import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_lowerCamelCase					:   Dict							=		getLogger(__name__)


_lowerCamelCase					:   Optional[int]							=		'''cuda''' if torch.cuda.is_available() else '''cpu'''





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Optional[int] = 8      , UpperCAmelCase__     :							Any = DEFAULT_DEVICE      , UpperCAmelCase__     :							Optional[Any]=False      , UpperCAmelCase__     :							Any="summarization"      , UpperCAmelCase__     :							List[str]=None      , **UpperCAmelCase__     :							Tuple      , ):
					SCREAMING_SNAKE_CASE    			=       Path(__SCREAMING_SNAKE_CASE  ).open("w"      , encoding="utf-8"  )
					SCREAMING_SNAKE_CASE    			=       str(__SCREAMING_SNAKE_CASE  )
					SCREAMING_SNAKE_CASE    			=       AutoModelForSeqaSeqLM.from_pretrained(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  )
					if fpaa:
										SCREAMING_SNAKE_CASE    			=       model.half()

					SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE  )
					logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"  )  # if this is wrong, check config.model_type.

					SCREAMING_SNAKE_CASE    			=       time.time()
					# update config with task specific params
					use_task_specific_params(__SCREAMING_SNAKE_CASE      , __SCREAMING_SNAKE_CASE  )
					if prefix is None:
										SCREAMING_SNAKE_CASE    			=       prefix or getattr(model.config      , "prefix"      , ""  ) or ""
					for examples_chunk in tqdm(list(chunks(__SCREAMING_SNAKE_CASE      , __SCREAMING_SNAKE_CASE  )  )  ):
										SCREAMING_SNAKE_CASE    			=       [prefix + text for text in examples_chunk]
										SCREAMING_SNAKE_CASE    			=       tokenizer(__SCREAMING_SNAKE_CASE      , return_tensors="pt"      , truncation=__SCREAMING_SNAKE_CASE      , padding="longest"  ).to(__SCREAMING_SNAKE_CASE  )
										SCREAMING_SNAKE_CASE    			=       model.generate(
										    input_ids=batch.input_ids      , attention_mask=batch.attention_mask      , **__SCREAMING_SNAKE_CASE      , )
										SCREAMING_SNAKE_CASE    			=       tokenizer.batch_decode(__SCREAMING_SNAKE_CASE      , skip_special_tokens=__SCREAMING_SNAKE_CASE      , clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE  )
										for hypothesis in dec:
															fout.write(hypothesis + "\n"  )
															fout.flush()
					fout.close()
					SCREAMING_SNAKE_CASE    			=       int(time.time() - start_time  )  # seconds
					SCREAMING_SNAKE_CASE    			=       len(__SCREAMING_SNAKE_CASE  )
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs      , 4  )}





def 	__lowerCamelCase							():
					return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple=True  ):
					SCREAMING_SNAKE_CASE    			=       argparse.ArgumentParser()
					parser.add_argument("model_name"      , type=__SCREAMING_SNAKE_CASE      , help="like facebook/bart-large-cnn,t5-base, etc."  )
					parser.add_argument("input_path"      , type=__SCREAMING_SNAKE_CASE      , help="like cnn_dm/test.source"  )
					parser.add_argument("save_path"      , type=__SCREAMING_SNAKE_CASE      , help="where to save summaries"  )
					parser.add_argument("--reference_path"      , type=__SCREAMING_SNAKE_CASE      , required=__SCREAMING_SNAKE_CASE      , help="like cnn_dm/test.target"  )
					parser.add_argument("--score_path"      , type=__SCREAMING_SNAKE_CASE      , required=__SCREAMING_SNAKE_CASE      , default="metrics.json"      , help="where to save metrics"  )
					parser.add_argument("--device"      , type=__SCREAMING_SNAKE_CASE      , required=__SCREAMING_SNAKE_CASE      , default=__SCREAMING_SNAKE_CASE      , help="cuda, cuda:1, cpu etc."  )
					parser.add_argument(
					    "--prefix"      , type=__SCREAMING_SNAKE_CASE      , required=__SCREAMING_SNAKE_CASE      , default=__SCREAMING_SNAKE_CASE      , help="will be added to the begininng of src examples"  )
					parser.add_argument("--task"      , type=__SCREAMING_SNAKE_CASE      , default="summarization"      , help="used for task_specific_params + metrics"  )
					parser.add_argument("--bs"      , type=__SCREAMING_SNAKE_CASE      , default=8      , required=__SCREAMING_SNAKE_CASE      , help="batch size"  )
					parser.add_argument(
					    "--n_obs"      , type=__SCREAMING_SNAKE_CASE      , default=-1      , required=__SCREAMING_SNAKE_CASE      , help="How many observations. Defaults to all."  )
					parser.add_argument("--fp16"      , action="store_true"  )
					parser.add_argument("--dump-args"      , action="store_true"      , help="print the custom hparams with the results"  )
					parser.add_argument(
					    "--info"      , nargs="?"      , type=__SCREAMING_SNAKE_CASE      , const=datetime_now()      , help=(
					        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
					        " lang=en-ru. If no value is passed, the current datetime string will be used."
					    )      , )
					# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       parser.parse_known_args()
					SCREAMING_SNAKE_CASE    			=       parse_numeric_n_bool_cl_kwargs(__SCREAMING_SNAKE_CASE  )
					if parsed_args and verbose:
										print(F"parsed the following generate kwargs: {parsed_args}"  )
					SCREAMING_SNAKE_CASE    			=       [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path  ).readlines()]
					if args.n_obs > 0:
										SCREAMING_SNAKE_CASE    			=       examples[: args.n_obs]
					Path(args.save_path  ).parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE  )

					if args.reference_path is None and Path(args.score_path  ).exists():
										warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."  )

					if args.device == "cpu" and args.fpaa:
										# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
										raise ValueError("Can't mix --fp16 and --device cpu"  )

					SCREAMING_SNAKE_CASE    			=       generate_summaries_or_translations(
					    __SCREAMING_SNAKE_CASE      , args.save_path      , args.model_name      , batch_size=args.bs      , device=args.device      , fpaa=args.fpaa      , task=args.task      , prefix=args.prefix      , **__SCREAMING_SNAKE_CASE      , )

					if args.reference_path is None:
										return {}

					# Compute scores
					SCREAMING_SNAKE_CASE    			=       calculate_bleu if "translation" in args.task else calculate_rouge
					SCREAMING_SNAKE_CASE    			=       [x.rstrip() for x in open(args.save_path  ).readlines()]
					SCREAMING_SNAKE_CASE    			=       [x.rstrip() for x in open(args.reference_path  ).readlines()][: len(__SCREAMING_SNAKE_CASE  )]
					SCREAMING_SNAKE_CASE    			=       score_fn(__SCREAMING_SNAKE_CASE      , __SCREAMING_SNAKE_CASE  )
					scores.update(__SCREAMING_SNAKE_CASE  )

					if args.dump_args:
										scores.update(__SCREAMING_SNAKE_CASE  )
					if args.info:
										SCREAMING_SNAKE_CASE    			=       args.info

					if verbose:
										print(__SCREAMING_SNAKE_CASE  )

					if args.score_path is not None:
										json.dump(__SCREAMING_SNAKE_CASE      , open(args.score_path      , "w"  )  )

					return scores


if __name__ == "__main__":
		# Usage for MT:
		# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
		run_generate(verbose=True)





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCamelCase					:   Union[str, Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							["""input_features""", """is_longer"""]




							def __init__(						self   :					str	,							_UpperCamelCase   :					Optional[int]=64	,							_UpperCamelCase   :					Any=48_000	,							_UpperCamelCase   :					Optional[Any]=480	,							_UpperCamelCase   :					List[Any]=10	,							_UpperCamelCase   :					Any=1_024	,							_UpperCamelCase   :					List[Any]=0.0	,							_UpperCamelCase   :					Any=False	,							_UpperCamelCase   :					float = 0	,							_UpperCamelCase   :					float = 14_000	,							_UpperCamelCase   :					int = None	,							_UpperCamelCase   :					str = "fusion"	,							_UpperCamelCase   :					str = "repeatpad"	,							**_UpperCamelCase   :					Optional[Any]	,							)					->				Dict:



												'''simple docstring'''

												super().__init__(
												    feature_size=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							padding_value=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							**_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       top_db
												SCREAMING_SNAKE_CASE    			=       truncation
												SCREAMING_SNAKE_CASE    			=       padding
												SCREAMING_SNAKE_CASE    			=       fft_window_size
												SCREAMING_SNAKE_CASE    			=       (fft_window_size >> 1) + 1
												SCREAMING_SNAKE_CASE    			=       hop_length
												SCREAMING_SNAKE_CASE    			=       max_length_s
												SCREAMING_SNAKE_CASE    			=       max_length_s * sampling_rate
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       frequency_min
												SCREAMING_SNAKE_CASE    			=       frequency_max
												SCREAMING_SNAKE_CASE    			=       mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins	,							num_mel_filters=_UpperCamelCase	,							min_frequency=_UpperCamelCase	,							max_frequency=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							norm=_UpperCamelCase	,							mel_scale="htk"	,							)
												SCREAMING_SNAKE_CASE    			=       mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins	,							num_mel_filters=_UpperCamelCase	,							min_frequency=_UpperCamelCase	,							max_frequency=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							norm="slaney"	,							mel_scale="slaney"	,							)




							def 	__snake_case(						self   :					str      )					->				Dict[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       copy.deepcopy(self.__dict__      )
												SCREAMING_SNAKE_CASE    			=       self.__class__.__name__
												if "mel_filters" in output:
																	del output["mel_filters"]
												if "mel_filters_slaney" in output:
																	del output["mel_filters_slaney"]
												return output




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					np.array	,							_UpperCamelCase   :					Optional[np.array] = None      )					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       spectrogram(
												    _UpperCamelCase	,							window_function(self.fft_window_size	,							"hann"      )	,							frame_length=self.fft_window_size	,							hop_length=self.hop_length	,							power=2.0	,							mel_filters=_UpperCamelCase	,							log_mel="dB"	,							)
												return log_mel_spectrogram.T




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					str      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       np.array_split(list(range(0	,							total_frames - chunk_frames + 1      )      )	,							3      )
												if len(ranges[1]      ) == 0:
																	# if the audio is too short, we just use the first chunk
																	SCREAMING_SNAKE_CASE    			=       [0]
												if len(ranges[2]      ) == 0:
																	# if the audio is too short, we just use the first chunk
																	SCREAMING_SNAKE_CASE    			=       [0]
												# randomly choose index for each part
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[0]      )
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[1]      )
												SCREAMING_SNAKE_CASE    			=       np.random.choice(ranges[2]      )

												SCREAMING_SNAKE_CASE    			=       mel[idx_front : idx_front + chunk_frames, :]
												SCREAMING_SNAKE_CASE    			=       mel[idx_middle : idx_middle + chunk_frames, :]
												SCREAMING_SNAKE_CASE    			=       mel[idx_back : idx_back + chunk_frames, :]

												SCREAMING_SNAKE_CASE    			=       torch.tensor(mel[None, None, :]      )
												SCREAMING_SNAKE_CASE    			=       torch.nn.functional.interpolate(
												    _UpperCamelCase	,							size=[chunk_frames, 64]	,							mode="bilinear"	,							align_corners=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       mel_shrink[0][0].numpy()
												SCREAMING_SNAKE_CASE    			=       np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,							axis=0      )
												return mel_fusion




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					np.array	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[str]      )					->				np.array:



												'''simple docstring'''

												if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																						SCREAMING_SNAKE_CASE    			=       True
																						# random crop to max_length (for compatibility) -> this should be handled by self.pad
																						SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) - max_length
																						SCREAMING_SNAKE_CASE    			=       np.random.randint(0	,							overflow + 1      )
																						SCREAMING_SNAKE_CASE    			=       waveform[idx : idx + max_length]
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters_slaney      )[None, :]
																	elif truncation == "fusion":
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters      )
																						SCREAMING_SNAKE_CASE    			=       max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																						SCREAMING_SNAKE_CASE    			=       mel.shape[0]
																						if chunk_frames == total_frames:
																											# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																											# In this case, we just use the whole audio.
																											SCREAMING_SNAKE_CASE    			=       np.stack([mel, mel, mel, mel]	,							axis=0      )
																											SCREAMING_SNAKE_CASE    			=       False
																						else:
																											SCREAMING_SNAKE_CASE    			=       self._random_mel_fusion(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																											SCREAMING_SNAKE_CASE    			=       True
																	else:
																						raise NotImplementedError(F"data_truncating {truncation} not implemented"      )

												else:
																	SCREAMING_SNAKE_CASE    			=       False
																	# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																	if waveform.shape[0] < max_length:
																						if padding == "repeat":
																											SCREAMING_SNAKE_CASE    			=       int(max_length / len(_UpperCamelCase      )      )
																											SCREAMING_SNAKE_CASE    			=       np.stack(np.tile(_UpperCamelCase	,							n_repeat + 1      )      )[:max_length]
																						if padding == "repeatpad":
																											SCREAMING_SNAKE_CASE    			=       int(max_length / len(_UpperCamelCase      )      )
																											SCREAMING_SNAKE_CASE    			=       np.stack(np.tile(_UpperCamelCase	,							_UpperCamelCase      )      )
																						SCREAMING_SNAKE_CASE    			=       np.pad(_UpperCamelCase	,							(0, max_length - waveform.shape[0])	,							mode="constant"	,							constant_values=0      )

																	if truncation == "fusion":
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters      )
																						SCREAMING_SNAKE_CASE    			=       np.stack([input_mel, input_mel, input_mel, input_mel]	,							axis=0      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       self._np_extract_fbank_features(_UpperCamelCase	,							self.mel_filters_slaney      )[None, :]

												return input_mel, longer





							def __call__(						self   :					Dict	,							_UpperCamelCase   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,							_UpperCamelCase   :					str = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							**_UpperCamelCase   :					Tuple	,							)					->				BatchFeature:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       truncation if truncation is not None else self.truncation
												SCREAMING_SNAKE_CASE    			=       padding if padding else self.padding

												if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																						raise ValueError(
																						    F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
																						    F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
																						    F" was sampled with {self.sampling_rate} and not {sampling_rate}."      )
												else:
																	logger.warning(
																	    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
																	    "Failing to do so can result in silent errors that might be hard to debug."      )

												SCREAMING_SNAKE_CASE    			=       isinstance(_UpperCamelCase	,							np.ndarray      ) and len(raw_speech.shape      ) > 1
												if is_batched_numpy and len(raw_speech.shape      ) > 2:
																	raise ValueError(F"Only mono-channel audio is supported for input to {self}"      )
												SCREAMING_SNAKE_CASE    			=       is_batched_numpy or (
												    isinstance(_UpperCamelCase	,							(list, tuple)      ) and (isinstance(raw_speech[0]	,							(np.ndarray, tuple, list)      ))
												)

												if is_batched:
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase	,							dtype=np.floataa      ) for speech in raw_speech]
												elif not is_batched and not isinstance(_UpperCamelCase	,							np.ndarray      ):
																	SCREAMING_SNAKE_CASE    			=       np.asarray(_UpperCamelCase	,							dtype=np.floataa      )
												elif isinstance(_UpperCamelCase	,							np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
																	SCREAMING_SNAKE_CASE    			=       raw_speech.astype(np.floataa      )

												# always return batch
												if not is_batched:
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase      )]

												# convert to mel spectrogram, truncate and pad if needed.
												SCREAMING_SNAKE_CASE    			=       [
												    self._get_input_mel(_UpperCamelCase	,							max_length if max_length else self.nb_max_samples	,							_UpperCamelCase	,							_UpperCamelCase      )
												    for waveform in raw_speech
												]

												SCREAMING_SNAKE_CASE    			=       []
												SCREAMING_SNAKE_CASE    			=       []
												for mel, longer in padded_inputs:
																	input_mel.append(_UpperCamelCase      )
																	is_longer.append(_UpperCamelCase      )

												if truncation == "fusion" and sum(_UpperCamelCase      ) == 0:
																	# if no audio is longer than 10s, then randomly select one audio to be longer
																	SCREAMING_SNAKE_CASE    			=       np.random.randint(0	,							len(_UpperCamelCase      )      )
																	SCREAMING_SNAKE_CASE    			=       True

												if isinstance(input_mel[0]	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase	,							dtype=np.floataa      ) for feature in input_mel]

												# is_longer is a list of bool
												SCREAMING_SNAKE_CASE    			=       [[longer] for longer in is_longer]

												SCREAMING_SNAKE_CASE    			=       {"input_features": input_mel, "is_longer": is_longer}
												SCREAMING_SNAKE_CASE    			=       BatchFeature(_UpperCamelCase      )

												if return_tensors is not None:
																	SCREAMING_SNAKE_CASE    			=       input_features.convert_to_tensors(_UpperCamelCase      )

												return input_features




