('audio'		,					Audio(sampling_rate=UpperCAmelCase__      )      )

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

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

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

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

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


if __name__ == "__main__":
		lowerCamelCase      				=     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      				=     parser.parse_args()

		main(args)






