





"""simple docstring"""




import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


a		:				Dict  			=    trt.Logger(trt.Logger.WARNING)
a		:				Optional[Any]  			=    absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

a		:				str  			=    logging.getLogger(__name__)

a		:				List[str]  			=    argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '''--onnx_model_path''',
    default=None,
    type=str,
    required=True,
    help='''Path to ONNX model: ''',
)

parser.add_argument(
    '''--output_dir''',
    default=None,
    type=str,
    required=True,
    help='''The output directory where the model checkpoints and predictions will be written.''',
)

# Other parameters

parser.add_argument(
    '''--tokenizer_name''',
    default='''''',
    type=str,
    required=True,
    help='''Pretrained tokenizer name or path if not the same as model_name''',
)

parser.add_argument(
    '''--version_2_with_negative''',
    action='''store_true''',
    help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
    '''--null_score_diff_threshold''',
    type=float,
    default=0.0,
    help='''If null_score - best_non_null is greater than the threshold predict null.''',
)

parser.add_argument(
    '''--max_seq_length''',
    default=384,
    type=int,
    help=(
        '''The maximum total input sequence length after WordPiece tokenization. Sequences '''
        '''longer than this will be truncated, and sequences shorter than this will be padded.'''
    ),
)
parser.add_argument(
    '''--doc_stride''',
    default=128,
    type=int,
    help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)

parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')

parser.add_argument(
    '''--n_best_size''',
    default=20,
    type=int,
    help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
    '''--max_answer_length''',
    default=30,
    type=int,
    help=(
        '''The maximum length of an answer that can be generated. This is needed because the start '''
        '''and end predictions are not conditioned on one another.'''
    ),
)

parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')

parser.add_argument(
    '''--dataset_name''',
    type=str,
    default=None,
    required=True,
    help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--dataset_config_name''',
    type=str,
    default=None,
    help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
    '''--fp16''',
    action='''store_true''',
    help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
    '''--int8''',
    action='''store_true''',
    help='''Whether to use INT8''',
)

a		:				Optional[int]  			=    parser.parse_args()

if args.tokenizer_name:
		a		:				List[Any]  			=    AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
		raise ValueError(
		    '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
		    '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
		)

logger.info('''Training/evaluation parameters %s''', args)

a		:				Tuple  			=    args.per_device_eval_batch_size

a		:				Dict  			=    (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a		:				Dict  			=    True

a		:				List[Any]  			=    '''temp_engine/bert-fp32.engine'''
if args.fpaa:
		a		:				Optional[int]  			=    '''temp_engine/bert-fp16.engine'''
if args.inta:
		a		:				List[str]  			=    '''temp_engine/bert-int8.engine'''

# import ONNX file
if not os.path.exists('''temp_engine'''):
		os.makedirs('''temp_engine''')

a		:				Optional[Any]  			=    1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
		with open(args.onnx_model_path, '''rb''') as model:
				if not parser.parse(model.read()):
						for error in range(parser.num_errors):
								print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
		a		:				List[str]  			=    [network.get_input(i) for i in range(network.num_inputs)]
		a		:				str  			=    [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

		with builder.create_builder_config() as config:
				a		:				str  			=    1 << 50
				if STRICT_TYPES:
						config.set_flag(trt.BuilderFlag.STRICT_TYPES)
				if args.fpaa:
						config.set_flag(trt.BuilderFlag.FPaa)
				if args.inta:
						config.set_flag(trt.BuilderFlag.INTa)
				a		:				Optional[int]  			=    builder.create_optimization_profile()
				config.add_optimization_profile(profile)
				for i in range(len(input_names)):
						profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
				a		:				int  			=    builder.build_engine(network, config)

				# serialize_engine and store in file (can be directly loaded and deserialized):
				with open(engine_name, '''wb''') as f:
						f.write(engine.serialize())






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					Optional[int]					, _lowercase		:					List[Any]					, _lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]					, _lowercase		:					str					, _lowercase		:					Tuple      )				->int:






	'''simple docstring'''






	a   :	List[Any]			=       np.asarray(inputs["input_ids"]					, dtype=np.intaa      )
	a   :	Any			=       np.asarray(inputs["attention_mask"]					, dtype=np.intaa      )
	a   :	Optional[Any]			=       np.asarray(inputs["token_type_ids"]					, dtype=np.intaa      )

	# Copy inputs
	cuda.memcpy_htod_async(d_inputs[0]					, input_ids.ravel()					, _lowercase      )
	cuda.memcpy_htod_async(d_inputs[1]					, attention_mask.ravel()					, _lowercase      )
	cuda.memcpy_htod_async(d_inputs[2]					, token_type_ids.ravel()					, _lowercase      )
	# start time
	a   :	Optional[int]			=       time.time()
	# Run inference
	context.execute_async(
	    bindings=[int(_lowercase      ) for d_inp in d_inputs] + [int(_lowercase      ), int(_lowercase      )]					, stream_handle=stream.handle      )
	# Transfer predictions back from GPU
	cuda.memcpy_dtoh_async(_lowercase					, _lowercase					, _lowercase      )
	cuda.memcpy_dtoh_async(_lowercase					, _lowercase					, _lowercase      )
	# Synchronize the stream and take time
	stream.synchronize()
	# end time
	a   :	str			=       time.time()
	a   :	Any			=       end_time - start_time
	a   :	List[Any]			=       (h_outputa, h_outputa)
	# print(outputs)
	return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
a		:				int  			=    Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_info()
else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
		set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		a		:				int  			=    load_dataset(args.dataset_name, args.dataset_config_name)
else:
		raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

a		:				Dict  			=    raw_datasets['''validation'''].column_names

a		:				Tuple  			=    '''question''' if '''question''' in column_names else column_names[0]
a		:				Dict  			=    '''context''' if '''context''' in column_names else column_names[1]
a		:				Any  			=    '''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
a		:				Any  			=    tokenizer.padding_side == '''right'''

if args.max_seq_length > tokenizer.model_max_length:
		logger.warning(
		    F'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
		    F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
		)

a		:				int  			=    min(args.max_seq_length, tokenizer.model_max_length)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any      )				->Union[str, Any]:






	'''simple docstring'''






	a   :	List[Any]			=       [q.lstrip() for q in examples[question_column_name]]

	# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
	# in one example possible giving several features when a context is long, each of those features having a
	# context that overlaps a bit the context of the previous feature.
	a   :	Optional[int]			=       tokenizer(
	    examples[question_column_name if pad_on_right else context_column_name]					, examples[context_column_name if pad_on_right else question_column_name]					, truncation="only_second" if pad_on_right else "only_first"					, max_length=_lowercase					, stride=args.doc_stride					, return_overflowing_tokens=_lowercase					, return_offsets_mapping=_lowercase					, padding="max_length"					, )

	# Since one example might give us several features if it has a long context, we need a map from a feature to
	# its corresponding example. This key gives us just that.
	a   :	List[str]			=       tokenized_examples.pop("overflow_to_sample_mapping"      )

	# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
	# corresponding example_id and we will store the offset mappings.
	a   :	str			=       []

	for i in range(len(tokenized_examples["input_ids"]      )      ):
		# Grab the sequence corresponding to that example (to know what is the context and what is the question).
		a   :	Tuple			=       tokenized_examples.sequence_ids(_lowercase      )
		a   :	Optional[Any]			=       1 if pad_on_right else 0

		# One example can give several spans, this is the index of the example containing this span of text.
		a   :	Dict			=       sample_mapping[i]
		tokenized_examples["example_id"].append(examples["id"][sample_index]      )

		# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
		# position is part of the context or not.
		a   :	Any			=       [
		    (o if sequence_ids[k] == context_index else None)
		    for k, o in enumerate(tokenized_examples["offset_mapping"][i]      )
		]

	return tokenized_examples


a		:				Union[str, Any]  			=    raw_datasets['''validation''']
# Validation Feature Creation
a		:				Any  			=    eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='''Running tokenizer on validation dataset''',
)

a		:				Optional[int]  			=    default_data_collator

a		:				Tuple  			=    eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
a		:				Optional[Any]  			=    DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple					, _lowercase		:					str					, _lowercase		:					int					, _lowercase		:					int="eval"      )				->List[str]:






	'''simple docstring'''






	a   :	Union[str, Any]			=       postprocess_qa_predictions(
	    examples=_lowercase					, features=_lowercase					, predictions=_lowercase					, version_2_with_negative=args.version_2_with_negative					, n_best_size=args.n_best_size					, max_answer_length=args.max_answer_length					, null_score_diff_threshold=args.null_score_diff_threshold					, output_dir=args.output_dir					, prefix=_lowercase					, )
	# Format the result to the format the metric expects.
	if args.version_2_with_negative:
		a   :	Optional[Any]			=       [
		    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
		]
	else:
		a   :	List[str]			=       [{"id": k, "prediction_text": v} for k, v in predictions.items()]

	a   :	Optional[Any]			=       [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
	return EvalPrediction(predictions=_lowercase					, label_ids=_lowercase      )


a		:				Union[str, Any]  			=    load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
		# setup for TRT inferrence
		for i in range(len(input_names)):
				context.set_binding_shape(i, INPUT_SHAPE)
		assert context.all_binding_shapes_specified






		def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]      )				->List[Any]:






			'''simple docstring'''






			return trt.volume(engine.get_binding_shape(_lowercase      )      ) * engine.get_binding_dtype(_lowercase      ).itemsize

		# Allocate device memory for inputs and outputs.
		a		:				Dict  			=    [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

		# Allocate output buffer
		a		:				str  			=    cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
		a		:				Union[str, Any]  			=    cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
		a		:				List[Any]  			=    cuda.mem_alloc(h_outputa.nbytes)
		a		:				List[str]  			=    cuda.mem_alloc(h_outputa.nbytes)

		# Create a stream in which to copy inputs/outputs and run inference.
		a		:				int  			=    cuda.Stream()

		# Evaluation
		logger.info('''***** Running Evaluation *****''')
		logger.info(F'''  Num examples = {len(eval_dataset)}''')
		logger.info(F'''  Batch size = {args.per_device_eval_batch_size}''')

		a		:				str  			=    0.0
		a		:				List[Any]  			=    0
		a		:				Any  			=    timeit.default_timer()

		a		:				Optional[int]  			=    None
		for step, batch in enumerate(eval_dataloader):
				a			,				a		:				Optional[int]  			=    model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
				total_time += infer_time
				niter += 1

				a			,				a		:				Any  			=    outputs
				a		:				Tuple  			=    torch.tensor(start_logits)
				a		:				Union[str, Any]  			=    torch.tensor(end_logits)

				# necessary to pad predictions and labels for being gathered
				a		:				int  			=    accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
				a		:				List[str]  			=    accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

				a		:				Tuple  			=    (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
				a		:				str  			=    logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

		if all_preds is not None:
				a		:				Any  			=    nested_truncate(all_preds, len(eval_dataset))

		a		:				Union[str, Any]  			=    timeit.default_timer() - start_time
		logger.info('''  Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
		# Inference time from TRT
		logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
		logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1000))







		logger.info('''Total Number of Inference =  %d''', niter)

a		:				Union[str, Any]  			=    post_processing_function(eval_examples, eval_dataset, all_preds)
a		:				int  			=    metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







logger.info(F'''Evaluation metrics: {eval_metric}''')









"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


a		:				List[Any]  			=    random.Random()






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					int=1.0					, _lowercase		:					Optional[int]=None					, _lowercase		:					Union[str, Any]=None      )				->Optional[Any]:






	'''simple docstring'''






	if rng is None:
		a   :	Tuple			=       global_rng

	a   :	Tuple			=       []
	for batch_idx in range(shape[0]      ):
		values.append([]      )
		for _ in range(shape[1]      ):
			values[-1].append(rng.random() * scale      )

	return values


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=400   ,      lowerCAmelCase__=2000   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=1_6000   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__="hann_window"   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=7600   ,      lowerCAmelCase__=1E-10   ,      lowerCAmelCase__=True   ,      )			->				Optional[Any]:
								a   :	int			=       parent
								a   :	Tuple			=       batch_size
								a   :	Dict			=       min_seq_length
								a   :	Any			=       max_seq_length
								a   :	Optional[int]			=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								a   :	Union[str, Any]			=       feature_size
								a   :	Tuple			=       padding_value
								a   :	str			=       sampling_rate
								a   :	Dict			=       do_normalize
								a   :	str			=       num_mel_bins
								a   :	List[str]			=       hop_length
								a   :	str			=       win_length
								a   :	Optional[Any]			=       win_function
								a   :	List[str]			=       fmin
								a   :	Any			=       fmax
								a   :	Optional[int]			=       mel_floor
								a   :	Tuple			=       return_attention_mask






							def 					__a     (     self   )			->				Optional[Any]:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "do_normalize": self.do_normalize,
								    "num_mel_bins": self.num_mel_bins,
								    "hop_length": self.hop_length,
								    "win_length": self.win_length,
								    "win_function": self.win_function,
								    "fmin": self.fmin,
								    "fmax": self.fmax,
								    "mel_floor": self.mel_floor,
								    "return_attention_mask": self.return_attention_mask,
								}






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Tuple:
								def _flatten(lowerCAmelCase__   ):
									return list(itertools.chain(*lowerCAmelCase__   )   )

								if equal_length:
									a   :	Union[str, Any]			=       floats_list((self.batch_size, self.max_seq_length)   )
								else:
									# make sure that inputs increase in size
									a   :	str			=       [
									    _flatten(floats_list((x, self.feature_size)   )   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Dict:
								if equal_length:
									a   :	str			=       [floats_list((self.max_seq_length, self.num_mel_bins)   ) for _ in range(self.batch_size   )]
								else:
									# make sure that inputs increase in size
									a   :	Any			=       [
									    floats_list((x, self.num_mel_bins)   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Optional[int]			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs


@require_torch
class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Tuple         =SpeechTaFeatureExtractor






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       SpeechTaFeatureExtractionTester(self   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								self.assertTrue(np.all(np.mean(lowerCAmelCase__   ,      axis=0   ) < 1E-3   )   )
								self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__   ,      axis=0   ) - 1   ) < 1E-3   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Any			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test not batched input
								a   :	Optional[int]			=       feat_extract(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	Optional[Any]			=       feat_extract(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	List[Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Dict			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]

								a   :	int			=       ["longest", "max_length", "do_not_pad"]
								a   :	Tuple			=       [None, 1600, None]
								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	Dict			=       feat_extract(lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      return_tensors="np"   )
									a   :	List[Any]			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self.assertTrue(input_values[0][800:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self.assertTrue(input_values[0][1000:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				str:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	List[str]			=       range(800   ,      1400   ,      200   )
								a   :	List[str]			=       [floats_list((1, x)   )[0] for x in lengths]

								a   :	Any			=       ["longest", "max_length", "do_not_pad"]
								a   :	Any			=       [None, 1600, None]

								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	List[Any]			=       feat_extract(lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      padding=lowerCAmelCase__   )
									a   :	Dict			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	int			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Union[str, Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="max_length"   ,      return_tensors="np"   )
								a   :	List[Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1]   )
								self._check_zero_mean_unit_variance(input_values[2]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Tuple			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	List[Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Union[str, Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length < longest -> then pad to max_length
								self.assertTrue(input_values.shape == (3, 1000)   )

								a   :	List[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	int			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=2000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Dict			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1200)   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Any			=       np.random.rand(100   ).astype(np.floataa   )
								a   :	Optional[Any]			=       np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
									a   :	str			=       feature_extractor.pad([{"input_values": inputs}]   ,      return_tensors="np"   )
									self.assertTrue(np_processed.input_values.dtype == np.floataa   )
									a   :	List[str]			=       feature_extractor.pad([{"input_values": inputs}]   ,      return_tensors="pt"   )
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )






							def 					__a     (     self   )			->				Tuple:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Union[str, Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Tuple			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test feature size
								a   :	Union[str, Any]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								self.assertTrue(input_values.ndim == 3   )
								self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins   )

								# Test not batched input
								a   :	Dict			=       feature_extractor(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	List[Any]			=       feature_extractor(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	Optional[int]			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	Any			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test 2-D numpy arrays are batched.
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in (800, 800, 800)]
								a   :	List[Any]			=       np.asarray(lowerCAmelCase__   )
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				str:
								a   :	Optional[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Union[str, Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								self.assertTrue(all(len(lowerCAmelCase__   ) == len(lowerCAmelCase__   ) for x, y in zip(lowerCAmelCase__   ,      processed_features[input_name]   )   )   )

								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="np"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	Dict			=       batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Tuple:
								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	Optional[Any]			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="pt"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	List[str]			=       batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Tuple			=       feat_extract.num_mel_bins  # hack!

								a   :	List[Any]			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )[input_name]
								a   :	Any			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="pt"   )[input_name]

								self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1E-2   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Any			=       self.feat_extract_dict
								a   :	Optional[Any]			=       True
								a   :	Union[str, Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	Any			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	int			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Union[str, Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Dict			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(list(processed.attention_mask.shape   )   ,      list(processed[input_name].shape[:2]   )   )
								self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       self.feat_extract_dict
								a   :	str			=       True
								a   :	Optional[Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	List[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	str			=       BatchFeature({input_name: speech_inputs}   )
								a   :	Optional[Any]			=       min(lowerCAmelCase__   )

								a   :	List[Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Any			=       feat_extract.pad(
								    lowerCAmelCase__   ,      padding="max_length"   ,      max_length=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(
								    list(processed_pad.attention_mask.shape   )   ,      [processed_pad[input_name].shape[0], max_length]   )
								self.assertListEqual(
								    processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()   ,      [max_length for x in speech_inputs]   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								from datasets import load_dataset

								a   :	Tuple			=       load_dataset("hf-internal-testing/librispeech_asr_dummy"   ,      "clean"   ,      split="validation"   )
								# automatic decoding with librispeech
								a   :	Optional[Any]			=       ds.sort("id"   ).select(range(lowerCAmelCase__   )   )[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	List[Any]			=       torch.tensor(
								    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
								     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
								     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
								     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
								     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
								     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]   )
								# fmt: on

								a   :	List[str]			=       self._load_datasamples(1   )
								a   :	Union[str, Any]			=       SpeechTaFeatureExtractor()
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 9_3680)   )
								self.assertTrue(torch.allclose(input_values[0, :30]   ,      lowerCAmelCase__   ,      atol=1E-6   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	Tuple			=       torch.tensor(
								    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
								     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
								     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
								     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]   )
								# fmt: on

								a   :	Dict			=       self._load_datasamples(1   )
								a   :	Tuple			=       SpeechTaFeatureExtractor()
								a   :	Optional[int]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 366, 80)   )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,      lowerCAmelCase__   ,      atol=1E-4   )   )


